To see the other types of publications on this topic, follow the link: MIT/BIH data base.
Journal articles on the topic 'MIT/BIH data base'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'MIT/BIH data base.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
G., K. Singh, Sharma A., and Velusami S. "Automatic Detection of diagnostic features using real-time ECG signals: Application to patients prone to Cardiac Arrhythmias." International Journal of BioSciences and Technology (IJBST) ISSN: 0974-3987 2, no. 7 (2009): 96–125. https://doi.org/10.5281/zenodo.1436599.
Full textAbstract:
<strong>ABSTRACT</strong> A composite method for the automatic detection of diagnostic features related to the depolarization sequence (P-QRS complex) of the heart, for arrhythmia classification, using single lead ECG is presented. The non-syntactic approach based upon slope and amplitude thresholds along with a set of empirical criteria is employed for segmenting QRS complexes from a variety of noisy ECG recordings acquired from the MIT/BIH arrhythmia database. The background noise is removed from the non-QRS portions using an appropriate filtering method that causes no change in the amplitudes or boundaries of P and T waves. In R-R intervals, the isoelectric line is determined by developing a technique based upon the method of least squares approximation. Amplitude threshold bands are set up above and below the isoelectric line to detect P and /or T wave peaks. Using a group of decision logic rules, framed on the basis of an exhaustive study of normal and arrhythmic ECG signals and detailed consultations with two independent cardiologists, the P waves are discriminated from the T waves. In total, 37 useful diagnostic features have been deduced pertaining to the QRS and P waves in the time domain. The QRS detection algorithm of the composite method was validated using 32,800 beats of several records of the MIT/BIH database for which a detection accuracy of 99.96% was achieved within the tolerance limits recommended by the CSE Working Party. The composite method for detection of both P and QRS wave features has been validated using all the 25 records for lead II of the CSE Dataset-3, before being applied to approximately 8000 beats of the MIT/BIH database. As regards noisy signals, only those were analysed that had a baseline wander not exceeding 0.25 Hz<strong>.</strong> <strong>Keywords: </strong>Arrhythmia, ECG signal, P-QRS complex, MIT/BIH data base, atrio-ventricular conduction ratio, diagnostic feature http://www.ijbst.org/Home/papers-published/ijbst-2009-volume-2-issue-7
2
Ziti Fariha Mohd Apandi, Ryojun Ikeura, Soichiro Hayakawa, and Shigeyoshi Tsutsumi. "QRS Detection Based on Discrete Wavelet Transform for ECG Signal with Motion Artifacts." Journal of Advanced Research in Applied Sciences and Engineering Technology 40, no. 1 (2024): 118–28. http://dx.doi.org/10.37934/araset.40.1.118128.
Full textAbstract:
Motion artifacts in ECG signals recorded during physical exercises activities can affect the diagnosis of arrhythmia. To minimize the faults in arrhythmia detection, it was important to choose accurate algorithm for detecting QRS in ECG signal with noises produced during physical movements of the patients. Therefore, choosing the QRS detection algorithm with good competency for the signal affected by noises and motion artifacts is needed for arrhythmia detection analysis. The QRS detection based on Discrete Wavelet Transform was implemented and presented in this paper. The performance of the algorithm was assessed using the MIT-BIH Arrhythmia Database and MIT-BIH Noise Stress Database. For the MIT-BIH Arrhythmia database, the average Sensitivity (Se) and positive Predictivity (+P) of the algorithm were 98.24% and 98.61%, respectively. The algorithms had a lower average false negative rate (FNR) than the pan Tompkins algorithm when applied to the MIT-BIH noise stress test database, which was 0.033% for record 118 and 0.032% for record 119, respectively. The results demonstrated that the algorithms perform well when dealing with arrhythmia data and motion artifact at various levels of signal to noise ratio.
3
Yan, Wei, and Zhen Zhang. "Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database." Journal of Healthcare Engineering 2021 (December 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/1819112.
Full textAbstract:
Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.
4
YANG, GUANGYING. "ELECTROCARDIOGRAM ARRHYTHMIA PATTERN RECOGNITION BASED ON AN IMPROVED WAVELET NEURAL NETWORK." Journal of Mechanics in Medicine and Biology 13, no. 01 (2013): 1350018. http://dx.doi.org/10.1142/s0219519413500188.
Full textAbstract:
Electrocardiography (ECG) is a transthoracic interpretation of the electrical activity of the heart over a period of time, as detected by electrodes attached to the outer surface of the skin and recorded by a device external to the body. ECG signal classification is very important for the clinical detection of arrhythmia. This paper presents an application of an improved wavelet neural network structure to the classification of the ECG beats, because of the high precision and fast learning rate. Feature extraction method in this paper is wavelet transform. Our experimental data set is taken from the MIT-BIH arrhythmia database. The correct detection rate of QRS wave is 95% by testing the data of MIT-BIH database. The proposed methods are applied to a large number of ECG signals consisting of 600 training samples and 120 test samples from the MIT-BIH database. The samples equally represent six different ECG signal types, including normal beat, atrial premature beat, ventricular premature beat, left bundle branch block, right bundle branch block and paced beat. In comparison with pattern recognition methods of BP neural networks, RBF neural networks and Support Vector Machines (SVM), the results in this experiment prove that the wavelet neural network method has a better recognition rate when classifying electrocardiogram signals. The experimental results prove that supposed method in this paper is effective for arrhythmia pattern recognition field.
5
Auliya, Ghina, and Jannes Effendi. "Detection of Atrial Fibrillation Based on Long Short-Term Memory." Computer Engineering and Applications Journal 10, no. 1 (2021): 21–31. http://dx.doi.org/10.18495/comengapp.v10i1.361.
Full textAbstract:
Atrial fibrillation is a quivering or irregular heartbeat (arrhythmia) that can lead to blood clots, stroke, heart failure, and even sudden cardiac death. This study used several public datasets of electrocardiogram (ECG) signals, including MIT-BIH Atrial Fibrillation, China Physiological Signal Challenge 2018, MIT-BIH Normal Sinus Rhythm based on QT-Database, and Fantasia Database. All datasets were divided into 3 cases with the experiment windows size 10, 5, and 2 seconds for two classes, namely Normal and Atrial Fibrillation. The recurrent neural networks method is appropriate for processing sequential data such as ECG signals, and k-fold Cross-Validation can help evaluate models effectively to achieve high performance. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 94.56% 94.67%, 94.67%, 94.43%, and 94.51%.
6
Wang, Ludi, Xiaoguang Zhou, Ying Xing, and Siqi Liang. "A Fast and Simple Adaptive Bionic Wavelet Transform: ECG Baseline Shift Correction." Cybernetics and Information Technologies 16, no. 6 (2016): 60–68. http://dx.doi.org/10.1515/cait-2016-0077.
Full textAbstract:
Abstract An ECG baseline shift correction method is presented on the base of the adaptive bionic wavelet transform. After modifying the bionic wavelet transform according to the characteristics of the ECG signal, we propose a novel adaptive BWT algorithm. Using the contaminated and actual data in the MIT-BIH database, the method of fast and simple adaptive bionic wavelet transform can effectively correct the baseline shift under the premise of maintaining the geometric characteristics of the ECG signal. Evaluation of the proposed method shows that the average improvement SNR of FABWT is 2.187 dB more than the CWT-based best case result.
7
João Vitor Mendes Pinto dos Santos and Thamiles Rodrigues de Melo. "Machine Learning-Based Cardiac Arrhythmia Detection in Electrocardiogram Signals." JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH 7, no. 2 (2024): 113–16. http://dx.doi.org/10.34178/jbth.v7i2.378.
Full textAbstract:
The cardiovascular system is vital for human physiology, regulating blood circulation. Cardiovascular Diseases (CVDs), including cardiac arrhythmias, can disrupt the heartbeat rhythm, impacting blood circulation. Black-box computational modeling of this system can facilitate the development of novel methods and devices to assist in diagnosing and treating CVDs. Artificial Neural Networks (ANNs) represent an effective black-box approach. Implementation involves selecting a database, separating training and test sets, and defining the model structure. The MIT-BIH database is commonly utilized to train computational models to detect cardiac arrhythmias. However, preliminary results with the ANN model trained using MIT-BIH data failed to meet the expected objectives, presenting numerous challenges. Nonetheless, given its nascent stage, there remains potential for optimizations, rendering it a prospective tool for diagnosing cardiac arrhythmias.
8
Mathunjwa, Bhekumuzi M., Yin-Tsong Lin, Chien-Hung Lin, Maysam F. Abbod, Muammar Sadrawi, and Jiann-Shing Shieh. "ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features." Sensors 22, no. 4 (2022): 1660. http://dx.doi.org/10.3390/s22041660.
Full textAbstract:
In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.
9
Rajeshwari, M. R., and K. S. Kavitha. "Enhanced tolerance-based intuitionistic fuzzy rough set theory feature selection and ResNet-18 feature extraction model for arrhythmia classification." Multiagent and Grid Systems 18, no. 3-4 (2023): 241–61. http://dx.doi.org/10.3233/mgs-220317.
Full textAbstract:
Arrhythmia classification on Electrocardiogram (ECG) signals is an important process for the diagnosis of cardiac disease and arrhythmia disease. The existing researches in arrhythmia classification have limitations of imbalance data problem and overfitting in classification. This research applies Fuzzy C-Means (FCM) – Enhanced Tolerance-based Intuitionistic Fuzzy Rough Set Theory (ETIFRST) for feature selection in arrhythmia classification. The selected features from FCM-ETIFRST were applied to the Multi-class Support Vector Machine (MSVM) for arrhythmia classification. The ResNet18 – Convolution Neural Network (CNN) was applied for feature extraction in input signal to overcome imbalance data problem. Conventional feature extraction along with CNN features are applied for FCM-ETIFRST feature selection process. The FCM-ETIFRST method in arrhythmia classification is evaluated on MIT-BIH and CPCS 2018 dataset. The FCM-ETIFRST has 98.95% accuracy and Focal loss-CNN has 98.66% accuracy on MIT-BIH dataset. The FCM-ETIFRST method has 98.45% accuracy and Explainable Deep learning Model (XDM) method have 93.6% accuracy on CPCS 2018 dataset.
10
Wang, Di, Yujuan Si, Weiyi Yang, Gong Zhang, and Jia Li. "A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding." Electronics 8, no. 6 (2019): 667. http://dx.doi.org/10.3390/electronics8060667.
Full textAbstract:
For good performance, most existing electrocardiogram (ECG) identification methods still need to adopt a denoising process to remove noise interference beforehand. This specific signal preprocessing technique requires great efforts for algorithm engineering and is usually complicated and time-consuming. To more conveniently remove the influence of noise interference and realize accurate identification, a novel temporal-frequency autoencoding based method is proposed. In particular, the raw data is firstly transformed into the wavelet domain, where multi-level time-frequency representation is achieved. Then, a prior knowledge-based feature selection is proposed and applied to the transformed data to discard noise components and retain identity-related information simultaneously. Afterward, the stacked sparse autoencoder is introduced to learn intrinsic discriminative features from the selected data, and Softmax classifier is used to perform the identification task. The effectiveness of the proposed method is evaluated on two public databases, namely, ECG-ID and Massachusetts Institute of Technology-Biotechnology arrhythmia (MIT-BIH-AHA) databases. Experimental results show that our method can achieve high multiple-heartbeat identification accuracies of 98.87%, 92.3%, and 96.82% on raw ECG signals which are from the ECG-ID (Two-recording), ECG-ID (All-recording), and MIT-BIH-AHA database, respectively, indicating that our method can provide an efficient way for ECG biometric identification.
11
Xiong, Hui, Chunhou Zheng, Jinzhen Liu, and Limei Song. "ECG Signal In-Band Noise De-Noising Base on EMD." Journal of Circuits, Systems and Computers 28, no. 01 (2018): 1950017. http://dx.doi.org/10.1142/s0218126619500178.
Full textAbstract:
The electrocardiogram (ECG) signal is widely used for diagnosis of heart disorders. However, ECG signal is a kind of weak signal to be interfered with heavy background interferences. Moreover, there are some overlaps between the interference frequency sub-bands and the ECG frequency sub-bands, so it is difficult to inhibit noise in the ECG signal. In this paper, the ECG signal in-band noise de-noising method based on empirical mode decomposition (EMD) is proposed. This method uses random permutation to process intrinsic mode functions (IMFs). It abstracts QRS complexes to separate them from noise so that the clean ECG signal is obtained. The method is validated through simulations on the MIT-BIH Arrhythmia Database and experiments on the measured test data. The results indicate that the proposed method can restrain noise, improve signal noise ratio (SNR) and reduce mean squared error (MSE) effectively.
12
Kulkarni, S. P. "DWT and ANN Based Heart Arrhythmia Disease Diagnosis from MIT-BIH ECG Signal Data." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 1 (2015): 276–79. http://dx.doi.org/10.17762/ijritcc2321-8169.150156.
Full text
13
Zhang, Sheng, Jie Gao, Jie Yang, and Shun Yu. "A Mallat Based Wavelet ECG De-Noising Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 2267–70. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2267.
Full textAbstract:
A Mallat based wavelet de-noising algorithm in ECG analysis is studied. We use bior3.7 wavelet based on Mallat algorithm for ECG decomposition. Then we choose composite threshold and wavelet reconfiguration algorithm for signal de-noising to achieve an effective result. Data get from MIT/BIH is examined using the method. The result shows that it can not only remove the power frequency disturbance, EMG interference and baseline drift emerging in ECG, but also preserve the ECG characteristics.
14
Zhao, Zhi Qiang, Min Jie Fu, Yong Hui Chen, et al. "Study on ECG Signal Wavelet Denoising Algorithm Based on the MSP430 Platform." Applied Mechanics and Materials 513-517 (February 2014): 3504–8. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3504.
Full textAbstract:
A novel ECG filtering algorithm was researched, which was suitable for the MSP430 platform. Several ECG signal wavelet denoising algorithms were simulated on matlab to compare their filtering effect. The mexican-hat wavelet denoising algorithm can get a better effect on filtering of ECG signal (No. 203 data from the MIT-BIH ECG database). The time complexity of the algorithm is O(n2), and the SNR can also be up to 66%.
15
Lin, Haicai, Ruixia Liu, and Zhaoyang Liu. "ECG Signal Denoising Method Based on Disentangled Autoencoder." Electronics 12, no. 7 (2023): 1606. http://dx.doi.org/10.3390/electronics12071606.
Full textAbstract:
The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a method for denoising ECG based on disentangled autoencoders. A disentangled autoencoder is an improved autoencoder suitable for denoising ECG data. In our proposed method, we use a disentangled autoencoder model based on a fully convolutional neural network to effectively separate the clean ECG data from the noise. Unlike conventional autoencoders, we disentangle the features of the coding hidden layer to separate the signal-coding features from the noise-coding features. We performed simulation experiments on the MIT-BIH Arrhythmia Database and found that the algorithm had better noise reduction results when dealing with four different types of noise. In particular, using our method, the average improved signal-to-noise ratios for the three noises in the MIT-BIH Noise Stress Test Database were 27.45 db for baseline wander, 25.72 db for muscle artefacts, and 29.91 db for electrode motion artefacts. Compared to a denoising autoencoder based on a fully convolutional neural network (FCN), the signal-to-noise ratio was improved by an average of 12.57%. We can conclude that the model has scientific validity. At the same time, our noise reduction method can effectively remove noise while preserving the important information conveyed by the original signal.
16
Alamr, Abrar, and Abdelmonim Artoli. "Unsupervised Transformer-Based Anomaly Detection in ECG Signals." Algorithms 16, no. 3 (2023): 152. http://dx.doi.org/10.3390/a16030152.
Full textAbstract:
Anomaly detection is one of the basic issues in data processing that addresses different problems in healthcare sensory data. Technology has made it easier to collect large and highly variant time series data; however, complex predictive analysis models are required to ensure consistency and reliability. With the rise in the size and dimensionality of collected data, deep learning techniques, such as autoencoder (AE), recurrent neural networks (RNN), and long short-term memory (LSTM), have gained more attention and are recognized as state-of-the-art anomaly detection techniques. Recently, developments in transformer-based architecture have been proposed as an improved attention-based knowledge representation scheme. We present an unsupervised transformer-based method to evaluate and detect anomalies in electrocardiogram (ECG) signals. The model architecture comprises two parts: an embedding layer and a standard transformer encoder. We introduce, implement, test, and validate our model in two well-known datasets: ECG5000 and MIT-BIH Arrhythmia. Anomalies are detected based on loss function results between real and predicted ECG time series sequences. We found that the use of a transformer encoder as an alternative model for anomaly detection enables better performance in ECG time series data. The suggested model has a remarkable ability to detect anomalies in ECG signal and outperforms deep learning approaches found in the literature on both datasets. In the ECG5000 dataset, the model can detect anomalies with 99% accuracy, 99% F1-score, 99% AUC score, 98.1% recall, and 100% precision. In the MIT-BIH Arrhythmia dataset, the model achieved an accuracy of 89.5%, F1 score of 92.3%, AUC score of 93%, recall of 98.2%, and precision of 87.1%.
17
Lassoued, Hela, Raouf Ketata, and Hajer Ben Mahmoud. "Optimal Neuro Fuzzy Classification for Arrhythmia Data Driven System." International Journal of Innovative Technology and Exploring Engineering 11, no. 1 (2021): 70–80. http://dx.doi.org/10.35940/ijitee.a9628.1111121.
Full textAbstract:
This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS configuration needs mainly the choice of an initial Fuzzy Inference System (FIS) and the training algorithm. Indeed, two clustering algorithms which are the fuzzy c-means (FCM) and the subtractive ( SUBCLUST) algorithms, are applied to generate the initial FIS. Besides, for tuning the ANFIS membership function and rule base parameters, Gradient descent and evolutionary training algorithms are also evaluated. Gradient descent consists of the backpropagation (BP) method and its hybridization with the least square algorithm (Hybrid). However, the evolutionary training methods involve the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA). Therefore, eight (8) ANFIS are configured and assessed. Accordingly, a comparison study between their obtained Root Mean Square Error (RMSE) is analyzed. At the end, we have selected an optimal ANFIS which uses the SUBTRUCT algorithm to generate the initial FIS and the GA to tune its parameters. Moreover, to guarantee the effectiveness of this work, a comparison study with related works is done.
18
Hela, Lassoued, Ketata Raouf, and Ben Mahmoud Hajer. "Optimal Neuro-Fuzzy Classification for Arrhythmia Data Driven System." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 1 (2021): 70–80. https://doi.org/10.35940/ijitee.A9628.1111121.
Full textAbstract:
This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS configuration needs mainly the choice of an initial Fuzzy Inference System (FIS) and the training algorithm. Indeed, two clustering algorithms which are the fuzzy c-means (FCM) and the subtractive ( SUBCLUST) algorithms, are applied to generate the initial FIS. Besides, for tuning the ANFIS membership function and rule base parameters, Gradient descent and evolutionary training algorithms are also evaluated. Gradient descent consists of the backpropagation (BP) method and its hybridization with the least square algorithm (Hybrid). However, the evolutionary training methods involve the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA). Therefore, eight (8) ANFIS are configured and assessed. Accordingly, a comparison study between their obtained Root Mean Square Error (RMSE) is analyzed. At the end, we have selected an optimal ANFIS which uses the SUBTRUCT algorithm to generate the initial FIS and the GA to tune its parameters. Moreover, to guarantee the effectiveness of this work, a comparison study with related works is done.
19
Maramgere Ramaiah, Rajeshwari, and Kavitha Kuntaegowdanalli Srikantegowda. "CORONARY HEART DISEASE CLASSIFICATION USING IMPROVED PENGUIN EMPEROR OPTIMIZATION-BASED LONG SHORT TERM MEMORY NETWORK." IIUM Engineering Journal 24, no. 2 (2023): 67–85. http://dx.doi.org/10.31436/iiumej.v24i2.2698.
Full textAbstract:
Ventricular fibrillation (VF) is the most life-threatening and dangerous type of Cardiac Arrhythmia (CA), with a mortality rate of 10-15% in a year. Therefore, early detection of cardiac arrhythmia is important to reduce the mortality rate. Many machine learning algorithms have been proposed and have proven their usefulness in the classification and detection of heart problems. In this research manuscript, a novel Long Short Term Memory (LSTM) classifier with Improved Penguin Optimization (IPEO) is implemented for VF classification. The IPEO is used in finding optimal hyperparameters that overcome the overfitting problem. The presented model is tested, trained, and validated using two standard datasets that are available publicly: Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) and the China Physiological Signal Challenge (CPSC) 2018 dataset. Both of them consist of ECG recordings for five seconds of coronary heart disease (CHD) patients. Furthermore, Fuzzy C-Means and Enhanced Fuzzy Rough Set method (FCM-ETIFRST) are used for feature selection to extract informative features and to cluster membership degree, non-membership degree, and hesitancy degree. On the MIT-BIH dataset, the proposed model achieved accuracy, sensitivity, specificity, precision, and Matthews’s correlation coefficient (MCC) of 99.75%, 98.29%, 98.39%, 98.35%, and 97.79% respectively. On the CPSC 2018 dataset, the proposed model achieved accuracy of 99.79%, sensitivity of 99.11%, specificity of 98.20%, precision of 99.43%, and MCC of 98.57%. Hence, the results proved that the proposed method provides better results in the classification of VF. ABSTRAK: Pemfibrilan Ventrikel (VF) adalah ancaman nyawa nombor satu dan jenis Aritmia Jantung (CA) berbahaya dengan kadar kematian 10-15% setahun. Oleh itu, pengesanan awal Aritmia Jantung sangat penting bagi mengurangkan kadar kematian. Terdapat banyak algoritma pembelajaran mesin yang telah dicadangkan dan terbukti berkesan dalam pengelasan dan pengesanan sakit jantung. Kajian ini mencadangkan kaedah baru pengelasan Memori Ingatan Jangka Panjang Pendek (LSTM) dengan Pengoptimuman Penambahbaikan Penguin (IPEO) yang dilaksanakan bagi klasifikasi VF. IPEO digunakan bagi mencari hiperparameter yang dapat mengatasi masalah padanan berlebihan. Model yang dicadangkan diuji, dilatih dan disahkan menggunakan dua dataset piawai yang dapat diperoleh secara terbuka: Institut Teknologi Hospital Massachusetts-Beth Israel (MIT-BIH) dan Cabaran Signal Psikologi Cina 2018 (CPSC). Kedua-dua data ini mempunyai rakaman ECG selama lima saat daripada pesakit Penyakit Jantung Koronari (CHD). Malah, kajian itu turut menggunakan Purata-C Kabur dan Kaedah Set Kasar Kabur Dipertingkat (FCM-ETIFRST) untuk pemilihan bagi mengekstrak ciri-ciri dan mengelaskan kelompok tahap keahlian, bukan ahli dan tahap keraguan. Bagi dataset MIT-BIH, model yang dicadangkan mencapai ketepatan, tahap sensitif, tahap spesifik, kejituan dan pekali kaitan Matthews (MCC) sebanyak 99.75%, 98.29%, 98.39%, 98.35%, dan 97.79% masing-masing. Bagi dataset CPSC 2018 pula, model yang dicadangkan mencapai ketepatan sebanyak 99.79%, 99.11% tahap sensitif , 98.20% tahap spesifik, 99.43% kejituan dan 98.57% MCC. Oleh itu, dapatan kajian membuktikan kaedah yang dicadangkan menunjukkan keputusan lebih baik dalam pengelasan VF.
20
Charfi, Faiza, and Ali Kraiem. "Comparative Study of ECG Classification Performance Using Decision Tree Algorithms." International Journal of E-Health and Medical Communications 3, no. 4 (2012): 102–20. http://dx.doi.org/10.4018/jehmc.2012100106.
Full textAbstract:
The electrocardiogram (ECG) signal has often been reported to play an important role in the primary diagnosis, prognosis, and survival analysis of heart diseases. Electrocardiography has brought several valuable impacts on the practice of medicine. This paper deals with the feature extraction and automatic analysis of different ECG signal waves using derivative based/ Pan-Tompkins based algorithms. The ECG signal contains an important amount of information that can be exploited in different way. It allows for the analysis of cardiac health condition. The discrimination of ECG signals using the Data Mining Decision Tree techniques is of crucial importance in the cardiac disease therapy and control of cardiac arrhythmias. Different ECG signals from MIT/BIH Arrhythmia data base are used for ECG features extraction and analysis. Two pathologies are considered: atrial fibrillation and right bundle branch block. Some decision tree classification algorithms currently in use, including C4.5, Improved C4.5, CHAID (Chi square Automatic Interaction Detector) and Improved CHAID are performed for performance analysis. Promising results have been achieved using the C4.5 classifier, with an overall accuracy of 96.87%.
21
Kirkbas, Ali, and Aydin Kizilkaya. "Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier." Sensors 25, no. 4 (2025): 1220. https://doi.org/10.3390/s25041220.
Full textAbstract:
This paper seeks to solve the classification problem of cardiac arrhythmias by using a small number of electrocardiogram (ECG) recordings. To offer a reasonable solution to this problem, a technique that combines a common matrix approach (CMA)-based classifier model with the Fourier decomposition method (FDM) is proposed. The FDM is responsible for generating time–frequency (T-F) representations of ECG recordings. The classification process is performed with feature images applied as input to the classifier model. The feature images are obtained after two-dimensional principal component analysis (2DPCA) of data matrices related to ECG recordings. Each data matrix is created by concatenating the ECG record itself, the Fourier transform, and the T-F representation on a single matrix. To verify the efficacy of the proposed method, various experiments are conducted with the MIT-BIH, Chapman, and PTB-XL databases. In the assessments using the MIT-BIH database under the inter-patient paradigm, we achieved a mean overall accuracy rate of 99.81%. The proposed method outperforms the majority of recent efforts, yielding rates exceeding 99% on nearly five performance metrics for the recognition of V- and S-class arrhythmias. It is found that, in the classification of four types of arrhythmias using ECG recordings from the Chapman database, our model surpasses recent works by reaching mean overall accuracy rates of 99.76% and 99.45% for the raw and de-noised ECG recordings, respectively. Similarly, five different forms of arrhythmias from the PTB-XL database were recognized with a mean overall accuracy of 98.71%.
22
Ullah, Hadaate, Md Belal Bin Heyat, Faijan Akhtar, et al. "An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal." Diagnostics 13, no. 1 (2022): 87. http://dx.doi.org/10.3390/diagnostics13010087.
Full textAbstract:
The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan–Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.
23
Ghahremani, Amir, and Christoph Lofi. "ImECGnet: Cardiovascular Disease Classification from Image-Based ECG Data Using a Multibranch Convolutional Neural Network." Journal of Image and Graphics 11, no. 1 (2023): 9–14. http://dx.doi.org/10.18178/joig.11.1.9-14.
Full textAbstract:
Reliable Cardiovascular Disease (CVD) classification performed by a smart system can assist medical doctors in recognizing heart illnesses in patients more efficiently and effectively. Electrocardiogram (ECG) signals are an important diagnostic tool as they are already available early in the patients’ health diagnosis process and contain valuable indicators for various CVDs. Most ECG processing methods represent ECG data as a time series, often as a matrix with each row containing the measurements of a sensor lead; and/or the transforms of such time series like wavelet power spectrums. While methods processing such time-series data have been shown to work well in benchmarks, they are still highly dependent on factors like input noise and sequence length, and cannot always correlate lead data from different sensors well. In this paper, we propose to represent ECG signals incorporating all lead data plotted as a single image, an approach not yet explored by literature. We will show that such an image representation combined with our newly proposed convolutional neural network specifically designed for CVD classification can overcome the aforementioned shortcomings. The proposed (Convolutional Neural Network) CNN is designed to extract features representing both the proportional relationships of different leads to each other and the characteristics of each lead separately. Empirical validation on the publicly available PTB, MIT-BIH, and St.-Petersburg benchmark databases shows that the proposed method outperforms time seriesbased state-of-the-art approaches, yielding classification accuracy of 97.91%, 99.62%, and 98.70%, respectively.
24
Ray, Shashwati, and Vandana Chouhan. "Electrocardiogram reconstruction based on Hermite interpolating polynomial with Chebyshev nodes." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 837. http://dx.doi.org/10.11591/ijeecs.v36.i2.pp837-845.
Full textAbstract:
Electrocardiogram (ECG) signals generate massive volume of digital data, so they need to be suitably compressed for efficient transmission and storage. Polynomial approximations and polynomial interpolation have been used for ECG data compression where the data signal is described by polynomial coefficients only. Here, we propose approximation using hermite polynomial interpolation with chebyshev nodes for compressing ECG signals that consequently denoises them too. Recommended algorithm is applied on various ECG signals taken from MIT-BIH arrhythmia database without any additional noise as the signals are already contaminated with noise. Performance of the proposed algorithm is evaluated using various performance metrics and compared with some recent compression techniques. Experimental results prove that the proposed method efficiently compresses the ECG signals while preserving the minute details of important morphological features of ECG signal required for clinical diagnosis.
25
Shashwati, Ray Vandana Chouhan. "Electrocardiogram reconstruction based on Hermite interpolating polynomial with Chebyshev nodes." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 837–45. https://doi.org/10.11591/ijeecs.v36.i2.pp837-845.
Full textAbstract:
Electrocardiogram (ECG) signals generate massive volume of digital data, so they need to be suitably compressed for efficient transmission and storage. Polynomial approximations and polynomial interpolation have been used for ECG data compression where the data signal is described by polynomial coefficients only. Here, we propose approximation using hermite polynomial interpolation with chebyshev nodes for compressing ECG signals that consequently denoises them too. Recommended algorithm is applied on various ECG signals taken from MIT-BIH arrhythmia database without any additional noise as the signals are already contaminated with noise. Performance of the proposed algorithm is evaluated using various performance metrics and compared with some recent compression techniques. Experimental results prove that the proposed method efficiently compresses the ECG signals while preserving the minute details of important morphological features of ECG signal required for clinical diagnosis.
26
Nurmaini, Siti, Annisa Darmawahyuni, Akhmad Noviar Sakti Mukti, Muhammad Naufal Rachmatullah, Firdaus Firdaus, and Bambang Tutuko. "Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification." Electronics 9, no. 1 (2020): 135. http://dx.doi.org/10.3390/electronics9010135.
Full textAbstract:
The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice.
27
Olanrewaju, Rashidah Funke, S. Noorjannah Ibrahim, Ani Liza Asnawi, and Hunain Altaf. "Classification of ECG signals for detection of arrhythmia and congestive heart failure based on continuous wavelet transform and deep neural networks." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1520. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1520-1528.
Full textAbstract:
According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.
28
Olanrewaju, Rashidah Funke, S. Noorjannah Ibrahim, Ani Liza Asnawi, and Hunain Altaf. "Classification of ECG signals for detection of arrhythmia and congestive heart failure based on continuous wavelet transform and deep neural networks." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1520–28. https://doi.org/10.11591/ijeecs.v22.i3.pp1520-1528.
Full textAbstract:
According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.
29
Victor, Johnson Olanrewaju, XinYing Chew, Khai Wah Khaw, and Ming Ha Lee. "A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification." Journal of Informatics and Web Engineering 2, no. 2 (2023): 90–110. http://dx.doi.org/10.33093/jiwe.2023.2.2.7.
Full textAbstract:
The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of hidden ailments resulting in numerous deaths. Early heart monitoring using an electrocardiogram (ECG) through the advancement of computer-aided diagnostic (CAD) systems is widely used. Meanwhile, the use of human reading of ECG results are faced with many challenges of inaccurate and unreliable interpretations. Over two decades, studies provided artificial intelligence (AI) technique using machine learning (ML) algorithms as a fast and reliable technique for ECG heartbeat classification. Moreover, in recent times, deep learning (DL) techniques have been focused on providing automatic feature extraction and better classification performance. On the other hand, the challenge with the ECG data is its imbalance nature. Therefore, this paper proposes a cost-based dual convolutional attention transfer DL model for ECG classification. The proposed model uses PhysionNet-MIT-BIH and Physikalisch-Technische Bundesanstalt (PTB) Diagnostics datasets. The first part uses the MIT-BIH for ECG categorization, while representations learned from the first classifier are used for PTB analysis through transfer learning (TL). The proposed model is evaluated and compared with well-performing conventional ML models based on their F1-score and accuracy scores. Our experimental finding show that the proposed model outperformed the well-performing ML models as well as competitive with past studies for both the classification and TL part, having obtained 98.45% for both F1-score and accuracy. The proposed model is applicable to real-life trials and experiments for ECG heartbeat and other similar domains.
30
Berrahou, Nadia, Abdelmajid El Alami, Rachid El Alami, and Hassan Qjidaa. "Synergistic Approaches for Accurate Arrhythmia Prediction: A Hybrid AI Model Integrating Higuchi Dimensional Fractal, RR-intervals and Attention-based Convolutional Neural Network in ECG Signal Analysis." Statistics, Optimization & Information Computing 13, no. 2 (2024): 547–67. https://doi.org/10.19139/soic-2310-5070-2091.
Full textAbstract:
In recent years, numerous methods for detecting arrhythmias using a 12-lead ECG have emerged, with deep learning approaches notably demonstrating effectiveness and gaining widespread adoption. However, the classification of inter-patient ECG data for arrhythmia detection remains a significant challenge. Despite the increased utilization of deep learning methodologies, a noticeable gap persists in achieving optimal performance in inter-patient ECG classification. In this paper, we introduce a new method based on a 1D deep learning model that incorporates an attention mechanism into convolutional neural networks for arrhythmia detection. 1D-CNN layers automatically extract morphological characteristics from ECG data, providing an accurate technique for spatial feature extraction. Simultaneously, the attention mechanism enables the model to focus on crucial segments of a signal. To enhance temporal context, four RR-interval features are included, and the potential of the Higuchi Dimensional Fractal is explored as a method for extracting additional features from ECG signals. Consequently, the classification layers benefit from the combination of both temporal and deep features, contributing to the final arrhythmia classification. We validated the proposed method using the MIT-BIH arrhythmia dataset, employing an inter-patient paradigm for model training and validation. Additionally, to assess its generalization ability, we tested it on the INCART dataset. The proposed method attained an average accuracy of 98.75% for three classes and 97.96% for four classes on the MIT-BIH arrhythmia dataset. On the INCART dataset, it achieves an average accuracy of 98.12% for three classes. The experimental results indicate the superiority of this method in comparison to existing methods for recognizing arrhythmias. Thus, our method demonstrates enhanced generalization and potential effectiveness in identifying arrhythmias in real-world datasets characterized by class imbalances, showcasing its practical applicability.
31
Zeng, Yuni, Hang Lv, Mingfeng Jiang, et al. "Deep arrhythmia classification based on SENet and lightweight context transform." Mathematical Biosciences and Engineering 20, no. 1 (2022): 1–17. http://dx.doi.org/10.3934/mbe.2023001.
Full textAbstract:
<abstract> <p>Arrhythmia is one of the common cardiovascular diseases. Nowadays, many methods identify arrhythmias from electrocardiograms (ECGs) by computer-aided systems. However, computer-aided systems could not identify arrhythmias effectively due to various the morphological change of abnormal ECG data. This paper proposes a deep method to classify ECG samples. Firstly, ECG features are extracted through continuous wavelet transform. Then, our method realizes the arrhythmia classification based on the new lightweight context transform blocks. The block is proposed by improving the linear content transform block by squeeze-and-excitation network and linear transformation. Finally, the proposed method is validated on the MIT-BIH arrhythmia database. The experimental results show that the proposed method can achieve a high accuracy on arrhythmia classification.</p> </abstract>
32
Thiamchoo, Nantarika, and Pornchai Phukpattaranont. "R Peak Detection Algorithm based on Continuous Wavelet Transform and Shannon Energy." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 10, no. 2 (2017): 167–75. http://dx.doi.org/10.37936/ecti-cit.2016102.64837.
Full textAbstract:
The R peak detection algorithm is a necessary tool for monitoring and diagnosing the cardiovascular disease. This paper presents the R peak detection algorithm based on continuous wavelet transform (CWT) and Shannon energy. We evaluate the proposed algorithm with the 48 record of ECG data from MIT-BIH arrhythmia database. Results show that the proposed algorithm gives very good DER (0.48%-0.50%) compared to those from previous publications (0.168%-0.87%). We demonstrated that the use of the CWT with a single scaling parameter is capable of removing noises. In addition, we found that Shannon energy cannot improve the DER value but it can highlight the R peak from the low QRS complex in ECG beat leading to the improvement in the robustness of the R peak detection algorithm.
33
Rizal, Achmad, Riandini ., and Teni Tresnawati. "Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy." International Journal of Engineering & Technology 7, no. 4.44 (2018): 161. http://dx.doi.org/10.14419/ijet.v7i4.44.26975.
Full textAbstract:
One of the abnormalities in the heart that can be assessed from an ECG signal is premature ventricle contraction (PVC). PVC is a form of arrhythmia in the form of irregularity in beat ECG signals. In this study, a multilevel wavelet entropy method was developed to distinguish PVC and normal ECG signals automatically. Data was taken from the MIT-BIH arrhythmia database with the process carried out is normalization, median filtering, beat-parsing, MWE calculation and classification using SVM. The results of the experiment showed that MWE level 5 with DB2 as mother wavelet and Quadratic SVM as classifier resulted in the highest accuracy of 94.9%. MWE level 5 means only five features needed for classification. The number of features is very little compared to previous research with a quite high accuracy.
34
Vergassola, R., W. Zong, M. R. Berthold, and R. Silipo. "Knowledge-based and Data-driven Models in Arrhythmia Fuzzy Classification." Methods of Information in Medicine 40, no. 05 (2001): 397–402. http://dx.doi.org/10.1055/s-0038-1634199.
Full textAbstract:
Summary Objectives: Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge-based and a data-driven fuzzy system applied to an electrocardiography classification problem. Methods: For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures. A second set of fuzzy rules is automatically constructed on thirty-nine MIT-BIH database’s records. The performances of the two classifiers on thirteen different records are comparable and up to a certain extent complementary. The two fuzzy models are then analyzed, by using the concept of information gain to estimate the impact of each ECG measure on each fuzzy decision process. Results: Both systems rely on the beat prematurity degree and the QRS complex width and neglect the P wave existence and the ST segment features. The PR interval is not well characterized across the fuzzy medical rules while it plays an important role in the data-driven fuzzy system. The T wave area shows a higher information gain in the knowledge based decision process, and is not very much exploited by the data-driven system. Conclusions: The main difference between a human designed and a data driven ECG arrhythmia classifier is found about the PR interval and the T wave.
35
Eleyan, Alaa, and Ebrahim Alboghbaish. "Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework." Computers 13, no. 2 (2024): 55. http://dx.doi.org/10.3390/computers13020055.
Full textAbstract:
Cardiovascular diseases (CVDs) like arrhythmia and heart failure remain the world’s leading cause of death. These conditions can be triggered by high blood pressure, diabetes, and simply the passage of time. The early detection of these heart issues, despite substantial advancements in artificial intelligence (AI) and technology, is still a significant challenge. This research addresses this hurdle by developing a deep-learning-based system that is capable of predicting arrhythmias and heart failure from abnormalities in electrocardiogram (ECG) signals. The system leverages a model that combines long short-term memory (LSTM) networks with convolutional neural networks (CNNs). Extensive experiments were conducted using ECG data from both the MIT-BIH and BIDMC databases under two scenarios. The first scenario employed data from five distinct ECG classes, while the second focused on classifying data from three classes. The results from both scenarios demonstrated that the proposed deep-learning-based classification approach outperformed existing methods.
36
Shukla, Neha, Anand Pandey, Anand Prakash Shukla, and Sanjeev Chandra Neupane. "ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices." Journal of Sensors 2022 (July 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/2449956.
Full textAbstract:
The advancement in deep learning techniques has helped researchers acquire and process multimodal data signals from different healthcare domains. Now, the focus has shifted towards providing end-to-end solutions, i.e., processing these data and developing models that can be directly implemented on edge devices. To achieve this, the researchers try to solve two problems: (I) reduce the complex feature dependencies and (II) reduce the complexity of the deep learning model without compromising accuracy. In this paper, we focus on the later part of reducing the complexity of the model by using the knowledge distillation framework. We have introduced knowledge distillation on the Vision Transformer model to study the MIT-BIH Arrhythmia Database. A tenfold crossvalidation technique was used to validate the model, and we obtained a 99.7% F1 score and 99.3% accuracy. The model was further tested on the Xilinx Alveo U50 FPGA accelerator, and it is found fit for any low-powered wearable device implementation.
37
Gurrala, Vijayakumar, Padmasai Yarlagadda, and Padmaraju Koppireddi. "Detection of Sleep Apnea Based on the Analysis of Sleep Stages Data Using Single Channel EEG." Traitement du Signal 38, no. 2 (2021): 431–36. http://dx.doi.org/10.18280/ts.380221.
Full textAbstract:
Sleep is a basic need for a human being’s intellectual and physiological restoration and overlaying nearly one 1/3 length of a daytime. A first-rate and deep sleep is required for green regeneration of the body. Sleep disorders hamper the performance of an individual. Sleep Apnea is the one amongst the disorders that affect many. Most of Apnea related works consider Electrocardiogram (ECG) and respiratory signals /or combinations, instead of considering all Polysomnographic signals (PSG). It is evident that for the detection of Apnea related sleep disorders it is required to consider one or few signals rather considering all PSG signals. In this work, we advocate a way that might be carried out to perceive the information of sleep stages which might be crucial in diagnosing and treating sleep disorders. It differentiates sleep stages and derives new features from the sleep EEG that allows helping physicians with the analysis and treatment of associated sleep issues. This theory depends on exclusive EEG datasets from Physionet with the use of MIT-BIH polysomnographic database that have been received and described through scientists for the analysis and prognosis of sleep ranges. Experimental results on 18 records with 10197 epochs show that an Apnea detection accuracy of 95.9% obtained for Machine learning classifier with Ensemble Bagged Tree classifier.
38
Ullah, Amin, Sadaqat ur Rehman, Shanshan Tu, Raja Majid Mehmood, Fawad, and Muhammad Ehatisham-ul-haq. "A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal." Sensors 21, no. 3 (2021): 951. http://dx.doi.org/10.3390/s21030951.
Full textAbstract:
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.
39
Li, Runchuan, Wenzhi Zhang, Shengya Shen, et al. "An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm." Journal of Healthcare Engineering 2021 (July 9, 2021): 1–19. http://dx.doi.org/10.1155/2021/9913127.
Full textAbstract:
Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification. The analysis results are output in the form of reports. In this system, by comparing and analyzing the classification accuracy of different feature sets and classifiers, the optimal classification algorithm is obtained and applied to the system. The algorithm accuracy of the system is tested based on the MIT-BIH data set. The result shows that AdaBoost + Random Forest achieved 99.11% accuracy with optimal feature sets. The intelligent heartbeat classification system based on this algorithm has also achieved good results on clinical data.
40
T, Tamilselvan. "Bidirectional RNN based early prediction of CVDs using ECG Signals for Type 2 diabetic patients." Journal of University of Shanghai for Science and Technology 24, no. 02 (2022): 301–19. http://dx.doi.org/10.51201/jusst/22/0247.
Full textAbstract:
The ECG signal is important for early diagnosis of heart abnormalities. Type 2 Diabetic individuals’ ECG signals provide pertinent data about their hearts and are one of the most important diagnostic techniques used by doctors to identify cardiovascular diseases. The suggested study uses feature extraction and Bi-RNN based classification to analyse ECG signals of Type 2 patients. To reduce noise from the ECG signal, a hybrid preprocessing filter made up of a Median and Savitzky-Golay filter. Undecimated dual tree complex wavelet transform (UDTCWT) along with Detrended fluctuation (DA) analysis and empirical orthogonal function (EOF) analysis are then used to extract features. These features are classified with Bidirectional RNN. The proposed method was tested on the MIT-BIH, Physionet and DICARDIA databases, and the findings show that it achieves an average accuracy of 97.6% when compared to conventional techniques.
41
Abdulbaqi, Azmi Shawkat, Israa Falih Muslim, Asraa A. Abd Al-Ameer, and Ahmed J. Obaid. "Healthcare surveillance based on cloud computing utilizing mobile devices." Journal of Discrete Mathematical Sciences & Cryptography 26, no. 4 (2023): 1189–96. http://dx.doi.org/10.47974/jdmsc-1566.
Full textAbstract:
Cardiologists utilized electrocardiograms (ECGs) to observe cardiac disease in everyday life. A system for reading ECGs outside the hospital based on a Mobile Instrument (MobileInst) was developed to make it easier for specialists to observe their patients’ ECGs. A MobileInst receives an ECG signal and sends the signals to an ECG instrument based on the proposed system. By using MobileInst, ECG equipment alarms can be detected. A Cloud Alarm Service (SCA), which records ECG signaling and alert information, receives alarm information when MobileInst receives alarm signals. MobileInst displays the ECG signaling and alarm data when the messages are received by Alert Server. Using the suggested scheme, specialists can keep track of their patients’ ailments through ECG signaling sent to their MobileInst devices. Signals for this study were collected from the MIT-BIH PhysioBank database. In observing patients, the technique was found to be accurate in providing accurate results.
42
Sumanta, Kuila, Maity Sayandeep, Kumar Mal Suman, and Joardar Subhankar. "Performance Analysis of ECG Arrhythmia Classification based on Different SVM Methods." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9, no. 12 (2020): 45–49. https://doi.org/10.5281/zenodo.5839644.
Full textAbstract:
Heart arrhythmias are the different types of heartbeats which are irregular in nature. In Tachycardia the heartbeat works too fast and in case of Bradycardia it works too slow. In the study of different cardiac conditions automatic detection of heart arrhythmia is done by the classification and feature extraction of Electrocardiogram(ECG) data. Various Support Vector Machine based methods are used to analyze and classify ECG signals for arrhythmia detection. There are several Support Vector Machine (SVM) methods used to classify the ECG data such as one against all, one against one and fuzzy decision function. This classification detects the existence of the arrhythmia and it helps the physicians to treat the heart patient with more accurate way. To train SVM, the MIT BIH Arrhythmia database is used which works with the heart disorder like sinus bradycardy, old inferior myocardial infarction, coronary artery disease, right bundle branch block. All three methods are implemented in proper way, and their rate of accuracy with SVM classifier is optimal when it is processed with the one-against-all method. The data sets of ECG arrhythmia are usually complex in nature, so for the SVM based classification one-against-all method has great impact and will fetch better result.
43
Liu, Mingxin, Ningning Shao, Chaoxuan Zheng, and Ji Wang. "Real Time Arrhythmia Monitoring and Classification Based on Edge Computing and DNN." Wireless Communications and Mobile Computing 2021 (May 15, 2021): 1–9. http://dx.doi.org/10.1155/2021/5563338.
Full textAbstract:
In this paper, we investigate how to incorporate intelligence into the human-centric IoT edges to detect arrhythmia, a heart condition often associated with morbidity and even mortality. We propose a classification algorithm based on the intrapatient convolutional neural network model and the interpatient attention residual network model to automatically identify the type of arrhythmia in the edges. As the imbalance categories in the MIT-BIH arrhythmia database which needs to be used in the algorithm, we slice and overlap the original ECG signal to homogenize the heartbeat sets of different types, and then the preprocessed data was used to train the two proposed network models; the results reached an overall accuracy rate of 99.03% and an F1 value of 0.87, respectively. The proposed algorithm model can be used as a real-time diagnostic tool for the remote E-health system in next generation wireless communication networks.
44
Shadhon Chandra Mohonta and Md. Firoj Ali. "A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network." MIST INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY 10 (December 29, 2022): 37–41. http://dx.doi.org/10.47981/j.mijst.10(03)2022.341(37-41).
Full textAbstract:
Electrocardiogram (ECG) signal is informative as well as non-invasive clinical tool to diagnose cardiac diseases of human heart. However, the diagnosis requires professionals’ clarification and is also time-consuming. To make the diagnosis proficient, a novel convolutional neural network (CNN) has been proposed for automatic arrhythmia detection. In this work, the ECG data collected from the MIT-BIH database have been preprocessed, and segmented in short ECG segments of 60 s. Then, all these segments have been transformed into scalogram images obtained from time-frequency analysis using continuous wavelet transform (CWT). Finally, these scalogram images have been exploited as an input for our designed CNN classifier to classify cardiac arrhythmia. In this approach, the overall accuracy, sensitivity, and specificity are 99.39%, 98.79%, and 100% respectively. Proposed CNN model has significant advantages, and it can be used to differentiate the healthy and arrhythmic patients effectively.
45
HASEENA, H., PAUL K. JOSEPH, and ABRAHAM T. MATHEW. "ARTIFICIAL NEURAL NETWORK BASED ECG ARRHYTHMIA CLASSIFICATION." Journal of Mechanics in Medicine and Biology 09, no. 04 (2009): 507–25. http://dx.doi.org/10.1142/s0219519409003103.
Full textAbstract:
Reliable and computationally efficient means of classifying electrocardiogram (ECG) signals has been the subject of considerable research effort in recent years. This paper explores the potential applications of a talented, versatile computation model called the Artificial Neural Network (ANN) in the field of ECG signal classification. Two types of ANNs: Multi-Layered Feed Forward Network (MLFFN) and Probabilistic Neural Networks (PNN) are used to classify seven types of ECG beats. It includes six types of arrhythmia data and normal data. Here, parametric modeling strategies are used in conjunction with ANN classifiers to discriminate ECG signals. Instead of giving the ECG data as such, parameters such as fourth order Auto Regressive model coefficients and Spectral Entropy of the signals has been selected. On testing with the Massachusetts Institute of Technology-Beth Israel Hospital (MIT/BIH) arrhythmia database, it has been observed that PNN has better performance than conventionally used MLFFN in ECG arrhythmia classification. MLFFN with Back Propagation Algorithm gives a classification accuracy of 97.54% and PNN gives 98.96%. The classification by PNN also has an advantage that the computation time for classification is lower than that of MLFFN.
46
Qin, Jing, Fujie Gao, Zumin Wang, Lu Liu, and Changqing Ji. "Arrhythmia Detection Based on WGAN-GP and SE-ResNet1D." Electronics 11, no. 21 (2022): 3427. http://dx.doi.org/10.3390/electronics11213427.
Full textAbstract:
A WGAN-GP-based ECG signal expansion and an SE-ResNet1D-based ECG classification method are proposed to address the problem of poor modeling results due to the imbalanced sample distribution of ECG data sets. The network architectures of WGAN-GP and SE-ResNet1D are designed according to the characteristics of ECG signals so that they can be better applied to the generation and classification of ECG signals. First, ECG data were generated using WGAN-GP on the MIT-BIH arrhythmia database to balance the dataset. Then, the experiments were performed using the AAMI category and inter-patient data partitioning principles, and classification experiments were performed using SE-ResNet1D on the imbalanced and balanced datasets, respectively, and compared with three networks, VGGNet, DenseNet and CNN+Bi-LSTM. The experimental results show that using WGAN-GP to balance the dataset can improve the accuracy and robustness of the model classification, and the proposed SE-ResNet1D outperforms the comparison model, with a precision of 95.80%, recall of 96.75% and an F1 measure of 96.27% on the balanced dataset. Our methods have the potential to be a useful diagnostic tool to assist cardiologists in the diagnosis of arrhythmias.
47
Sun, Ao, Wei Hong, Juan Li, and Jiandong Mao. "An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm." Sensors 24, no. 19 (2024): 6306. http://dx.doi.org/10.3390/s24196306.
Full textAbstract:
Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention mechanism (SE module), convolutional neural network (CNN), and long short-term memory neural network (LSTM). The data of this model use the MIT-BIH arrhythmia database, and after noise reduction of raw ECG data by the EEMD denoising algorithm, a CNN-LSTM is used to learn features from the data, and the fusion channel attention mechanism is used to adjust the weight of the feature map. The CNN-LSTM-SE model is compared with the LSTM, CNN-LSTM, and LSTM-attention models, and the models are evaluated using Precision, Recall, and F1-Score. The classification performance of the tested CNN-LSTM-SE classification prediction model is better, with a classification accuracy of 98.5%, a classification precision rate of more than 97% for each label, a recall rate of more than 98%, and an F1-score of more than 0.98. It meets the requirements of arrhythmia classification prediction and has a certain practical value.
48
Lu, Peng, Yang Gao, Hao Xi, et al. "KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement." Journal of Healthcare Engineering 2021 (April 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/6684954.
Full textAbstract:
Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep neural networks have become a popular method to efficiently and accurately simulate nonlinear patterns of ECG data in a data-driven manner but require more resources. Therefore, it is crucial to design deep learning (DL) algorithms that are more suitable for resource-constrained mobile devices. In this paper, KecNet, a lightweight neural network construction scheme based on domain knowledge, is proposed to model ECG data by effectively leveraging signal analysis and medical knowledge. To evaluate the performance of KecNet, we use the Association for the Advancement of Medical Instrumentation (AAMI) protocol and the MIT-BIH arrhythmia database to classify five arrhythmia categories. The result shows that the ACC, SEN, and PRE achieve 99.31%, 99.45%, and 98.78%, respectively. In addition, it also possesses high robustness to noisy environments, low memory usage, and physical interpretability advantages. Benefiting from these advantages, KecNet can be applied in practice, especially wearable and lightweight mobile devices for arrhythmia classification.
49
Topolski, Mariusz, and Jędrzej Kozal. "Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform." PLOS ONE 16, no. 12 (2021): e0260764. http://dx.doi.org/10.1371/journal.pone.0260764.
Full textAbstract:
Feature extraction is an important part of data processing that provides a basis for more complicated tasks such as classification or clustering. Recently many approaches for signal feature extraction were created. However, plenty of proposed methods are based on convolutional neural networks. This class of models requires a high amount of computational power to train and deploy and large dataset. Our work introduces a novel feature extraction method that uses wavelet transform to provide additional information in the Independent Component Analysis mixing matrix. The goal of our work is to combine good performance with a low inference cost. We used the task of Electrocardiography (ECG) heartbeat classification to evaluate the usefulness of the proposed approach. Experiments were carried out with an MIT-BIH database with four target classes (Normal, Vestibular ectopic beats, Ventricular ectopic beats, and Fusion strikes). Several base wavelet functions with different classifiers were used in experiments. Best was selected with 5-fold cross-validation and Wilcoxon test with significance level 0.05. With the proposed method for feature extraction and multi-layer perceptron classifier, we obtained 95.81% BAC-score. Compared to other literature methods, our approach was better than most feature extraction methods except for convolutional neural networks. Further analysis indicates that our method performance is close to convolutional neural networks for classes with a limited number of learning examples. We also analyze the number of required operations at test time and argue that our method enables easy deployment in environments with limited computing power.
50
Vavekanand, Raja, Kira Sam, Suresh Kumar, and Teerath Kumar. "CardiacNet: A Neural Networks Based Heartbeat Classifications using ECG Signals." Studies in Medical and Health Sciences 1, no. 2 (2024): 1–17. http://dx.doi.org/10.48185/smhs.v1i2.1188.
Full textAbstract:
Obtaining information about the electrical activity of the heart in the form of electrocardiograms (ECG) has become a standard way of monitoring patients’ heart rhythm and function. It is used for diagnosing a variety of cardiac anomalies such as arrhythmia and other heart diseases. However, the interpretation of ECGs requires the expertise of trained physicians, thus bearing the need for tools that automatically classify such signals. In this study we train deep convolutional neural networks (CNNs) to perform binary classification of ECG beats to normal and abnormal. We use transfer learning in order to build models that are fine-tuned on specific patients’ data, after pre-training a generic network on a set of different ECGs selected from the MIT-BIH arrhythmia database. We then compare the performance of the fine-tuned networks against that of individual networks, which are trained only on the ECG data of a single patient, in order to evaluate the overall efficacy of transfer learning on the given problem. We managed to achieve adequate results on both scenarios as the individual classifiers yielded an average of 94.6% balanced accuracy on the test set, whereas the fine-tuned models a marginally worse 93.5%.