Journal articles: 'Arrhythmia classifier'

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Priti, Suryawanshi, and Shailesh Hambarde Dr. "Prediction of Cardiac Arrhythmia using Random Forest Machine Learning Algorithm." Journal of Emerging Technologies and Innovative Research 11, no. 3 (2024): d199—d204. https://doi.org/10.5281/zenodo.10828678.

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   Arrhythmia disease is a common disorder that affects the heart's rhythm and can lead to serious complications. The accurate prediction of arrhythmia is crucial for early diagnosis and effective treatment of patients. In recent years, machine learning algorithms have emerged as a promising approach for predicting arrhythmia disease. Early and precise detection of cardiac arrhythmias is crucial for improved patient outcomes. This investigation delves into the efficacy of a Random Forest (RF) classifier for automated arrhythmia detection using electrocardiogram (ECG) data. The research evaluates the RF model's performance on a publicly available ECG dataset, benchmarking it against existing methodologies. The findings substantiate the effectiveness of the RF classifier in arrhythmia detection, achieving superior accuracy, robustness, and interpretability.

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Vimal, C., and B. Sathish. "Random Forest Classifier Based ECG Arrhythmia Classification." International Journal of Healthcare Information Systems and Informatics 5, no. 2 (2010): 1–10. http://dx.doi.org/10.4018/jhisi.2010040101.

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Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results indicate that a prediction accuracy of more than 98% can be obtained using the proposed method. This system can be further improved and fine-tuned for practical applications.

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SATHYAMANGALAM NATARAJAN, SHIVAPPRIYA, ARUN KUMAR SHANMUGAM, JUDE HEMANTH DURAISAMY, and HARIKUMAR RAJAGURU. "PREDICTION OF CARDIAC ARRHYTHMIA USING MULTI CLASS CLASSIFIERS BY INCORPORATING WAVELET TRANSFORM BASED FEATURES." DYNA 97, no. 4 (2022): 418–24. http://dx.doi.org/10.6036/10458.

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Timely diagnosis and earlier detection of the dangerous heart conditions will reduce the mortality rate and save life of the patient. For that, it is necessary to automate the classi?cation and prediction of Cardiac Arrhythmia. Raw ECG signal is extracted from the MIT-BIH Arrhythmia database, followed by preprocessing and feature extraction using wavelet transform method. Further the extracted features are used for the classification of four different cardiac arrhythmias such as Bradycardia, Tachycardia, Left and Right Bundle Branch Block. Comparative study on the five different classifiers namely Decision trees, Support Vector Machine (SVM), Discriminant Analysis, k-Nearest Neighbor Classifiers (KNN), Ensemble Classifiers, and its variants are experimented in the proposed work. Among these, the weighted KNN classifier gives higher accuracy (90.3%) and prediction speed (10,000 observations per second) with reduced training time (4.329 seconds), compared with the existing state of the art methods. The prediction speed is 10,000 numbers of observations per second which identifies the heart problem earlier, and so appropriate treatment can be given to the patient. To further improve the classification accuracy, three optimizable classifiers namely Optimizable KNN, optimizable SVM, optimizable ensemble are used for the hyper parameter tunning and weight optimization. The optimizable SVM provides better perform (accuracy 93.4 %) among the three optimizable classifiers as well as the existing state of the art works. Therefore, the proposed work used for earlier Cardiac arrhythmia disease diagnosis and prognosis. Keywords: ECG, Cardiac Arrhythmia, Wavelet Transform, Multi class Classifiers, Decision trees, Support Vector Machine (SVM), Discriminant Analysis, k-Nearest Neighbor Classifiers, Ensemble Classifiers, Optimizable classifier.

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Kumar M., Arun, and Arvind Chakrapani. "Classification of ECG signal using FFT based improved Alexnet classifier." PLOS ONE 17, no. 9 (2022): e0274225. http://dx.doi.org/10.1371/journal.pone.0274225.

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Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.

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Jan, Atif, Numan Khurshid, and Muhammad Irfan Khattak. "Developing Resource Efficient Heart Arrhythmia Classifier." International Journal of Computer Applications 109, no. 16 (2015): 35–39. http://dx.doi.org/10.5120/19274-1014.

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Leong, P. H. W., and M. A. Jabri. "A low-power VLSI arrhythmia classifier." IEEE Transactions on Neural Networks 6, no. 6 (1995): 1435–45. http://dx.doi.org/10.1109/72.471380.

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DAS, MANAB KUMAR, and SAMIT ARI. "ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET." Journal of Mechanics in Medicine and Biology 14, no. 05 (2014): 1450066. http://dx.doi.org/10.1142/s0219519414500663.

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In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.

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Arvanaghi, Roghayyeh, Sabalan Daneshvar, Hadi Seyedarabi, and Atefeh Goshvarpour. "CLASSIFICATION OF CARDIAC ARRHYTHMIAS USING ARTERIAL BLOOD PRESSURE BASED ON DISCRETE WAVELET TRANSFORM." Biomedical Engineering: Applications, Basis and Communications 29, no. 05 (2017): 1750034. http://dx.doi.org/10.4015/s101623721750034x.

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Early and correct diagnosis of cardiac arrhythmias is an important step in the treatment of patients. In the recent decades, a wide area of bio-signal processing is allocated to cardiac arrhythmia classification. Unlike other studies, which have employed Electrocardiogram (ECG) signal as a main signal to classify the arrhythmia and sometimes they have used other vital signals as an auxiliary signal to fill missing data and robust detections. In this study, the Arterial Blood Pressure (ABP) is used to classify six types of heart arrhythmias. In other words, in this study for first time, the arrhythmias are classified according ABP signal information. Discrete Wavelet Transform (DWT) is used to de-noise and decompose ABP signal. On feature extraction stage, three types of features including frequency, power, and entropy are extracted. In classification stage, Least Square Support Vector Machine (LS-SVM) is employed as a classifier. The accuracy, sensitivity, and specificity rates of 95.75%, 96.77%, and 96.32% are achieved, respectively. Currently, the classification of cardiac arrhythmias is based on the ABP signal which has some advantages. The recording of ABP signal is done by means of one electrode and therefore it has resulted in lower costs compared with the ECG signal. Finally, it has been shown that ABP has very important and valuable information about the heart performance and can be used in arrhythmia classification.

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Rizqyawan, Muhammad Ilham, Artha Ivonita Simbolon, and Dwi Esti Kusumandari. "Weighted SVM with RR Interval based Features for Android-based Arrhythmia Classifier." Internetworking Indonesia Journal 10, no. 2 (2018): 57–62. https://doi.org/10.5281/zenodo.3257962.

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ECG is one of the most popular fields in biosignals research. One of the popular area in ECG research is automatic Arrhythmia classification. In this paper, we presented an effort to make an Arrhythmia classifier for Android. We use RRI based features and SVM as the classification method. Then we conduct an experiment with three different SVM configuration to see how much improvement can be made by using these configurations. By looking at kappa score as the metrics, the configuration 2 is greatly improve the classifier (169% increase). And by using hyper-parameter tuning we further optimize the classifier as can be seen on result of configuration 3 (10.5% increase).

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Thirrunavukkarasu, R. R., and T. Meera Devi. "Empirical Mode Decomposition with Fuzzy Weight Beetle Swarm Optimization (EMD-FWBSO) Denoising and Enhanced Kernel Support Vector Machine (EKSVM) Classifier for Arrhythmia in Electrocardiogram Recordings." Journal of Medical Imaging and Health Informatics 11, no. 11 (2021): 2778–89. http://dx.doi.org/10.1166/jmihi.2021.3870.

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Elderly persons are generally prone to CHDs (Chronic Heart Diseases). Arrhythmia is a persistent CHD with high mortalities resulting from cardiac failures, heart strokes, and CADs (Coronary Artery Diseases). Arrhythmia can be detected using ECG (Electrocardiogram) signals. ECG signals need to be pre-processed for removing noises present in signals. Since denoising is a significant step in ECG signals. Recently Support Vector Machine -Radial Bias Function (SVM-RBF) classifier is introduced for arrhythmia classification, it doesn’t remove noises presented from the ECG signals. The major aim of the work is to design a new classifier with removed noises and enhanced ECG signal. In this work, EMDs (Empirical Mode Decompositions) is introduced for noise removing which works recursively and dependent on signals called sifting. In EMD, IMFs (Intrinsic Mode Functions) decompose noisy signals into intrinsic oscillatory components adaptively using sifting. Further, FWBSOs (Fuzzy Weight Beetle Swarm Optimizations) are used in this work for optimizing EMDs and IMFs. This work in the initial phase reconstructs ECG signals which are filtered by IMFs. These filters are followed by extraction of morphological features from waves of P-QRS-T while ECG segments are selected using PCAs and DTWs. In the final phase, EKSVMs (Enhanced Kernel Support Vector Machines) classifies extracted features automatically by categorizing ECG signals into Normal and Ventricular Ectopic Beats. This work’s resulted are evaluated with performance metrics of Sensitivity, F-measure, Positive Productivity and Accuracy. This work uses database of MIT-BIH arrhythmia in a 5 fold cross validation for its predictions. The proposed EKSVMs classifier is compared to existing classifiers such as K-Nearest Neighbors (KNN), Enhanced Particle Swarm Optimisation-Multiple Layer Perception (EPSO-MLP) and SVM-RBF. The experiments of the proposed classifier and existing methods are carried out on MATLAB R2018a.

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Cai, Jing, Ge Zhou, Mengkun Dong, Xinlei Hu, Guangda Liu, and Weiguang Ni. "Real-Time Arrhythmia Classification Algorithm Using Time-Domain ECG Feature Based on FFNN and CNN." Mathematical Problems in Engineering 2021 (May 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/6648432.

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To solve the problem of real-time arrhythmia classification, this paper proposes a real-time arrhythmia classification algorithm using deep learning with low latency, high practicality, and high reliability, which can be easily applied to a real-time arrhythmia classification system. In the algorithm, a classifier detects the QRS complex position in real time for heartbeat segmentation. Then, the ECG_RRR feature is constructed according to the heartbeat segmentation result. Finally, another classifier classifies the arrhythmia in real time using the ECG_RRR feature. This article uses the MIT-BIH arrhythmia database and divides the 44 qualified records into two groups (DS1 and DS2) for training and evaluation, respectively. The result shows that the recall rate, precision rate, and overall accuracy of the algorithm’s interpatient QRS complex position prediction are 98.0%, 99.5%, and 97.6%, respectively. The overall accuracy for 5-class and 13-class interpatient arrhythmia classification is 91.5% and 75.6%, respectively. Furthermore, the real-time arrhythmia classification algorithm proposed in this paper has the advantages of practicability and low latency. It is easy to deploy the algorithm since the input is the original ECG signal with no feature processing required. And, the latency of the arrhythmia classification is only the duration of one heartbeat cycle.

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Cao, Minh, Tianqi Zhao, Yanxun Li, Wenhao Zhang, Peyman Benharash, and Ramin Ramezani. "ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique." Journal of Physics: Conference Series 2547, no. 1 (2023): 012031. http://dx.doi.org/10.1088/1742-6596/2547/1/012031.

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Abstract Electrocardiogram (ECG) is a simple non-invasive measure to identify heart-related issues such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine learning is being utilized in a wide range of healthcare related applications and datasets, many arrhythmia classifiers using deep learning methods have been proposed in recent years. However, sizes of the available datasets from which to build and assess machine learning models is often very small and the lack of well-annotated public ECG datasets is evident. In this paper, we propose a deep transfer learning framework that is aimed to perform classification on a small size training dataset. The proposed method is to fine-tune a general-purpose image classifier ResNet-18 with MIT-BIH arrhythmia dataset in accordance with the AAMI EC57 standard. This paper further investigates many existing deep learning models that have failed to avoid data leakage against AAMI recommendations. We compare how different data split methods impact the model performance. This comparison study implies that future work in arrhythmia classification should follow the AAMI EC57 standard when using any including MIT-BIH arrhythmia dataset.

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Sharanya S, Sridhar PA, Poornakala J, Muppala Vasishta, and Tharani U. "Convolution Neural Network Based Ecg Classifier." International Journal of Research in Pharmaceutical Sciences 10, no. 3 (2019): 1626–30. http://dx.doi.org/10.26452/ijrps.v10i3.1327.

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Classification of Electrocardiogram (ECG) signals plays a significant role in the identification of the functioning of the heart. This work pertains with the ECG signals, where the classifier is developed for identification of normal or abnormal conditions of the heart. The raw ECG signals are collected from an online database (www.physioNet.org) for classification. The raw ECG signal is pre-processed for noise removal, and the frequency spectrum is analysed to compare raw and denoised ECG signal. Attributes (P, Q, R, S, T time intervals) from denoised ECG signal is analysed and classified using Convolution Neural Network (CNN). The paper reports a classification technique to differentiate ECG signals from the MIT-BIH database (arrhythmia database, arrhythmia p-wave annotations, atrial fibrillation). The CNN analyses the deviation between nominal ranges of attributes (amplitude and time interval) and classifies between the abnormality and normal ECG wave. This work provides a simple method for interpreting ECG related condition for the clinician and helps medical practitioners to make diagnostic decisions.

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Jabri, Marwan, and Edward Tinker. "Classifier architectures for single chamber arrhythmia recognition." Applied Intelligence 6, no. 3 (1996): 215–24. http://dx.doi.org/10.1007/bf00126627.

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Rezaei, Mercedeh J., John R. Woodward, Julia Ramírez, and Patricia Munroe. "A Novel Two-Stage Heart Arrhythmia Ensemble Classifier." Computers 10, no. 5 (2021): 60. http://dx.doi.org/10.3390/computers10050060.

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Atrial fibrillation (AF) and ventricular arrhythmia (Arr) are among the most common and fatal cardiac arrhythmias in the world. Electrocardiogram (ECG) data, collected as part of the UK Biobank, represents an opportunity for analysis and classification of these two diseases in the UK. The main objective of our study is to investigate a two-stage model for the classification of individuals with AF and Arr in the UK Biobank dataset. The current literature addresses heart arrhythmia classification very extensively. However, the data used by most researchers lack enough instances of these common diseases. Moreover, by proposing the two-stage model and separation of normal and abnormal cases, we have improved the performance of the classifiers in detection of each specific disease. Our approach consists of two stages of classification. In the first stage, features of the ECG input are classified into two main classes: normal and abnormal. At the second stage, the features of the ECG are further categorised as abnormal and further classified into two diseases of AF and Arr. A diverse set of ECG features such as the QRS duration, PR interval and RR interval, as well as covariates such as sex, BMI, age and other factors, are used in the modelling process. For both stages, we use the XGBoost Classifier algorithm. The healthy population present in the data, has been undersampled to tackle the class imbalance present in the data. This technique has been applied and evaluated using an ECG dataset from the UKBioBank ECG taken at rest repository. The main results of our paper are as follows: The classification performance for the proposed approach has been measured using F1 score, Sensitivity (Recall) and Specificity (Precision). The results of the proposed system are 87.22%, 88.55% and 85.95%, for average F1 Score, average sensitivity and average specificity, respectively. Contribution and significance: The performance level indicates that automatic detection of AF and Arr in participants present in the UK Biobank is more precise and efficient if done in a two-stage manner. Automatic detection and classification of AF and Arr individuals this way would mean early diagnosis and prevention of more serious consequences later in their lives.

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N. S. V Rama Raju, N., V. Malleswara Rao, and I. Srinivasa Rao. "Automatic detection and classification of cardiac arrhythmia using neural network." International Journal of Engineering & Technology 7, no. 3 (2018): 1482. http://dx.doi.org/10.14419/ijet.v7i3.14084.

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This paper proposes a Neural Network classifier model for the automatic identification of the ventricular and supraventricular arrhythmias cardiac arrhythmias. The wavelet transform (DWT) and dual tree complex wavelet transform (DTCWT) is applied for QRS complex detec-tion. After segmentation both feature of DWT and DTCWT is combined for feature extraction, statistical feature has been calculated to re-duce the overhead of classifier. An adaptive filtering with the soft computed wavelet thersholding to the signals before the extraction is done in pre-processing. Different ECG database is considered to evaluate the propose work with MIT-BIH database Normal Sinus Rhythm Da-tabase (NSRD) , and MIT-BIH Supraventricular Arrhythmia Database (svdb) .The evaluated outcomes of ECG classification claims 98 -99 % of accuracy under different training and testing situation.

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Guerra, Roger de T., Cristina K. Yamaguchi, Stefano F. Stefenon, Leandro dos S. Coelho, and Viviana C. Mariani. "Deep Learning Approach for Automatic Heartbeat Classification." Sensors 25, no. 5 (2025): 1400. https://doi.org/10.3390/s25051400.

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Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional arrhythmia detection methods are time-consuming, error-prone, and often subjective, making it difficult for doctors to discern between distinct patterns of arrhythmia. To understand ECG signals, this study presents a multi-class classifier and an autoencoder with long short-term memory (LSTM) network layers for extracting signal properties on a dataset from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital (MIT-BIH). The suggested model had an accuracy rate of 98.57% on the arrhythmia dataset and 97.59% on the supraventricular dataset. In contrast to other deep learning models, the proposed model eliminates the problem of the gradient disappearing in classification tasks.

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Zhou, Haiying, Xiancheng Zhu, Sishan Wang, et al. "A Novel Cardiac Arrhythmias Detection Approach for Real-Time Ambulatory ECG Diagnosis." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (2017): 1758004. http://dx.doi.org/10.1142/s0218001417580046.

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In view of requirements of low-resource consumption and high-efficiency in real-time Ambulatory Electrocardiograph Diagnosis (AED) applications, a novel Cardiac Arrhythmias Detection (CAD) algorithm is proposed. This algorithm consists of three core modules: an automatic-learning machine that models diagnostic criteria and grades the emergency events of cardiac arrhythmias by studying morphological characteristics of ECG signals and experiential knowledge of cardiologists; a rhythm classifier that recognizes and classifies heart rhythms basing on statistical features comparison and linear discriminant with confidence interval estimation; and an arrhythmias interpreter that assesses emergency events of cardia arrhythmias basing on a two rule-relative interpretation mechanisms. The experiential results on off-line MIT-BIH cardiac arrhythmia database as well as online clinical testing explore that this algorithm has 92.8% sensitivity and 97.5% specificity in average, so that it is suitable for real-time cardiac arrhythmias monitoring.

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Gautam, Desh D., Vinod K. Giri, and Krishn G. Upadhyay. "Detection of Ventricular Arrhythmias using HRV Analysis and Quadratic Features." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 6 (2020): 847–55. http://dx.doi.org/10.2174/2352096512666191021112835.

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Background : Ventricular Arrhythmias, one of the fatal heart diseases, requires timely recognition. The nonlinear and random nature of heart rate makes the diagnosis challenging. Introduction: The research work in this paper is divided into three phases. In the first phase, classification of some of the ventricular arrhythmias is done in four classes as Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB) with some Normal (N) samples and the analysis of classifying algorithms to improve the classifiers accuracy. A Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN), and K Nearest Neighbor (KNN) algorithms were used to train and test the classifier, with the help of online available MIT-BIH Arrhythmia Database. Then, in the second phase, the variance analysis of the data is carried out using Principle Component Analysis (PCA) to improve the classifier performance. In the last phase, the whole process is repeated after including Quadratic features with the best performing classifier only. Methods: Signal processing, generation of Heart Rate Variability (HRV) signals from the available Electrocardiogram (ECG) signals and training, testing of ANN classifier was done in MATLAB environment, and the training and testing of SVM, and Random Forest classifier was done in R project software. Results: Random Forest shows the best result among all classifiers with 86.11% accuracy, 87.1% after applying PCA with top 16 features, and 91.4% after including quadratic features with top 28 features. Conclusion: The present study envisages helping ECG and HRV data analyses while selecting the AI techniques for classification purposes according to data.

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Ilbeigipour, Sadegh, Amir Albadvi, and Elham Akhondzadeh Noughabi. "Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming." Journal of Healthcare Engineering 2021 (April 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/6624829.

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One of the major causes of death in the world is cardiac arrhythmias. In the field of healthcare, physicians use the patient’s electrocardiogram (ECG) records to detect arrhythmias, which indicate the electrical activity of the patient’s heart. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient’s life. In this research, we used the Structured Streaming module built top on the open-source Apache Spark platform for the first time to implement a machine learning pipeline for real-time cardiac arrhythmias detection and evaluate the impact of using this new module on classification performance metrics and the rate of delay in arrhythmia detection. The ECG data collected from the MIT/BIH database for the detection of three class labels: normal beats, RBBB, and atrial fibrillation arrhythmias. We also developed three decision trees, random forest, and logistic regression multiclass classifiers for data classification where the random forest classifier showed better performance in classification than the other two classifiers. The results show previous results in performance metrics of the classification model and a significant decrease in pipeline runtime by using more class labels compared to previous studies.

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DON, S., DUCKWON CHUNG, DUGKI MIN, and EUNMI CHOI. "ANALYSIS OF ELECTROCARDIOGRAM SIGNALS OF ARRHYTHMIA AND ISCHEMIA USING FRACTAL AND STATISTICAL FEATURES." Journal of Mechanics in Medicine and Biology 13, no. 01 (2013): 1350008. http://dx.doi.org/10.1142/s0219519413500085.

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In this study, we present a three-stage method for detecting abnormalities and classifying electrocardiogram (ECG) beats using a k-nearest neighbor (k-NN) classifier and Gaussian mixture model (GMM). In the first stage, a signal filtering method is used to remove the ECG beat baseline wander. In the second stage, features are extracted based on Higuchi's fractal dimension (HFD) and statistical features. In the third stage, k-NN and GMM are used as classifiers to classify arrhythmia and ischemia. A total of 30,000 ECG segments obtained from the MIT-BIH Arrhythmia and European ST-T Ischemia databases were used to quantify this approach. 60% of the beats were used for training the classifier and the remaining 40%, for validating it. An overall accuracy of 99% and 98.24% was obtained for k-NN and GMM, respectively. This result is significantly better than that of currently used state-of-the-art classification approaches for arrhythmia and ischemia.

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Badr, Malek, Shaha Al-Otaibi, Nazik Alturki, and Tanvir Abir. "Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks." Computational Intelligence and Neuroscience 2022 (August 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/1094830.

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The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.

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Anandha Praba, R., L. Suganthi, E. S. Selva Priya, and J. Jeslin Libisha. "Efficient Cardiac Arrhythmia Detection Using Machine Learning Algorithms." Journal of Physics: Conference Series 2318, no. 1 (2022): 012011. http://dx.doi.org/10.1088/1742-6596/2318/1/012011.

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Abstract The most common type of chronic and life-threatening disease is cardiovascular disease (CVD). For the early prediction of arrhythmia, electrocardiogram (ECG) is recorded from the patients, non-invasively using surface electrode. In this approach, Empirical Mode Decomposition (EMD) is performed for noise removal followed by Pan Tompkins algorithm for feature extraction. To reduce the amount of signal characteristics and computation time, Principal Component Analysis (PCA) is utilized. Finally, two classifiers, The Support Vector Machine (SVM) and the Naive Bayes (NB) classifier is used to determine the cardiac abnormality from the ECG signal. The comparison is made between the two classifiers and their accuracy will be analysed. We obtained 89% accuracy for SVM and 99% for NB classifier. Lakhs of samples will be available in the Physionet. The amplitude of the signal is 0.1 Mv and time period (T) is 10ms and the frequency of 100Hz. The Confusion Matrix can then be used to assess how well an ECG signal is performing. A MATLAB program is used which has the capacity to observe the ECG bio-signal on a computer.

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Mitchell, Henry, Nicole Rosario, Carme Hernandez, Stuart R. Lipsitz, and David M. Levine. "Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data." Open Heart 10, no. 2 (2023): e002228. http://dx.doi.org/10.1136/openhrt-2022-002228.

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BackgroundComputer-assisted interpretation of single-lead ECG is the preliminary method for clinicians to flag and further evaluate an arrhythmia of clinical importance for acutely ill patients. Critical scrutiny of novel detection algorithms is lacking, particularly in external real-world data sets. This study’s objective was to evaluate a hybrid machine learning model’s ability to classify eight arrhythmias from a single-lead ECG signal from acutely ill patients.MethodsThis cross-sectional external retrospective evaluation of a previously trained hybrid machine learning model against an ECG reading team in the setting of home hospital care (acute care delivered at home substituting for traditional hospital care) draws from patients admitted at two hospitals in Boston, Massachusetts, USA between 12 June 2017 and 23 November 2019. We calculated classifier statistics for each arrhythmia, all arrhythmias and strips where the model identified normal sinus rhythm.ResultsThe model analysed 2 680 162 min of single-lead ECG data from 423 patients and identified 691 478 arrhythmias. Patients had a mean age of 70 years (SD, 18), 60% were female and 45% were white. For any arrhythmia, the model had a sensitivity of 98%, a specificity of 100%, an accuracy of 98%, a positive predictive value of 100%, a negative predictive value of 93% and an F1Score of 99%. Performance was best for pause (F1Score, 99%) and worst for paroxysmal supraventricular tachycardia (F1Score, 92%). The model’s false positive rate for any arrhythmia was 0.2%, ranging from 0.4% for pause to 7.2% for paroxysmal supraventricular tachycardia. The false negative rate for any arrhythmia was 1.9%.ConclusionsA hybrid machine learning model was effective at classifying common cardiac arrhythmias from a single-lead ECG in real-world data.

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Deepak H. A. and Vijayakumar T. "Cardiac Arrhythmia, CHF, and NSR Classification With NCA-Based Feature Fusion and SVM Classifier." International Journal of Software Innovation 11, no. 1 (2023): 1–24. http://dx.doi.org/10.4018/ijsi.315659.

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An arrhythmia is an irregular heartbeat that causes abnormal heart rhythms. Manual analysis of electrocardiogram (ECG) signals is not sufficient to quickly detect cardiac arrhythmias. This study proposes a deep learning approach based on a convolutional neural network (CNN) architecture for the classification of cardiac arrhythmias (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). First, the ECG signal is converted into a 2D image using time-frequency conversion. The scalogram is constructed using a continuous wavelet transform to extract dynamic features. With CNN, each ECG signal is broken down into heartbeats, and then each heartbeat is converted into a 2D grayscale image of the heartbeat. Morphological feature extraction was performed by segmenting the QRS complex and detecting P and T waves. A third approach to feature extraction is dual-tree complex wavelet transform (DT-CWT). In addition, all extracted features are combined using neighborhood component analysis (NCA), and features are selected to classify using a support vector machine (SVM) classifier.

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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Calcium Channel Parameters With KNN And ANN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 919–25. http://dx.doi.org/10.18137/cardiometry.2022.25.919925.

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Aim: Aim of this research is to analyze and compare ventricular cardiac arrhythmia classification using calcium channel parameters with Artificial Neural Network (ANN) and K- Nearest Neighbour (KNN) classifier. Materials and Methods: For the classification of arrhythmias, A.V.Panifilov (AVP) is used. THVCM contains well defined Calcium channel dynamics and its properties. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier such as K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifiers to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Results: The results obtained from Normal, Tachycardia and Bradycardia data are imported to the ANN and KNN classifier. In which KNN shows accuracy value (12.3950%), standard deviation (0.96490) and Standard error mean (0.21576). And ANN shows accuracy value (35.3400%), standard deviation (3.22285) and Standard error mean (0.72065). Conclusion: From the results, it is concluded that ANN produces better results when compared with KNN classification in terms of accuracy.

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Sundari, Tribhuvanam, C. Nagaraj H, and P. S. Naidu V. "Analysis and classification of ECG beat based on wavelet decomposition and SVM." Indian Journal of Science and Technology 13, no. 24 (2020): 2404–17. https://doi.org/10.17485/IJST/v13i24.452.

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Abstract Objectives: To extract the features of single arrhythmia ECG beat. To develop efficient algorithms for automated detection of arrhythmia based on ECG. Methods/Statistical analysis: The methodology includes pre-processing and segmentation of ECG. Extraction of ECG features are to support the ECG beat classification and analysis of cardiac abnormalities using machine learning techniques. Wavelet decomposition is considered for feature extraction and classification with multiclass support vector machine. Findings: This work evaluates the suitability of the wavelet features of ECG for classifier. The proposed arrhythmia classifier results in an accuracy up to 98% for various classes of arrhythmia considered in this work. Novelty/Applications: This work is an assistive tool for medical practitioners to examine ECG in a limited time with their expertise to make the accurate abnormality diagnosis of the arrhythmia.   Keywords: Arrhythmia; classification; feature extraction; support vector machine; wavelet decomposition

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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Sodium Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 911–18. http://dx.doi.org/10.18137/cardiometry.2022.25.911918.

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Aim: Aim of this research is to analyze and compare ventricular Cardiac Arrhythmia (CA) classification using Sodium Channel (Na+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbour (KNN) classifiers. Materials and Methods: Ten Tusscher Human Ventricular Cell Model (THVCM) (data) is used for arrhythmias classification. THVCM has well defined sodium (Na+) channel dynamics. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier, K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifier to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Result: Ventricular normal, tachycardia and bradycardia data are fed into novel ANN and KNN classifiers. The results obtained from classifiers for 20 samples are fed to SPSS. In that ANN shows accuracy of 35.6% with standard deviation (3.17822) and Standard error mean (0.71067). Similarly KNN produces an accuracy value of 18.05% with standard deviation (1.19593) and Standard error mean (0.26739). Conclusion: As per the results, it clearly shows that the novel ANN has better accuracy for classification than KNN.

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Ben Itzhak, Sagi, Shir Sharony Ricon, Shany Biton, Joachim A. Behar, and Jonathan A. Sobel. "Effect of temporal resolution on the detection of cardiac arrhythmias using HRV features and machine learning." Physiological Measurement 43, no. 4 (2022): 045002. http://dx.doi.org/10.1088/1361-6579/ac6561.

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Abstract Objective. Arrhythmia is an abnormal cardiac rhythm that affects the pattern and rate of the heartbeat. Wearable devices with the functionality to measure and store heart rate (HR) data are growing in popularity and enable diagnosing and monitoring arrhythmia on a large scale. The typical sampling resolution of HR data available from non-medical grade wearable devices varies from seconds to several minutes depending on the device and its settings. However, the impact of sampling resolution on the performance and quality of arrhythmia detection has not yet been quantified. Approach. In this study, we investigated the detection and classification of three arrhythmias, namely atrial fibrillation, bradycardia, tachycardia, from down-sampled HR data with various temporal resolution (5-, 15-, 30- and 60 s averages) in 1 h segments extracted from an annotated Holter ECG database acquired at the University of Virginia Heart Station. For the classification task, a total of 15 common heart rate variability (HRV) features were engineered based on the HR time series of each patient. Three different types of machine learning classifiers were evaluated, namely logistic regression, support vector machine and random forest. Main results. A decrease in temporal resolution drastically impacted the detection of atrial fibrillation but did not substantially affect the detection of bradycardia and tachycardia. A HR resolution up to 15 s average demonstrated reasonable performance with a sensitivity of 0.92 and a specificity of 0.86 for a multiclass random forest classifier. Significance. HRV features extracted from low resolution long HR recordings have the potential to increase the early detection of arrhythmias in undiagnosed individuals.

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Jain, Rajat, Pranam R. Betrabet, B. Ashwath Rao, and N. V. Subba Reddy. "Classification of Cardiac Arrhythmia using improved Feature Selection methods and Ensemble Classifiers." Journal of Physics: Conference Series 2161, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2161/1/012003.

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Abstract Arrhythmia is one of the life-threatening heart diseases which is diagnosed and analyzed using electrocardiogram (ECG) recordings and other symptoms namely rapid heartbeat or chest-pounding, shortness of breath, near fainting spells, insufficient pumping of blood from the heart, etc along with sudden cardiac arrest. Arrhythmia records a hasty and aberrant ECG. In this implementation, the arrhythmia dataset is collected from the UCI machine learning repository and then classified the records into sixteen stated classes using multiclass classification. The large feature set of the dataset is reduced using improved feature selection techniques such as t-Distributed Stochastic Neighbor Embedding (TSNE), Principal Component Analysis (PCA), Uniform Manifold Approximation, and Projection (UMAP) and then an Ensemble Classifier is built to analyse the classification accuracy on arrhythmia dataset to conclude when and which approach gives optimal results.

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SREE, S. VINITHA, DHANJOO N. GHISTA, and KWAN-HOONG NG. "CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 05 (2012): 1240032. http://dx.doi.org/10.1142/s0219519412400325.

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An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

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Deng, Jiawen, Jieru Ma, Jie Yang, et al. "An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection." Applied Sciences 14, no. 1 (2023): 342. http://dx.doi.org/10.3390/app14010342.

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Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.

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Boublenza, Amina, M. Amine Chikh, and Sarra Bouchikhi. "Classifier Set Selection for Cardiac Arrhythmia Recognition Using Diversity." Journal of Medical Imaging and Health Informatics 5, no. 3 (2015): 513–19. http://dx.doi.org/10.1166/jmihi.2015.1413.

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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.

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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.

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Kurniawan, Arief, Eko Mulyanto Yuniarno, Eko Setijadi, Mochamad Yusuf Alsagaff, Gijsbertus Jacob Verkerke, and I. Ketut Eddy Purnama. "Detection of multi-class arrhythmia using heuristic and deep neural network on edge device." International Journal of Advances in Intelligent Informatics 9, no. 3 (2023): 429. http://dx.doi.org/10.26555/ijain.v9i3.1061.

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Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based on the QRS complex morphology. We proposed an edge device that detects QRS complexes based on variance analysis (QVAT) and the arrhythmia classification based on the QRS complex spectrogram. The classifier uses two-dimensional convolutional neural network (2D CNN) deep learning. We use a single board computer and neural network compute stick to implement the edge device. The outcomes are a prototype device cardiologists use as a supporting tool for analysing ECG signals, and patients can also use it for self-tests to figure out their heart health. To evaluate the performance of our edge device, we tested using the MIT-BIH database because other methods also use the data. The QVAT sensitivity and predictive positive are 99.81% and 99.90%, respectively. Our classifier's accuracy, sensitivity, predictive positive, specificity, and F1-score are 99.82%, 99.55%, 99.55%, 99.89%, and 99.55%, respectively. The experiment result of arrhythmia classification shows that our method outperforms the others. Still, for r-peak detection, the QVAT implemented in an edge device is comparable to the other methods. In future work, we can improve the performance of r-peak detection using the double-check algorithm in QVAT and cross-check the QRS complex detection by adding 1 class to the classifier, namely the non-QRS class.

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CHIKH, M. A., and OMAR BEHADADA. "A PVC BEATS RECOGNITION USING FUZZY CLASSIFIER." Journal of Mechanics in Medicine and Biology 10, no. 02 (2010): 327–39. http://dx.doi.org/10.1142/s021951941000337x.

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This article describes a fuzzy classifier for the identification of premature ventricular complexes (PVCs) in surface electrocardiograms (ECGs). The classifier uses features extracted from the ECG beat, such as the width of QRS complex and RR interval. The performance of the algorithm is evaluated on the MIT-BIH Arrhythmia Database following the AAMI recommendations. The results of the experiments of the recognition of PVCs have confirmed the reliability and advantage of the proposed approach.

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LAKSHMI, KALAPALA SRI, NARAGANI GAYATHRI, PIRAKALA YESWITHA, MOTHUKURI HARSHA VARDHAN, and PIDUGU HEMANTH KUMAR. "ARRHYTHMIA DISEASE DIAGNOSIS BASED ON ECG TIME–FREQUENCY DOMAIN FUSION AND CONVOLUTIONAL NEURAL NETWORK." Fuzzy Systems and Soft Computing 20, no. 01 (2025): 01–10. https://doi.org/10.36893/fssc.2025.v20.011.

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This task looks to work on cardiovascular conclusion by using Electrocardiogram (ECG) signals for the brief location of arrhythmia side effects, fundamental for powerful treatment and anticipation of adverse consequences. Customary ECG symptomatic procedures often disregard timerecurrence area data, prompting extended and less effective investigations. A remarkable strategy is proposed to ease this limitation, using Convolutional Neural Networks (CNNs) to examine improved arrhythmia recognition methods. The task tries to make a solid arrhythmia demonstrative framework by consolidating time-recurrence space information to improve indicative effectiveness and accuracy. The model-building step envelops the execution of numerous algorithms: CNN, LSTM, CNN + LSTM, Casting a ballot Classifier (incorporating Irregular Woodland and AdaBoost), and Stacking Classifier (blending Random Forest and MLP with LightGBM). Primer preliminaries using the MITTouch and PTDBD datasets yield empowering results, with the CNN model achieving an accuracy of 99.43%. Extra enhancements are looked for by group strategies as CNN+LSTM, Casting a ballot Classifier, and Stacking Classifier, holding back nothing levels. This work implies a significant movement in cardiovascular diagnostics, utilizing deep learning and gathering strategies to improve the utilization of ECG signal data for upgraded patient results.

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Jeong, Yeji, Jaewon Lee, and Miyoung Shin. "Enhancing Inter-Patient Performance for Arrhythmia Classification with Adversarial Learning Using Beat-Score Maps." Applied Sciences 14, no. 16 (2024): 7227. http://dx.doi.org/10.3390/app14167227.

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Research on computer-aided arrhythmia classification is actively conducted, but the limited generalization capacity constrains its applicability in practical clinical settings. One of the primary challenges in deploying such techniques in real-world scenarios is the inter-patient variability and the consequent performance degradation. In this study, we leverage our previous innovation, the n-beat-score map (n-BSM), to introduce an adversarial framework to mitigate the issue of poor performance in arrhythmia classification within the inter-patient paradigm. The n-BSM is a 2D representation of the ECG signal, capturing its constituent beat characteristics through beat-score vectors derived from a pre-trained beat classifier. We employ adversarial learning to eliminate patient-dependent features during the training of the beat classifier, thereby generating the patient-independent n-BSM (PI-BSM). This approach enables us to concentrate primarily on the learning characteristics associated with beat type rather than patient-specific features. Through a beat classifier pre-trained with adversarial learning, a series of beat-score vectors are generated for the beat segments that make up a given ECG signal. These vectors are then concatenated chronologically to form a PI-BSM. Utilizing PI-BSMs as the input, an arrhythmia classifier is trained to differentiate between distinct types of rhythms. This approach yields a 14.27% enhancement in the F1-score in the MIT-BIH arrhythmia database and a 4.97% improvement in cross-database evaluation using the Chapman–Shaoxing 12-lead ECG database.

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Padmavathi, Kora, and K. Sri Ramakrishna. "Detection of Atrial Fibrillation using Autoregressive modeling." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 1 (2015): 64. http://dx.doi.org/10.11591/ijece.v5i1.pp64-70.

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A‎atrial fibrillation (AF) is the arrhythmia that commonly causes death in the adults. We measured AR coefficients using Burg’s method for each 15 second segment of ECG. These features are classified using the different statistical classifiers: kernel SVM and KNN classifier. The performance of the algorithm was evaluated on signals from MIT Physionet database.. The effect of AR model order and data length was tested on the classification results. This method shows better results can be used for practical use in the clinics.‏

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Ahmed, Frahan, Li Chen, and Toukir Ahmed Md. "A Qualitative Overview of Fuzzy Logic in ECG Arrhythmia Classification." International Journal of Engineering Works (ISSN: 2409-2770) 05, no. 11 (2018): 232–39. https://doi.org/10.5281/zenodo.1486134.

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Achieving elevated efficiency for the classification of the ECG signal is a noteworthy issue in the present world. Electrocardiogram (ECG) is a technique to identify heart diseases. However, the detection of the actual type of heart diseases is indispensable for further treatment. Various techniques have been invented and explored to categorize the heart diseases which are recognized as arrhythmias. This paper aims to investigate the development of various techniques of arrhythmia classification on the basis of fuzzy logic along with an elaborative discussion on accepted techniques. Moreover, a comparative study on their efficiency has been analyzed to emphasize the scope of novel research areas. 

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Chen, Hanjie, Saptarshi Das, John Morgan, and Koushik Maharatna. "An effective PSR-based arrhythmia classifier using self-similarity analysis." Biomedical Signal Processing and Control 69 (August 2021): 102851. http://dx.doi.org/10.1016/j.bspc.2021.102851.

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Kusumandari, D. E., M. I. Rizqyawan, M. Yazir, M. Turnip, A. Darma, and A. Turnip. "Application of convolutional neural network classifier for wireless arrhythmia detection." Journal of Physics: Conference Series 1080 (August 2018): 012048. http://dx.doi.org/10.1088/1742-6596/1080/1/012048.

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Tran, Hoai Linh, Van Nam Pham, and Duc Thao Nguyen. "A hardware implementation of intelligent ECG classifier." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 3 (2015): 905–19. http://dx.doi.org/10.1108/compel-05-2014-0119.

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Purpose – The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications. Design/methodology/approach – The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded into the device. The device performance was tested with the ECG signals from the MIT-BIH database to prove the correctness of the hardware implementations. Findings – The FPAA and Programmable System on Chip (PSoC) technologies allow us to integrate many signal processing blocks in a compact device. In this paper the device has the same performance in ECG signal processing and classifying as achieved on PC simulators. This confirms the correctness of the implementation. Research limitations/implications – The device was fully tested with the signals from the MIT-BIH databases. For new patients, we have tested the device in collecting the ECG signals and QRS detections. We have not created a new database of ECG signals, in which the beats are examined by doctors and annotated the type of the rhythm (normal or abnormal, which type of arrhythmia, etc.) so we have not tested the classification mode of the device on real ECG signals. Social implications – The compact design of an intelligent ECG classifier offers a portable solution for patients with heart diseases, which can help them to detect the arrhythmia on time when the doctors are not nearby. This type of device not only may help to improve the patients’ safety but also contribute to the smart, inter-networked life style. Originality/value – The device integrate a number of solutions including software, hardware and algorithms into a single, compact device. Thank to the advance of programmable ICs such as FPAA and PSoC, the designed device can acquire one channel of ECG signals, extract the features and classify the arrhythmia type (if detected) using the neuro-fuzzy TSK network in online mode.

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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).

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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.

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CHU, JINGHUI, HONG WANG, and WEI LU. "A NOVEL TWO-LEAD ARRHYTHMIA CLASSIFICATION SYSTEM BASED ON CNN AND LSTM." Journal of Mechanics in Medicine and Biology 19, no. 03 (2019): 1950004. http://dx.doi.org/10.1142/s0219519419500040.

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Arrhythmia classification is useful during heart disease diagnosis. Although well-established for intra-patient diagnoses, inter-patient arrhythmia classification remains difficult. Most previous work has focused on the intra-patient condition and has not followed the Association for the Advancement of Medical Instrumentation (AAMI) standards. Here, we propose a novel system for arrhythmia classification based on multi-lead electrocardiogram (ECG) signals. The core of the design is that we fuse two types of deep learning features with some common traditional features and select discriminating features using a binary particle swarm optimization algorithm (BPSO). Then, the feature vector is classified using a weighted support vector machine (SVM) classifier. For a better generalization of the model and to draw fair comparisons, we carried out inter-patient experiments and followed the AAMI standards. We found that, when using common metrics aimed at multi-classification either macro- or micro-averaging, our system outperforms most other state-of-the-art methods.

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Yılmaz, Ersen. "An Expert System Based on Fisher Score and LS-SVM for Cardiac Arrhythmia Diagnosis." Computational and Mathematical Methods in Medicine 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/849674.

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An expert system having two stages is proposed for cardiac arrhythmia diagnosis. In the first stage, Fisher score is used for feature selection to reduce the feature space dimension of a data set. The second stage is classification stage in which least squares support vector machines classifier is performed by using the feature subset selected in the first stage to diagnose cardiac arrhythmia. Performance of the proposed expert system is evaluated by using an arrhythmia data set which is taken from UCI machine learning repository.

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BENALI, R., N. DIB, and F. REGUIG BEREKSI. "CARDIAC ARRHYTHMIA DIAGNOSIS USING A NEURO-FUZZY APPROACH." Journal of Mechanics in Medicine and Biology 10, no. 03 (2010): 417–29. http://dx.doi.org/10.1142/s021951941000354x.

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The ventricular premature contractions (VPC) are cardiac arrhythmias that are widely encountered in the cardiologic field. They can be detected using the electrocardiogram (ECG) signal parameters. A novel method for detecting VPC from the ECG signal is proposed using a new algorithm (Slope) combined with a fuzzy-neural network (FNN). To achieve this objective, an algorithm for QRS detection is first implemented, and then a neuro-fuzzy classifier is developed. Its performances are evaluated by computing the percentages of sensitivity (SE), specificity (SP), and correct classification (CC). This classifier allows extraction of rules (knowledge base) to clarify the obtained results. We use the medical database (MIT-BIH) to validate our results.

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ÖZDEMİR, Ahmet Turan, and Kenan DANIŞMAN. "A comparative study of two different FPGA-based arrhythmia classifier architectures." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 23 (2015): 2089–106. http://dx.doi.org/10.3906/elk-1305-41.

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Huang, Junguang, Zijing Liu, and Hao Liu. "An Efficient Arrhythmia Classifier Using Convolutional Neural Network with Incremental Quantification." Journal of Physics: Conference Series 1966, no. 1 (2021): 012022. http://dx.doi.org/10.1088/1742-6596/1966/1/012022.

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Taher, Fatma, Hamoud Alshammari, Lobna Osman, Mohamed Elhoseny, Abdulaziz Shehab, and Eman Elayat. "Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach." Computers, Materials & Continua 75, no. 2 (2023): 4485–99. http://dx.doi.org/10.32604/cmc.2023.036118.

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Journal articles on the topic 'Arrhythmia classifier'

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