Journal articles: 'Morphological features of ECG'

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Anwar, Syed Muhammad, Maheen Gul, Muhammad Majid, and Majdi Alnowami. "Arrhythmia Classification of ECG Signals Using Hybrid Features." Computational and Mathematical Methods in Medicine 2018 (November 12, 2018): 1–8. http://dx.doi.org/10.1155/2018/1380348.

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Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes. RR interval information is used as a dynamic feature. The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification. Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features. These hybrid features are combined and fed to a neural network to classify arrhythmia. The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats. The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.

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Lu, Peng, Hao Xi, Bing Zhou, Hongpo Zhang, Yusong Lin, Liwei Chen, Yang Gao, Yabin Zhang, and Yanhua Hu. "A New Multichannel Parallel Network Framework for the Special Structure of Multilead ECG." Journal of Healthcare Engineering 2020 (December 3, 2020): 1–15. http://dx.doi.org/10.1155/2020/8889483.

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Electrocardiogram (ECG) contains the rhythmic features of continuous heartbeat and morphological features of ECG waveforms and varies among different diseases. Based on ECG signal features, we propose a combination of multiple neural networks, the multichannel parallel neural network (MLCNN-BiLSTM), to explore feature information contained in ECG. The MLCNN channel is used in extracting the morphological features of ECG waveforms. Compared with traditional convolutional neural network (CNN), the MLCNN can accurately extract strong relevant information on multilead ECG while ignoring irrelevant information. It is suitable for the special structures of multilead ECG. The Bidirectional Long Short-Term Memory (BiLSTM) channel is used in extracting the rhythmic features of ECG continuous heartbeat. Finally, by initializing the core threshold parameters and using the backpropagation algorithm to update automatically, the weighted fusion of the temporal-spatial features extracted from multiple channels in parallel is used in exploring the sensitivity of different cardiovascular diseases to morphological and rhythmic features. Experimental results show that the accuracy rate of multiple cardiovascular diseases is 87.81%, sensitivity is 86.00%, and specificity is 87.76%. We proposed the MLCNN-BiLSTM neural network that can be used as the first-round screening tool for clinical diagnosis of ECG.

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Kamath, Chandrakar. "ECG classification using morphological features derived from symbolic dynamics." International Journal of Biomedical Engineering and Technology 9, no. 4 (2012): 325. http://dx.doi.org/10.1504/ijbet.2012.049217.

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Can Ye, B. V. K. Vijaya Kumar, and M. T. Coimbra. "Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals." IEEE Transactions on Biomedical Engineering 59, no. 10 (October 2012): 2930–41. http://dx.doi.org/10.1109/tbme.2012.2213253.

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Rodrigues, João, David Belo, and Hugo Gamboa. "Noise detection on ECG based on agglomerative clustering of morphological features." Computers in Biology and Medicine 87 (August 2017): 322–34. http://dx.doi.org/10.1016/j.compbiomed.2017.06.009.

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Choi, Gyu Ho, Hoon Ko, Witold Pedrycz, Amit Kumar Singh, and Sung Bum Pan. "Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics." Sensors 20, no. 24 (December 12, 2020): 7130. http://dx.doi.org/10.3390/s20247130.

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Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.

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Yücelbaş, Şule, Cüneyt Yücelbaş, Gülay Tezel, Seral Özşen, Serkan Küççüktürk, and Şebnem Yosunkaya. "Pre-determination of OSA degree using morphological features of the ECG signal." Expert Systems with Applications 81 (September 2017): 79–87. http://dx.doi.org/10.1016/j.eswa.2017.03.049.

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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 (August 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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Liang, Yongbo, Zhencheng Chen, Rabab Ward, and Mohamed Elgendi. "Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database." Diagnostics 8, no. 3 (September 10, 2018): 65. http://dx.doi.org/10.3390/diagnostics8030065.

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Cardiovascular diseases (CVDs) have become the biggest threat to human health, and they are accelerated by hypertension. The best way to avoid the many complications of CVDs is to manage and prevent hypertension at an early stage. However, there are no symptoms at all for most types of hypertension, especially for prehypertension. The awareness and control rates of hypertension are extremely low. In this study, a novel hypertension management method based on arterial wave propagation theory and photoplethysmography (PPG) morphological theory was researched to explore the physiological changes in different blood pressure (BP) levels. Pulse Arrival Time (PAT) and photoplethysmogram (PPG) features were extracted from electrocardiogram (ECG) and PPG signals to represent the arterial wave propagation theory and PPG morphological theory, respectively. Three feature sets, one containing PAT only, one containing PPG features only, and one containing both PAT and PPG features, were used to classify the different BP categories, defined as normotension, prehypertension, and hypertension. PPG features were shown to classify BP categories more accurately than PAT. Furthermore, PAT and PPG combined features improved the BP classification performance. The F1 scores to classify normotension versus prehypertension reached 84.34%, the scores for normotension versus hypertension reached 94.84%, and the scores for normotension plus prehypertension versus hypertension reached 88.49%. This indicates that the simultaneous collection of ECG and PPG signals could detect hypertension.

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Belova, G. V., O. S. Rudenko, T. A. Karmakova, M. E. Yudakova, and V. S. Sidorenko. "Barrett’s esophagus: genetic features." Experimental and Clinical Gastroenterology 174, no. 5 (June 29, 2020): 36–41. http://dx.doi.org/10.31146/1682-8658-ecg-177-5-36-41.

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The aim of the study was to evaluate the genetic characteristics of the pathological process in patients with biopsy-proven PB. Materials and methods: Dynamic observation and treatment of patients with morphologically confi rmed diagnosis of Barrett’s esophagus (PB) was performed at the Bank of Russia Multidisciplinary medical center in Moscow in the period from 2014 to 2019.the study included 52 patients. Among them, men — 33 (63.4%) and women –19 (36.5%) aged from 28 to 70 years, the peak incidence was mainly in the age range from 50–71 years. As a result, morphological types of PB epithelium were identifi ed at the time of the study: cardiac — 8(15.3%), small — bowel — 25 (48%), large-bowel without dysplasia-17 (32.6%) and large-bowel with mild dysplasia foci –2 (3.8%). The expression of the p53, P63, and Ki-67 genes responsible for cell proliferation and diff erentiation was studied using an immunohistochemical method. Results. As a result of the study, it was found that genetic changes in patients with morphologically confi rmed PB increase from the cardiac type of epithelium in the segment to the detection of dysplasia foci on the background of colonic metaplasia. An exception is tokokishechnaya metaplasia, which may be an indirect sign of the development of highly diff erentiated epithelium in response to gastroesophageal or duodenogastroesophageal refl ux Summary. 1. Mutation of the p 53 genes responsible for regulating cell transcription and activating apoptosis and the P63 gene responsible for cell diff erentiation occurs in all morphological types of PB, with the number of mutations increasing as metastatic changes progress in accordance with the Correa cascade 2. The combination of p53 and P63 gene mutations in almost equal proportions indicates parallel processes of cell proliferation and diff erentiation disorders. 3. Ki-67 expression increases from the cardiac type of epithelium in the segment to the detection of dysplasia foci on the background of metaplastic epithelium. An exception is tokokishechnaya metaplasia, which may be an indirect sign of the development of highly diff erentiated epithelium in response to gastroesophageal or duodenogastroesophageal refl ux. Conclusion. Studies have shown that there are a number of genetic features of the pathological process in patients with morphologically confi rmed PB, the combination of which is more reliable and reliable assessment of the risk of neoplastic changes than individual indicators. To date, it remains relevant to develop a marker panel that is suitable for use in clinical practice, informative for both assessing individual risk and stratifying risk groups, and useful for monitoring the eff ectiveness of treatment.

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Elangovan, Ramanujam, and Padmavathi S. "A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification." International Journal of Artificial Intelligence and Machine Learning 9, no. 2 (July 2019): 39–56. http://dx.doi.org/10.4018/ijaiml.2019070103.

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Cardiovascular disease diagnosis from an ECG signal plays an important and significant role in the health care system. Recently, numerous researchers have developed an automatic time series-based multi-step diagnosis system for the fast and accurate diagnosis of ECG abnormalities. The multi-step procedure involves ECG signal acquisition, signal pre-processing, feature extraction, and classification. Among which, the feature extraction plays a vital role in the field of accurate diagnosis. The features may be different types such as statistical, morphological, wavelet or any other signal-based approach. This article discusses various time series motif-based feature extraction techniques with respect to a different dimension of ECG signal.

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Lin, Chun-Cheng, and Chun-Min Yang. "Heartbeat Classification Using Normalized RR Intervals and Morphological Features." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/712474.

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This study developed an automatic heartbeat classification system for identifying normal beats, supraventricular ectopic beats, and ventricular ectopic beats based on normalized RR intervals and morphological features. The proposed heartbeat classification system consists of signal preprocessing, feature extraction, and linear discriminant classification. First, the signal preprocessing removed the high-frequency noise and baseline drift of the original ECG signal. Then the feature extraction derived the normalized RR intervals and two types of morphological features using wavelet analysis and linear prediction modeling. Finally, the linear discriminant classifier combined the extracted features to classify heartbeats. A total of 99,827 heartbeats obtained from the MIT-BIH Arrhythmia Database were divided into three datasets for the training and testing of the optimized heartbeat classification system. The study results demonstrate that the use of the normalized RR interval features greatly improves the positive predictive accuracy of identifying the normal heartbeats and the sensitivity for identifying the supraventricular ectopic heartbeats in comparison with the use of the nonnormalized RR interval features. In addition, the combination of the wavelet and linear prediction morphological features has higher global performance than only using the wavelet features or the linear prediction features.

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Singh, Yogendra Narain. "Individual Identification Using Linear Projection of Heartbeat Features." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/602813.

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This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively.

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Choi, Gyu Ho, Kiho Lim, and Sung Bum Pan. "Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles." Sensors 21, no. 1 (December 30, 2020): 202. http://dx.doi.org/10.3390/s21010202.

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Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the intensity of noise is strong because the driver’s motion artifact is included. Existing time, frequency, and phase normalization methods have a problem of distorting P, QRS Complexes, and T waves, which are morphological features of an ECG, or normalizing to signals containing noise. In this paper, we propose an adaptive threshold filter-based driver identification system to solve the problem of distortion of the ECG morphological features when normalized and the motion artifact noise of the ECG that causes the identification performance deterioration in the driving environment. The experimental results show that the proposed method improved the average similarity compared to the results without normalization. The identification performance was also improved compared to the results before normalization.

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Silvestri, Francesca, Simone Acciarito, and Gauray Mani Khanal. "Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features." International Journal on Advanced Science, Engineering and Information Technology 9, no. 2 (April 5, 2019): 601. http://dx.doi.org/10.18517/ijaseit.9.2.8296.

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Matveev, Mikhail, Ivaylo Christov, Vessela Krasteva, Giovanni Bortolan, Dimitar Simov, Nikolay Mudrov, and Irena Jekova. "Assessment of the stability of morphological ECG features and their potential for person verification/identification." MATEC Web of Conferences 125 (2017): 02004. http://dx.doi.org/10.1051/matecconf/201712502004.

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Tao, Liang, Baoning Liu, and Wei Liang. "Automated Detection of Arrhythmia for Hybrid Neural Network of LSTM-Residual with Multi-Information Fusion." Mathematical Problems in Engineering 2021 (July 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/2831064.

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Arrhythmia is a common cardiovascular disease; the electrocardiogram (ECG) is widely used as an effective tool for detecting arrhythmia. However, real-time arrhythmia detection monitoring is difficult, so this study proposes a long short-term memory-residual model. Individual beats provide morphological features and combined with adjacent segments provide temporal features. Our proposed model captures the time-domain and morphological ECG signal information simultaneously and fuses the two information types. At the same time, the attention block is applied to the network to further strengthen the useful information, capture the hidden information in the ECG signal, and improve the model classification performance. Our model was finally trained and tested on the MIT-BIH arrhythmia database, and the entire dataset was divided into intrapatient and interpatient modes. Accuracies of 99.11% and 85.65%, respectively, were obtained under the two modes. Experimental results demonstrate that our proposed method is an efficient automated detection method.

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Yücelbaş, Şule, Cüneyt Yücelbaş, Gülay Tezel, Seral Özşen, and Şebnem Yosunkaya. "Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal." Expert Systems with Applications 102 (July 2018): 193–206. http://dx.doi.org/10.1016/j.eswa.2018.02.034.

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Salami, H. F., N. B. Shlevkov, and S. F. Sokolov. "The potential and limitations of standard electrocardiography for the differential diagnosis of wide QRS complex tachycardias." Almanac of Clinical Medicine 47, no. 4 (September 16, 2019): 350–60. http://dx.doi.org/10.18786/2072-0505-2019-47-040.

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The possibility to identify potentially life-threatening ventricular arrhythmias by analysis of standard electrocardiography (ECG) parameters without the use of sophisticated and expensive diagnostic techniques, such as electrophysiological heart studies, has been an important achievement of the last decades. Current differential diagnostic methods for wide QRS complex tachycardias can be categorized into 4 groups: 1) identification the ECG signs diagnostic of ventricular tachycardia, such as atrioventricular dissociation and/or sinus captures and/or fusion QRS complexes; 2) analysis of the distinct morphological features of QRS complexes during wide QRS complex tachycardias; 3) analysis of the ECG characteristics in between wide QRS complex tachycardia attacks; 4) comparison of the morphological QRS features during wide QRS complex tachycardia and during sinus rhythm periods. The list of ECG algorithms for differential diagnosis of wide QRS complex tachycardias has been growing and updated year after year; however, all of them have their limitations. Most frequently, the differential diagnosis between ventricular and aberrant supraventricular tachycardias is based on the morphological criteria of wide QRS complexes developed by H.J. Wellens et al., P. Brugada et al., M. Griffith et al., K.E. Kindwal et al., A. Vereckei et al. At the same time, there is a continuous search for new algorithms or criteria that could be easily used without decreased specificity and/or sensitivity of the diagnosis of ventricular tachycardia. The aim of this review is to comprehensively analyze the literature data and results of our own studies on the potential and limitation of standard ECG for the differential diagnosis of ventricular and various aberrant supraventricular tachycardias.

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Darmawahyuni, Annisa, Siti Nurmaini, Sukemi, Wahyu Caesarendra, Vicko Bhayyu, M. Naufal Rachmatullah, and Firdaus. "Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier." Algorithms 12, no. 6 (June 7, 2019): 118. http://dx.doi.org/10.3390/a12060118.

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The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.

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Mazaheri, Vajihe, and Hamed Khodadadi. "Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm." Expert Systems with Applications 161 (December 2020): 113697. http://dx.doi.org/10.1016/j.eswa.2020.113697.

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Lenis, Gustavo, Patrick Reichensperger, David Sommer, Christian Heinze, Martin Golz, and Olaf Dössel. "Detection of microsleep events in a car driving simulation study using electrocardiographic features." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 283–87. http://dx.doi.org/10.1515/cdbme-2016-0063.

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AbstractMicrosleep events (MSE) are short intrusions of sleep under the demand of sustained attention. They can impose a major threat to safety while driving a car and are considered one of the most significant causes of traffic accidents. Driver’s fatigue and MSE account for up to 20% of all car crashes in Europe and at least 100,000 accidents in the US every year. Unfortunately, there is not a standardized test developed to quantify the degree of vigilance of a driver. To account for this problem, different approaches based on biosignal analysis have been studied in the past. In this paper, we investigate an electrocardiographic-based detection of MSE using morphological and rhythmical features. 14 records from a car driving simulation study with a high incidence of MSE were analyzed and the behavior of the ECG features before and after an MSE in relation to reference baseline values (without drowsiness) were investigated. The results show that MSE cannot be detected (or predicted) using only the ECG. However, in the presence of MSE, the rhythmical and morphological features were observed to be significantly different than the ones calculated for the reference signal without sleepiness. In particular, when MSE were present, the heart rate diminished while the heart rate variability increased. Time distances between P wave and R peak, and R peak and T wave and their dispersion increased also. This demonstrates a noticeable change of the autonomous regulation of the heart. In future, the ECG parameter could be used as a surrogate measure of fatigue.

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Satoh, Hiroshi, Fumitaka Matoh, Katsunori Shiraki, Takeji Saitoh, Masao Saotome, Tsuyoshi Urushida, Hideki Katoh, and Hideharu Hayashi. "P-9 Delayed Enhancement on Cardiac Magnetic Resonance and Clinical, Morphological and ECG Features in Hypertrophic Cardiomyopathy." CVD Prevention and Control 4 (May 2009): S55. http://dx.doi.org/10.1016/s1875-4570(09)60203-5.

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Alqudah, Ali Mohammad, and Amin Alqudah. "Morphological Based Method for Automated Extraction and Classification of ECG ST-T Wave." Instrumentation Mesure Métrologie 20, no. 1 (February 28, 2021): 15–22. http://dx.doi.org/10.18280/i2m.200103.

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The wave starting from the beginning of the S wave until the end of the T wave is known as ST-T. ST-T wave extraction and classification is a very important technique in the diagnosis of myocardial ischemia. The myocardial ischemia which is also called cardiac ischemia can cause damage to the heart muscle. A sudden, severe blockage of a coronary artery may lead to a heart attack or other severe complications, and may also cause serious abnormal heart rhythms which will be reflected in the electrocardiogram trace. This paper aims to automate the real-time technique detection of ST-T waves that help in the diagnosis of myocardial ischemia and to classify the patient state. The proposed method uses the ECG wave morphological features that have been extracted using the detrended cumulative area, which is used to detect the ST-T wave. The proposed technique was tested and validated and it revealed promising results. The proposed method scored a sensitivity of 90.13% for K-mean Clustering and 96.3% for the SVM classifier for the ST interval detection. The method was tested on the European ST-T Database.

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Nanjundegowda, Raghu, and Vaibhav Meshram. "Arrhythmia recognition and classification using kernel ICA and higher order spectra." International Journal of Engineering & Technology 7, no. 2 (March 12, 2018): 256. http://dx.doi.org/10.14419/ijet.v7i2.9535.

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Electrocardiogram (ECG) is one of the monitoring methodology for the identification of arrhythmia disease. The conventional methodologies of arrhythmia identification are based on morphological features or certain transformation technique. These conventional techniques are partially successful in arrhythmia identification, because it treats heart as a linear structure. In this paper, ECG based arrhythmia identification is assessed by employing MIT-BIH arrhythmia dataset. The proposed approach contains two major steps: feature extraction and classification. Initially, a combination of non-linear and linear feature extraction is carried-out using Principal Component Analysis (PCA), Kernel Independent Component Analysis (KICA) and Higher Order Spectrum (HOS) for achieving optimal feature subsets. The linear experiments on ECG data achieves high performance in noise free data and the non-linear experiments distinguish the ECG data more effectively, extract hidden information and also helps to attain better performance under noisy conditions. After finding the feature information, a binary classifier Support Vector Machine (SVM) is employed for classifying the normality and abnormality of arrhythmia. In experimental analysis, the proposed approach distinguishes the normality and abnormality of arrhythmia ECG signals in terms of specificity, sensitivity and accuracy. Experimental outcome shows that the proposed approach improved accuracy in arrhythmia detection up to 0.5-1% compared to the existing methods: neural network and SVM based radial basis function.

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Fu, Fan, Wentao Xiang, Yukun An, Bin Liu, Xianqing Chen, Songsheng Zhu, and Jianqing Li. "Comparison of Machine Learning Algorithms for the Quality Assessment of Wearable ECG Signals Via Lenovo H3 Devices." Journal of Medical and Biological Engineering 41, no. 2 (April 2021): 231–40. http://dx.doi.org/10.1007/s40846-020-00588-7.

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Abstract Purpose Electrocardiogram (ECG) signals collected from wearable devices are easily corrupted with surrounding noise and artefacts, where the signal-to-noise ratio (SNR) of wearable ECG signals is significantly lower than that from hospital ECG machines. To meet the requirements for monitoring heart disease via wearable devices, eliminating useless or poor-quality ECG signals (e.g., lead-falls and low SNRs) can be solved by signal quality assessment algorithms. Methods To compensate for the deficiency of the existing ECG quality assessment system, a wearable ECG signal dataset from heart disease patients collected by Lenovo H3 devices was constructed. Then, this paper compares the performance of three machine learning algorithms, i.e., the traditional support vector machine (SVM), least-squares SVM (LS-SVM) and long short-term memory (LSTM) algorithms. Different non-morphological signal quality indices (i.e., the approximate entropy (ApEn), sample entropy (SaEn), fuzzy measure entropy (FMEn), Hurst exponent (HE), kurtosis (K) and power spectral density (PSD) features) extracted from the original ECG signals are fed into the three algorithms as input. Results The true positive rate, true negative rate, sensitivity and accuracy are used to evaluate the performance of each method, and the LSTM algorithm achieves the best results on these metrics (97.14%, 86.8%, 97.46% and 95.47%, respectively). Conclusions Among the three algorithms, the LSTM-based quality assessment method is the most suitable for the signals collected by the Lenovo H3 devices. The results also show that the combination of statistical features can effectively evaluate the quality of ECG signals.

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Zhu, Huaiyu, Yisheng Zhao, Yun Pan, Hanshuang Xie, Fan Wu, and Ruohong Huan. "Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting." Sensors 21, no. 16 (August 5, 2021): 5290. http://dx.doi.org/10.3390/s21165290.

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Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.

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Miranda-Moura, Maria Tereza M., Vanessa Uemura da Fonseca, Naisandra Bezerra da Silva, Maria de Lourdes Freitas, Osiel Benedito de Almeida, Hugo Alexandre de Oliveira Rocha, Paula de Carvalho Papa, and Carlos Eduardo Bezerra de Moura. "Morphological features and vascularization study of caprine cyclic corpus luteum." Pesquisa Veterinária Brasileira 30, no. 4 (April 2010): 351–57. http://dx.doi.org/10.1590/s0100-736x2010000400012.

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Corpus luteum is a temporary endocrine gland that regulates either the estrous cycle and pregnancy. It presents extreme dependency on the adequate blood supply. This work aims to evaluate goat corpus luteum (CL) vascular density (VD) over the estrous cycle. For that purpose, 20 females were submitted to estrus synchronization/ovulation treatment using a medroxyprogesterone intra-vaginal sponge as well as intramuscular (IM) application of cloprostenol and equine chorionic gonadotrophine (eCG). After sponge removal, estrus was identified at about 72hs. Once treatment was over, female goats were then subdivided into 4 groups (n=5 each) and slaughtered on days 2, 12, 16 and 22 after ovulation (p.o). Ovaries were collected, withdrawn and weighted. CL and ovaries had size and area recorded. Blood samples were collected and the plasma progesterone (P4) was measured through RIA commercial kits. The VD was 24.42±6.66, 36.26±5.61, 8.59±2.2 and 3.97±1.12 vessels/mm² for days 2, 12, 16 and 22 p.o, respectively. Progesterone plasma concentrations were 0.49±0.08, 2.63±0.66, 0.61±0.14 and 0.22±0.04ng/ml for days 2, 12, 16 e 22 p.o, respectively. Studied parameters were affected by the estrous cycle phase. Values greater than 12 p.o were observed. In the present work we observed that ovulation occurred predominantly in the right ovary (70% of the animals), which in turn presented bigger measures than the contra lateral one. There is a meaningful relationship between the weight and size of the ovary and these of CL (r=0.87, r=0.70, respectively, p<0.05). It is possible to conclude that morphology of goat's ovaries and plasma progesterone concentration changed according to estrous cycle stages. We propose these parameters can be used as indicators of CL functional activity.

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Pooyan, Mohammad, and Fateme Akhoondi. "Providing an efficient algorithm for finding R peaks in ECG signals and detecting ventricular abnormalities with morphological features." Journal of Medical Signals & Sensors 6, no. 4 (2016): 218. http://dx.doi.org/10.4103/2228-7477.195090.

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Fan, Xiaomao, Zhejing Hu, Ruxin Wang, Liyan Yin, Ye Li, and Yunpeng Cai. "A novel hybrid network of fusing rhythmic and morphological features for atrial fibrillation detection on mobile ECG signals." Neural Computing and Applications 32, no. 12 (June 24, 2019): 8101–13. http://dx.doi.org/10.1007/s00521-019-04318-2.

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Білобородова, T. O., І. С. Скарга-Бандурова, and В. С. Дерев’янченко. "A wearable system for real-time outpatient ECG monitoring." ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля, no. 5(253) (September 5, 2019): 26–30. http://dx.doi.org/10.33216/1998-7927-2019-253-5-26-30.

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Functional state of the cardiovascular system is an important factor for human physical well-being. To perform analysis of the cardiovascular state, the wearable continuous ECG monitoring system is essential. In this paper, a wearable ECG monitoring system based on IoT is proposed. The systems architecture is presented. Wearable devices design employs few optimal components for the acquisition of acceptable ECG signal. The R peaks corresponding to each heartbeat, and T waves, a morphological feature of the ECG are detected. It enables to perform heart rate and heart rate variability analyses, as well as extract, store and analyze the long term ECG measurements.

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Huang, Jian-Jia, Chung-Yu Chang, Jen-Kuang Lee, and Hen-Wai Tsao. "RESOLVING SINGLE-LEAD ECG FROM EMG INTERFERENCE IN HOLTER RECORDING BASED ON EEMD." Biomedical Engineering: Applications, Basis and Communications 26, no. 01 (February 2014): 1450008. http://dx.doi.org/10.4015/s1016237214500082.

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The aim of this study was to propose an electrocardiogram (ECG) de-noising framework based on ensemble empirical mode decomposition (EEMD) to eliminate electromyography (EMG) interference without signal distortion. ECG signals are easily corrupted by EMG, especially in Holter monitor recordings. The frequency component overlapping between EMG and ECG is a challenge in signal processing that remains to be solved. The aim of the present study, therefore, was to resolve ECG signals from recorded segments with EMG noise. Two units were put into our proposed framework; first, modified moving average filter for signal preprocessing to cancel baseline wandering, and second, EEMD to cancel EMG. In order to enhance the de-noising capability (such as signal distortion in traditional EEMD), we developed a novel EEMD signal reconstruction algorithm using a statistical ECG model. We tested the proposed framework using MIT-BIH database, artificial and single-lead recorded real-world noisy signals. Correlation coefficients and ECG morphological features were used to evaluate the performance of the proposed algorithm. Our results showed that the proposed de-noising algorithm successfully resolved ECG signals from baseline wandering and EMG interference without distorting the signal waveform.

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Zhou, Haiying, Xiancheng Zhu, Sishan Wang, Kui Zhou, Zheng Ma, Jian Li, Kun-Mean Hou, and Christophe De Vaulx. "A Novel Cardiac Arrhythmias Detection Approach for Real-Time Ambulatory ECG Diagnosis." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (March 9, 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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Yang, Hui, and Zhiqiang Wei. "A Novel Approach for Heart Ventricular and Atrial Abnormalities Detection via an Ensemble Classification Algorithm Based on ECG Morphological Features." IEEE Access 9 (2021): 54757–74. http://dx.doi.org/10.1109/access.2021.3071273.

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Gautham, A., and V. Karthik Raj. "DESIGNING OF A SINGLE ARM SINGLE LEAD ECG SYSTEM FOR WET AND DRY ELECTRODE: A COMPARISON WITH TRADITIONAL SYSTEM." Biomedical Engineering: Applications, Basis and Communications 28, no. 03 (June 2016): 1650021. http://dx.doi.org/10.4015/s1016237216500216.

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Electrocardiography is a non-invasive medical diagnostic procedure used to record the electrical activity of the heart as a waveform. An electrocardiogram (ECG) can be utilized to evaluate the electrical activity of the heart, also the rate and regularity of the heart beat and other related diagnoses. ECG systems have evolved along since its invention and researches are going on continuously to decrease the complexity of ECG systems. This paper discusses the designing of a single arm single lead ECG system to acquire ECG signals from areas of left arm alone. The proposed system uses pre-gelled disposable surface electrodes and dry copper electrodes. The single arm approach in ECG acquisition reduces the complexity of the system to a greater extent and also improves the ease of use and patient comfort. The paper discusses the various designing aspects and the working of the single arm single lead ECG system. A hardware only approach has been used here in the design of the ECG system. ECGs were obtained from 10 healthy subjects using the proposed system, which were compared along with ECG acquired from a commercially used system. The obtained ECG had morphological features similar to a normal ECG waveform. The results of the comparison were very promising and all the values recorded were in the normal range of values for the respective parameters in comparison.

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Chuang, Yu-Hung, Chia-Ling Huang, Wen-Whei Chang, and Jen-Tzung Chien. "Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography." Sensors 20, no. 24 (December 17, 2020): 7246. http://dx.doi.org/10.3390/s20247246.

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Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI. Incorporating the derived vectorcardiography (VCG) techniques can help monitor the three-dimensional electrical activities of human hearts. This study presents a patient-specific reconstruction method based on long short-term memory (LSTM) network to exploit both intra- and inter-lead correlations of ECG signals. MI-induced changes in the morphological and temporal wave features are extracted from the derived VCG using spline approximation. After the feature extraction, a classifier based on multilayer perceptron network is used for MI classification. Experiments on PTB diagnostic database demonstrate that the proposed system achieved satisfactory performance to differentiating MI patients from healthy subjects and to localizing the infarcted area.

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Marton, Yuval, Nizar Habash, and Owen Rambow. "Dependency Parsing of Modern Standard Arabic with Lexical and Inflectional Features." Computational Linguistics 39, no. 1 (March 2013): 161–94. http://dx.doi.org/10.1162/coli_a_00138.

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We explore the contribution of lexical and inflectional morphology features to dependency parsing of Arabic, a morphologically rich language with complex agreement patterns. Using controlled experiments, we contrast the contribution of different part-of-speech (POS) tag sets and morphological features in two input conditions: machine-predicted condition (in which POS tags and morphological feature values are automatically assigned), and gold condition (in which their true values are known). We find that more informative (fine-grained) tag sets are useful in the gold condition, but may be detrimental in the predicted condition, where they are outperformed by simpler but more accurately predicted tag sets. We identify a set of features (definiteness, person, number, gender, and undiacritized lemma) that improve parsing quality in the predicted condition, whereas other features are more useful in gold. We are the first to show that functional features for gender and number (e.g., “broken plurals”), and optionally the related rationality (“humanness”) feature, are more helpful for parsing than form-based gender and number. We finally show that parsing quality in the predicted condition can dramatically improve by training in a combined gold+predicted condition. We experimented with two transition-based parsers, MaltParser and Easy-First Parser. Our findings are robust across parsers, models, and input conditions. This suggests that the contribution of the linguistic knowledge in the tag sets and features we identified goes beyond particular experimental settings, and may be informative for other parsers and morphologically rich languages.

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Despić, Miloje, and Sarah Murray. "On binary features and disagreeing natural classes: Evidence from Cheyenne and Serbian." Linguistic Review 35, no. 2 (June 1, 2018): 219–41. http://dx.doi.org/10.1515/tlr-2017-0025.

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Abstract In this paper we offer new arguments for bivalence of morphological features. In the domain of person, we argue in support of the system using the features [±speaker] and [±hearer], on the basis of plural marking in Cheyenne. In the domain of gender, we argue in support of the system using the features [±masculine] and [±feminine], on the basis of gender agreement in Serbian coordinate structures. The property of binary systems crucial for our proposal is that they allow combinations of disagreeing feature values, given that in such systems every morphological category is represented as a combination of two values. Our main empirical goal is to show that some languages treat such combinations of disagreeing feature values (as well as combinations of agreeing values) as natural classes (e.g., Noyer, Rolf. 1992. Features, positions and affixes in autonomous morphological structure. Ph.D. dissertation. Massachusetts Institute of Technology).

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Zavyalova, M. V., S. V. Vtorushin, N. V. Krakhmal, Yu Yu Rakina, and A. P. Koshel. "Evaluation of cancer invasion markers determining the risk of lymph node metastasis in pancreatic cancer." Experimental and Clinical Gastroenterology, no. 3 (June 22, 2020): 60–65. http://dx.doi.org/10.31146/1682-8658-ecg-175-3-60-65.

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The aim of the study was to evaluate the expression features of cancer invasion markers in ductal pancreatic adenocarcinoma and determine their relationship with the frequency of lymph node metastasis.Materials and methods: 84 cases of pancreatic ductal adenocarcinoma were studied with morphological and immunohistochemical studies using antibodies MMP2, Integrin β1 and β3, β-catenin, Twist and Snail, which associated with increased invasive properties of the tumor according to the literature.Results: The study showed the presence of relationships between the expression indicators of the studied markers in different structures of the parenchymal component of the tumor with the frequency of lymph node metastasis in pancreatic cancer.Conclusion: The results of the study show the possibility of using these markers as additional morphological parameters that allow us to assess and predict the risk of lymphogenous dissemination of pancreatic cancer.

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Nishanov, Yu N., M. S. Palvanova, M. T. Yuldasheva, and R. M. Shermatov. "Features of the blood supply to the wall of the small intestine and its Peyer’s patches." Experimental and Clinical Gastroenterology, no. 3 (June 22, 2020): 66–70. http://dx.doi.org/10.31146/1682-8658-ecg-175-3-66-70.

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Purpose and objectives:1. To study the blood supply of Peyer’s patches in the norm.2. To reveal the features of the blood supply of the Peyer’s patches in different sections of the small intestine.The peculiarity of the blood supply to the wall of the small intestine is that it depends on the morphostructure of the wall of this intestine.The distribution of arterial vessels in the submucosa is such that trey are divided into ordinal vessels (1, 2, 3 and 4 orders), which branch into layers and zones of the intestine, end also localized in Peyer’s patches.According to the results of the study, it was reveated that the features of the blood supply to the layers of the wall of the small intestine is that 4 zones of the wall of the intestine differ: mesenteric, antimesenteric (liber) and two lateral zones that differ in morphological and morphometric indicators.

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Korzheva, I. Yu, N. E. Chernekhovskaya, E. V. Galaeva, N. V. Vychuzhanina, and E. A. Khoreva. "Therapeutic and diagnostic algorithm for colon lipoma." Experimental and Clinical Gastroenterology 1, no. 5 (August 19, 2021): 101–3. http://dx.doi.org/10.31146/1682-8658-ecg-189-5-101-103.

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Clinical relevance: Colon lipomas are not rare tumors, but they occur with blurred clinical features and patients arrive with complications- obstructive intestinal obstruction or bleeding. In this regard, the authors presented a clinical case of a 57-yearold patient whose diagnosis was established only by morphological examination of the surgical material.The purpose of the study was to demonstrate clinical observation of colon lipoma.Materials and methods: The case history of a 57-year-old patient diagnosed with colonic lipoma is presented.

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Bank, Sebastian. "The algebraic structure of morphosyntactic features." Nordlyd 41, no. 2 (April 8, 2015): 239. http://dx.doi.org/10.7557/12.3414.

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The most common way of separating homophony from syncretism — which is a basic challenge for any inflectional analysis: to distinguish between accidental and systematic form-identity — is attributing only the latter to a coherent feature combination instantiating a natural class. Features predetermine which form-identities can or cannot be analyzed as natural-class syncretism. Hence, they are crucial for the restrictiveness and predictions of morphological grammar. However, most current theoretical frameworks (e.g.] Anderson 1992, Corbett & Fraser 1993, Halle & Marantz 1993, Stump 2001) do not make explicit their assumptions regarding the formal status of features. They miss out on state-of-the-art formalisms to introduce feature notations like Formal Concept Analysis (FCA, going back to Wille 1982, Ganter & Wille 1999) which provides a formal model of conceptualization in general. In this paper, I will show how FCA provides an all-embracing terminology to reproduce, visualize, and compare feature systems from different morphological frameworks, enables more precise and consistent morphological analyses, and crucially serves to rule out excessively powerful notations where the feature combinatorics are decoupled from the distributional facts they represent.

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da Silva Bitecourt, Frederico, Carina Oliveira Dumont Horta, Karen Santos Lima, Bruno Bastos Godoi, Fernanda Luiza Menezes Bello, Cíntia Maria Rodrigues, Luana Pereira Leite Schetino, and Kinulpe Honorato-Sampaio. "Morphological study of apoptosis in granulosa cells and ovulation in a model of atresia in rat preovulatory follicles." Zygote 26, no. 4 (August 2018): 336–41. http://dx.doi.org/10.1017/s0967199418000291.

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SummaryPrevious studies have established a model of atresia in preovulatory follicles after stimulation of immature rats with equine chorionic gonadotropin (eCG). This gonadotropin recruits a follicular pool and the deprivation of preovulatory luteinizing hormone (LH) surge induces the atresia in preovulatory follicles. The present study investigated the occurrence of ovulation and provided some morphological features of granulosa cell (GC) apoptosis of atretic follicles at 0, 48, 72 and 120 h after eCG stimulation. Histological sections of ovaries from untreated animals (0 h) showed primordial, primary, secondary and early antral follicles. After 48 h ovaries showed large antral follicles. Preovulatory follicles were observed at 72 h, and two out of five rats displayed cumulus–oocyte complexes (COCs) in the oviducts. All animals exhibited corpora lutea after 120 h. We observed increased estradiol (E2) levels 48 h after eCG treatment that might trigger an endogenous preovulatory gonadotropin surge. Higher progesterone (P4) level, which is the hallmark of a functional corpus luteum, was observed at 120 h. Atresia in secondary and antral follicles was observed by pyknotic granulosa cell nuclei in histology and positive immunolabelling for cleaved caspase 3. We also observed macrophages in secondary and antral follicles in atresia. Transmission electron microscopy revealed GCs with compacted chromatin against the nuclear envelope, nuclear fragmentation, cell shrinkage and fragmentation. No preovulatory follicles showed apoptosis of GCs. In conclusion, our results suggested the occurrence of an endogenous gonadotropin surge, promoting ovulation and preventing atresia of preovulatory follicles.

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Nagata, Yasunobu, Ran Zhao, Hassan Awada, Cassandra M. Kerr, Abhinav Goyal, Inom Mirzaev, Sunisa Kongkiatkamon, et al. "How Morphologic Features Are Shaped By Underlying Somatic Genotype in MDS." Blood 134, Supplement_1 (November 13, 2019): 1716. http://dx.doi.org/10.1182/blood-2019-127285.

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Morphology has been the pillars of the MDS diagnosis. In the genetic era, the subjectivity of pathologic evaluation in the absence of integration with mechanistic correlations is a clear limitation of current diagnostic schemes. Well-known genotype/phenotype associations, including e.g., linking of SF3B1 mutations to RARS or JAK2/SF3B1 mutations to RARS-T, suggest that somatic events can shape morphologic features and vice versa. However, the complexity of morphologic and genetic changes precludes identification of many consequential genotype/phenotype associations. As objective image analytic tools are being developed, this project aims at determining the most mutually predictive relationships between combinations of morphologic features and genomic changes. We have analyzed 1,079 MDS patients for somatic CNVs and mutations in a targeted panel of 33 genes frequently mutated in myeloid cancers. Our stringent bioanalytic pipeline removed artifacts, SNPs and errors. We applied this pipeline to discovery (2/3) and validation (1/3) cohorts. Bone marrow morphological alterations were mapped to binary features called by an independent pathologist in a blinded fashion based on uniformly defined criteria occurring in >10% of cells. A total of 10 such features were investigated. For instance, myeloid, erythroid and megakaryocytic dysplasia occurred in 54%, 70% and 72% of patients, respectively. 89% had at least one cytopenia, 57% multiple cytopenias, and 50% had at least one myeloproliferative feature (e.g., monocytosis). In addition, all cases were partitioned in accordance with two risk groups defined by IPSS-R (lower risk <3.5 and higher risk >3.5). NGS analysis identified 1,929 somatic mutations, but for proper correlation with morphologic definitions only mutations with a clonal burden >10% were used. Our initial univariate analysis yielded 52 significant associations (q<.1). For instance, myeloid dysplasia was more associated with ASXL1, NRAS,SRSF2, STAG2, and TET2 mutations. Univariate associative analyses failed to yield signatures due to the extensive presence of the heterogeneity in genotype profiles. Noting patterns of interdependence exhibited by the 24 individual morphologic features, an unsupervised cluster analysis based on the consensus clustering method was used to identify intrinsic patterns of co-occurrence and morphologic subtypes of MDS. A semi-supervised machine learning (ML) technique based on Bayesian partial exchangeability investigated the extent to which morphologic subtypes of MDS can be discriminated on the basis of patterns of mutation incidence and co-occurrence. To augment existing clinical models of prognosis, ML was applied to low risk patient MDS subtypes. A single model, selected and subsequently validated using our independent test set, yielded genetic signatures demonstrating morphologic orientation. An unsupervised cluster analysis revealed 5 discrete morphological MDS groups on the basis of 24 features (Fig.1a). High-risk subtypes were clustered in group 1 (G1), whereas low risk subtypes clustered in the other 4 groups, each of which had unique prominent morphological features (Fig.1b). G2 had pancytopenia, G3 had monocytosis, G4 had elevated megakaryocytes, and G5 had erythroid dysplasia. Exhibiting prognostic utility, overall survival differed significantly among these groups (Fig.1c). Mutational frequencies also differed between these groups, e.g., G1 frequently had mutations of RUNX1, TP53, and STAG2. 8 genetic signatures characterize the morphologic phenotypes of low risk MDS patients (S1-8; Fig.1d). Signature-A (SA) was enriched for TET2 mutations, SB had the co-occurrence of TET2 and SRSF2 mutations, whereas SG was enriched in SF3B1 mutations. Deviation in survival (p<0.026) demonstrated the prognostic value of these genetic signatures among low risk patients. The genetic signatures were predictive of specific morphological features (Fig.1e). SB dominated G3 (73%), and SC were frequent for G4 (57%). Among 18 morphologic/genetic associations identified in the discovery set, 9 were confirmed in the validation cohort. For instance, we validated that SB was enriched in monocytosis and TET2 mutations; while SD in anemia and SF3B1 mutations. This is the first comprehensive analysis that links somatic molecular lesions to morphological patterns to identify distinct phenotype/genotype associations. Disclosures Nazha: Jazz Pharmacutical: Research Funding; Abbvie: Consultancy; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau; Daiichi Sankyo: Consultancy; Tolero, Karyopharma: Honoraria; Incyte: Speakers Bureau. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees. Hobbs:Amgen: Research Funding; SimulStat Inc.: Consultancy. Maciejewski:Novartis: Consultancy; Alexion: Consultancy.

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BAERMAN, MATTHEW. "Morphological reversals." Journal of Linguistics 43, no. 1 (February 27, 2007): 33–61. http://dx.doi.org/10.1017/s0022226706004440.

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The term morphological reversal describes the situation where the members of a morphological opposition switch their functions in some context (as with Hebrew gender marking, where -Ø~-a marks masculine~feminine with adjectives but feminine~masculine with numerals). There is a long tradition of polemic against the notion that morphology can encode systematic reversals, and an equally long tradition of reintroducing them under different names (e.g. polarity, exchange rules or morphosyntactic toggles). An examination of some unjustly neglected examples (number in Nehan, aspect in Tübatulabal, tense in Trique and argument marking in Neo-Aramaic) confirms the existence of morphological reversal, particularly as a mechanism of language change. This is strong evidence for the separateness of morphological paradigms from the features that they encode.

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Opitz, Andreas, and Thomas Pechmann. "Gender Features in German." Linguistic Perspectives on Morphological Processing 11, no. 2 (July 18, 2016): 216–41. http://dx.doi.org/10.1075/ml.11.2.03opi.

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Current theoretical approaches to inflectional morphology make extensive use of the two concepts of abstract feature decomposition and underspecification. Psycholinguistic models of inflection, in contrast, generally lack such more differentiated morphological analyses. This paper reports a series of behavioral experiments that investigate the processing of grammatical gender of nouns in German. The results of these experiments support the idea that elements in the mental lexicon may be underspecified with regard to their grammatical features. However, contrary to all established morphological and psycholinguistic approaches, we provide evidence that even the lexical representation of bare noun stems is characterized by underspecified gender information. The observation that the domain of underspecification of grammatical features extends from inflectional markers to noun stems, supports the idea that underspecification is a more general characteristic of the mental lexicon. We conclude that this finding is mainly driven by economical reasons: a feature (or feature value) that is never used for grammatical operations (e.g., inflectional marking or evaluation of agreement) is not needed in the language system at all.

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Deitcher, Yair, Guy Eyal, Lida Kanari, Matthijs B. Verhoog, Guy Antoine Atenekeng Kahou, Huibert D. Mansvelder, Christiaan P. J. de Kock, and Idan Segev. "Comprehensive Morpho-Electrotonic Analysis Shows 2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex." Cerebral Cortex 27, no. 11 (September 15, 2017): 5398–414. http://dx.doi.org/10.1093/cercor/bhx226.

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Abstract There have been few quantitative characterizations of the morphological, biophysical, and cable properties of neurons in the human neocortex. We employed feature-based statistical methods on a rare data set of 60 3D reconstructed pyramidal neurons from L2 and L3 in the human temporal cortex (HL2/L3 PCs) removed after brain surgery. Of these cells, 25 neurons were also characterized physiologically. Thirty-two morphological features were analyzed (e.g., dendritic surface area, 36 333 ± 18 157 μm2; number of basal trees, 5.55 ± 1.47; dendritic diameter, 0.76 ± 0.28 μm). Eighteen features showed a significant gradual increase with depth from the pia (e.g., dendritic length and soma radius). The other features showed weak or no correlation with depth (e.g., dendritic diameter). The basal dendritic terminals in HL2/L3 PCs are particularly elongated, enabling multiple nonlinear processing units in these dendrites. Unlike the morphological features, the active biophysical features (e.g., spike shapes and rates) and passive/cable features (e.g., somatic input resistance, 47.68 ± 15.26 MΩ, membrane time constant, 12.03 ± 1.79 ms, average dendritic cable length, 0.99 ± 0.24) were depth-independent. A novel descriptor for apical dendritic topology yielded 2 distinct classes, termed hereby as “slim-tufted” and “profuse-tufted” HL2/L3 PCs; the latter class tends to fire at higher rates. Thus, our morpho-electrotonic analysis shows 2 distinct classes of HL2/L3 PCs.

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Casas, Manuel M., Roberto L. Avitia, Jose Antonio Cardenas-Haro, Jugal Kalita, Francisco J. Torres-Reyes, Marco A. Reyna, and Miguel E. Bravo-Zanoguera. "A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification." Applied Sciences 11, no. 15 (July 22, 2021): 6711. http://dx.doi.org/10.3390/app11156711.

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Arrhythmias are the most common events tracked by a physician. The need for continuous monitoring of such events in the ECG has opened the opportunity for automatic detection. Intra- and inter-patient paradigms are the two approaches currently followed by the scientific community. The intra-patient approach seems to resolve the problem with a high classification percentage but requires a physician to label key samples. The inter-patient makes use of historic data of different patients to build a general classifier, but the inherent variability in the ECG’s signal among patients leads to lower classification percentages compared to the intra-patient approach. In this work, we propose a new unsupervised algorithm that adapts to every patient using the heart rate and morphological features of the ECG beats to classify beats between supraventricular origin and ventricular origin. The results of our work in terms of F-score are 0.88, 0.89, and 0.93 for the ventricular origin beats for three popular ECG databases, and around 0.99 for the supraventricular origin for the same databases, comparable to supervised approaches presented in other works. This paper presents a new path to make use of ECG data to classify heartbeats without the assistance of a physician despite the needed improvements.

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Pandey, Saroj Kumar, and Rekh Ram Janghel. "Classification of electrocardiogram signal using an ensemble of deep learning models." Data Technologies and Applications 55, no. 3 (February 16, 2021): 446–60. http://dx.doi.org/10.1108/dta-05-2020-0108.

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PurposeAccording to the World Health Organization, arrhythmia is one of the primary causes of deaths across the globe. In order to reduce mortality rate, cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. The objective of this paper was to implement a better heartbeat classification model which will work better than the other implemented heartbeat classification methods.Design/methodology/approachIn this paper, the ensemble of two deep learning models is proposed to classify the MIT-BIH arrhythmia database into four different classes according to ANSI-AAMI standards. First, a convolutional neural network (CNN) model is used to classify heartbeats on a raw data set. Secondly, four features (wavelets, R-R intervals, morphological and higher-order statistics) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes.FindingsAmong these, the highest accuracy obtained is 98.58% using ensemble method with product rule. The results show that the ensemble of CNN and BLSTM has offered satisfactory performance compared to other techniques discussed in this study.Originality/valueIn this study, we have developed a new combination of two deep learning models to enhance the performance of arrhythmia classification using segmentation of input ECG signals. The contributions of this study are as follows: First, a deep CNN model is built to classify ECG heartbeat using a raw data set. Second, four types of features (R-R interval, HOS, morphological and wavelet) were extracted from the raw data set and then applied to the bidirectional LSTM model to classify the ECG heartbeat. Third, combination rules (sum rules, product rules and majority voting rules) were tested to ensure the accumulated probabilities of the CNN and LSTM models.

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Athif, Mohamed, Pamodh Chanuka Yasawardene, and Chathuri Daluwatte. "Detecting atrial fibrillation from short single lead ECGs using statistical and morphological features." Physiological Measurement 39, no. 6 (June 19, 2018): 064002. http://dx.doi.org/10.1088/1361-6579/aac552.

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Journal articles on the topic 'Morphological features of ECG'

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