Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
TLDR
The proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database and can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.Abstract:
The heart disease is one of the most serious health problems in today’s world. Over 50 million persons have cardiovascular diseases around the world. Our proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database (strongly imbalanced data) for one lead (modified lead II), from 29 people. In this work, we have used long-duration (10 s) ECG signal segments (13 times less classifications/analysis). The spectral power density was estimated based on Welch’s method and discrete Fourier transform to strengthen the characteristic ECG signal features. Our main contribution is the design of a novel three-layer (48 + 4 + 1) deep genetic ensemble of classifiers (DGEC). Developed method is a hybrid which combines the advantages of: (1) ensemble learning, (2) deep learning, and (3) evolutionary computation. Novel system was developed by the fusion of three normalization types, four Hamming window widths, four classifiers types, stratified tenfold cross-validation, genetic feature (frequency components) selection, layered learning, genetic optimization of classifiers parameters, and new genetic layered training (expert votes selection) to connect classifiers. The developed DGEC system achieved a recognition sensitivity of 94.62% (40 errors/744 classifications), accuracy = 99.37%, specificity = 99.66% with classification time of single sample = 0.8736 (s) in detecting 17 arrhythmia ECG classes. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.read more
Citations
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Classification of myocardial infarction with multi-lead ECG signals and deep CNN
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A new approach for arrhythmia classification using deep coded features and LSTM networks.
TL;DR: A novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
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A new machine learning technique for an accurate diagnosis of coronary artery disease
Moloud Abdar,Wojciech Książek,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Ru San Tan,Vladimir Makarenkov,Paweł Pławiak +7 more
TL;DR: An innovative machine learning methodology is described that enables an accurate detection of CAD and applies it to data collected from Iranian patients and shows that machine-learning techniques optimized by the proposed approach can lead to highly accurate models intended for both clinical and research use.
Journal ArticleDOI
Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals
Turker Tuncer,Sengul Dogan,Paweł Pławiak,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: DWT coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique are employed for automated detection of arrhythmia detection and the results show that the proposed method is more superior than other already reported classical ensemble learning and deep learning methods for arrhythmmia detection using ECG signals.
Journal ArticleDOI
Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review
Fatma Murat,Ozal Yildirim,Muhammed Talo,Ulas Baran Baloglu,Yakup Demir,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +7 more
TL;DR: A detailed examination of deep learning methods for ECG arrhythmia detection is provided, and suggestions for further research in this area are presented.
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