A Multitier Deep Learning Model for Arrhythmia Detection

An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient–doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected.

[1]  Honghua Dai,et al.  Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network , 2019, IEEE Access.

[2]  Selcan Kaplan Berkaya,et al.  A survey on ECG analysis , 2018, Biomed. Signal Process. Control..

[3]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[4]  U. Rajendra Acharya,et al.  A deep convolutional neural network model to classify heartbeats , 2017, Comput. Biol. Medicine.

[5]  R. Rangayyan,et al.  Biomedical Signal Analysis , 2015 .

[6]  Abdullah M. Iliyasu,et al.  Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections , 2020, Viruses.

[7]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[8]  Mohamed Hammad,et al.  Detection of abnormal heart conditions based on characteristics of ECG signals , 2018, Measurement.

[9]  Mohammad Ali Tinati,et al.  Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals , 2016, Pattern Recognit. Lett..

[10]  U. Rajendra Acharya,et al.  Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals , 2017, Comput. Biol. Medicine.

[11]  Masoumeh Haghpanahi,et al.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.

[12]  An-Yeu Wu,et al.  Low-Complexity Privacy-Preserving Compressive Analysis Using Subspace-Based Dictionary for ECG Telemonitoring System , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[13]  Ming-Feng Yeh,et al.  Real-time ECG telemonitoring system design with mobile phone platform , 2008 .

[14]  Shoushui Wei,et al.  An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection , 2018, Journal of Medical Imaging and Health Informatics.

[15]  Bor-Shyh Lin,et al.  Design of a Wearable 12-Lead Noncontact Electrocardiogram Monitoring System , 2019, Sensors.

[16]  U. Rajendra Acharya,et al.  Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats , 2018, Comput. Biol. Medicine.

[17]  Masahiro Inoue,et al.  A Smart Shirt Made with Conductive Ink and Conductive Foam for the Measurement of Electrocardiogram Signals with Unipolar Precordial Leads , 2015 .

[18]  Mohamed Hammad,et al.  Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint , 2019, IEEE Access.

[19]  Md. Saiful Islam,et al.  End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection , 2020, Sensors.

[20]  Siti Mariyam Shamsuddin,et al.  Particle Swarm Optimization: Technique, System and Challenges , 2011 .

[21]  Onur Avci,et al.  1D Convolutional Neural Networks and Applications: A Survey , 2019, Mechanical Systems and Signal Processing.

[22]  Madhuchhanda Mitra,et al.  Automated Identification of Myocardial Infarction Using Harmonic Phase Distribution Pattern of ECG Data , 2018, IEEE Transactions on Instrumentation and Measurement.

[23]  Paul Lukowicz,et al.  AMON: a wearable multiparameter medical monitoring and alert system , 2004, IEEE Transactions on Information Technology in Biomedicine.

[24]  P K Buchholz,et al.  Understanding the ECG. , 1972, RN.

[25]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[26]  David Menotti,et al.  Improving automatic cardiac arrhythmia classification: Joining temporal-VCG, complex networks and SVM classifier , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[27]  Younghoon Kim,et al.  Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[28]  Mukhtiar Singh,et al.  Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network , 2020, Comput. Methods Programs Biomed..

[29]  Chengjin Qin,et al.  Automated heartbeat classification based on deep neural network with multiple input layers , 2020, Knowl. Based Syst..

[30]  Lisheng Xu,et al.  Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal , 2020, Sensors.

[31]  Jing Zhang,et al.  ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network , 2020, Artif. Intell. Medicine.

[32]  Yong J. Yuan,et al.  Wearable Medical Monitoring Systems Based on Wireless Networks: A Review , 2016, IEEE Sensors Journal.

[33]  Andrea Ridolfi,et al.  BIOTEX—Biosensing Textiles for Personalised Healthcare Management , 2010, IEEE Transactions on Information Technology in Biomedicine.

[34]  Feng Jiang,et al.  Very deep feature extraction and fusion for arrhythmias detection , 2018, Neural Computing and Applications.

[35]  Ulas Bagci,et al.  A collaborative computer aided diagnosis (C‐CAD) system with eye‐tracking, sparse attentional model, and deep learning☆ , 2018, Medical Image Anal..

[36]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[37]  U. Rajendra Acharya,et al.  Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals , 2017, Inf. Sci..

[38]  U. Rajendra Acharya,et al.  Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals , 2019, Neural Computing and Applications.

[39]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[40]  Yee Leung,et al.  A system for personalized health care with ECG and EEG signals for analysis , 2017, 2017 International Smart Cities Conference (ISC2).

[41]  Annalisa Bonfiglio,et al.  Smart Garments for Emergency Operators: The ProeTEX Project , 2010, IEEE Transactions on Information Technology in Biomedicine.

[42]  Mohamed Hammad,et al.  ResNet‐Attention model for human authentication using ECG signals , 2020, Expert Syst. J. Knowl. Eng..

[43]  E. W. Hancock,et al.  Recommendations for the standardization and interpretation of the electrocardiogram. Part I: The electrocardiogram and its technology. A scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Car , 2007, Heart rhythm.

[44]  Mohamed Hammad,et al.  A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication , 2019, Future Gener. Comput. Syst..

[45]  Mohamed Hammad,et al.  A novel biometric based on ECG signals and images for human authentication , 2016, Int. Arab J. Inf. Technol..

[46]  Li Wan,et al.  Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram. , 2019, Journal of electrocardiology.

[47]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[48]  Mohamed Hammad,et al.  Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network , 2019, Comput. Secur..

[49]  Annisa Darmawahyuni,et al.  An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique , 2019, Applied Sciences.

[50]  Khan Muhammad,et al.  A Novel Deep Transfer Learning Method for Detection of Myocardial Infarction , 2019, ArXiv.

[51]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[52]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[53]  Sengul Dogan,et al.  Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals , 2019, Knowl. Based Syst..

[54]  Pradeep Singh,et al.  Cardiac Arrhythmia Classification Using Machine Learning Techniques , 2019 .

[55]  Jianqiang Li,et al.  Interpretation of Electrocardiogram (ECG) Rhythm by Combined CNN and BiLSTM , 2020, IEEE Access.

[56]  Pavani Lakshmi Penmatsa,et al.  Smart Detection and Transmission of Abnormalities in ECG via Bluetooth , 2016, 2016 IEEE International Conference on Smart Cloud (SmartCloud).

[57]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[58]  Zhengchun Hua,et al.  Automated arrhythmia classification based on a combination network of CNN and LSTM , 2020, Biomed. Signal Process. Control..

[59]  Mohamed Hammad,et al.  Fingerprint classification based on a Q-Gaussian multiclass support vector machine , 2017, ICBEA '17.