Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms

Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main objective is developing the compact deep learning based detect system which minimally uses the dataset but delivers the confident accuracy rate of the Arrhythmia detection. This compact system can be implemented in wearable devices or real-time monitoring equipment because the feature extraction step is not required for complex ECG waveforms, only the R-peak data is needed. The results of both tests indicated that the Compact Arrhythmia Detection System (CADS) matched the performance of conventional systems for the detection of arrhythmia in two consecutive test runs. All features of the CADS are fully implemented and publicly available in MATLAB.

[1]  T. Straeter.  On the Extension of the Davidon-Broyden Class of Rank One, Quasi-Newton Minimization Methods to an Infinite Dimensional Hilbert Space with Applications to Optimal Control Problems , 1971 .

[2]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

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

[4]  Bogdan Wilamowski,et al.  Comparison of different neural network architectures for digit image recognition , 2011, 2011 4th International Conference on Human System Interactions, HSI 2011.

[5]  Ali Tariq Bhatti,et al.  R-Peak detection in ECG signal compression for Heartbeat rate patients at 1 KHz using High Order Statistic Algorithm , 2015 .

[6]  B. S. Harish,et al.  A Survey on various Machine Learning Approaches for ECG Analysis , 2017 .

[7]  Hongxun Yao,et al.  Visualizing and Comparing Convolutional Neural Networks , 2014, ArXiv.

[8]  William Robson Schwartz,et al.  ECG-based heartbeat classification for arrhythmia detection: A survey , 2016, Comput. Methods Programs Biomed..

[9]  Manuel G. Penedo,et al.  Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers , 2019, Biomed. Signal Process. Control..

[10]  Majid Sarrafzadeh,et al.  ECG Heartbeat Classification: A Deep Transferable Representation , 2018, 2018 IEEE International Conference on Healthcare Informatics (ICHI).

[11]  Tomasz Szandala.  Comparison of Different Learning Algorithms for Pattern Recognition with Hopfield's Neural Network , 2015, BICA.

[12]  Xuan Zeng,et al.  HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications , 2017, IEEE Access.

[13]  Zhitao Lin,et al.  Automatic QRS complex detection using two-level convolutional neural network , 2018, BioMedical Engineering OnLine.

[14]  George B. Moody,et al.  An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave , 2014, Journal of open research software.

[15]  M. Spach,et al.  The nature of electrical propagation in cardiac muscle. , 1983, The American journal of physiology.

[16]  Fajr Ibrahem Alarsan,et al.  Analysis and classification of heart diseases using heartbeat features and machine learning algorithms , 2019, Journal of Big Data.

[17]  Marimuthu Palaniswami,et al.  Detecting Subclinical Diabetic Cardiac Autonomic Neuropathy by Analyzing Ventricular Repolarization Dynamics , 2016, IEEE Journal of Biomedical and Health Informatics.

[18]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[19]  Sholom M. Weiss,et al.  An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods , 1989, IJCAI.

[20]  Ali Cinar,et al.  Interpreting ECG data by integrating statistical and artificial intelligence tools. , 2002, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[21]  Jason Cong,et al.  Minimizing Computation in Convolutional Neural Networks , 2014, ICANN.

[22]  John R. Koza,et al.  Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming , 1996 .

[23]  David Menotti,et al.  Learning Deep Off-the-Person Heart Biometrics Representations , 2017, IEEE Transactions on Information Forensics and Security.

[24]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[25]  Xuemei Guo,et al.  A deep learning approach for fetal QRS complex detection , 2018, Physiological measurement.

[26]  Ann Sergeant.  Analysis and interpretation of the electrocardiogram , 2010 .

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

[28]  Malay Mitra,et al.  Cardiac Arrhythmia Classification Using Neural Networks with Selected Features , 2013 .

[29]  Witold Pedrycz,et al.  ECG signal processing, classification, and interpretation : , 2012 .

[30]  Paul D. Yoo,et al.  An Enhanced Machine Learning-Based Biometric Authentication System Using RR-Interval Framed Electrocardiograms , 2019, IEEE Access.

[31]  Se Young Chun,et al.  Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test , 2019, IEEE Access.

[32]  Sander M. Bohte,et al.  Editorial: Artificial Neural Networks as Models of Neural Information Processing , 2017, Front. Comput. Neurosci..

[33]  Leif Sörnmo,et al.  Chapter 7 – ECG Signal Processing , 2005 .

[34]  W J Tompkins,et al.  Applications of artificial neural networks for ECG signal detection and classification. , 1993, Journal of electrocardiology.

[35]  Khalil El-Khatib,et al.  Continuous authentication by electrocardiogram data , 2009, 2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH).

[36]  G. Moody,et al.  The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. , 1992, European heart journal.

[37]  Kazi Khairul Islam,et al.  Arrhythmia Detection Technique using basic ECG Parameters , 2015 .

[38]  Cornel Pater.  Methodological considerations in the design of trials for safety assessment of new drugs and chemical entities , 2005, Current controlled trials in cardiovascular medicine.

[39]  Rafael Martínez-Peláez,et al.  Segmentation of the ECG Signal by Means of a Linear Regression Algorithm , 2019, Sensors.

[40]  Ali Işın,et al.  Cardiac arrhythmia detection using deep learning , 2017 .

[41]  Jaime S. Cardoso,et al.  Evolution, Current Challenges, and Future Possibilities in ECG Biometrics , 2018, IEEE Access.

[42]  A. Mincholé,et al.  Artificial intelligence for the electrocardiogram , 2019, Nature Medicine.

[43]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[44]  Sander Bohte,et al.  Editorial: Artificial Neural Networks as Models of Neural Information Processing , 2017, Front. Comput. Neurosci..

[45]  Ernesto Damiani,et al.  Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000 , 2019 .