ECG Arrhythmia Detection with Deep Learning

Arrhythmia is any irregularity of heart rate that cause an abnormality in your heart rhythm. Manual analysis of Electrocardiogram (ECG) signal is not enough for quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning approach for detection of five different arrhythmia types based on 2D convolutional neural networks (CNN) architecture. ECG signals were obtained from MIT-BIH arrhythmia database. For CNN architecture, each ECG signal was segmented into heartbeats, then each heartbeat was transformed into 2D grayscale heartbeat image. 2D CNN model was used due to success of image recognition. The proposed model result demonstrate that CNN and ECG image formation give highest result when classified different types of ECG arrhythmic signals.

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

[2]  B.V.K. Vijaya Kumar,et al.  Arrhythmia detection and classification using morphological and dynamic features of ECG signals , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[3]  Jaya Prakash Sahoo,et al.  Analysis of ECG signal for Detection of Cardiac Arrhythmias , 2011 .

[4]  Chun-Cheng Lin,et al.  Heartbeat Classification Using Normalized RR Intervals and Morphological Features , 2014 .

[5]  Jirí Bíla,et al.  Fast fourier transform for feature extraction and neural network for classification of electrocardiogram signals , 2015, 2015 Fourth International Conference on Future Generation Communication Technology (FGCT).

[6]  Ibrahim Hamed,et al.  Automatic Arrhythmia Detection Using Support Vector Machine Based on Discrete Wavelet Transform , 2016 .

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

[8]  Jinsul Kim,et al.  An Automated ECG Beat Classification System Using Convolutional Neural Networks , 2016, 2016 6th International Conference on IT Convergence and Security (ICITCS).

[9]  Naveen Kumar Dewangan,et al.  ECG arrhythmia classification using discrete wavelet transform and artificial neural network , 2016, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[10]  An-Pin Chen,et al.  Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks , 2016, 2016 7th International Conference on Cloud Computing and Big Data (CCBD).

[11]  Lu Cao,et al.  Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System , 2016, Sensors.

[12]  U. Rajendra Acharya,et al.  Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network , 2017, Inf. Sci..

[13]  Santanu Sahoo,et al.  Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities , 2017 .

[14]  Daeyoung Kim,et al.  ECG arrhythmia classification using a 2-D convolutional neural network , 2018, ArXiv.

[15]  M. Nash,et al.  ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network , 2018, Physiological measurement.

[16]  Jiann-Shiun Yuan,et al.  ECG Arrhythmia Classification Using Transfer Learning from 2- Dimensional Deep CNN Features , 2018, 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[17]  Aydin Akan,et al.  Arrhythmia Detection on ECG Signals by Using Empirical Mode Decomposition , 2018, 2018 Medical Technologies National Congress (TIPTEKNO).

[18]  Ying Liu,et al.  A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification , 2018, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[19]  Bin Yao,et al.  ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network , 2019, IEEE Access.

[20]  Aydin Akan,et al.  Cardiac Arrhythmia Detection from 2D ECG Images by Using Deep Learning Technique , 2019, 2019 Medical Technologies Congress (TIPTEKNO).

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

[22]  Muhammad Bilal,et al.  Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation , 2020, Remote. Sens..