Patch-Based ECG Segmentation for Arrhythmia Classification Using Hybrid Deep Learning

Abstract

The human heart abnormality can be detected using electrocardiogram (ECG) signal which is an electrical     activity waveform with P, QRS and T waves. ECG abnormality normally analyzed using shape, wave form peaks and time duration of waves etc.  Traditional techniques require manual interpretation to recognize the hear abnormality, hence time consuming process. In this research, automated deep learning model that understand each patches in ECG wave for accurate abnormality classification is implemented. We propose novel patch segmentation with a 180-timeframe timestamps for best feature engineering and learning of ECG waveforms in the MIT-BH dataset.  The segmented features support the model to focus on main features, which helps to improve the learning efficiency and prediction accuracy of machine learning and deep learning algorithms. Waves are segmented as patches in 180 time stamps, and features are extracted for learning and classification. A machine learning model random forest with SMOTE and a hybrid deep learning model CNN-LSTM with SMOTE are used to train and test the patched ECG waveform. Further to test the generalizability of model, we tested the PTBXL dataset from physio net to MIT trained CNN-LSTM model.  The result shows that our proposed model on random forest acquired a maximum of 98.3 % accuracy in binary classification and 99.2% in multiclass classification. CNN-LSTM with patch segmentation achieves 96.5% accuracy. For PTBXL dataset testing we achieved 91% of accuracy.