A GPSR-Based Sparse ECG Signal Recovery Approach for Hybrid Compressive Sensing Framework and Its Classification Using Deep Convolution Neural Network

Research on Compressive Sensing (CS) framework for ECG signals are of 3 categories. Category (1) involves CS for a single frame of ECG signal and performance analysis of reconstructed frame. Category (2) involves CS for every frame of the complete ECG signals and arrhythmia classification using deep learning approach on the reconstructed ECG signals. Category (3) is same as category (1) but the classification involves direct compressed measurements without reconstruction. The key merit of category (2) is that the signal can also be visually inspected by doctors. This paper falls in category (2) and proposes a hybrid CS algorithm for ECG signal reconstruction using discrete wavelet transform (DWT) and discrete cosine transform (DCT) both as sensing and sparsifying operator and the classification of reconstructed signal is achieved using DCNN for two different image representations (a) CWT and (b). Using MIT-BIT ECG dataset, for the proposed hybrid CS implementation the reconstruction is achieved with PRD=2.591, SNR=36.530 dB and SSIM=0.745587 and the classification accuracy achieved for binary class problem with CR=0.5 is 93% for CWT image representation and 98.4% for STFT image representation. The proposed hybrid CS method performs better than the conventional non hybrid method for all the performance metrics and the classification results are better than other existing state of art methods for category (2) & (3). The same experiment is extended to a 3-class DCNN classification problem and the accuracy is 93.33% for the STFT image.