Abnormal ECG Detection in Wearable Devices Using Compressed Learning

The electrocardiogram (ECG) is a broadly used diagnostic tool for assessing heart functionality. However, ECG data can be large and difficult to transmit or store. The concept of compressed sensing refers to the reconstruction of sparse signals from a limited number of measurements, potentially reducing the size of ECG data while preserving diagnostic information. The purpose of this study is to investigate the use of Compressed Learning (CL) to detect abnormal ECGs. Data from three distinct groups of individuals, namely those with congestive heart failure (CHF), cardiac arrhythmia (ARR), and those with normal sinus rhythms (NSR), have been obtained from a publicly available database. Three measurement matrices, such as Random Gaussian, Random Bernoulli, and Structured Fourier Matrices, were used at different compression ratios. The compressed ECG data was further classified using Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Neural Networks. The results show that CL can significantly reduce the size of ECG data without sacrificing diagnostic accuracy. The SVM classifier achieved high training and testing accuracy of 82%. This study suggests that CL can be a useful tool for ECG data compression and abnormalities detection and could potentially be integrated into clinical practice for more efficient ECG analysis.