Diagnosis of Arrhythmia from Compressively Sensed ECG Signals Using Machine Learning Algorithms

Abstract

Cardiovascular diseases (CVDs) represent a significant health concern in the present era, with Electrocardiogram (ECG) serving as a crucial bio-signal for their detection. Efficient health monitoring necessitates rapid and precise diagnosis, thereby mandating the utilization of Compressive Sensing (CS) alongside Machine Learning (ML) algorithms. CS functions as a sensing methodology that reduces sample numbers by capturing sparse or compressible representations, simplifying and expediting the acquisition process. In this proposed study, ECG signals are compressively sensed and preprocessed using CS reconstruction algorithms, followed by the application of various ML algorithms for diagnostic purposes. The assessment of the reconstructed ECG signal entails the assessment of Peak Signal-to-Noise Ratio (PSNR) values and Percentage Root-mean-square Difference (PRD). Concurrently, ML algorithms are evaluated based on metrics including accuracy, specificity, and sensitivity. This work demonstrates exceptional performance in terms of acquisition time and computational complexity through the application of CS technology. Comparative analysis with the existing methodologies for CVD diagnosis reveals the proposed approach’s remarkable efficacy. Notably, the reduction in data volume and hardware complexity serves as a significant advantage over conventional methods. The integration of CS and ML algorithms in the proposed methodology proves highly effective in diagnosing CVDs, achieving a classification accuracy of 94.7%. These results underscore the methodology’s ability to deliver both speed and accuracy in diagnosis, positioning it as a promising approach for health monitoring.