A Low Power Wearable ECG Module for Heart Rate Variability Classification System

Abstract

The Electrocardiogram (ECG) technology has emerged as one of the significant and easily available diagnostic techniques in the clinical cardiovascular domain to detect the diseases related to the heart and monitoring the electrical activity of the heart. The primary use of the ECG is to detect several cardiovascular diseases (CVD) namely myocardial infarction, Arrhythmia, Atrial Fibrillation and Ventricular Hypertrophy, etc. In recent time the research is going on automatic heartbeat detection, miniaturization of low power ECG processing wearable module and wireless patient s information transformation with IoT and MEMS-based chip design and integration. It is possible by the personalized heartbeat classification of a patient and accumulation of medical data with the integration of high-performance computing devices by using a reliable processing unit. newline ECG signal attained is corrupted with several mechanical and electrical noise namely Power Line Interference (PLI), Baseline Wander (BLW), electrode movement/ electromyography (EMG), motion artifacts. For accurate analysis, the clinician requires a noiseless ECG signal. The Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) filtering techniques help to remove the noise of the ECG signal. The FIR filters are chosen due to their linear response and hardware implementable nature. The FIR filters require large computational complexity for general purpose multiplier during the implementation, which limits its speed and demands more power and resources. This issue has been partially resolved by employing a Field Programmable Gate Arrays (FPGA) processing unit. The re-programmability, high speed, low power consumption and low execution time employ FPGA as an efficient processing unit in the area of biomedical applications designing. In this research, the most advanced and popular board namely Zynq-7000 zedboard is utilized to design the wearable ECG denoising module for the multistage classification system. newline