An Energy-Efficient Wireless Sensor Network for Telemetry Operation: A Case Study of Cardiovascular Problem

An Electrocardiogram (ECG) is the first diagnostic tool a medical practitioner uses to measure the electrical and mucular fitness of an individual heart. The use of ECG is so important because heart-related diseases are silent killers. In recent times, advancement and the further development of wearable devices and ECG sensors have made it possible to continuously measure and analyze the electrocardiogram and myocardium signals of the heart. However, it requires significant training and adeptness to interpret the recorded ECG correctly and effectively. An energy-efficient wireless sensor infrastructure for improved operation of cardiovascular problems in the development economy using the concept of fog technology was presented. The ECG data was collected from Federal Medical Center Umuahia Abia State, Nigeria. Discrete Wavelet Transform (DWT) at first to perform preprocessing of ECG data to eliminate noise from motion artifacts, power line interference, and high frequency sources, followed by the undecimated Wavelet Transform (UWT) at first to extract relevant features, which are of high interest to a cardiologist. The proposed system classifies a recorded heartbeat into four classes, namely Normal Beat, Premature Ventricular Contraction (PVC), Premature Atrial Contraction (PAC), and Myocardial Infarction. The study found that processing and analyzing health data at the fog resulted in total energy savings of 36% and 52% when compared to conventional processing.