Design and Implementation of PQRST ASIC for Energy-Efficient Cardiovascular Disease Diagnosis Using ECG Signal Feature Extraction

Abstract

Cardiovascular problems have become a major source of concern for people all over the world. The significance of disease necessitates proper diagnosis as well as right and early treatment. ECG is the most extensively utilized important signal nowadays, providing precise information regarding the function of the cardiovascular. A novel illness diagnostic algorithm created on the forward searching for ECG signal processing techniques is implemented in an Application Specific Integrated Circuit (ASIC) for the cardiovascular diagnosis of diseases on a feature extraction method. The CMOS small leakage research strategies are used to create an ASIC. The PQRST ASIC has a surface area of with a supply voltage of the PQRST ASIC dissipates of energy. ECGs can provide a great deal of information on the normal and anomalous functioning of the heartbeat. The form of the ECG is similar to the irregularities of a heart. One cardiovascular phase of an ECG signal is made up of the feature facts P-QRS-T. The magnitude and frequency principles of a P-QRS-T section define how a human's heart pumps. An anomaly in ECG signals occurs when the electrical impulses of a heart are unpredictable, quicker, or less than usual. The ASIC output has been sent to a feature extraction method to diagnose the ECG signal, which provides a show that the design can be emailed to a cardiologist. This research looks at numerous strategies provided by researchers for extracting features from ECG signals. Using the Manually curated PTB diagnostics ECG database, the ASIC and feature extraction are validated for the diagnosis of bundle branch blockage, hypertrophic, arrhythmias, and myocardial infarction. The proposed ASIC, in conjunction with the feature extraction method, is best suited for an energy-effective peripheral cardiovascular disease recognition technique.