Detection and Prediction of Sudden Cardiac Death (SCD) For Personal Healthcare

Abstract

Sudden Cardiac Death (SCD) is one of continuing challenges to the modern clinician. It is responsible for an estimated 400,000 deaths per year in the United States and millions of deaths worldwide. This research developed a personal cardiac homecare system by sensing Lead-I ECG signals for detecting and predicting SCD events, which also builds in ECG identity verification. A MIT/BIH SCD Holter Database plus our ECG database were investigated. The system includes a self-made ECG amplifier, a NI DAQ card, a laptop computer, LabView and MatLab programs. The wavelet analysis was applied to detect SCD and the overall performance is 87.5% correct detection rate. In addition, artificial neural networks (ANN) were used to predict SCD events. The correct prediction rates by applying least mean square (LMS), decision based neural network (DBNN), and back propagation (BP) neural network were 67.44%, 58.14% and 55.81% respectively.