A Wearable Auto-Patient Adaptive ECG Processor for Shockable Cardiac Arrhythmia

摘要

A non-machine learning patient-specific shockable cardiac arrhythmia (SCA) classification processor based on single lead electrocardiogram (ECG) is presented. The proposed SCA detection processor integrates a hardware-efficient reduced-set-of-five (RSF5) feature extraction engine to extract SCA and non-SCA, self-adaptive patient-specific threshold engine for the peak and interval detection from the ECG, and simplified decision logic to discriminate the arrhythmia in real-time. The SCAD processor consumes 0.89pJ/classification while classifying with an average sensitivity, and specificity of 98.66%, and 99.75%, respectively.