A Fully Analog Autonomous QRS Complex Detection and Low-Complexity Asystole, Extreme Bradycardia, and Tachycardia Classification System

This paper presents a power-efficient fully analog classifier architecture for the detection of critical cardiac abnormalities . asystole, extreme bradycardia and tachycardia. To reduce power consumption and hardware complexity, an analog QRS complex detection circuit and arithmetic counter based classification modules are introduced. The QRS detection circuit is autonomous and consumes an average current of 34 nA only, state-of-the-art QRS detection designs which are DSP assisted and consume tens of micro-watts of power. Furthermore, a heart rate estimator provides the number of QRS complexes per minute. Each of the proposed modules are successfully validated through real ECG test signals taken from the PhysioNet database. The proposed beat detector circuit exhibits a sensitivity of 97.85% and positive prediction of 98.3%. Experimental results based on bedside monitor data, shows that the proposed classification module provides an overall sensitivity of 96.25% and positive prediction of 96.97%. The complete classification architecture, implemented fully on analog platform, is simulated in UMC 0.18 μm CMOS process and consumes 119 nW power with an active silicon area of 450 μm×800 μm. Moreover, the low power implementation makes it suitable for long-term battery operated remote ECG monitoring systems. IEE