Ultra-Low Power CAN Detection and VA Prediction

In this chapter, an ECG processor on-chip for full ECG feature extraction and cardiac autonomic neuropathy (CAN) is presented. Absolute value curve length transform (ACLT) is performed for QRS detection, whereas full feature extraction (detecting QRS_on, QRS_off, P-, and T-waves) is achieved by low-pass differentiation. Proposed QRS detector attains a sensitivity of 99.37% and predictivity of 99.38%. Extracted RR interval along with QT interval enables CAN severity detector. CAN is cardiac arrhythmia usually seen in diabetic patients and have prevalent effect in sudden cardiac death. In this chapter, the first hardware real-time implementation of the CAN severity detector is proposed. Detection is based on RR variability and QT variability analysis. RR variability metrics are based on mean RR interval and RMSSD of RR interval. The proposed architecture is implemented in 65 nm technology, and it consumes only 75 nW at 0.6 V, when operating at 250 Hz. Ultra-low power dissipation of the system enables it to be integrated into wearable healthcare devices. This chapter also presents an architecture for VA prediction. The architecture was optimized for ultra-low power operation compared to prior state-of-the-art design.