A Nanowatt Real-Time Cardiac Autonomic Neuropathy Detector

This paper presents an electrocardiogram (ECG) processor on chip for full ECG feature extraction and cardiac autonomic neuropathy (CAN) classification. Full ECG extraction is performed using absolute value curve length transform (A-CLT) for $\text{QRS}_{\text{peak}}$ detection and using low-pass differentiation for other ECG features such as $\text{QRS}_{\text{on}}$, $\text{QRS}_{\text{off}}$, Pwave, and Twave. The proposed QRS detector attained a sensitivity of 99.37% and predictivity of 99.38%. The extracted $\text{QRS}_{\text{peak}}$ to $\text{QRS}_{\text{peak}}$ intervals (RR intervals) along with QT intervals enable CAN severity detection, which is a cardiac arrhythmia usually seen in diabetic patients leading to increased risk of sudden cardiac death. This paper presents the first hardware real-time implementation of CAN severity detector that is based on RR variability and QT variability analysis. RR variability metrics are based on mean RR interval and root mean square of standard differences of the RR intervals. The proposed architecture was implemented in 65-nm technology and consumed 75 nW only at 0.6 V, when operating at 250 Hz. Ultralow power dissipation of the system enables it to be integrated into wearable healthcare devices.