A 1.06- \mu W Smart ECG Processor in 65-nm CMOS for Real-Time Biometric Authentication and Personal Cardiac Monitoring

• Published in: IEEE journal of solid-state circuits Vol. 54; no. 8; pp. 2316 - 2326
• Main Authors: Yin, Shihui, Kim, Minkyu, Kadetotad, Deepak, Liu, Yang, Bae, Chisung, Kim, Sang Joon, Cao, Yu, Seo, Jae-Sun
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.2019
• Subjects: Arrhythmia detection; Authentication; Biomedical monitoring; biometric authentication; cardiac monitoring; ECG; Electrocardiography; Feature extraction; Finite impulse response filters; Lasso regression; Monitoring; sparse neural network (NN); weight compression
• ISSN: 0018-9200; 1558-173X
• DOI: 10.1109/JSSC.2019.2912304

Many wearable devices employ the sensors for physiological signals (e.g., electrocardiogram or ECG) to continuously monitor personal health (e.g., cardiac monitoring). Considering private medical data storage, secure access to such wearable devices becomes a crucial necessity. Exploiting the ECG sensors present on wearable devices, we investigate the possibility of using ECG as the individually unique source for device authentication. In particular, we propose to use ECG features toward both cardiac monitoring and neural-network-based biometric authentication. For such complex functionalities to be seamlessly integrated in wearable devices, an accurate algorithm must be implemented with ultralow power and a small form factor. In this paper, a smart ECG processor is presented for ECG-based authentication as well as cardiac monitoring. Data-driven Lasso regression and low-precision techniques are developed to compress neural networks for feature extraction by 24.4<inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula>. The 65-nm testchip consumes 1.06 <inline-formula> <tex-math notation="LaTeX">\mu \text{W} </tex-math></inline-formula> at 0.55 V for real-time ECG authentication. For authentication, equal error rates of 1.70%/2.18%/2.48% (best/average/worst) are achieved on the in-house 645-subject database. For cardiac monitoring, 93.13% arrhythmia detection sensitivity and 89.78% specificity are achieved for 42 subjects in the MIT-BIH arrhythmia database.