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Published December 2019 | public
Journal Article

A Real-Time Vital-Sign Monitoring in the Physical Domain on a Mixed-Signal Reconfigurable Platform

Abstract

This work presents a mixed-signal physical-computation-electronics for monitoring three vital signs; namely heart rate, blood pressure, and blood oxygen saturation; from electrocardiography, arterial blood pressure, and photoplethysmography signals in real-time. The computational circuits are implemented on a reconfigurable and programmable signal-processing platform, namely field-programmable analog array (FPAA). The design leverages the core enabling technology of FPAA, namely floating-gate CMOS devices, and an on-chip low-power microcontroller to achieve energy-efficiency while not compromising accuracy. The custom physical-computation-electronics operating in CMOS subthreshold region, performs low-level (i.e., physiologically-relevant feature extraction) and high-level (i.e., detecting arrhythmia) signal processing in an energy-efficient manner. The on-chip microcontroller is used (1) in the programming mode for controlling the charge storage at the analog-memory elements to introduce patient-dependency into the system and (2) in the run mode to quantify the vital signs. The system has been validated against digital computation results from MATLAB using datasets collected from three healthy subjects and datasets from the MIT/BIH open source database. Based on all recordings in the MIT/BIH database, ECG R-peak detection sensitivity is 94.2%. The processor detects arrhythmia in three MIT/BIH recordings with an average sensitivity of 96.2%. The cardiac processor achieves an average percentage mean error bounded by 3.75%, 6.27%, and 7.3% for R-R duration, systolic blood pressure, and oxygen saturation level calculations; respectively. The power consumption of the ECG, blood-pressure and photo-plethysmography processing circuitry are 126 nW, 251 nW and 1.44 μW respectively in a 350nm process. Overall, the cardiac processor consumes 1.82 μW.

Additional Information

© 2019 IEEE. Manuscript received July 8, 2019; revised September 23, 2019; accepted October 22, 2019. Date of publication October 28, 2019; date of current version December 31, 2019. The authors would like to thank Omer Inan and the members of the Inan Research Lab at Georgia Tech for assistance with data collection from healthy subjects and sharing the data.

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023