Non-contact Quickest Abnormal Heart Rate Detection using MIMO Radar

This paper proposes to employ a multiple-input multiple-output (MIMO) radar-based quickest change detection (QCD) for non-contact sudden abnormal heart rate (HR) alerting, where the HR changes from a normal to an abnormal value at an unknown change time. By developing a signal model for the non-contact sudden abnormal HR alerting using MIMO radar, we propose a sequential generalized likelihood ratio test (GLRT) based abnormal HR detector to detect the HR change immediately and evaluate the abnormal HR alerting performance employing mean time to false alarm (MTFA) and worst case average detection delay (WADD). Numerical examples validate the correctness of the theoretical analysis and demonstrate the efficiency of the proposed method by comparing with the state-of-the-art methods.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10889244
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
0.00

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article

Non-contact Quickest Abnormal Heart Rate Detection using MIMO Radar

Pei‐Chao Wang, Qian He
Non-Invasive Vital Sign Monitoring
article

Non-contact Quickest Abnormal Heart Rate Detection using MIMO Radar

Pei‐Chao Wang, Qian He
article en

Abstract

This paper proposes to employ a multiple-input multiple-output (MIMO) radar-based quickest change detection (QCD) for non-contact sudden abnormal heart rate (HR) alerting, where the HR changes from a normal to an abnormal value at an unknown change time. By developing a signal model for the non-contact sudden abnormal HR alerting using MIMO radar, we propose a sequential generalized likelihood ratio test (GLRT) based abnormal HR detector to detect the HR change immediately and evaluate the abnormal HR alerting performance employing mean time to false alarm (MTFA) and worst case average detection delay (WADD). Numerical examples validate the correctness of the theoretical analysis and demonstrate the efficiency of the proposed method by comparing with the state-of-the-art methods.

University of Electronic Science and Technology of China (CN)
Nature
Openalex Percentile: Top 96%
Non-Invasive Vital Sign Monitoring
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