Physical reservoir computing for optical stethoscope-based heart sound biometric identification

Heart sound signal has emerged as a promising solution to biometric identification. In this paper, we use an optical flow algorithm to retrieve optical stethoscope-based heart sound signals from a laser-camera system. We apply physical reservoir computing (RC) for the classification algorithm. As a...

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Bibliographic Details
Published in:IEEE transactions on artificial intelligence pp. 1 - 14
Main Authors: Ding, Yuqi, Li, Haobo, Liang, Xiangpeng, Vaskeviciute, Marija, Faccio, Daniele, Heidari, Hadi
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Adaptive optics
Biomedical optical imaging
Biometric identification
Cameras
Classification algorithms
edge computing
Heart
Machine learning
neuromorphic processor
Optical reflection
optical stethoscope
rotating neuron reservoir
Speckle
Stethoscope
ISSN:2691-4581, 2691-4581
Online Access:Get full text
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Summary:Heart sound signal has emerged as a promising solution to biometric identification. In this paper, we use an optical flow algorithm to retrieve optical stethoscope-based heart sound signals from a laser-camera system. We apply physical reservoir computing (RC) for the classification algorithm. As a bio-inspired algorithm, physical RC has attracted growing research interests in recent years. We aim to create an efficient identification system by applying a recently proposed physical RC model called rotating neuron reservoir (RNR) as the processing core. Unlike conventional machine learning classifiers, RNR is a hardware-based neuromorphic model that preserves the majority of computing in the analogue domain, holding the promise of a next-generation machine learning accelerator. At the same time, the RNR, as a recurrent neural network (RNN), is suitable for time series data processing. The proposed system is verified by an experimentally collected heart sound dataset by laser-camera system achieving an overall accuracy of 89.03% in identifying twelve testing subjects. Additionally, the elevated heart sound from 8 subjects have been blended with their normal heart sounds to assess the robustness of the proposed system. The classification accuracy reaches over 83% in this mixed test. Furthermore, the identification system was assessed under individuals with different types of heart murmurs and abnormal heart sounds, achieving an overall accuracy of around 90%. The successful demonstration promises a novel application of physical RC for future biometric identification.
ISSN:2691-4581
2691-4581
DOI:10.1109/TAI.2025.3617382