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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Vydané v:IEEE transactions on artificial intelligence s. 1 - 14
Hlavní autori: Ding, Yuqi, Li, Haobo, Liang, Xiangpeng, Vaskeviciute, Marija, Faccio, Daniele, Heidari, Hadi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 2025
Predmet:
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
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Abstract 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.
AbstractList 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.
Author Heidari, Hadi
Liang, Xiangpeng
Faccio, Daniele
Li, Haobo
Ding, Yuqi
Vaskeviciute, Marija
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  organization: James Watt School of Engineering, University of Glasgow, Glasgow, UK
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Snippet Heart sound signal has emerged as a promising solution to biometric identification. In this paper, we use an optical flow algorithm to retrieve optical...
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SubjectTerms 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
Title Physical reservoir computing for optical stethoscope-based heart sound biometric identification
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