Key result
Analysis of carotid artery blood flow sounds using a convolutional neural network differentiated between different users with over 95% accuracy.
Why the study?
State-of-the-art behavioral and physiological biometric traits are vulnerable to tampering and forgery.
Observational (n=7)
Carotid artery blood flow sounds analyzed via convolutional neural networks show potential as a novel biometric identification method with >95% accuracy in a small proof-of-concept study.
Small study shows carotid sound recording feasibility; leaves open validation of biometric accuracy and security in larger cohorts.
BACKGROUND: Biometric sensing is a security method for protecting information and property. State-of-the-art biometric traits are behavioral and physiological in nature. However, they are vulnerable to tampering and forgery. METHODS: The proposed approach uses blood flow sounds in the carotid artery as a source of biometric information. A handheld sensing device and an associated desktop application were built. Between 80 and 160 carotid recordings of 11 s in length were acquired from seven individuals each. Wavelet-based signal analysis was performed to assess the potential for biometric applications. RESULTS: The acquired signals per individual proved to be consistent within one carotid sound recording and between multiple recordings spaced by several weeks. The averaged continuous wavelet transform spectra for all cardiac cycles of one recording showed specific spectral characteristics in the time-frequency domain, allowing for the discrimination of individuals, which could potentially serve as an individual fingerprint of the carotid sound. This is also supported by the quantitative analysis consisting of a small convolutional neural network, which was able to differentiate between different users with over 95% accuracy. CONCLUSION: The proposed approach and processing pipeline appeared promising for the discrimination of individuals. The biometrical recognition could clinically be used to obtain and highlight differences from a previously established personalized audio profile and subsequently could provide information on the source of the deviation as well as on its effects on the individual's health. The limited number of individuals and recordings require a study in a larger population along with an investigation of the long-term spectral stability of carotid sounds to assess its potential as a biometric marker. Nevertheless, the approach opens the perspective for automatic feature extraction and classification.
No takes yet. Share an insight, caveat, or question.
Salvi et al. (2021) reported an observational. Carotid artery blood flow sound analysis was evaluated on Differentiation between different users (accuracy). Analysis of carotid artery blood flow sounds using a convolutional neural network differentiated between different users with over 95% accuracy.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: