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2021
Journal Article
Titel
Vascular Auscultation of Carotid Artery: Towards Biometric Identification and Verification of Individuals
Abstract
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.
Author(s)
Salvi, Rutuja
IDTM GmbH-Ingenieurgesellschaft für Diagnostischen und Therapeutische Medizintechnik mit Beschränkter Haftung, 45657 Recklinghausen, Germany
Fuentealba, Patricio
IDTM GmbH-Ingenieurgesellschaft für Diagnostischen und Therapeutische Medizintechnik mit Beschränkter Haftung, 45657 Recklinghausen, Germany and Instituto de Electricidad y Electrónica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5111187, Chile
Sühn, Thomas
INKA-Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University, 39120 Magdeburg, Germany und SURAG Medical GmbH-Surgical Audio Guidance, 39120 Magdeburg, Germany
Spiller, Moritz
INKA-Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University, 39120 Magdeburg, Germany und SURAG Medical GmbH-Surgical Audio Guidance, 39120 Magdeburg, Germany
Illanes, Alfredo
INKA-Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University, 39120 Magdeburg, Germany und SURAG Medical GmbH-Surgical Audio Guidance, 39120 Magdeburg, Germany