IDTM GmbH-Ingenieurgesellschaft für Diagnostischen und Therapeutische Medizintechnik mit Beschränkter Haftung, 45657 Recklinghausen, Germany.
Instituto de Electricidad y Electrónica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5111187, Chile.
Sensors (Basel). 2021 Oct 7;21(19):6656. doi: 10.3390/s21196656.
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.
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.
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.
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.
生物识别传感是一种保护信息和财产安全的方法。最先进的生物识别特征是行为和生理性质的。然而，它们容易受到篡改和伪造。
该方法使用颈动脉血流声音作为生物识别信息的来源。构建了一个手持式传感设备和一个相关的桌面应用程序。从七个人中每人采集了 80 到 160 个长度为 11 秒的颈动脉记录。进行了基于小波的信号分析，以评估生物识别应用的潜力。
每个个体的采集信号在一次颈动脉声音记录内和相隔数周的多次记录之间是一致的。一个记录的所有心动周期的平均连续小波变换谱在时频域中显示出特定的光谱特征，允许个体之间的区分，这可能作为颈动脉声音的个体指纹。这也得到了一个小型卷积神经网络的定量分析的支持，该网络能够以超过 95%的准确率区分不同的用户。
所提出的方法和处理管道似乎有希望用于个体的区分。生物识别识别可以在临床上用于获取并突出与之前建立的个性化音频配置文件的差异，随后可以提供关于偏差来源及其对个体健康影响的信息。个体数量和记录的限制需要在更大的人群中进行研究，并调查颈动脉声音的长期光谱稳定性，以评估其作为生物识别标志物的潜力。然而，该方法为自动特征提取和分类开辟了前景。
