Key result
Wavelet transformed seismocardiogram biometrics achieved state-of-the-art identification and authentication performance when combined with deep learning, machine learning, or structural similarity.
Why the study?
Although seismocardiogram has become easily accessible due to sensor technology advances, seismocardiogram biometrics have not been widely explored.
Wavelet transformed seismocardiogram data combined with machine learning or deep learning models provides a highly effective method for biometric identification and authentication.
Answers are grounded in this paper and cite their sources.
May enable cardiac signal-based biometrics; leaves open real-world validation and security testing.
Seismocardiogram (SCG) has become easily accessible in the past decade owing to the advance of sensor technology. However, SCG biometric has not been widely explored. In this paper, we propose combining wavelet transform together with deep learning models, machine learning classifiers, or distance metric to perform SCG biometric matching tasks. We validate the proposed methods on the publicly available dataset from PhysioNet database. The dataset contains one hour long electrocardiogram, breathing, and SCG data of 20 subjects. We train the models on the first five minute SCG and conduct identification on the last five minute SCG. We evaluate the identification and authentication performance with recognition rate and equal error rate, respectively. Based on the results, we show that wavelet transformed SCG biometric can achieve state-of-the-art performance when combined with deep learning models, machine learning classifiers, or structural similarity metric.
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Hsu et al. (2021) studied this question. Wavelet transform combined with deep learning models, machine learning classifiers, or distance metric was evaluated on Identification and authentication performance (recognition rate and equal error rate). Wavelet transformed seismocardiogram biometrics achieved state-of-the-art identification and authentication performance when combined with deep learning, machine learning, or structural similarity.
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