This article presents an unsupervised, automated procedure for the analysis of SeismoCardioGram (SCG) signals. SCG is a measure of chest vibrations, induced by the mechanical activity of the heart, that allows to extract relevant parameters, including Heart Rate (HR) and HR Variability (HRV). An initial self-calibration is performed, solely based on SCG traces, yielding a suitable heartbeat template (personalized for each subject). Then, beat detection and timing annotation are performed in two steps: at first, candidate beats are identified and validated, by means of suitably defined detection signals; then, precise timing annotation is achieved by best aligning such candidate beats to the previously extracted template. The algorithm has been validated on two separate datasets, featuring different acquisition setups: the first one is the publicly available CEBS database, reporting SCG signals from subjects lying in supine position, whereas the second one was acquired using a custom setup, involving sitting subjects. Results show good sensitivity and precision scores (98.5%, 98.6% for the CEBS database, and 99.1%, 97.9% for the Custom one, respectively). Also, comparison with ECG gold-standard is given, showing good agreement between beat-to-beat intervals computed from SCG and the ECG gold-standard: on average, R2 scores of 99.3% and 98.4% are achieved on CEBS and Custom datasets, respectively. Furthermore, a low RMS Error is achieved on the CEBS and Custom dataset, amounting to 4.6 ms and 6.2 ms, respectively (i.e. 2.3 Ts and 3.1 Ts, where Ts is the sampling period): such results well compare to related literature. Validation on two different datasets indicates the robustness of the proposed methodology.
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