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This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p < 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors.

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space).

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This paper presents an automated procedure for acquisition and analysis of BallistoCardioGraphy (BCG) traces. A tri-axial accelerometer and a microcontroller unit are used to record heart-induced recoil forces generated from a lying subject. The problem of BCG J-peak annotation is split into two sub-tasks: candidates extraction, based on a detection signal, and actual annotation, guided by subject-specific search windows. Such procedure is derived from an automatic calibration, which is carried out with no need of concurrent ElectroCardioGram (ECG) or user intervention. The algorithm also implements post-annotation checks for refinement of annotation, which effectively reduces the number of missed J-peaks. The impact of each algorithm phase is analyzed, assessing statistical significance of each step; finally, performance is optimized in a data-driven fashion. Results show that the proposed methodology is able to achieve high sensitivity and precision (the median score is 98.9% and 98.1%, respectively) in J-peak detection. The quality of J-peaks timing annotation is further demonstrated by a very low discrepancy between BCG and ECG HR estimates. Over all population, the standard deviation of such error was found to be approximately 6.56 ms, whereas the Mean Absolute Error just 4.7 ms (i.e. ≈ 1.18; Ts, where Ts = 4 ms is the sampling period). Such scores, indeed, improve over recent related literature.