Key result
An unsupervised methodology using a Convolutional Variational AutoEncoder achieved 98.5% sensitivity and 98.6% precision in seismocardiogram heartbeat detection with a 4.6 ms root mean square error.
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An unsupervised machine learning approach using Convolutional Variational AutoEncoders can accurately detect heartbeats and extract meaningful morphological representations from Seismocardiogram signals.
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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Mora et al. (2020) studied Healthy (n=20). Convolutional Variational AutoEncoder (VAE) for SCG analysis vs. Standard ElectroCardioGraphy (ECG) was evaluated on Heartbeat detection sensitivity. An unsupervised methodology using a Convolutional Variational AutoEncoder achieved 98.5% sensitivity and 98.6% precision in seismocardiogram heartbeat detection with a 4.6 ms root mean square error.
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