Reconstruction of 3-Axis Seismocardiogram from Right-to-left and Head-to-foot Components Using A Long Short-Term Memory Network

TL;DR

This study develops a deep learning model using LSTM networks to predict the dorsoventral seismocardiogram from right-to-left and head-to-foot components, enabling 3-axis SCG reconstruction from dual-axis accelerometer data.

Contribution

It introduces a novel deep learning approach to reconstruct 3-axis SCG signals from two axes, enhancing non-invasive cardiac monitoring capabilities.

Findings

LSTM model achieved a mean square error of 0.09 in predictions.

The approach demonstrates potential for accurate 3-axis SCG reconstruction.

Deep learning can effectively model complex physiological signals.

Abstract

This pilot study aims to develop a deep learning model for predicting seismocardiogram (SCG) signals in the dorsoventral direction from the SCG signals in the right-to-left and head-to-foot directions ($\textrm{SCG}_x$ and $\textrm{SCG}_y$). The dataset used for the training and validation of the model was obtained from 15 healthy adult subjects. The SCG signals were recorded using tri-axial accelerometers placed on the chest of each subject. The signals were then segmented using electrocardiogram R waves, and the segments were downsampled, normalized, and centered around zero. The resulting dataset was used to train and validate a long short-term memory (LSTM) network with two layers and a dropout layer to prevent overfitting. The network took as input 100-time steps of $\textrm{SCG}_x$ and $\textrm{SCG}_y$, representing one cardiac cycle, and outputted a vector that mapped to the target variable being predicted. The results showed that the LSTM model had a mean square error of 0.09 between the predicted and actual SCG segments in the dorsoventral direction. The study demonstrates the potential of deep learning models for reconstructing 3-axis SCG signals using the data obtained from dual-axis accelerometers.