Deep Learning for identifying systolic complexes in SCG traces: a cross-dataset analysis

TL;DR

This study evaluates deep learning methods for detecting the systolic complex in seismocardiographic signals across different datasets, highlighting the importance of personalization and multi-channel data for improved accuracy in real-world scenarios.

Contribution

It provides the first cross-dataset analysis of deep learning approaches for systolic complex detection, emphasizing personalization and multi-channel data integration.

Findings

Deep learning is effective for systolic complex detection.

Personalization reduces domain shift effects.

Multi-channel data improves detection accuracy.

Abstract

The seismocardiographic signal is a promising alternative to the traditional ECG in the analysis of the cardiac activity. In particular, the systolic complex is known to be the most informative part of the seismocardiogram, thus requiring further analysis. State-of-art solutions to detect the systolic complex are based on Deep Learning models, which have been proven effective in pioneering studies. However, these solutions have only been tested in a controlled scenario considering only clean signals acquired from users maintained still in supine position. On top of that, all these studies consider data coming from a single dataset, ignoring the benefits and challenges related to a cross-dataset scenario. In this work, a cross-dataset experimental analysis was performed considering also data from a real-world scenario. Our findings prove the effectiveness of a deep learning solution, while showing the importance of a personalization step to contrast the domain shift, namely a change in data distribution between training and testing data. Finally, we demonstrate the benefits of a multi-channels approach, leveraging the information extracted from both accelerometers and gyroscopes data.