  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.
