Making Noise - Improving Seismocardiography Based Heart Analysis With Denoising Autoencoders

Seismocardiography is a method commonly used to monitor and prevent cardiovascular diseases. However, noise and artifacts in the signals often interfere with the assessment of cardiac health and the analysis of the signal morphology. Therefore, this work presents a new approach to denoise seismocardiography signals by applying fully convolutional denoising autoencoders. In order to investigate the suitability and robustness of this approach, a series of experiments have been carried out with respect to the optimal configuration for the denoising task and a comparison with wavelet denoising as a traditional approach. Furthermore, the practical applicability of the method is tested with the use case of transforming noisy seismocardiography signals into electrocardiography signals. Our approach using autoencoders outperforms the commonly used wavelet denoising. Additionally, we demonstrate that the widespread usage of Butterworth filters may not only be unnecessary but even detrimental. Finally, the generalizability of the method is verified on unseen data. With those combined improvements in noise reduction, the assessment of cardiac health using seismocardiography in the presence of noise may be facilitated in the future.