Signal-Level Fusion With Convolutional Neural Networks for Capacitively Coupled ECG in the Car.

Unobtrusive measurement technologies for vital signs, such as capacitively coupled electrocardiography (cECG), allow for health monitoring outside the clinical domain, for example in automotive applications. While cECG has the potential to deliver accurate information about the driver's heart, the signal quality is very volatile compared to standard ECG and can be corrupted easily by motion artifacts. In this work, we present a signal-level fusion algorithm based on a convolutional neural network (CNN) to locate individual heartbeats in three-channel cECG signals. To design and optimize the network's structure, data from the PhysioNet / CinC challenge “Robust Detection of Heart Beats in Multimodal Data” was used as an independent source. To train and test the algorithm, we used cECG data from six subjects and three different driving scenarios (highway, city, and proving ground) that is freely available as part of our UnoViS-database. Data consisted of 31 recordings with a total duration of 13.4 hours. Leave-one-subject-out cross validation was performed to assess the algorithm's performance. We achieved a sensitivity of 88.0% and a positive predictive value of 95.2% compared to the reference ECG, with a root-mean-square R-Peak localization error of 20.81 ms. The developed algorithm is available for download via the UnoViS-Website.