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Transforming Seismocardiograms into Electrocardiograms by Applying Convolutional Autoencoders

: Haescher, Marian; Hoepfner, Florian; Chodan, Wencke; Kraft, Dimitri; Aehnelt, Mario; Urban, Bodo


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020. Proceedings : May 4-8, 2020, Barcelona, Spain
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-5090-6631-5
ISBN: 978-1-5090-6632-2
International Conference on Acoustics, Speech and Signal Processing (ICASSP) <45, 2020, Barcelona>
Conference Paper
Fraunhofer IGD ()
Fraunhofer IGD-R ()
Electrocardiography (ECG); cardiology; neural networks; Lead Topic: Individual Health; Research Line: Human computer interaction (HCI)

Electrocardiograms constitute the key diagnostic tool for cardiologists. While their diagnostic value is yet unparalleled, electrode placement is prone to errors, and sticky electrodes pose a risk for skin irritations and may detach in long-term measurements. Heart.AI presents a fundamentally new approach, transforming motion-based seismocardiograms into electrocardiograms interpretable by cardiologists. Measurements are conducted simply by placing a sensor on the user’s chest. To generate the transformation model, we trained a convolutional autoencoder with the publicly available CEBS dataset. The transformed ECG strongly correlates with the ground truth (r=.94, p.01), and important features (number of R-peaks, QRS-complex durations) are modeled realistically (Bland-Altman analyses, p0.12). On a 5- point Likert scale, 15 cardiologists rated the morphological and rhythmological validity as high (4.63/5 and 4.8/5, respectively). Our electrodeless approach solves crucial problems of ECG measurements while being scalable, accessible and inexpensive. It contributes to telemedicine, especially in low-income and rural regions worldwide.