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  4. Transforming Seismocardiograms into Electrocardiograms by Applying Convolutional Autoencoders
 
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2020
Conference Paper
Titel

Transforming Seismocardiograms into Electrocardiograms by Applying Convolutional Autoencoders

Abstract
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, p>0.12). 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.
Author(s)
Haescher, Marian
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hoepfner, Florian
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Chodan, Wencke
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kraft, Dimitri
Univ. Rostock
Aehnelt, Mario
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Urban, Bodo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020. Proceedings
Konferenz
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020
Thumbnail Image
DOI
10.1109/ICASSP40776.2020.9053130
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Electrocardiography (ECG)

  • cardiology

  • neural networks

  • Lead Topic: Individual Health

  • Research Line: Human computer interaction (HCI)

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