ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model with CNN and LSTM

Kohei Yamamoto, Ryosuke Hiromatsu, Tomoaki Ohtsuki

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


An Electrocardiogram (ECG) is a typical method used to detect heartbeat, and an ECG signal analysis enables the detection of some heart diseases. However, the ECG-based heartbeat detection requires device attachment, which is not preferred for daily use. A Doppler sensor could be a device used to enable the non-contact heartbeat detection. In this paper, we propose a Doppler sensor-based ECG signal reconstruction method by a hybrid deep learning model with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An ECG signal can be reconstructed by relating features of a heartbeat signal obtained by a Doppler sensor to those of the ECG signal. Thus, we construct the deep learning model that extracts the spatial and temporal features from the heartbeat signal by CNN and LSTM. Based on the extracted features, the ECG signal is reconstructed. We conducted experiments to observe heartbeat against 9 healthy subjects without heart disease. The experimental results showed that our method performed ECG signal reconstruction with a correlation coefficient of 0.86 between the reconstructed and actual ECG signals, even without attaching devices. The results indicate that it is possible to remotely reconstruct an ECG signal from a heartbeat signal via a Doppler sensor.

Original languageEnglish
Article number9139941
Pages (from-to)130551-130560
Number of pages10
JournalIEEE Access
Publication statusPublished - 2020


  • Doppler sensor
  • ECG
  • Heartbeat
  • deep learning
  • microwaves

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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