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Contactless real-time heartbeat detection via 24 ghz continuous-wave doppler radar using artificial neural networks

Maleševic, Nebojša LU ; Petrovic, Vladimir ; Belic, Minja ; Antfolk, Christian LU ; Mihajlovic, Veljko and Jankovic, Milica (2020) In Sensors (Switzerland) 20(8).
Abstract

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small... (More)

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference (“ground truth”) in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial neural network, Doppler radar, Heart rate, Real-time processing
in
Sensors (Switzerland)
volume
20
issue
8
article number
2351
publisher
MDPI AG
external identifiers
  • pmid:32326190
  • scopus:85083977106
ISSN
1424-8220
DOI
10.3390/s20082351
language
English
LU publication?
yes
id
bef49428-fce6-4041-b468-2ae9c35309ee
date added to LUP
2020-05-20 13:07:51
date last changed
2020-10-20 03:06:57
@article{bef49428-fce6-4041-b468-2ae9c35309ee,
  abstract     = {<p>The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (&lt;1 s). The performance of the proposed method was evaluated with respect to an ECG reference (“ground truth”) in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.</p>},
  author       = {Maleševic, Nebojša and Petrovic, Vladimir and Belic, Minja and Antfolk, Christian and Mihajlovic, Veljko and Jankovic, Milica},
  issn         = {1424-8220},
  language     = {eng},
  month        = {04},
  number       = {8},
  publisher    = {MDPI AG},
  series       = {Sensors (Switzerland)},
  title        = {Contactless real-time heartbeat detection via 24 ghz continuous-wave doppler radar using artificial neural networks},
  url          = {http://dx.doi.org/10.3390/s20082351},
  doi          = {10.3390/s20082351},
  volume       = {20},
  year         = {2020},
}