Registered report protocol: Developing an artifact index for capacitive electrocardiography signals acquired with an armchair

Warnecke, Joana M.; Wang, Ju; Cakir, Tolga; Spicher, Nicolai; Ganapathy, Nagarajan; Deserno, Thomas M.

doi:10.1371/journal.pone.0254780

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…We obtain the ground truth with an ECG sensor (BiosignalPlux Explorer, Plux Wireless Biosignals, Lisbon, Portugal) connected to three adhesive electrodes, which we attach to the usual positions on the chest 25 . We make a test recording to ensure the electrodes are in the correct position.…”

Section: Methodsmentioning

confidence: 99%

Robust in-vehicle heartbeat detection using multimodal signal fusion

Warnecke,

Lasenby,

Deserno

2023

Sci Rep

Self Cite

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A medical check-up during driving enables the early detection of diseases. Heartbeat irregularities indicate possible cardiovascular diseases, which can be determined with continuous health monitoring. Therefore, we develop a redundant sensor system based on electrocardiography (ECG) and photoplethysmography (PPG) sensors attached to the steering wheel, a red, green, and blue (RGB) camera behind the steering wheel. For the video, we integrate the face recognition engine SeetaFace to detect landmarks of face segments continuously. Based on the green channel, we derive colour changes and, subsequently, the heartbeat. We record the ECG, PPG, video, and reference ECG with body electrodes of 19 volunteers during different driving scenarios, each lasting 15 min: city, highway, and countryside. We combine early, signal-based late, and sensor-based late fusion with a hybrid convolutional neural network (CNN) and integrated majority voting to deliver the final heartbeats that we compare to the reference ECG. Based on the measured and the reference heartbeat positions, the usable time was 51.75%, 58.62%, and 55.96% for the driving scenarios city, highway, and countryside, respectively, with the hybrid algorithm and combination of ECG and PPG. In conclusion, the findings suggest that approximately half the driving time can be utilised for in-vehicle heartbeat monitoring.

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Section: Methodsmentioning

confidence: 99%

Robust in-vehicle heartbeat detection using multimodal signal fusion

Warnecke,

Lasenby,

Deserno

2023

Sci Rep

Self Cite

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“…This will help to improve the existing state-of-the-art cECG signal quality. 10,17,22,23 Our study was, however, limited to 11 minutes of data recording from 20 subjects, all of them being free from any chronic heart complications. The subjects were assigned the task of reading, which might not take into account all the types of body movements, leading to only partial learning of the neural network.…”

Section: Discussionmentioning

confidence: 99%

“…If the calculated HR of cECG was in the 90% range of HR from the Plux, the reliability criteria was met. 17 The calculated HR from reference and cECG in the window of 5 seconds is as shown in Figure 4 for one of the subject. The gaps of HR from cECG represent the unreliable regions of the signal.…”

Section: Methodsmentioning

confidence: 99%

Reliability estimation of armchair-based capacitive ECG using video-based pose estimation

Srivastava¹,

Ershadi²,

Haghi³

et al. 2023

Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications

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Background: Annually and globally, cardiovascular diseases yield the death of 17.9 million people. Continuous and unobtrusive monitoring of vital signs supports an early detection of abnormalities and diseases, such as atrial fibrillation. Here, we analyze capacitive electrocardiography (cECG) recorded in an armchair at home. However, processing such data is challenging, as body movements and other artifacts disturb the signal quality. Methods: In this paper, we suggest video-based pose estimation to assess the reliability of cEGC. In 20 subjects, we measured reference and capacitive ECG synchronized with a video recording for key-point-based movement analysis with the OpenPifPaf pose estimation algorithm. We considered all 17 human body joints to compute a movement index and label all data in windows of 5 s as reliable vs. unreliable, according to that index. Then, we compared the heart rates obtained from complete and reliable cEGC windows with the corresponding windows from the reference ground truth ECG. Result: The left and right hip joints are most significantly influencing the signal’s quality. In addition, the joints’ movement distance from the original position limited to the range 460.84 pixels and 382.22 pixels, respectively, deliver a reliable cECG signal. Conclusion: Video-based pose estimation delivers reliable and unreliable periods of cECG recordings and improves continuous health monitoring at home.

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Section: Ground Truthmentioning

confidence: 99%

Robust In-Vehicle Heartbeat Detection Using Multimodal Signal Fusion

Warnecke

Lasenby

Deserno

2023

Preprint

Self Cite

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A medical checkup during driving enables the early detection of diseases. Heartbeat irregularities indicate possible cardiovas-cular diseases, which can be determined with continuous health monitoring. Therefore, we develop a redundant sensor system based on electrocardiography (ECG) and photoplethysmography (PPG) sensors attached to the steering wheel, and a red, green, and blue (RGB) camera behind the steering wheel. For the video, we integrate the face recognition engine SeetaFace to continuously detect landmarks of face segments. Based on the green channel, we derive colour changes and subsequently the heartbeat. We record the ECG, PPG, video, and reference ECG with body electrodes of 19 volunteers during different driving scenarios, each lasting 15 min: city, highway, and countryside. We combine early, signal-based late, and sensor-based late fusion with a hybrid convolutional neural network (CNN) and integrated majority voting to deliver the final heartbeats that we compare to the reference ECG. Based on the measured and the reference heartbeat positions, the usable time was 51.75%, 58.62%, and 55.96% for the driving scenarios city, highway, and countryside, respectively, with the hybrid algorithm and combination of ECG and PPG. In conclusion, the findings suggest that approximately half the driving time can be utilised for in-vehicle heartbeat monitoring.

show abstract

Registered report protocol: Developing an artifact index for capacitive electrocardiography signals acquired with an armchair

Cited by 4 publications

References 32 publications

Robust in-vehicle heartbeat detection using multimodal signal fusion

Robust in-vehicle heartbeat detection using multimodal signal fusion

Reliability estimation of armchair-based capacitive ECG using video-based pose estimation

Robust In-Vehicle Heartbeat Detection Using Multimodal Signal Fusion

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