Improving the Accuracy of R-Peak Detection in a Wearable Armband Device for Daily Life Electrocardiogram Monitoring Using a Deep Convolutional Denoising Encoder-Decoder Network

Continuous long-term heart rate (HR) monitoring using wearable devices is desirable to aid in the diagnosis of many health-related conditions. Recently, we have developed an armband device that does not use obstructive leads, has dry electrodes which are convenient for long-term electrocardiogram (E...

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Veröffentlicht in:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Jg. 2022; S. 4291 - 4294
Hauptverfasser: Hajeb-Mohammadalipour, Shirin, Hossain, Md-Billal, Chon, Ki H.
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2022
Schlagworte:
Clinical Relevance─This study provides a method to remove significant EMG artifacts that may blur the identification of R-peaks in the armband ECG recordings. This approach can more accurately detect HR and may increase the percent of data that are usable
Electrocardiography
Electromyography
even during daytime recordings
Heart rate
Noise reduction
Recording
Training
Wearable computers
ISSN:2694-0604, 2694-0604
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Zusammenfassung:Continuous long-term heart rate (HR) monitoring using wearable devices is desirable to aid in the diagnosis of many health-related conditions. Recently, we have developed an armband device that does not use obstructive leads, has dry electrodes which are convenient for long-term electrocardiogram (ECG) recording, and has been shown to be an effective alternate approach for continuous ECG monitoring. However, motion artifacts (MA) due to electromyogram (EMG) contractions are acknowledged as the major challenge of an armband. In this study, we used a deep convolutional neural network denoising encoder-decoder (CNNDED) to enhance the accuracy of R-peak detection in MA-corrupted ECG recordings obtained by an armband device. We collected simultaneous 24-hour ECG recordings using both the armband device and a Holter monitor on 10 subjects. Each 10-sec ECG segment was converted to a time-frequency representation and subsequently used as the input to CNNDED. During the training process, the model learned to accentuate the location of R peaks by amplifying their values in each ECG beat and suppressing the remaining waveforms. For the training output, the model used the R-peak location information from the simultaneously collected Holter ECG data, which were considered as the reference. The performance of CNNDED was evaluated on an independent test data set using the standard performance metrics. The mean relative error of the estimated HR with respect to the Holter data was 17.5 and 7.3 beats/min, pre- and post-CNNDED, respectively. The mean relative difference of the root mean square of successive difference values were 0.23 and 0.06 before and after applying CNNDED, respectively. Although further study is needed, the current preliminary results suggest that CNNDED can improve detection of R peaks even when they are completely buried in the presence of EMG artifacts.
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ISSN:2694-0604
2694-0604
DOI:10.1109/EMBC48229.2022.9871609