Heartbeat-aware convolutional neural network for R-peak detection of wearable device ECG data / 南方医科大学学报
Journal of Southern Medical University
; (12): 375-383, 2022.
Article
em Zh
| WPRIM
| ID: wpr-936326
Biblioteca responsável:
WPRO
ABSTRACT
OBJECTIVE@#To develop a method for R-peak detection of ECG data from wearable devices to allow accurate estimation of the physiological parameters including heart rate and heart rate variability.@*METHODS@#A fully convolutional neural network was applied to predict the R-peak heatmap of ECG data and locate the R-peak positions. The heartbeat-aware (HA) module was introduced to enable the model to learn to predict the heartbeat number and R-peak heatmap simultaneously, thereby improving the capability of the model for extraction of the global context. The R-R interval estimated by the predicted heartbeat number was adopted to calculate the minimum horizontal distance for peak positioning. To achieve real-time R-peak detection on mobile devices, the deep separable convolution was adopted to reduce the number of parameters and the computational complexity of the model.@*RESULTS@#The proposed model was trained only with ECG data from wearable devices. At a tolerance window interval of 150 ms, the proposed method achieved R peak detection sensitivities of 100% for both wearable device ECG dataset and a public dataset (i.e. LUDB), and the true positivity rates exceeded 99.9%. As for the ECG signal of a 10 s duration, the CPU time of the proposed method for R-peak detection was about 23.2 ms.@*CONCLUSION@#The proposed method has good performance for R-peak detection of both wearable device ECG data and routine ECG data and also allows real-time R-peak detection of the ECG data.
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Base de dados:
WPRIM
Assunto principal:
Algoritmos
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Processamento de Sinais Assistido por Computador
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Redes Neurais de Computação
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Eletrocardiografia
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Dispositivos Eletrônicos Vestíveis
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Frequência Cardíaca
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
Idioma:
Zh
Revista:
Journal of Southern Medical University
Ano de publicação:
2022
Tipo de documento:
Article