Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG
Journal of Korean Medical Science
;
: e399-2020.
Artículo
en Inglés
| WPRIM
| ID: wpr-899716
ABSTRACT
Background@#This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. @*Methods@#A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer.An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. @*Results@#F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. @*Conclusion@#The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Tipo de estudio:
Estudio diagnóstico
/
Estudio pronóstico
Idioma:
Inglés
Revista:
Journal of Korean Medical Science
Año:
2020
Tipo del documento:
Artículo
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