| Autor: |
Choksatchawathi, Tanut, Sawadwuthikul, Guntitat, Thuwajit, Punnawish, Keawlee, Thitikorn, Kunaseth, Narin, Luenam, Phoomraphee, Mateepithaktham, Thee, Sudhawiyangkul, Thapanun, Wilaiprasitporn, Theerawit |
| Rok vydání: |
2023 |
| Předmět: |
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| DOI: |
10.48550/arxiv.2306.10863 |
| Popis: |
In this paper, we utilized obstructive sleep apnea and cardiovascular disease-related photoplethysmography (PPG) features in constructing the input to deep learning (DL). The features are pulse wave amplitude (PWA), beat-to-beat or RR interval, a derivative of PWA, a derivative of RR interval, systolic phase duration, diastolic phase duration, and pulse area. Then, we develop DL architectures to evaluate the proposed features' usefulness. Eventually, we demonstrate that in human-machine settings where the medical staff only needs to label 20% of the PPG recording length, our proposed features with the developed DL architectures achieve 79.95% and 73.81% recognition accuracy in MESA and HeartBEAT datasets. This simplifies the labelling task of the medical staff during the sleep test yet provides accurate apnea event recognition. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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