Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals.
Obstructive sleep apnea (OSA) is a sleeping disorder that can cause multiple complications. Our aim is to build an automatic deep learning model for OSA event detection using combined signals from the electrocardiogram (ECG) and thoracic movement signals. We retrospectively obtained 420 cases of PSG...
| Publicado en: | Acta Oto-Laryngologica Vol. 144; no. 1; pp. 52 - 58 |
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| Autores principales: | , , , |
| Formato: | research tables/charts tracings Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jan2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176072848&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176072848 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00016489 B6D jtl: Acta Oto-Laryngologica issn: 00016489 maglogo: Y pubinfo: dt: Jan2024 vid: 144 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 176072848 174867173 176072848 176072848 10.1080/00016489.2024.2301732 176072848 ppf: 52 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals. aug: au: Li, Zufei Jia, Yajie Li, Yanru Han, Demin affil: Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China sug: subj: Sleep Apnea, Obstructive Diagnosis Prediction Models Deep Learning Algorithms Electrocardiography Thorax Physiology Human Retrospective Design ROC Curve Descriptive Statistics Young Adult Adult Middle Age Male Female Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Obstructive sleep apnea (OSA) is a sleeping disorder that can cause multiple complications. Our aim is to build an automatic deep learning model for OSA event detection using combined signals from the electrocardiogram (ECG) and thoracic movement signals. We retrospectively obtained 420 cases of PSG data and extracted the signals of ECG, as well as the thoracic movement signal. A deep learning algorithm named ResNeSt34 was used to construct the model using ECG with or without thoracic movement signal. The model performance was assessed by parameters such as accuracy, precision, recall, F1-score, receiver operating characteristic (ROC), and area under the ROC curve (AUC). The model using combined signals of ECG and thoracic movement signal performed much better than the model using ECG alone. The former had accuracy, precision, recall, F1-score, and AUC values of 89.0%, 88.8%, 89.0%, 88.2%, and 92.9%, respectively, while the latter had values of 84.1%, 83.1%, 84.1%, 83.3%, and 82.8%, respectively. The automatic OSA event detection model using combined signals of ECG and thoracic movement signal with the ResNeSt34 algorithm is reliable and can be used for OSA screening. 背景：阻塞性睡眠呼吸暂停（OSA）是一种可引起多种并发症的睡眠障碍。 目的：我们的目的是利用心电图 (ECG) 和胸部运动信号的组合信号构建一种用于 OSA 事件检测的自动深度学习模型。 材料和方法：我们回顾性地获得了420个病例的PSG数据, 并提取了心电图信号以及胸部运动信号。运用一种名为ResNeSt34的深度学习算法, 根据具有或不具有胸部运动信号的ECG来构建模型。模型性能的评估是根据准确度、精密度、召回率、F1分数、接收器操作特性（ROC）和ROC曲线下面积（AUC）等参数来进行的。 结果：采用心电信号和胸部运动信号组合的模型比单独使用心电信号的模型表现更好。前者的准确度、精密度、召回率、F1评分和AUC值分别为89.0%、88.8%、89.0%、8.82%和92.9%, 而后者的分别为84.1%、83.1%、84.1%、83.3%和82.8%。 结论和意义：采用ResNeSt34算法, 结合心电图信号和胸部运动信号的OSA事件自动检测模型是可靠的, 可用于OSA筛查。 pubtype: Academic Journal doctype: research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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