{"title":"基于深度学习模型的单导联心电图衍生经验模式分解睡眠呼吸暂停识别模型","authors":"R. K. Sree","doi":"10.22214/ijraset.2024.63676","DOIUrl":null,"url":null,"abstract":"Abstract: While polysomnography (PSG) is the gold standard for detecting sleep apnea (SA), the insertion of several disruptive devices may impair the quality of the patient's sleep, and its interpretation requires specialised training from a sleep scientist or technician. Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used in recent years to automatically detect SA and lessen the negative effects of PSG. Currently, the majority of suggested methods concentrate on feature engineering and machine learning (ML) techniques, which call for previous expert knowledge and expertise. This paper uses a deep learning (DL) framework based on 1D and 2D deep CNN with empirical mode decomposition (EMD) of a preprocessed ECG signal to propose a SA detection method to distinguish between a normal and apnea occurrence. The EMD is the perfect tool for removing crucial elements that characterise the underlying physiological or biological processes. Based on 5- fold cross-validation (5fold-CV), the segment-level classification performance had 93.8% accuracy with 94.9% sensitivity and 92.7% specificity. As a result, this work effectively created a unique and reliable SA detection system based on the ECG decomposed signal utilising EMD and deep CNN.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"43 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Single-Lead Electrocardiogram-Derivative Empirical Mode Decomposition-Based Deep Learning Model for Sleep Apnea Identification\",\"authors\":\"R. K. Sree\",\"doi\":\"10.22214/ijraset.2024.63676\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract: While polysomnography (PSG) is the gold standard for detecting sleep apnea (SA), the insertion of several disruptive devices may impair the quality of the patient's sleep, and its interpretation requires specialised training from a sleep scientist or technician. Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used in recent years to automatically detect SA and lessen the negative effects of PSG. Currently, the majority of suggested methods concentrate on feature engineering and machine learning (ML) techniques, which call for previous expert knowledge and expertise. This paper uses a deep learning (DL) framework based on 1D and 2D deep CNN with empirical mode decomposition (EMD) of a preprocessed ECG signal to propose a SA detection method to distinguish between a normal and apnea occurrence. The EMD is the perfect tool for removing crucial elements that characterise the underlying physiological or biological processes. Based on 5- fold cross-validation (5fold-CV), the segment-level classification performance had 93.8% accuracy with 94.9% sensitivity and 92.7% specificity. As a result, this work effectively created a unique and reliable SA detection system based on the ECG decomposed signal utilising EMD and deep CNN.\",\"PeriodicalId\":13718,\"journal\":{\"name\":\"International Journal for Research in Applied Science and Engineering Technology\",\"volume\":\"43 4\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal for Research in Applied Science and Engineering Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22214/ijraset.2024.63676\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Research in Applied Science and Engineering Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22214/ijraset.2024.63676","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
摘要:虽然多导睡眠图(PSG)是检测睡眠呼吸暂停(SA)的黄金标准,但插入多个干扰性设备可能会影响患者的睡眠质量,而且其解读需要睡眠科学家或技术人员的专业培训。近年来,心率变异性(HRV)和心电图(ECG)推导的呼吸(EDR)已被用于自动检测 SA 并减轻 PSG 的负面影响。目前,大多数建议的方法都集中在特征工程和机器学习(ML)技术上,这需要先前的专业知识和专业技能。本文使用基于一维和二维深度 CNN 的深度学习(DL)框架,结合预处理心电信号的经验模式分解(EMD),提出了一种 SA 检测方法,以区分正常和呼吸暂停的发生。EMD 是去除表征潜在生理或生物过程的关键元素的完美工具。基于 5 倍交叉验证(5fold-CV),分段级分类的准确率为 93.8%,灵敏度为 94.9%,特异度为 92.7%。因此,这项研究利用 EMD 和深度 CNN 有效地创建了一个基于心电图分解信号的独特而可靠的 SA 检测系统。
A Single-Lead Electrocardiogram-Derivative Empirical Mode Decomposition-Based Deep Learning Model for Sleep Apnea Identification
Abstract: While polysomnography (PSG) is the gold standard for detecting sleep apnea (SA), the insertion of several disruptive devices may impair the quality of the patient's sleep, and its interpretation requires specialised training from a sleep scientist or technician. Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used in recent years to automatically detect SA and lessen the negative effects of PSG. Currently, the majority of suggested methods concentrate on feature engineering and machine learning (ML) techniques, which call for previous expert knowledge and expertise. This paper uses a deep learning (DL) framework based on 1D and 2D deep CNN with empirical mode decomposition (EMD) of a preprocessed ECG signal to propose a SA detection method to distinguish between a normal and apnea occurrence. The EMD is the perfect tool for removing crucial elements that characterise the underlying physiological or biological processes. Based on 5- fold cross-validation (5fold-CV), the segment-level classification performance had 93.8% accuracy with 94.9% sensitivity and 92.7% specificity. As a result, this work effectively created a unique and reliable SA detection system based on the ECG decomposed signal utilising EMD and deep CNN.