Chirag Mehta;Pranav Sai Ananthoju;Swarubini PJ;Nagarajan Ganapathy
{"title":"利用心电图传感和优化深度网络自动评估孕期微压力","authors":"Chirag Mehta;Pranav Sai Ananthoju;Swarubini PJ;Nagarajan Ganapathy","doi":"10.1109/LSENS.2024.3444810","DOIUrl":null,"url":null,"abstract":"Elevated stress levels during pregnancy increase the risk of delivering a premature or low-birthweight infant. Recently, microecological momentary assessment (micro-EMA) has been explored extensively. However, capturing more distinct physiological responses to micro-EMA is still challenging. In this letter, we propose a methodology for micro-EMA-based stress detection using feature extraction and classifiers. For this, an online publicly available micro-EMA database (N=18) is considered. The ECG signals are preprocessed. Ten features are extracted and applied to the classifiers, namely, support vector machine, decision tree, gradient boosting (GradB), adaptive boosting, 1-D convolution network (DL), and, DL with fine-tuning (DLFT). Performance is evaluated using leave-one-subject-out cross-validation. The proposed approach is able to discriminate stress in pregnant mothers. Using DLFT, the approach yields an average classification F1 score, precision, and recall of 76.50 %, 72.40%, and 86.25%, respectively. Thus, the proposed approach could be extended for integrated monitoring systems, enabling real-time stress detection during pregnancy.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Microstress Assessment During Pregnancy Using ECG Sensing and Optimized Deep Networks\",\"authors\":\"Chirag Mehta;Pranav Sai Ananthoju;Swarubini PJ;Nagarajan Ganapathy\",\"doi\":\"10.1109/LSENS.2024.3444810\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Elevated stress levels during pregnancy increase the risk of delivering a premature or low-birthweight infant. Recently, microecological momentary assessment (micro-EMA) has been explored extensively. However, capturing more distinct physiological responses to micro-EMA is still challenging. In this letter, we propose a methodology for micro-EMA-based stress detection using feature extraction and classifiers. For this, an online publicly available micro-EMA database (N=18) is considered. The ECG signals are preprocessed. Ten features are extracted and applied to the classifiers, namely, support vector machine, decision tree, gradient boosting (GradB), adaptive boosting, 1-D convolution network (DL), and, DL with fine-tuning (DLFT). Performance is evaluated using leave-one-subject-out cross-validation. The proposed approach is able to discriminate stress in pregnant mothers. Using DLFT, the approach yields an average classification F1 score, precision, and recall of 76.50 %, 72.40%, and 86.25%, respectively. Thus, the proposed approach could be extended for integrated monitoring systems, enabling real-time stress detection during pregnancy.\",\"PeriodicalId\":13014,\"journal\":{\"name\":\"IEEE Sensors Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10639474/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10639474/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
孕期压力水平升高会增加早产儿或低体重儿的出生风险。最近,人们对微生态瞬间评估(micro-EMA)进行了广泛的探索。然而,捕捉微生态瞬时评估中更多不同的生理反应仍具有挑战性。在这封信中,我们利用特征提取和分类器提出了一种基于微生态瞬间评估的压力检测方法。为此,我们考虑了一个在线公开微 EMA 数据库(N=18)。心电信号经过预处理。提取十个特征并应用于分类器,即支持向量机、决策树、梯度提升(GradB)、自适应提升、一维卷积网络(DL)和微调 DL(DLFT)。使用 "留一主体 "交叉验证法对性能进行了评估。所提出的方法能够分辨怀孕母亲的压力。使用 DLFT,该方法的平均分类 F1 得分、精确度和召回率分别为 76.50 %、72.40 % 和 86.25 %。因此,建议的方法可以扩展到综合监测系统中,实现孕期压力的实时检测。
Automated Microstress Assessment During Pregnancy Using ECG Sensing and Optimized Deep Networks
Elevated stress levels during pregnancy increase the risk of delivering a premature or low-birthweight infant. Recently, microecological momentary assessment (micro-EMA) has been explored extensively. However, capturing more distinct physiological responses to micro-EMA is still challenging. In this letter, we propose a methodology for micro-EMA-based stress detection using feature extraction and classifiers. For this, an online publicly available micro-EMA database (N=18) is considered. The ECG signals are preprocessed. Ten features are extracted and applied to the classifiers, namely, support vector machine, decision tree, gradient boosting (GradB), adaptive boosting, 1-D convolution network (DL), and, DL with fine-tuning (DLFT). Performance is evaluated using leave-one-subject-out cross-validation. The proposed approach is able to discriminate stress in pregnant mothers. Using DLFT, the approach yields an average classification F1 score, precision, and recall of 76.50 %, 72.40%, and 86.25%, respectively. Thus, the proposed approach could be extended for integrated monitoring systems, enabling real-time stress detection during pregnancy.