Machine Learning Based Real-Time Diagnosis of Mental Stress Using Photoplethysmography

光容积图 心率变异性 精神压力 人工智能 计算机科学 特征(语言学) 压力(语言学) 区间(图论) 支持向量机 特征提取 心率 模式识别(心理学) 机器学习 医学 数学 内科学 血压 计算机视觉 滤波器(信号处理) 组合数学 语言学 哲学
作者
Talha Anwar,Seemab Zakir
出处
期刊:Journal of Biomimetics, Biomaterials and Biomedical Engineering 卷期号:55: 154-167 被引量:4
标识
DOI:10.4028/p-01r9mn
摘要

Mental stress is a natural response to life activities. However, acute and prolonged stress may cause psychological and heart diseases. Heart rate variability (HRV) is considered an indicator of mental stress and physical fitness. The standard way of obtaining HRV is using electrocardiography (ECG) as the time interval between two consecutive R-peaks. ECG signal is collected by attaching electrodes on different locations of the body, which need a proper clinical setup and is costly as well; therefore, it is not feasible to monitor stress with ECG. Photoplethysmography (PPG) is considered an alternative for mental stress detection using pulse rate variability (PRV), the time interval between two successive peaks of PPG. This study aims to diagnose daily life stress using low-cost portable PPG devices instead of lab trials and expensive devices. Data is collected from 27 subjects both in rest and in stressed conditions in daily life routine. Thirty-six time domain, frequency domain, and non-linear features are extracted from PRV. Multiple machine learning classifiers are used to classify these features. Recursive feature elimination, student t-test and genetic algorithm are used to select these features. An accuracy of 72% is achieved using stratified leave out cross-validation using K-Nearest Neighbor, and it increased up to 81% using a genetic algorithm. Once the model is trained with the best features selected with the genetic algorithm, we used the trained weights for the real-time prediction of mental stress. The results show that using a low-cost device; stress can be diagnosed in real life. The proposed method enable the regular monitoring of stress in short time that help to control the occurrence of psychological and cardiovascular diseases.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
FengYun完成签到,获得积分0
刚刚
酷炫邑发布了新的文献求助10
1秒前
兮pqsn发布了新的文献求助10
1秒前
李健应助xx采纳,获得10
2秒前
田様应助DHL采纳,获得10
2秒前
pluto应助butu采纳,获得10
3秒前
3秒前
3秒前
4秒前
ChrisKim发布了新的文献求助10
4秒前
傅傅发布了新的文献求助10
4秒前
玉米烤肠发布了新的文献求助10
6秒前
zhujun完成签到,获得积分10
7秒前
小陈完成签到,获得积分10
7秒前
double发布了新的文献求助10
7秒前
一一应助小鼠星球采纳,获得20
8秒前
火箭Lucky完成签到 ,获得积分10
9秒前
9秒前
ItachiSkuya应助莉莉采纳,获得10
9秒前
慕青应助顺利毕业采纳,获得10
9秒前
poting应助小杨采纳,获得10
9秒前
11秒前
ceeray23应助李哈哈采纳,获得10
11秒前
萝卜花1968发布了新的文献求助10
12秒前
哒哒哒完成签到 ,获得积分10
13秒前
13秒前
脑洞疼应助123采纳,获得10
14秒前
14秒前
14秒前
15秒前
传奇3应助DHL采纳,获得10
15秒前
香蕉觅云应助阳光的芯采纳,获得10
16秒前
翟淑雨完成签到,获得积分10
17秒前
17秒前
大个应助鲜于冰彤采纳,获得10
17秒前
18秒前
笨笨完成签到,获得积分20
18秒前
LMA完成签到,获得积分20
18秒前
18秒前
大个应助Tan3837采纳,获得10
18秒前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2500
Востребованный временем 2500
Aspects of Babylonian celestial divination : the lunar eclipse tablets of enuma anu enlil 1500
Agaricales of New Zealand 1: Pluteaceae - Entolomataceae 1040
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 1000
Classics in Total Synthesis IV: New Targets, Strategies, Methods 1000
Devlopment of GaN Resonant Cavity LEDs 666
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3454966
求助须知:如何正确求助?哪些是违规求助? 3050269
关于积分的说明 9020709
捐赠科研通 2738874
什么是DOI,文献DOI怎么找? 1502329
科研通“疑难数据库(出版商)”最低求助积分说明 694480
邀请新用户注册赠送积分活动 693178