Stress Detection by Measuring Heart Rate Variability

2015 ◽  
Vol 3 (4) ◽  
pp. 2083-2089
Author(s):  
Dipali HPatil ◽  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Stress Detection

scholarly journals Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3461
Author(s):  
Blake Anthony Hickey ◽  
Taryn Chalmers ◽  
Phillip Newton ◽  
Chin-Teng Lin ◽  
David Sibbritt ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Smart Devices ◽  
Stress Detection ◽  
Wearable Technologies ◽  
Detection Of Stress ◽  
Anxiety Depression

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

scholarly journals Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Benedek Szakonyi ◽  
István Vassányi ◽  
Edit Schumacher ◽  
István Kósa
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Domain ◽  
Assisted Living ◽  
Acute Stress ◽  
Ambient Assisted Living ◽  
Portable Devices ◽  
Stress Detection ◽  
Non Linear ◽  
Variability Data

Abstract Background Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices. Methods Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed. Results The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively. Conclusion The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

scholarly journals The Use of Nonlinear Parameters of Heart Rate Variability for Stress Detection

2021 ◽  
pp. 265-274
Author(s):  
Dmitriy Dimitriev ◽  
◽  
Elena Saperova ◽  
Aleksey Dimitriev ◽  
El’dar Salimov ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sensitivity And Specificity ◽  
Recurrence Plot ◽  
Line Length ◽  
Detection Algorithm ◽  
Stress Detection ◽  
Linear Discriminant ◽  
Rr Interval ◽  
Diagonal Line

This paper presents a stress detection algorithm using heart rate variability (HRV) parameters. Five-minute electrocardiograms were recorded at rest and under exam stress (252 students were involved). The determined HRV parameters were applied to detect stress by means of several classification algorithms. We analysed linear indices in the time (standard deviation of NN intervals (SDNN) and root mean square of successive RR interval differences (RMSSD)) and frequency domains (low frequency (LF) and high frequency (HF) power as well as LF/HF ratio). To study nonlinear HRV indices, we evaluated approximate entropy (ApEn), sample entropy (SampEn), α1 (DFA1) and α2 (DFA2) scaling exponents, correlation dimension D2, and recurrence plot quantification measures (recurrence rate (REC), mean diagonal line length (Lmean), maximum diagonal line length (Lmax), determinism (DET), and Shannon entropy (ShanEn)). Receiver operating characteristic (ROC) was used to test the performance of the classifiers derived from HRV. The highest area under the ROC curve (AUC), sensitivity, and specificity were found for mean RR-interval, DFA1, DFA2, RMSSD, and Lmax. These parameters were used for stress/rest classification with the help of algorithms that are common in clinical and physiological applications, i.e. logistic regression (LR) and linear discriminant analysis (LDA). Classification performance for stress was quantified using accuracy, sensitivity and specificity measures. The LR achieved an accuracy of 68.25 % at an optimal cutoff value of 0.57. LDA determined stress with 67.46 % accuracy. Thus, HRV parameters can serve as an objective tool for stress detection.

scholarly journals Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

2019 ◽  
Author(s):  
Federica Landreani ◽  
Andrea Faini ◽  
Alba Martin-Yebra ◽  
Mattia Morri ◽  
Gianfranco Parati ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Supine Position ◽  
Mental Stress ◽  
Cardiac Activity ◽  
Stress Detection ◽  
Short Term ◽  
Reaction Forces ◽  
The Mean ◽  
Induced Reaction

Body acceleration due the heartbeat-induced reaction forces can be measured as smartphone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (SDNN and RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional ECG. Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 sec) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

Non-Contact Psychological Stress Detection Combining Heart Rate Variability and Facial Expressions

2021 ◽  
Vol 41 (3) ◽  
pp. 0310003
Author(s):  
孔令琴 Kong Lingqin ◽  
陈飞 Chen Fei ◽  
赵跃进 Zhao Yuejin ◽  
董立泉 Dong Liquan ◽  
刘明 Liu Ming ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Psychological Stress ◽  
Facial Expressions ◽  
Stress Detection
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