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

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.

Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method

“…However, the result considering a single signal (PPG) was 80.0%, which is lower than our accuracy of 86.5%. Another study [ 12 ] selected TSST and Stroop color test as stressors and used a random forest classifier and three-fold cross-validation. The results for overlapping and not overlapping HRV signals over 5 min are accuracies of 96.0% and 85.9%, respectively.…”

“…Stress measurement using physiological signals can compensate for the shortcomings of other methods and has been actively studied in recent years to detect the physiological responses that change due to the presence of a stressor. Stress measurement studies are mainly conducted on physiological signals, such as ECG [ 11 , 12 ], GSR [ 13 , 14 ], and EEG [ 15 , 16 ], and some studies have described the combined use of several physiological signals [ 17 , 18 ].…”

Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?

“…The performance of each classifier was evaluated using overall accuracy (denoted as accuracy in Tables 1 and 2), the AUC for the detection of positive outcomes (denoted as AUC in Tables 1 and 2), the mean of the F-measure for positive and negative outcomes (denoted as mF1 in Tables 1 and 2), and the weighted F-measure for positive and negative outcomes (denoted as wF1 in Tables 1 and 2). These are standard measures used to evaluate classification models [13,14]. The accuracy, mF1, and wF1 measures were used to evaluate the model as a whole, while the AUC was used to evaluate the performance of the model when predicting the presence of a corresponding diagnosis.…”

Cognitive assistance for action selection: Challenges and approaches

“…Another, also complementary feasible way to find out when assistance is needed could be found in the detection of signals on the user , for example by means of portable electroencephalography (EEG), electrocardiography (ECG), and eye tracking (ET) systems, or by measuring electrodermal activity (EDA). During activity execution, confusion or a lack of crucial information can trigger an acute stress response, which can be measured, for example, as a reduced heart rate variability (e.g., Camm et al, 1996 ; Szakonyi et al, 2021 ), increased skin conductance (Critchley, 2002 ), or decreased pupil dilation (Henckens et al, 2009 ), thus providing an indication that assistance is needed.…”