A Sensor-enabled Digital Trier Social Stress Test in an Enterprise Context.

The Trier Social Stress Test (TSST) protocol is a widely accepted method of inducing social and/or cognitive stress in participants and studying its effects. Traditionally, this protocol is administered in laboratory or university settings, which are less formal than in offices. In this paper, we report the results of the analysis of multi-modal sensor data collected from employees of an enterprise who underwent the test. We briefly discuss the adaptations that enabled administering it digitally in a semi-automatic mode with minimal researcher/test-administrator intervention. In our setup, noninvasive sensor-signals, including the Galvanic Skin Response and Photoplethysmogram, were collected during and outside the stress-inducing tasks. We analyze the data collected from twenty participants and show that the State Trait Anxiety Inventory (STAI) score is needed in assessing the effect of the digital version of the TSST. A support vector machine classifier yielded an F1 score of 0.723 with the STAI score taken as ground truth. We also introduce the idea of ground truth based on the change in the STAI scores to reduce variation due to subjective interpretation, for which an F1 score of 0.847 was obtained.

Robust Beat-to-Beat Interval from Wearable PPG using RLS and SSA.

Ambulatory Photoplethysmogram (PPG) is a more user-friendly choice for continuous cardiac monitoring as compared to Electrocardiogram (ECG). However, wearable PPG is often prone to motion artefacts. In this paper, we propose a novel pipeline for motion-resistant beat-to-beat interval extraction from noisy PPG. Firstly, the effects of motion artefacts are minimized by using Adaptive Recursive-Least-Square (RLS) Filtering and Singular Spectrum Analysis (SSA). Next, the signal peaks are identified and their locations are corrected by weighted local interpolation. Finally, outlier peak-to-peak intervals are marked as incorrigible. Experimental validation on the training dataset of IEEE Signal Processing Cup 2015 reveals that the proposed method achieves 1.68% mean peak detection error rate and 11.32 milliseconds mean absolute error of detected beat-to-beat intervals. The metric values outperform those obtained by the state-of-the-art techniques by at least 12.58 and 5.74 times respectively.

Trend Statistics Network and Channel invariant EEG Network for sleep arousal study.

Sleep is a very important part of life. Lack of sleep or sleep disorder can cause a negative impact on day to day life and can have long term serious consequences. In this work, we propose an end-to-end trainable neural network for automated sleep arousal scoring. The network consists of two main parts. Firstly, a trend statistics network computes the moving average of the filtered signals at different scales. Secondly, we propose a channel invariant EEG network to detect the arousals in any Electroencephalography (EEG) channel. Finally, we combine the features from various channels through a convolution network and a bi-directional long short-term memory to predict the probability of arousal. Further, we propose an objective function that uses only respiratory effort related arousal (RERA) and non-arousal regions to optimize the network. We also propose a method to estimate the respiratory disturbance index (RDI) from the probability predicted by the network. Evaluation on Physionet Challenge 2018 database shows that the proposed method detects RERA with mean area under the precision-recall curve (AUPRC) of 0.50 in a 10-fold cross validation setup. The mean absolute error of RDI prediction is 6.11, while a two-class RDI severity prediction yields a specificity of 75% and a sensitivity of 83%.

A Heart Rate Driven Kalman Filter for Continuous Arousal Trend Monitoring.

This paper proposes a continuous and unsupervised approach of monitoring the arousal trend of an individual from his heart rate using Kalman Filter. The state-space model of the filter characterizes the baseline arousal condition. Deviations from this baseline model are used to recognize the arousal trend. A publicly available dataset, DECAF, comprising the physiological responses of 30 subjects while watching 36 movie clips inducing different emotions, is used to validate the proposed technique. For each clip, annotations of arousal given by experts per second are used to quantify the ground truth of arousal change. Experimental results suggest that the proposed algorithm achieves a median correlation of 0.53 between the computed and expected arousal levels which is significantly higher than that achievable by the state-of-the-art technique.

Eliminating Individual Bias to Improve Stress Detection from Multimodal Physiological Data.

Stress monitoring is important for mental wellbeing and early detection of related disorders. The current work is focused on stress detection from multiple non-invasive physiological signals like Electroencephalogram (EEG), Photoplethysmogram (PPG) and Galvanic Skin Response (GSR). We show that, compared to using only the well known EEG band powers in different frequencies for stress detection, an early fusion with GSR and PPG features shows a significant improvement. Maximum Relevance Minimum Redundancy (mRMR) based feature selection is used to identify the most suitable physiological features correlating with stress. A major contribution of this work lies in eliminating subject-specific bias to improve the classification accuracy. We use self-reported values of Valence, Arousal and Dominance to cluster subjects and build separate classification models specific to clusters. The proposed approach is validated on a publicly available dataset comprising 146 data instances from 10 subjects. The performances of Leave-One- Subject-Out cross validation (LOSOCV) in terms of mean Fscores are 0.61 using EEG features only, 0.64 using early fusion of EEG, GSR and PPG features and 0.69 by applying our clustering technique before fusion and classification.