How Reliable Are Ultra-Short-Term HRV Measurements during Cognitively Demanding Tasks?

Abstract

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements’ reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland–Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.

Classification of Physiological States Through Machine Learning Algorithms Applied to Ultra-Short-Term Heart Rate and Pulse Rate Variability Indices on a Single-Feature Basis

“…MEAN and RMSSD) proved more effective in classifying the two physiological states. Although employing different methods and datasets, these results are in agreement with ours [2,3,5]. Herein, MEAN and RMSSD time domain features yielded higher accuracy values for RRI and PPI in all the classifiers, underscoring their high potential for classifying postural stress.…”

“…Using the above-mentioned seven features, the three classifiers were trained and tested by varying the length of the series from the initial 300 heartbeats down to 10 heartbeats. Although according to [3,20] a minimum of 1 minute is needed to reliably estimate HF and at least 2 minutes for the LF component with regard to frequency-domain HRV/PRV analysis, we have adopted the approach followed by several other studies which have instead computed such features on even shorter time series down to 10 s [2,5,9]. In our case, it was feasible to compute the information domain features using 60 heartbeats as a minimum.…”

Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

“…The collected data was segmented into 30-second time frames to extract physiological and behavioral features. This particular time window was chosen based on the findings of Bernardes et al [ 32 ], who determined that a 30-second duration is the smallest timeframe suitable for obtaining dependable HRV features that accurately evaluate psychological stress. Therefore, our dataset consisted of 48 participants, each with 70 minutes of data collection divided into 30-second time windows, resulting in 6720 datapoints.…”

Experimental Verification of the Possibility of Reducing Photoplethysmography Measurement Time for Stress Index Calculation

“…Due to the different scales of each selected feature, we used a normalizer as a scaling method to configure the shape used as the input to the regression model. Additionally, to predict the stress index, we constructed a dataset using the PRV time domain feature calculated by the PPG data acquisition time as the input data and the square root of the SI calculated from the PPG signal obtained for 1 min after the start of PPG acquisition as the label, as shown in Figure 5 [ 37 , 38 ]. As a result, the label is the square root of the SI derived from the PPG signal collected for 1 min from the PPG measurement time, and the input feature used is the HRV time domain index derived from the measurement time.…”