Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life

• Published in: BMC medical informatics and decision making Vol. 19; no. 1; pp. 12 - 13
• Main Authors: Castaldo, R., Montesinos, L., Melillo, P., James, C., Pecchia, L.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 17.01.2019; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Adult; Analysis; Anxiety; Artificial intelligence; Benchmarking; Biomedical engineering; Case reports; Case studies; Clinical decision-making; Colleges & universities; Correlation analysis; Data mining; Data-driven machine learning; Electrocardiography; Electrocardiography - methods; Entropy; Feasibility Studies; Feature extraction; Female; Health Informatics; Health physics; Heart failure; Heart rate; Heart Rate - physiology; Heart rate variability (HRV); Human performance; Humans; Informatics; Information Systems and Communication Service; knowledge support systems; Laboratories; Learning algorithms; Machine Learning; Male; Management of Computing and Information Systems; Medicine; Medicine & Public Health; Mental stress detection; Models, Theoretical; Nervous system; Physiology; Psychological stress; Research Article; Short term; Software; Statistical analysis; Statistical tests; Stress (Psychology); Stress, Psychological - diagnosis; theory; Ultra-short term HRV analysis; Young Adult; Heart rate variability (HRV); Ultra-short term HRV analysis; Mental stress detection; Data-driven machine learning
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-019-0742-y

Background This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress. Methods ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features. Results Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier. Conclusion This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.