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

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Bibtex

@article{f7cbc42d95a44c2485feca210dab55a7,
title = "Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection",
abstract = "Body acceleration due to heartbeat-induced reaction forces can be measured as mobile phone 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 (standard deviation of normal-to-normal interval-SDNN and root mean square of successive differences-RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing at rest conditions (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional electrocardiogram (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 s) of cardiac activity using smartphone's accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.",
keywords = "accelerometers, ballistocardiography, seismocardiography, smartphone, stress evaluation, ultra-short heart rate variability",
author = "Federica Landreani and Andrea Faini and Alba Martin-Yebra and Mattia Morri and Gianfranco Parati and Caiani, {Enrico Gianluca}",
year = "2019",
month = "8",
day = "28",
doi = "10.3390/s19173729",
language = "English",
volume = "19",
journal = "Sensors",
issn = "1424-3210",
publisher = "MDPI AG",
number = "17",

}