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

Landreani, Federica ; Faini, Andrea ; Martin-Yebra, Alba LU ; Morri, Mattia ; Parati, Gianfranco and Caiani, Enrico Gianluca (2019) In Sensors (Basel, Switzerland) 19(17).
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... (More)

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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
accelerometers, ballistocardiography, seismocardiography, smartphone, stress evaluation, ultra-short heart rate variability
in
Sensors (Basel, Switzerland)
volume
19
issue
17
publisher
MDPI AG
external identifiers
  • pmid:31466391
  • scopus:85071762468
ISSN
1424-3210
DOI
10.3390/s19173729
language
English
LU publication?
yes
id
f7cbc42d-95a4-4c24-85fe-ca210dab55a7
date added to LUP
2019-09-18 12:17:26
date last changed
2020-01-13 02:23:18
@article{f7cbc42d-95a4-4c24-85fe-ca210dab55a7,
  abstract     = {<p>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.</p>},
  author       = {Landreani, Federica and Faini, Andrea and Martin-Yebra, Alba and Morri, Mattia and Parati, Gianfranco and Caiani, Enrico Gianluca},
  issn         = {1424-3210},
  language     = {eng},
  month        = {08},
  number       = {17},
  publisher    = {MDPI AG},
  series       = {Sensors (Basel, Switzerland)},
  title        = {Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection},
  url          = {http://dx.doi.org/10.3390/s19173729},
  doi          = {10.3390/s19173729},
  volume       = {19},
  year         = {2019},
}