scholarly journals Acute Mental Stress Detection via Ultra-short term HRV Analysis

2015 ◽  
pp. 1068-1071 ◽  
Author(s):  
R. Castaldo ◽  
P. Melillo ◽  
L. Pecchia
Keyword(s):  
Mental Stress ◽  
Stress Detection ◽  
Short Term ◽  
Hrv Analysis

scholarly journals To What Extent Can We Shorten HRV Analysis in Wearable Sensing? A Case Study on Mental Stress Detection.

2017 ◽  
pp. 643-646 ◽  
Author(s):  
Rossana Castaldo ◽  
Luis Montesinos ◽  
Paolo Melillo ◽  
Sebastiano Massaro ◽  
Leandro Pecchia
Keyword(s):  
Mental Stress ◽  
Stress Detection ◽  
Wearable Sensing ◽  
Hrv Analysis

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

2019 ◽  
Author(s):  
Federica Landreani ◽  
Andrea Faini ◽  
Alba Martin-Yebra ◽  
Mattia Morri ◽  
Gianfranco Parati ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Supine Position ◽  
Mental Stress ◽  
Cardiac Activity ◽  
Stress Detection ◽  
Short Term ◽  
Reaction Forces ◽  
The Mean ◽  
Induced Reaction

Body acceleration due the heartbeat-induced reaction forces can be measured as smartphone 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 (SDNN and RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional 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 sec) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

scholarly journals Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis

2015 ◽  
Vol 18 ◽  
pp. 370-377 ◽  
Author(s):  
R. Castaldo ◽  
P. Melillo ◽  
U. Bracale ◽  
M. Caserta ◽  
M. Triassi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Mental Stress ◽  
Meta Analysis ◽  
Healthy Adults ◽  
Short Term ◽  
Stress Assessment ◽  
Hrv Analysis

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

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3729 ◽  
Author(s):  
Landreani ◽  
Faini ◽  
Martin-Yebra ◽  
Morri ◽  
Parati ◽  
...  
Keyword(s):  
Heart Rate ◽  
Mental Stress ◽  
Cardiac Activity ◽  
Mean Square ◽  
Stress Detection ◽  
Short Term ◽  
Reaction Forces ◽  
The Mean ◽  
Electrocardiogram Ecg ◽  
Induced Reaction

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.

scholarly journals Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 267
Author(s):  
Duan Liang ◽  
Shan Wu ◽  
Lan Tang ◽  
Kaicheng Feng ◽  
Guanzheng Liu
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sample Entropy ◽  
Biomedical Signal Processing ◽  
Apnea Hypopnea Index ◽  
Short Term ◽  
Autonomic Nervous System Dysfunction ◽  
Obstructive Sleep ◽  
Hrv Analysis ◽  
Frequency Power

Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (p < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (|r| = 0.602, p < 0.05), NPSampEn (|r| = 0.756, p < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal.

Real Time Mental Stress Detection Through Breath Analysis

2020 ◽  
pp. 403-410
Author(s):  
Paulo Santos ◽  
Peter Roth ◽  
Jorge M. Fernandes ◽  
Viktor Fetter ◽  
Valentina Vassilenko
Keyword(s):  
Real Time ◽  
Mental Stress ◽  
Breath Analysis ◽  
Stress Detection
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