Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis

Castaldo, Rossana; Xu, Wei; Melillo, Paolo; Pecchia, Leandro; Santamaría, Lorena; James, CJ

doi:10.1109/embc.2016.7591557

Cited by 63 publications

(46 citation statements)

References 16 publications

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“…Similarly, temporal, frequency and non-linear parameters on short-time HRV series, achieving a global performance of 64% using a support vector machine classifier, have been presented [41]. The same author reported an improved performance of 79% using additional non-lineal features using a complex tree classifier [42]. A global accuracy of 84.6% using exclusively RMSSD of ultra-short time HRV series and using a combination of binary tree classifiers has been reported [43].…”

Section: Discussionmentioning

confidence: 95%

Estimation of Mental Distress from Photoplethysmography

et al. 2018

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This paper introduces the design of a new wearable photoplethysmography (PPG) sensor and its assessment for mental distress estimation. In our design, a PPG sensor obtains blood volume information by means of an optical plethysmogram technique. A number of temporal, morphological and frequency markers are computed using time intervals between adjacent normal cardiac cycles to characterize pulse rate variability (PRV). In order to test the efficiency of the developed wearable for classifying distress versus calmness, the well-known International Affective Picture System has been used to induce different levels of arousal in forty-five healthy participants. The obtained results have shown that temporal features present a single discriminant power between emotional states of calm and stress, ranging from 67 to 72%. Moreover, a discriminant tree-based model is used to assess the possible underlying relationship among parameters. In this case, the combination of temporal parameters reaches 82.35% accuracy. Considering the low difficulty of metrics and methods used in this work, the algorithms are prepared to be embedded into a micro-controller device to work in real-time and in a long-term fashion.

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Section: Discussionmentioning

confidence: 95%

Estimation of Mental Distress from Photoplethysmography

et al. 2018

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“…Some researchers consider the LF/HF ratio (ratio of low- to high-frequency spectra) as an index of sympathovagal balance. 32 For example, Castaldo et al, 33 in their meta-analysis, showed that LF/HF ratio was significantly increased during stress, and HF power was significantly decreased. They assumed that there are sympathetic activation and parasympathetic withdrawal during stress reaction, which reflect in the HRV changes.…”

Section: Discussionmentioning

confidence: 99%

The differences between patients with panic disorder and healthy controls in psychophysiological stress profile

Kotianová¹,

Kotian²,

Šlepecký³

et al. 2018

NDT

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BackgroundAlarming somatic symptoms, in particular, cardiovascular symptoms, are the characteristic feature of panic attacks. Increased cardiac mortality and morbidity have been found in these patients. Power spectral analysis of electrocardiogram R–R intervals is known to be a particularly successful tool in the detection of autonomic instabilities in various clinical disorders. Our study aimed to compare patients with panic disorder and healthy controls in heart rate variation (HRV) parameters (very-low-frequency [VLF], low-frequency [LF], and high-frequency [HF] band components of R–R interval) in baseline and during the response to the mental task.Subjects and methodsWe assessed psychophysiological variables in 33 patients with panic disorder (10 men, 23 women; mean age 35.9±10.7 years) and 33 age- and gender-matched healthy controls (10 men, 23 women; mean age 35.8±12.1 years). Patients were treatment naïve. Heart rate, blood pressure, muscle tension, and HRV in basal conditions and after the psychological task were assessed. Power spectrum was computed for VLF (0.003–0.04 Hz), LF (0.04–0.15 Hz), and HF (0.15–0.40 Hz) bands using fast Fourier transformation.ResultsIn the baseline period, the VLF band was significantly lower in panic disorder group compared to controls (p<0.005). In the period of mental task, the LF/HF ratio was significantly higher in panic disorder patients compared to controls (p<0.05). No significant differences were found in the remaining parameters. There was a significant difference in ΔHF and ΔLF/HF ratio between patients and controls, with Δ increasing in patients and decreasing in controls.ConclusionThese findings revealed that patients suffering from panic disorder were characterized by relative sympathetic dominance (reactivity) in response to mental stress compared with healthy controls.

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“…In [13], the author present schizophrenia prediction algorithm which analyses the problem using decision tree. In [14], the author presents a ultra-short term HRV analysis scheme to detect the mental stress using academic questions. In [15], a genetic algorithm based diagnosis approach is presented.…”

Section: S Peerbasha M Mohamed Surputheenmentioning

confidence: 99%

Behavioral Pattern Based Psychotic Analysis for Improved Student Performance using Fuzzy Set

Peerbasha*,

Surputheen

2019

IJRTE

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The problem of bipolar disorder has been well studied and analyzed. To perform the detection of presence of BD, there are number of approaches available and the result of detection has been used in several ways. In order to improve the performance in BD detection and utilize the result in gauging the performance of students, a behavioral pattern base psychotic analysis model has been presented in this paper. The method maintains the behaviors, habits and interests of different students in different period of time. The student behaviors includes mood change, depression, sudden laughs, uninterested, short temper, lack of concentration, adamant, frustration, energy, sleep and so on. Such behaviors has been tracked for number of students for prolong period and stored in the behavior set. By reading the behavior set and with the identified samples of BD, the method generates set of behavioral patterns. The behavioral pattern has been generated for three different classes like lower, medium and high. For each class of behavioral pattern, the method generates set of fuzzy rules. Using the fuzzy rule, each student has been analyzed for their behavioral pattern in different time window. Based on the patterns, the method estimates BDCW (Bipolar Disorder Class Weight). Based on the weight measure, the presence of BD has been identified and classified under different class. Identified results have been used to generate academic pattern and helps to generate analysis result to improve the student performance. The proposed approach improve the performance of student development, monitoring and health development.

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Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis

Cited by 63 publications

References 16 publications

Estimation of Mental Distress from Photoplethysmography

Estimation of Mental Distress from Photoplethysmography

The differences between patients with panic disorder and healthy controls in psychophysiological stress profile

Behavioral Pattern Based Psychotic Analysis for Improved Student Performance using Fuzzy Set

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