An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders

Katsis, Christos D.; Katertsidis, Nikolaos S.; Fotiadis, Dimitrios I.

doi:10.1016/j.bspc.2010.12.001

Cited by 94 publications

(43 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, the power spectrum density (PSD, [14,31,33,39,40]) is extracted as a EEG frequency domain feature from the raw signals. The PSD is estimated with Welch's method in MATLAB (R2016a) using a Hamming window and different time window sizes (1,2,3,4,5,6,8,10,12,15,20,30 and 60 s) with no overlap as parameters. A number of (32 channels × 60 s/Tl) features are obtained per trial, where Tl is the size of the time window.…”

Section: The Construction Of Eeg Mfi Sequencesmentioning

confidence: 99%

“…As early as the 1980s, Minsky, one of the founders of artificial intelligence, proposed that a machine without emotions is not intelligent. Recently, research on human emotion recognition has been applied in many fields such as entertainment [1], safe driving [2,3], health care [4], social security [5], etc. Picard et al [6] believed that the emotional changes of the human were embodied in speech [7], facial expressions [8], body posture [9], the central nervous system, autonomic nerve physiological activities [10], etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

et al. 2017

View full text Add to dashboard Cite

Featured Application: The method presented in this study can be applied in many fields, such as mental health care, entertainment consumption behavior, society safety, and so on. For example, in the mental health care field, an automatic emotion analysis system can be constructed with our method to monitor the emotional variation of the subjects. With accurate and objective emotion analysis results from EEG signals, our method can provide useful treatment effect information to the medical staff.Abstract: The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study) is 75.21%.

show abstract

Section: The Construction Of Eeg Mfi Sequencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Reflects the variability cardiac, this variability decreases during stress [7,8,10,14] Variation amplitude peak to peak of BVP = (Max(i) -Min(i))…”

Section: Physiological Changes On Bvp Signal During Emotional Simentioning

confidence: 99%

“…We are interested in works using physiological signals as a measure so we quote in the following table the most important studies using physiological signals for measurement methods. [7] (lAPS)…”

Section: State Of the Artmentioning

confidence: 99%

Short-term anxiety recognition from blood volume pulse signal

Handouzi

Maaoui

Pruski

et al. 2014

2014 IEEE 11th International Multi-Conference on Systems, Signals &Amp; Devices (SSD14)

View full text Add to dashboard Cite

In this paper we focus our attention on the development of a virtual reality exposure system to induce anxiety and a strategy for recognizing it. We describe anxiety detection in short term from blood volume pulse measurement; detailing data collection, features extraction and classification.We built a model of anxiety detection using support vector machines and evaluate it on the collected data. Results show that the choice of relevant features allowed good anxiety recognition.

show abstract

“…While biochemical assays are invasive, expensive, not amenable to continuous measurements, and vulnerable to diurnal cycles (18)(19)(20)(21), electrophysiological measures are non-invasive and more suitable for continuous monitoring. Measures like galvanic skin response (GSR) (22)(23)(24)(25)(26)(27)(28)(29), heart rate (HR) (24)(25)(26)28,30), heart rate variability (HRV) (27,31,32), and electroencephalography (EEG) (33)(34)(35)(36) are increasingly being used to assess stress response. The relationship between these physiological measures and stress response, and both state and trait anxiety, however, remains poorly understood (37).…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to identifying objective biomarkers of anxiety and stress

Ramakrishnan

Pardes²,

Lynch³

et al. 2019

Preprint

View full text Add to dashboard Cite

Anxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state.Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety -a vulnerability foreshadowing development of anxiety and mood disorders -remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to non-stressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with ~96% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with ~84% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

show abstract

An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders

Cited by 94 publications

References 19 publications

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

Short-term anxiety recognition from blood volume pulse signal

A machine learning approach to identifying objective biomarkers of anxiety and stress

Contact Info

Product

Resources

About