Machine learning-based signal processing using physiological signals for stress detection

Ghaderi, Amir; Frounchi, Javad; Farnam, Alireza

doi:10.1109/icbme.2015.7404123

Cited by 84 publications

(34 citation statements)

References 8 publications

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“…Emotion recognition system can identify the critical states during driving by detecting the stress level assessments [2][3] [4]. Moreover, there are applications that affect daily lives without stress [5] with more pleasing life [6].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

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Abstract-Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional statements using peripheral physiological signals based on arousal-valence evaluation. These signals are the Electrocardiogram, Respiration Volume, Skin Temperature and Galvanic Skin Response. We explored the signals collected in the MAHNOB-HCI multimodal tagging database. We defined the emotion into three different ways: two and three classes using 1-9 discrete self-rating scales and another model using 9 emotional keywords to establish the three defined areas in arousal-valence dimensions. To perform the accuracies, we began by removing the artefacts and noise from the signals, and then we extracted 169 features. We finished by classifying the emotional states using the support vector machine. The obtained results showed that the electrocardiogram and respiration volume were the most relevant signals for human's feeling recognition task. Moreover, the obtained accuracies were promising comparing to recent related works for each of the three establishments of emotion modeling.

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Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

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“…This is necessary to circumvent effects that might eschew dimensionality. For example, KNN classifiers are used for stress detection in the monitoring of human physiological signals [21] as well as in the detection of seizure activity in a patient with epilepsy [22]. With Figure 3 as our example, the problem can be formulated as; thus, let x connotes input dataset (data point), while, its K nearest neighbors are denoted with Nk (x).…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

“…(2) ) T . When presented with an adequate hidden unit, an MLP having a minimum of two layers can equate a random mapping originating from a finite input domain to a finite output domain [21][22][23]. Nevertheless, discovering the ideal set of weights w for an MLP can be modelled as NP-complete optimization problem [24].…”

Section: Neural Networkmentioning

confidence: 99%

Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

et al. 2020

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Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.

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“…That is, many non-stressful epochs were wrongly identified as stressful epochs, limiting the utility of HRV in this context (55,56). Several recent studies have attempted to combine different physiological measures to improve the ability of an algorithm to identify stress states (24,25,29,33,(38)(39)(40)(41)(42). By combining select features from these physiological modalities using a support vector machine, we achieved high sensitivity (91%) and specificity (100%)-indeed more than that achieve using any independent measure alone (Supplementary Table 2).…”

Section: Identifying Stressful States and Non-stressful Statesmentioning

confidence: 99%

“…Several recent studies have used new developments in machine learning to integrate features from more than one physiological measure to evaluate stress response with high accuracy (24,25,29,33,(38)(39)(40)(41)(42). These algorithms, however, have not always been validated in conditions outside the ones in which they were trained.…”

Section: Introductionmentioning

confidence: 99%

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

Ramakrishnan

Pardes²,

Lynch³

et al. 2019

Preprint

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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.

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Machine learning-based signal processing using physiological signals for stress detection

Cited by 84 publications

References 8 publications

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

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

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