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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Varyan, Richa
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Measuring anxiety level on phobia using electrodermal activity, electrocardiogram and respiratory signals Ain, Khusnul; Rahma, Osmalina Nur; Purwanti, Endah; Varyan, Richa; Ittaqilah, Sayyidul Istighfar; Arfensia, Danny Sanjaya; Sosialita, Tiara Dyah; Qulub, Fitriyatul; Chai, Rifai
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp337-348

Abstract

People with spider phobia experience excessive anxiety reactions when exposed to spiders that will interfere with daily life. Diagnosing and measuring anxiety levels in patients with spider phobia is a complex challenge. Conventional diagnosis requires psychological evaluations and clinical interviews that take time and often result in a high degree of subjectivity. Therefore, there is a need for a more objective and efficient approach to measuring anxiety levels in patients. This study performs anxiety level classification based on electrodermal activity, electrocardiogram (ECG) and respiratory signals using the dataset of Arachnophobia subjects. Each raw data is preprocessed using 24 types of features. Feature performance is processed using the recursive feature elimination method. Data processing was performed in 3 anxiety levels (high, medium, low) and two anxiety levels (high, low) with the support vector machine method and hold-out validation method (7:3). The performance of the model is evaluated by showing the accuracy, precision, recall and F1 score values. The polynomial kernel can perform optimal classification and obtain 100% accuracy in 2 classes and three classes with 100% precision, recall, and F1 score values. This result shows excellent potential in measuring anxiety levels that correlate with mental health issues.
Classifying mental workload of esports players using machine learning Fawwaz, Aisy Al; Rahma, Osmalina; Ittaqillah, Sayyidul Istighfar; Shane Kurniawan, Angeline; Putri, Revita Novianti; Varyan, Richa; Adinda, Aura; Ain, Khusnul; Chai, Rifai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp469-480

Abstract

Electrodermal activity (EDA) peak counts, derived from both tonic and phasic components, are widely used as physiological proxies for mental workload in cognitively demanding tasks, such as esports. However, their specificity remains uncertain, particularly given potential confounding effect of time-on-task. This study analyzes 92 competitive gameplay sessions from a multimodal esports dataset using three decomposition techniques: convex decomposition (cvxEDA), sparse deconvolution (sparseEDA), and time varying sympathetic activity (TVSymp). From each method, phasic, and tonic peak counts (TPC), as well as their normalized rates, were extracted. We examined their relationship with self-reported workload through correlation analyses, partial correlations controlling for session duration, and linear mixed-effects models (LMMs). While both peak types exhibited strong positive correlations with gameplay duration (r=0.915 for phasic and r=0.856 for tonic), their association with perceived workload vanished once time was accounted for. Across methods, TVSymp yielded the highest discriminative validity with an area under curve (AUC) of 0.880 in classifying high versus low workload. Machine learning (ML) classifiers trained solely on EDA-based features under a leave-one-subject-out (LOSO) scheme outperformed multimodal models that incorporated heart rate variability (HRV). These results underscore need to disentangle temporal structure from cognitive signals when interpreting EDA and call into question the assumption that EDA peak counts alone reliably encode mental workload across individuals.
Co-Authors Adinda, Aura Arfensia, Danny Sanjaya Chai, Rifai Fawwaz, Aisy Al Fitriyatul Qulub Ittaqilah, Sayyidul Istighfar Ittaqillah, Sayyidul Istighfar Khusnul Ain Osmalina Nur Rahma Putri, Revita Novianti Rahma, Osmalina Shane Kurniawan, Angeline Solly Aryza Sosialita, Tiara Dyah