# Comparison of ECG Between Gameplay and Seated Rest: Machine Learning-Based Classification
## 4 Discussion  
The PR-AUC score further suggests strong discrimination ability, even in potentially imbalanced data scenarios. The improvement in performance for 10 min segments suggests that longer ECG recordings provide a more stable representation of heart-rate variability (HRV) features, leading to enhanced classification accuracy. The PR-AUC score of 0.906 confirms the robustness of the OCS model in distinguishing between gaming and resting states. Figures 1 and 2 showed the mean \(\pm\) S.D. In particular, indices such as OCS, ILF, and LOF tend to show S.D. larger than 1. This is likely due to the fact that while many of the values are close to 1, a few smaller values deviate significantly from 1, thereby increasing the overall variability. In recent years, machine learning (ML) approaches applied to heart-rate variability (HRV) analysis have been widely utilized for various health-related applications [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], such as detecting cardiovascular diseases, assessing fatigue, and monitoring stress levels [33, 34, 35, 36, 37]. However, research focusing on the HRV-based classification of gaming behavior remains relatively scarce. Given the increasing prevalence of excessive gaming and its associated health risks, the present study holds significance in demonstrating the feasibility of using HRV-based ML models to distinguish gaming activity from resting states. Our findings suggest that specific autonomic nervous system responses may be leveraged as biomarkers for identifying gaming behavior, providing a novel contribution to this field. To classify physiological states during gameplay and resting conditions, researchers often rely on machine learning methods applied to features extracted from electrocardiogram (ECG) signals, particularly heart-rate variability (HRV) metrics. HRV analysis provides insights into autonomic nervous system activity, which is sensitive to emotional and cognitive stressors, such as those experienced during gameplay. The extracted features typically include time-domain metrics (e.g., mean RR interval, SDNN, RMSSD), frequency-domain metrics (e.g., VLF, LF, and HF components via fast Fourier transform), and non-linear indices (e.g., sample entropy and Poincare plot-based features). Various machine learning models have been employed for this type of classification task. Traditional classifiers such as support vector machines (SVMs), Random Forests (RFs), and Logistic Regression (LGR) have shown effectiveness, especially in studies with limited data. More advanced methods like XGBoost offer robust performance through gradient boosting. Deep learning models, including convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) networks, have also been explored due to their ability to capture temporal patterns in ECG data, though they typically require larger datasets. Several studies support these approaches. For example, [PERSON] et al. [38] performed heart-rate variability classification using support vector machines and genetic algorithms, [PERSON] et al. [39] introduced emotion recognition techniques using physiological signals and video games, and [PERSON] et al. [40] performed a mental stress assessment based on electrocardiogram \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Participants** & **MRR [ms]** & **SDRR** & **VLF** & **LF [ln, ms2]** & **HF** & **LF/HF** & **LR** & **HF Freq [Hz]** \\ \hline R1 & 571 & 21 & 5.16 & 4.90 & 3.42 & 4.38 & 0.296 \\ R2 & 694 & 57 & 6.85 & 7.10 & 6.28 & 2.27 & 0.215 \\ R3 & 769 & 47 & 6.84 & 6.79 & 5.72 & 2.92 & 0.219 \\ R4 & 1055 & 113 & 8.44 & 7.38 & 6.16 & 3.40 & 0.247 \\ R5 & 575 & 28 & 5.66 & 4.75 & 3.47 & 3.59 & 0.253 \\ R6 & 706 & 45 & 6.71 & 5.88 & 5.68 & 1.23 & 0.268 \\ R7 & 775 & 38 & 6.19 & 5.74 & 5.14 & 1.82 & 0.234 \\ \hline Mean \(\pm\) S.D. & 735 \(\pm\) 151 & 50 \(\pm\) 28 & 6.55 \(\pm\) 0.937 & 6.08 \(\pm\) 0.970 & 5.12 \(\pm\) 1.12 & 2.08 \(\pm\) 1.02 & 0.247 \(\pm\) 0.026 \\ \hline \hline \end{tabular}
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