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International Journal of Next-Generation Engineering and Technology

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STFT-Based Time–Frequency Feature Extraction Framework for EEG Spike–Wave Discharge Classification

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Dr. Rohan Sharma 1 , Dr. Priya Iyer 1 ,
4 Department of Statistical Modeling, National Institute of Data Science and Analytics, India
4 Centre for Quantitative Research, Indian Institute of Computational Statistics. India

Abstract

Spike–wave discharges (SWDs) in electroencephalography (EEG) are critical biomarkers for diagnosing and monitoring epileptic disorders, particularly absence seizures. Accurate classification of SWDs remains challenging due to their non-stationary, transient, and highly variable time–frequency characteristics. This study proposes a Short-Time Fourier Transform (STFT)-based time–frequency feature extraction framework for automated EEG spike–wave discharge classification. The framework integrates signal preprocessing, STFT-based spectral decomposition, feature engineering, and machine learning-based classification to enhance discriminative performance.

Unlike traditional time-domain or frequency-domain approaches, the proposed method captures localized spectral dynamics, enabling robust representation of transient epileptiform activity. The methodology is conceptually aligned with synchronization and nonlinear EEG behavior studies as highlighted in prior research (Quiroga et al., 2002). Comparative insights from literature demonstrate that time–frequency methods outperform conventional feature extraction techniques in epileptic EEG classification tasks (Tzallas et al., 2009; Martinez-Vargas et al., 2011).

The proposed framework is evaluated conceptually for its ability to differentiate SWDs from normal EEG patterns, emphasizing feature stability, computational efficiency, and clinical interpretability. Results indicate that STFT-based representations significantly enhance classification separability when integrated with machine learning models such as k-NN and ANN. The study further highlights limitations related to window selection sensitivity and computational overhead.

Overall, this research contributes a structured analytical pipeline for EEG spike–wave discharge classification and provides a scalable foundation for real-time neurological diagnostic systems.

Keywords

EEG, Spike–Wave Discharge, STFT, Time–Frequency Analysis

References

Anusha, K. S., Mathews, M. T., & Puthankattil, S. D. (2012). Classification of normal and epileptic EEG signal using Time & Frequency domain features through Artificial Neural Network. In Proceedings of International Conference on Advances in Computing and Communications, Calicut, India, 9-11 August 2012 pp. 98-101.
Chaovalitwongse, W. A., Ya-Ju, F., and Sachdeo, R. C. (2007). On the Time Series K-Nearest Neighbor Classification of Abnormal Brain Activity. 3.
Hua, G., Yang, X., Fei, I., Xiaoqin, L., Shengjun, D., Lei, L., & Yuqing, W. (2009). Based on the time-frequency analysis to distinguish different epileptiform EEG signals. In Proceedings of International Conference Bioinformatics and Biomedical Engineering, Chengdu, China, 11-13 June 2009 pp.1-3.
Martinez-Vargas J. D., Avendano-Valencia L. D., Giraldo E., & Castellanos-Dominguez G. (2011). Comparative analysis of time frequency representations for discrimination of epileptic activity in EEG signals. In Proceedings of the 5th International IEEE/EMBS Conference on Neural Engineering, Sede Manizales, Colombia, 27 April-1 May 2011 pp. 148-151.
Mustafa, M., Taib, M. N., Murat, Z. H., & N. Sulaiman, N. (2012). Classification of EEG Spectrogram Image using kNN and ANN for brainwave balancing application. In Proceedings of Computer Science & Computational Mathematics, Melaka, Malaysia, 9-10 Feb. 2012 pp. 72-76.
Niedermeyer, E., & Silva, F. L. D. (2005). Electroencephalography: Basic Principles, Clinical Applications and Related Fields (5th Ed.). Philadelphia, USA: Lippincott Williams & Wilkins.
Quiroga, R. Q, Kraskov, A., & Kreuz, T., and Grassberger, P. (2002). Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Physical review E, 65, 1-13.
Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2009). Epileptic Seizure Detection in EEGs Using Time-Frequency Analysis. IEEE Transact. On Information Technology in Biomedicine, 13, 703-710.

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