Articles | Open Access |

SPIKE-WAVE DISCHARGE CLASSIFICATION USING THE SHORT-TIME FOURIER TRANSFORM (STFT) APPROACH

M. Mustfizur , Universiti Malaysia Pahang, Pekan Campus, 26600 Pekan, Pahang, Malaysia

Abstract

Spike-wave discharges (SWD) are crucial biomarkers in the diagnosis and monitoring of neurological disorders such as epilepsy. Accurate classification of SWD is essential for effective clinical interventions and improving patient outcomes. This study presents a novel approach for classifying spike-wave discharges using the Short-Time Fourier Transform (STFT). By leveraging STFT's capability to analyze non-stationary signals, we extract time-frequency features from EEG recordings to accurately distinguish SWD from other brain activities. The extracted features are then classified using machine learning algorithms, providing high accuracy in identifying SWD events. Performance evaluation demonstrates that the proposed STFT-based method offers significant improvements in classification accuracy and computational efficiency compared to traditional time-domain analysis. The study's findings highlight the potential of STFT in real-time applications for automated seizure detection, contributing to advancements in neurological disorder diagnostics.

Keywords

Spike-wave discharge, Short-Time Fourier Transform, EEG classification

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.

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.

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.

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.

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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M. Mustfizur. (2024). SPIKE-WAVE DISCHARGE CLASSIFICATION USING THE SHORT-TIME FOURIER TRANSFORM (STFT) APPROACH. The American Journal of Engineering and Technology, 6(10), 1–8. Retrieved from https://www.theamericanjournals.com/index.php/tajet/article/view/5481