# A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data

arXiv: https://arxiv.org/abs/2102.01647 · HTML: https://mlsanity.com/paper/2102.01647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 44 references that are in the map.
- First posted on arXiv 31 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach](https://mlsanity.com/paper/1801.05412/) (2018)
- [Linear and Quadratic Discriminant Analysis: Tutorial](https://mlsanity.com/paper/1906.02590/) (2019)
- [Effective and Extensible Feature Extraction Method Using Genetic Algorithm-Based Frequency-Domain Feature Search for Epileptic EEG Multi-classification](https://mlsanity.com/paper/1701.06120/) (2017)

## Built on by (most cited first, 1 of 1)

- [Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques](https://mlsanity.com/paper/2304.01214/) (2023)

## Abstract (arXiv)

This paper demonstrates the predictive superiority of discrete wavelet transform (DWT) over previously used methods of feature extraction in the diagnosis of epileptic seizures from EEG data. Classification accuracy, specificity, and sensitivity are used as evaluation metrics. We specifically show the immense potential of 2 combinations (DWT-db4 combined with SVM and DWT-db2 combined with RF) as compared to others when it comes to diagnosing epileptic seizures either in the balanced or the imbalanced dataset. The results also highlight that MFCC performs less than all the DWT used in this study and that, The mean-differences are statistically significant respectively in the imbalanced and balanced dataset. Finally, either in the balanced or the imbalanced dataset, the feature extraction techniques, the models, and the interaction between them have a statistically significant effect on the classification accuracy.