Detection of Epilepsy Using MFCC-Based Feature and XGBoost
- Resource Type
- Conference
- Authors
- Long, Jie-Min; Yan, Zhang-Fa; Shen, Yu-Lin; Liu, Wei-Jun; Wei, Qing-Yang
- Source
- 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018 11th International Congress on. :1-4 Oct, 2018
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Electroencephalography
Epilepsy
Feature extraction
Mel frequency cepstral coefficient
Discrete wavelet transforms
Support vector machines
Continuous wavelet transforms
Detection of epilepsy
MFCC
XGBoost
- Language
This paper develops a MFCC-based feature for detection of epilepsy, since inspired by some methods in speech signal processing, and tests the reliability of the feature through experiments. Our experimental results show that the method using MFCC-based feature and XGBoost has a high accuracy of 99.5% in epilepsy detection, reaching the level of the state-of-the-art method. This work has some inspiration for exploring better epilepsy detection methods.