Total views : 339

Early Detection of Epilepsy using Automatic Speech Recognition


  • Lovely Professional University, Jalandhar-Delhi G.T. Road, National Highway 1, Phagwara, Punjab –144411, India


Objectives: Epilepsy is a neurological disorder that is characterized by occurrence of seizures. The Electroencephalogram (EEG) signals are used as the primary source of data for the study of epilepsy. This study uses Mel Frequency Cepstral Coefficients(MFCC) for early detection of epilepsy in adults. Method: Use of MFCC is a de-facto method of Automatic Speech Recognition (ASR). Extending the use of the same method for EEG signals yields reliable results as the properties of EEG signals resemble the properties of speech signals. The training and test samples were taken from EEG database of the University of Bonn. Using the database a support vector machine was trained and then was used for testing. Findings: The use of MFCC and along with Support Vector Machine (SVM) has an average accuracy of 98.5%. Therefore, an epileptic EEG signal can be detected with a high accuracy. The results reaffirmed the fact that there is a high correlation between the speech signals and EEG signals. The newer methods of ASR may be explored for finer results. There is a significant improvement in accuracy over other methods of epilepsy detection.


Automatic speech recognition, Epilepsy detection, MFCC.

Full Text:

 |  (PDF views: 198)


  • Santhosh NS, Sinha S, Satishchandra P. Epilepsy: Indian perspective. Annals of Indian Academy of Neurology. 2014; 17(5):3–11.
  • Epilepsy. Media Center [Internet]. 2016 [updated 2016 Feb; cited 2016 Apr 22]. Available from: mediacentre/factsheets/fs999/en/
  • Smith SJM. EEG in the diagnosis, classification, and management of patients with epilepsy. Journal of Neurol Neurosurg Psychiatry. 2005; 76:ii2–ii7.
  • Temko A, Nadeu C, Marnane W, Boylan G, Light body G. EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures.IEEE Transactions on Information Technology in Biomedicine. 2011 Nov; 56(6):839–47.
  • Singh A, Chakraborty M. MFCC based pattern recognition for limb motor action. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2015 Jun; 4(6):5327–32.
  • Shen YT, Chung PC, Thonnet M, Chauvel P. Seizure detection on prolonged-EEG videos. IEEE International Symposium on Circuits and Systems. 2008:2030–3.
  • Hossan MA, Memon S, Gregory MA. A novel approach for MFCC features extraction. Signal Processing and Communication Systems (ICSPCS). 4th International Conference; 2010. p. 1–5; Queensland, Australia.
  • Parveen, Singh A. Detection of brain tumor in MRI images, using combination of fuzzy C-means and SVM.2nd International Conference on Signal Processing and Integrated Networks (SPIN); 2015. p. 98–102.
  • Epileptologie KF. Time Series EEG Data [Internet]. 2016 [cited 2016]. Available from: cms/front_content.php?idcat=193&lang=3
  • Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. Indications of nonlinear deterministic and finitedimensional structures in time series of brain electrical activity: Dependence on recording region and brain state.Physical Review E. 2001; 64(061907):72–84.
  • Index of/atm/eegmmidb [Internet]. 2016 [cited 2016].Available from:
  • Sharanreddy, Kulkarni PK. Multi-wavelet transform based epilepsy seizure detection. 2012 IEEE EMBS International Conference on Biomedical Engineering and Sciences; 2012.p. 288–93.
  • Kumar SP, Ajitha L. Early Detection of epilepsy using EEG signals. International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT); 2014. p. 1509–14.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.