Automated EEG signal analysis for identification of epilepsy seizures and brain tumour

• Published in: Journal of medical engineering & technology Vol. 37; no. 8; pp. 511 - 519
• Main Authors: Sharanreddy, M., Kulkarni, P. K.
• Format: Journal Article
• Language: English
• Published: England Informa UK Ltd 01.11.2013; Taylor & Francis
• Subjects: Adolescent; Adult; Approximate entropy (ApE); Artificial Intelligence; artificial intelligence (AI); Brain Neoplasms - physiopathology; brain tumour; Child; Child, Preschool; electroencephalogram (EEG); Electroencephalography - methods; Epilepsy - physiopathology; epilepsy seizure; Female; gliomas; Humans; Infant; Male; Middle Aged; multi-wavelet transforms (MWT); neural network (NN); Sensitivity and Specificity; Wavelet Analysis; Young Adult
• ISSN: 0309-1902; 1464-522X
• DOI: 10.3109/03091902.2013.837530

Abstract Electroencephalography (EEG) is a clinical test which records neuro-electrical activities generated by brain structures. EEG test results used to monitor brain diseases such as epilepsy seizure, brain tumours, toxic encephalopathies infections and cerebrovascular disorders. Due to the extreme variation in the EEG morphologies, manual analysis of the EEG signal is laborious, time consuming and requires skilled interpreters, who by the nature of the task are prone to subjective judegment and error. Further, manual analysis of the EEG results often fails to detect and uncover subtle features. This paper proposes an automated EEG analysis method by combining digital signal processing and neural network techniques, which will remove error and subjectivity associated with manual analysis and identifies the existence of epilepsy seizure and brain tumour diseases. The system uses multi-wavelet transform for feature extraction in which an input EEG signal is decomposed in a sub-signal. Irregularities and unpredictable fluctuations present in the decomposed signal are measured using approximate entropy. A feed-forward neural network is used to classify the EEG signal as a normal, epilepsy or brain tumour signal. The proposed technique is implemented and tested on data of 500 EEG signals for each disease. Results are promising, with classification accuracy of 98% for normal, 93% for epilepsy and 87% for brain tumour. Along with classification, the paper also highlights the EEG abnormalities associated with brain tumour and epilepsy seizure.