Informatics Publishing Limited

S. Sathiya ¹, V. Rathikarani ¹, M. Balasubramanian ¹, P. Dhanalakshmi ¹

Affiliations
1 Annamalai University, Annamalai Nagar-608002, IN

Abstract

This paper helps the neurologists to diagnose and determine weather a patient is epileptic or non epileptic. EEG standard is an acronym for Electroencephalogram has been traditionally used to diagnose the brain activity of a patient which corresponds to epilepsy and other brain disorders. The features are extracted using medical data signals and SVM is trained using the extracted signals. This paper focuses on developing a classification technique called SVM for multichannel EEG recordings. Traditional SVMs are used to exploit both spatial and temporal characteristics of EEG data. The proposed technique is tested on two EEG data sets acquired from ten patients respectively SVM is based on the principle of structural risk minimization and it can be used for non linear regression. This study is a necessary application of data mining to advance the diagnosis and treatment of human epilepsy. The performance system achieves the identification rate of 96.0% for 10 subjects.

Keywords

Electroencephalogram (EEG) Classification, Epilepsy Diagnosis, Discrete Wavelet Transform and Support Vector Machine.

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P. Dhanalakshmi ¹, S. Palanivel ¹, M. Arul ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Tamil Nadu, IN
2 Department of Business Administration, Annamalai University, Tamil Nadu, IN

Abstract

In this paper, we describe automatic segmentation methods for audio broadcast data. Today, digital audio applications are part of our everyday lives. Since there are more and more digital audio databases in place these days, the importance of effective management for audio databases have become prominent. Broadcast audio data is recorded from the Television which comprises of various categories of audio signals. Efficient algorithms for segmenting the audio broadcast data into predefined categories are proposed. Audio features namely Linear prediction coefficients (LPC), Linear prediction cepstral coefficients, and Mel frequency cepstral coefficients (MFCC) are extracted to characterize the audio data. Auto Associative Neural Networks are used to segment the audio data into predefined categories using the extracted features. Experimental results indicate that the proposed algorithms can produce satisfactory results.

Keywords

Linear Prediction Cepstral Coefficients, Mel Frequency Cepstral Coefficients, Auto Associative Neural Networks, Audio Segmentation, Audio Classification.

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