EEG Signal Sorting & ANN Principal Features Analysis for Brain Disease Diagnosis
Sangita V. Darandale1, M.B. Tadwalkar2
1Sangita V. Darandale, Department of Electronics and Telecomm Engineering, JSPMs, Jayawantrao Sawant College of Engg, Hadapsar, Pune 28, India.
2Prof. M.B.Tadwalkar, Department of Electronics and Telecomm Engineering, JSPMs,Jayawantrao Sawant College of Engg, Hadapsar, Pune 28, India.
Manuscript received on November 01, 2014. | Revised Manuscript Received on November 20, 2014. | Manuscript published on November 20, 2014. | PP: 23-27 | Volume-2, Issue-12, November 2014. | Retrieval Number: L05441121214/2014©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Automatic support system for EEG signal classification for brain diseases diagnosis is proposed in this study. The artificial neural network is used to diagnosis disease like epilepsy by classifying the EEG Signal. The manual analysis of the signal require more time. Also there is a requirement of intensive trained person, to minimize the diagnostics errors. Back Propagation Network with data processing techniques was employed. Decision is based on two stages: feature extraction using Principal Component Analysis and the classification using Back Propagation Network (BPN).The training performance as well as classification accuracy is evaluated for Back Propagation classifier performance. Back Propagation Network classifier is used for high speed and accuracy.
Keywords: EEG signals, Classification algorithms, Back propagation network, epilepsy disease.