P. Padmapriya ¹, K. Manikandan ¹, K. Jeyanthi ¹, V. Renuga ¹, J. Sivaraman ¹

Affiliations
1 Department of Biomedical Engineering, Vel Tech Multitech, Chennai – 600062, Tamil Nadu, IN

Abstract

Aim: This paper proposes the automatic support system for detecting the tumor cells by analyzing the scalp EEG by means of RBF technique. Objectives: To acquire the EEG signal from the various electrodes. The artificial neural network will be focused to split up the EEG signal whether cyst i.e. tumor or regular. Methods: The EEG signal is been acquired from the subject using EEG scalp electrodes. The various features such as mean, variance, co-variance, Eigen values and Eigen vectors are extracted from those signals using the Principal Component Analysis. Radial Basis Function (RBF) networks are feed-forward networks which uses a supervised training algorithm are used for function approximation, time series prediction and system control. The RBF is used to train and classify the signal whether the subject is normal or suffering from abnormalities. Results: The features are been extracted using the Principal Component Analysis and the features are skilled. Thus the acquired signals are been classified as cyst or normal. Conclusion: Thus in this paper an automatic system is been developed for diagnosing the tumor cells by means of analyzing EEG signal which is non-invasive method. It can also extend for analyzing other diseases seizures of epilepsy, Alzheimer's disease.

Keywords

EEG, Neural Network, Principal Component Analysis, Radial Basis Function

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S. Vinurajkumar ¹, V. Renuga ¹, K. Nithyakalyani ¹, K. Jeyanthi ¹, N. Malathy ², J. Sivaraman ¹

Affiliations
1 Department of Biomedical Engineering, Vel Tech Multitech, Chennai - 600062, Tamil Nadu, IN
2 Department of Electronics and Instrumentation Engineering, RMK Engineering College, Chennai - 601206, Tamil Nadu, IN

Abstract

Objectives: This study involves the measurement of obstructions in the blood flow of an arterial system. An attempt is made to find the correlation between the pressure and flow of the blood. Methods: The obstructed area produces vascular sounds which are acquired through disturbed blood flow patterns. The measurements are carried over by audio frequency analysis of the sound observed during arterial stenosis. These sounds can be measured using piezoelectric film sensor by placing it in the wrist and mid arm in the blood flow path. Results:The sounds that initiate in the narrowed arteries is measured by non-invasive, sensitive piezoelectric film sensor that captures the vibration caused by the flow of blood inside arteries and generate appropriate waveform for different flow conditions. Conclusion: The waveforms are analyzed in spectrum analyzer and compared with the normal flow patterns and if the values deviate from normal it is concluded for occlusion or clot that happened in the blood flow passage.

Keywords

Arterial Stenosis, Blood Flow, Occlusion, Piezoelectric Film Sensor

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