Recognizing ECG cardiac arrhythmia automatically is an essential task for diagnosing the abnormalities of cardiac muscle. The proposal of few algorithms has been made for classifying the ECG cardiac arrhythmias, however the system of classification efficiency is determined on the basis of its prediction and diagnosis accuracy. Hence, in this study the proposal of an efficient system has been made for classifying the ECG cardiac arrhythmia as an expertise. Discrete Wavelet Transform (DWT) is being utilized for the preprocessing mechanism of ECG signal, Independent Component Analysis (ICA) is being utilized for dimensionality reduction and Feature Extraction process of ECG signal and Multi-Layer Perceptron (MLP) neural network is being utilized for performing the task of classification. As an outcome of classification, the results have been acquired on categorizing Normal Beats under the class of Non-Ectopic beat, Atrial Premature Beat under the class of Supra-Ventricular ectopic beat and Ventricular Escape beat under the class of Ventricular ectopic beat on the basis of standardization given by ANSI/AAMI EC57: 1998. For the acquisition of ECG signal, MIT-BIH physionet arrhythmia database is being utilized in this study added to that its being utilized for training process and testing process of the classifier on the basis of MLP-NN. The results obtained from the simulation has been inferred that the accuracy of classification of the proposed algorithm is 96.50% on utilizing 10 files inclusive of normal beats, Atrial Premature Beat and Ventricular Escape beat.

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Administration of Anaesthesia is important for both surgery and intensive care. The intravenous anaesthetics are widely used to provide rapid onset, stable maintenance, and rapid recovery compared with inhaled anaesthetics. The aim of the project is to investigate a reliable and safe methodology for delivering total intravenous anaesthesia using closed loop control technology and bispectral analysis of human electroencephalogram (EEG) waveform .In this work a Bispectral Index (BIS), derived from power spectral and bispectral analysis on EEG waveform, is used to measure the depth of anaesthesia. . In comparison with conventional method, drug effect is measured during drug infusion in closed loop anaesthesia. This may provide superior safety, better patient care, better quality of anaesthesia. Main advantage of this method is to make recurrent and minor alterations to anaesthetic drug. A model based controller is used to control the drug infusion rate .The performance of the model based controller is compared with the PID controller. The results show that the model based controller gives better performance than others controllers and it reduces rise time , settling time and peak time.

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