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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.

There are three signal domains in which ECG data compression can be performed, namely time domain, frequency domain and parameter extraction. The present paper deals with the frequency domain method of compression using fast Fourier transform. The algorithm has been tested on the third set of the CSE database library. A performance evaluation has been made using two important parameters, namely compression ratio and percent root-mean-square difference besides visual comparison. Further, in order to know the clinical acceptable quality of the reconstructed signal peak, boundary and interval measurements were made both on the reconstructed and the original signal of the same record for comparison. The visual examination reveals that most of the noise in the original signal had been filtered out during the compression. This amounts to reduction of electromyographic noise to a considerable extent. The experimental observations show that a compression ratio of 8 is feasible while ensuring clinical acceptability.

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