Abstract-Electroencephalogram (EEG) is the recording of the electrical activity of the brain. One of the major fields of application of this relatively cheap and non-invasive diagnostic technique is epilepsy, which affects almost 1% of the world's population. Automatic seizure detection is very important in clinical practice and has to be achieved by analyzing the EEG signals. Inter-ictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after longterm monitoring became common in investigation of epileptic patients. In this paper, a comprehensive chaotic analysis of the normal, ictal and inter-ictal segments in EEG signals is studied using nonlinear dynamical parameters such as correlation dimension, fractal dimension exponent and entropies. These measures show distinct difference for normal, ictal and interictal segments in the EEG recordings. The results are further supported by ANOVA test which gives a p-value of less than 0.01 with 95% confidence. The results of the study done for two age groups of pediatric subjects, demonstrated the potential of these chaotic measures in quantifying and automatically detecting the presence of any seizure activity in the EEG recordings with high statistical significance.
The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the R-R interval etc. may help to identify the nature of disease afflicting the heart. However, human observer can not directly monitor these subtle details. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. Therefore, computer based analysis and display, is highly useful in diagnostics. The study demonstrates the feasibility of fuzzy logic based data fusion of the heterogeneous signals for the detection of life threatening states. Important parameters are derived from multimodal data and rule based approaches have been used. Fuzzified region for various abnormality conditions have been obtained which demonstrate the efficacy of the approach in various test cases. Comprehensive pictures showing the condition of the patient in various states will help physician in making a timely assessment in an intensive care set up.
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