Epilepsy is a disorder of the brain depicted by recurrent seizures. Electroencephalogram signals can be used to study the characteristics of epileptic seizures. In this study, we propose a method for the automated classification of electroencephalogram into normal, interictal and ictal classes using 6, 12, 18 and 23.6 s of data. We employed discrete wavelet transform to decompose electroencephalogram signals into frequency sub-bands. These discrete wavelet transform coefficients were then subjected to independent component analysis for reducing the data dimension. The independent component analysis features were then fed to six classifiers, namely, decision tree, K-nearest neighbor, probabilistic neural network, fuzzy, Gaussian mixture model and support vector machine to select the best classifier. We observed that the support vector machine classifier with radial basis function kernel function gave the best results with an average accuracy of 96%, sensitivity of 96% and specificity of 97% for 23.6 s of electroencephalogram data. Our results show that as the duration of the data increases, the classification accuracy increases. This proposed technique can be used as an automatic seizure monitoring software to aid the doctors in providing timely quality care for the patients suffering from epilepsy.

Study of the disease demographics in human population indicates that cardiac ailments are the primary cause of premature death, and a need for emergent technologies is felt to address the rising trend. However, development of automated heart sound analysis system and its usage at the grassroot levels for cardiac pre-screening have been hindered by the lack proper understanding of the intrinsic characteristics of cardiac auscultation. In this article we present an investigatory report based on a nationwide survey conducted on the practice of cardiac auscultation for determination of its effectiveness in diagnosis. The aims are to achieve better validation of heart sound acquisition methods and use the clinical feedback from cardiologists for improvements in the classification of the cardiac abnormalities. Results obtained from six different classifiers used in the study are illustrated, which show a remarkable specificity using an improvised classification hierarchy, derived based on clinical recommendations. The study addresses the needs for better understanding of the relevancy of heart sound signal parameters, recording transducers, recording location and the inherent complexity associated in interpretation of heart sounds, specially in noisy environments of out-patient departments and primary healthcare centers. Further, the inter-relationship between heart sound and other advance medical imaging modalities, and the need for more focused training in cardiac auscultation among the medical and paramedical staff is investigated.