Ouvrage : 1
Biomedical sensors and cloud-based smart applications are the main components of contemporary automated healthcare systems. A multichannel EEG signals collection, processing, transmission, and interpretation are needed in the framework of mobile epileptic seizure care. Conventional automatic epilepsy detection systems are time-invariant and have the drawback of collecting more information than required. This renders in the wastage of energy computation, storage, and activity of transmission. In this framework, techniques of real-time data compression can play a vital role. In this sense, this work describes an original tactic for the cloud-based efficient and automated epilepsy detection. The method is developed by smartly using the idea of adaptive rate processing. The aim is to gain a real-time compression advantage in order to ensure efficient EEG signal processing, retrieval, and interpretation as part of the development of a cloud-based healthcare system. The specific brain activities can cause EEG signals to differ and can influence the performance of automated classification. Therefore, the signal is conditioned in this analysis by the application of novel adaptive-order filtering. Onward, the adaptive rate discrete wavelet transform (DWT) derives subbands by decomposing the enhanced signal. In next step, rigorous classifiers are used to recognize various anticipated EEG signal categories. The efficiency of the method is evaluated by using a publically available Hauz Khas health center epilepsy dataset. Results show a significant gain in compression and aptitudes for a processing effectiveness as compared to fix rate counterparts. The accuracy rate, specificity, F-measure, and Kappa statistics are all used to test the suggested method. Results confirm that the suggested method secures a high accuracy of classification. It assures the benefit of embedding the proposed solution in existing automated epilepsy detectors to realize efficient cloud-based healthcare solutions.