Effective epileptic seizure detection based on the event-driven processing and machine learning for mobile healthcare

Mobile healthcare is a promising approach. It is realized by using the biomedical implants that are connected to the cloud. A framework for the precise and effective diagnosis of epileptic seizures is designed in this context. To achieve real-time compression and effective signal processing and transmission, it uses an intelligent event-driven electroencephalogram (EEG) signal acquisition. Experimental results show that grace of the event-driven nature an overall 3.3 fold compression and transmission bandwidth usage reduction is achieved by the devised method compared to the conventional counterparts. It promises a notable decrease in the post analysis and classification processing activity. The system performance is studied by using a standard three class EEG epileptic seizure dataset. The highest classification accuracy of 97.5% is secured for a mono-class. The best average classification accuracy of 96.4% is attained for three-classes. Comparison of the system with classical equivalents is made. Results demonstrate more than threefold and sevenfold of outperformance respectively in terms of compression gain and processing efficiency while confirming a comparable classification precision.