A Firefly based Deep Belief Signal Specification based Novel Hybrid Technique for EEG Signal Analysis

Abstract

ABSTRACT In the medical sector, electroencephalogram (EEG) signal analysis is a fascinating and indispensable diagnostic tool for brain diseases such as epilepsy. Neurobiological diseases are difficult and time-consuming to diagnose due to the excessive associated noise in the EEG signal, which can significantly diminish and impede accurate predictions of such diseases. Existing techniques rely on complex processing blocks, such as Granger causality, chaos analysis, mutual information estimation, etc., in order to function satisfactorily. This paper proposes the development of a novel EEG signal analysis system employing Deep Belief Signal Specification (FbDBSS) for the diagnosis of Schizophrenia (SZ). This signal is used to initially train the system alongside conventional EEG, followed by preprocessing. The resultant signal is sent to the classifier, which extracts its characteristics for use in the final analysis. MATLAB is used to implement the proposed design for determining the robustness score in terms of accuracy, precision, F-measure, recall (sensitivity), execution time, and power consumption. Achieving an accuracy of 97%, a precision of 96.8%, an F-measurement of 96%, a recall rate (i.e. sensitivity) of 97%, an execution time of 19 s, and a power consumption of 5dBW demonstrates the effectiveness of the proposed method for EEG signal analysis in health care monitoring.