Identification of PLMS Sleep Disorder using EEG Signal feature-based classification by Machine Learning Techniques

Abstract

It has been demonstrated that periodic leg movements during sleep (PLMS) are connected to alterations in features of EEG signal. Data mining evaluates hemispheric/cortical activity-related hemodynamic changes. We used data mining and machine learning to examine whether there are changes in brain hemodynamics associated with PLMS. Nighttime EEG recordings were made while brain activity was monitored in PLMS patients. Scores from EEG feature data were examined to find relevant differences. PLMS were consistently accompanied by variations in brain activity that increased in magnitude when related to changes in EEG and persisted even in the absence of any arousal that could be seen visually in the EEG. This study is the first to show PLMS-related alterations in brain activity. Clinical relevance of these observations has yet to be established. We have used wavelet decomposition with or without it to complete the aforementioned classification tasks. For binary disease to identify tasks employing the sleep stage of N3, we have achieved classification accuracy ranging between 92% and 96% and AUC ranging between 0.85 and 0.89. But, as the use of the suggested wavelet-based features is performed, a superior classification accuracy is achieved, with an AUC of 0.99 and a range of 94–98%. This is because the best wavelet-based features have a high degree of discrimination.