Computational Framework for Early Detection of Neurological Disorders Using Multi-Modal Signal Analysis

Abstract

Non-invasive and reliable methods that allow early detection of neurological disorders have been a critical issue as the biomedical signals are complex and heterogeneous. Conventional single-modality diagnostic strategies have been found to be less effective in providing complete physiological patterns, and hence reduce the accuracy of the diagnosis. To overcome this shortcoming, this paper suggests a computational model to early detect neurological disorders through multi-modal signal analysis. The framework combines non-homogeneous sources of information, such as electroencephalogram (EEG) signals, magnetic resonance imaging (MRI) and clinical parameters, to increase the diagnosis reliability. The methodology involves signal preprocessing, feature extraction, multi-modal data fusion and classification phases. Noise and artifact are eliminated by filtering and normalization methods and time-domain and frequency-domain features are extracted. A weighted fusion algorithm is a mixture of tasks into a single form, which is processed by a machine learning-based classifier. Experimental results indicate that the proposed framework is much more effective than single-modality methods in accuracy, precision, recall, and F1-score, allowing it to be used, more effectively, in early diagnosis and supporting intelligent clinical decision systems.