AI for the detection of neurological condition: Parkinson's disease & emotions

Abstract

Artificial Intelligence (AI) is widely applied by many researchers in the measurement and analysis of signals and images in clinical medicine and the biological sciences. The role of machine learning in processing biomedical signals and its applications in medicine and healthcare is huge, and it is now in a very advanced stage. Several types of biomedical signals have been analyzed by using Deep Learning (DL), Neural Networks (NN), and Artificial Intelligence on Electrocardiogram (ECG) and Electroencephalogram (EEG) signals by many researchers. Parkinson's disease (PD) is a neurodegenerative disorder that progresses over time and is characterized by rigidity, tremor, postural instability, and non-motor symptoms caused by the loss of dopaminergic neurons in the substantia nigra. This paper analyses the current state of the art of EEG analysis using AI techniques for Parkinson's disease detection and emotion detection.