NLP Tools for Epileptic Seizure Prediction Using EEG Data: A Comparative Study of Three ML Models

Abstract

Natural Language Processing (NLP) is an ever-evolving field of computer science that involves the development of algorithms that can process, analyze and understand human language. One of the most exciting areas of NLP is the creation of NLP language models with applications across almost every industry. However, most people only associate NLP with its traditional use in language translation, sentiment analysis, and chatbots. In reality, there are many less-common uses for NLP models that have the potential to transform businesses, improve customer experiences, and even save lives. In the healthcare industry, NLP models can be used to analyze unstructured medical data and help diagnose and treat patients more efficiently. For example, NLP can be used to analyze clinical notes, lab results, and other data combing through vast amounts of data to identify patterns and create targeted treatment plans. NLP-based medical diagnosis is still in its infancy, but it has the potential to revolutionize the healthcare industry in the coming years. This article explores a less common use of machine-learning language models built on transformed EEG data for epilepsy prediction using the Kolmogorov-Chaitin algorithmic complexity as the first step in generating text-like data, which are finally used for building machine learning models.