Polysomnography Raw Data Extraction, Exploration, and Preprocessing
in: Handbook of AI and Data Sciences for Sleep Disorders, Richard B. Berry,Panos M. Pardalos,Xiaochen Xian, Editor, Springer Nature, Basel, pp.45-65, 2024
- Publication Type: Book Chapter / Chapter Research Book
- Publication Date: 2024
- Publisher: Springer Nature
- City: Basel
- Page Numbers: pp.45-65
- Editors: Richard B. Berry,Panos M. Pardalos,Xiaochen Xian, Editor
- TED University Affiliated: Yes
Abstract
Raw polysomnography (PSG) preprocessing is one of the first steps in any sleep disorder detection using artificial intelligence (AI) and data science (DS). This chapter mainly discusses the process of transforming raw PSG at the very beginning in a way that can be fed into a machine learning (ML) or deep learning (DL) model. This includes essential steps that come before building the actual model: starting from defining the problem, collecting raw PSG, then data exploration, and finally, preparing the data. PSG preprocessing is often highly specific to a particular dataset at hand, the main expected result of the learning model, and the equipment used for signal acquisition. For this reason, it is common in the literature to overlook raw PSG preprocessing or to mention it briefly without specifying details. Hence, giving a set of universally applicable steps is not easy. This chapter discusses the possible preprocessing steps that could be applied to the raw PSG data, which were tested empirically or proven theoretically.