Polysomnography Raw Data Extraction, Exploration, and Preprocessing

Malak A. Almarshad (), Saiful Islam (), Sultan Bahammam, Saad Al-Ahmadi () and Ahmed S. BaHammam ()
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Malak A. Almarshad: Computer Science Department, College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud Islamic University
Saiful Islam: Department of Computer Engineering, TED University
Sultan Bahammam: The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University
Saad Al-Ahmadi: The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University
Ahmed S. BaHammam: The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University

A chapter in Handbook of AI and Data Sciences for Sleep Disorders, 2024, pp 45-65 from Springer

Abstract: 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.

Keywords: Artificial intelligence (AI); Machine learning (ML); Deep learning (DL); Sleep study; PSG autoscoring; Time series classification; Time series regression; Biosignal; Sleep disorders (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-68263-6_2

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