Bio-Fingerprinting applied to polysomnographs

Polysomnography (PSG) is a multi-parametric test used in the study of sleep and as a diagnostic tool for sleep disorders. The first step in the quantitative analysis of polysomnographic recordings is the classification of sleep stages. It is possible to distinguish between wake, REM sleep, NREM sleep stages 1 to 4 and movement time. To classify sleep stages, it is important to identify where certain patterns occur, such as sleep spindles. A sleep spindle is an electroencephalography (EEG) pattern defined as a train of distinct waves with frequency 11–16 Hz (most commonly 12-14 Hz) with duration more or equal than 0.5 s. Spindles are a characteristic of stage 2 sleep as they define the transition from stage N1 (non-rapid eye movement, NREM1) to stage N2 (NREM2). Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. Some automatic detectors already exist, but they are not accurate. The aim of this project is to demonstrate that the performance of an existent sleep-spindle detector can improve by modifying the algorithm so that it can be adapted to the characteristics of each patient. In this project some analysis has been performed, demonstrating that a detection spindle algorithm can be made customizable for each patient. For the patient on which I have tested the best method found, the F1-score has increased from 0.48, result of the initial algorithm, to 0.55, result using 20 spindles in input.