Algorithms for the analysis of polysomnographic recordings with customizable criteria

Abstract

The diagnosis of Sleep Apnoea–Hypopnoea Syndrome requires the visual inspection of a recording containing a large number of physiological parameters of the patient – the polysomnogram. The purpose of this visual inspection is the identification and characterization of different types of pathological events that occur over these parameters. These events are defined by a set of morphological criteria. Based on these criteria, commercial tools have been developed to support clinicians in the task of visually inspecting polysomnograms. This article argues that the standard morphological criteria are just guiding recommendations that experienced physicians often adapt to each specific diagnostic context. Thus, tools that support the analysis of polysomnograms ideally should use flexible criteria that could be easily customizable by clinicians. In this paper, we propose algorithms capable of identifying pathological events relevant in the diagnosis of SAHS using custom criteria that are acquired directly from the clinician. These algorithms take advantage of the Fuzzy Set Theory to capture and handle the vagueness and uncertainty that are characteristics of medical knowledge. Knowledge acquisition using the traditional linguistic approach of the Fuzzy Sets Theory is supported by a desktop tool. However, the authors feel that some of the criteria that need to be acquired are more visual in nature than linguistic. An alternative mechanism for the visual acquisition of these criteria is proposed. Finally, when presenting the pathological events that have been identified, the tool uses several visual metaphors designed to simplify visual inspection of the polysomnogram. We have validated our proposal over 69 h of polysomnographic recordings arising from 12 patients that were subjected to a sleep study. 95.7% of the events identified were correct detections. The rate of false negatives was 1.6%.