Adaptive detection and severity level characterization algorithm for Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) via oximetry signal analysis

TL;DR

This paper presents an adaptive algorithm for detecting and characterizing the severity of OSAHS events using oximetry signals, aiming to improve automated diagnosis processes.

Contribution

It introduces a formal specification and pseudocode for an adaptive-threshold detection algorithm for OSAHS events based on oximetry data.

Findings

Algorithm effectively detects apnea/hypopnea events.

Provides severity characterization through event rate calculation.

Suitable as a module in machine learning-based diagnosis systems.

Abstract

In this paper, an abstract definition and formal specification is presented for the task of adaptive-threshold OSAHS events detection and severity characterization. Specifically, a low-level pseudocode is designed for the algorithm of raw oximetry signal pre-processing, calculation of the 'drop' and 'rise' frames in the related time series, detection of valid apnea/hypopnea events via SpO2 saturation level tracking, as well as calculation of corresponding event rates for OSAHS severity characterization. The designed algorithm can be used as the first module in a machine learning application where these data can be used as inputs or encoded into higher-level statistics (features) for pattern classifiers, in the context of computer-aided or fully automated diagnosis of OSAHS and related pathologies.