Detection of obstructive sleep apnoea using dynamic filter-banked features

摘要

There is a need for developing simple signal processing algorithms for less costly, reliable and noninvasive Obstructive Sleep Apnoea (OSA) diagnosing. One of the promising directions is to provide the OSA analysis based on the heart rate variability (HRV), which clearly shows a non-stationary behavior. So, a feature extraction approach, being capable of capturing the dynamic heart rate information and suitable for OSA detection, remains an open issue. Grounded on discriminating capability of frequency bands of HRV activity between normal and OSA patients, features can be extracted. However, some HRV normal spectrograms resemble like pathological ones, and vice versa; so, prior to extract the feature set, the energy spatial contribution contained in each subŨband should be clarified. This paper presents a methodology for OSA detection based on a set of short-time feature banked features that are extracted from the spectrogram of the HRV time series. The methodology introduces the spectral splitting scheme, which searches for spectral components with alike stochastic behavior improving the OSA detection accuracy. Two different splitting approaches are considered (heuristic and relevance-based); both of them performing minute-by-minute classification comparable with other outcomes that are reported in literature, but avoiding more complex methods or more computed features. For validation purposes, the methodology is tested on 1-min HRV-segments estimated from 50 Physionet database recordings. Using a parallel combining k-nn classifier, the assessed dynamic feature set reaches as much as 80% value of accuracy, for both considered approaches of spectral splitting. Attained results can be oriented in research focused on finding alternative methods used for less costly and noninvasive OSA diagnosing with the additional benefit of easier clinical interpretation of HRV-derived parameters.