A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals

Abstract

Objective. Sleep apnea significantly decreases the quality of life. The apnea hypopnea index (AHI) is the main indicator for sleep apnea diagnosis. This study explored a novel automatic algorithm to diagnose sleep apnea from nasal airflow (AF) and pulse oximetry (SpO2) signals. Approach. Of the 988 polysomnography (PSG) records from the sleep heart health study (SHHS), 45 were randomly selected for the development of an algorithm and the remainder for validation (n = 943). The algorithm detects apnea events by a digitization process, following the determination of the peak excursion (peak-to-trough amplitude) from AF envelope. Hypopnea events were determined from the AF envelope and oxygen desaturation with correction to time lag in SpO2. Total sleep time (TST) was estimated from an optimized percentage of artefact-free total recording time. AHI was estimated from the number of detected events divided by the estimated TST. The estimated AHI was compared to the scored SHHS data for performance evaluation. Main results. The validation showed good agreement between the estimated and scored AHI (intraclass correlation coefficient of 0.95 and mean ±95% limits of agreement of −1.6 ±12.5 events h−1). The diagnostic accuracies were found: 90.7%, 91%, and 96.7% for AHI cut-off ≥5, ≥15, and ≥30 respectively. Significance. The new algorithm is accurate over other existing methods for the automatic diagnosis of sleep apnea. It is applicable to any portable sleep screeners especially for the home diagnosis of sleep apnea.

An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

“…The recent work that accomplished most similar task to the presented one on the SHHS-1 database was described in [ 27 ]. The other algorithm was able to determine the exact location and duration of apnea and hypopnea episodes, as well as differentiate one from the other.…”

“…The other algorithm was able to determine the exact location and duration of apnea and hypopnea episodes, as well as differentiate one from the other. In this regard the goals of [ 27 ] and this work were the same. Unfortunately, results for individual episode classification accuracy were not provided, thus the two models cannot be compared in this regard.…”

“…The difference between the two works is in the kind of algorithm; Ref. [ 27 ] does not use a deep learning approach and instead focuses on extensive analysis of the signal itself. Additionally, the SpO 2 signal is required by the other model.…”

“…Additionally, the SpO 2 signal is required by the other model. Table 12 shows results from [ 27 ] and this work in the form of diagonal values from row-normalized confusion matrices for three AHI cut-offs: 5, 15 and 30.…”

“…This approach is based on bidirectional gated recurrent units and yields good results in AHI estimation. Additionally, Uddin et al [ 27 ] presented a very interesting algorithm that is not based on deep learning architecture. Signal processing techniques are used instead, utilizing airflow and SpO 2 signals.…”