Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification

TL;DR

This paper introduces a deep neural network approach using weighted averaged features from a single airflow signal to classify sleep apnea severity, achieving higher accuracy than previous methods on a large dataset.

Contribution

The study presents a novel deep learning scheme with weighted airflow features for sleep apnea classification, outperforming traditional classifiers on a large patient dataset.

Findings

Binary classification accuracy up to 92.69%

Multiclass accuracy of 63.70%

Outperforms SVM and AB-CART methods

Abstract

Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages of using DL for sleep Apnea-Hypopnea severity classification. To reduce the complexity of clinical diagnosis using Polysomnography (PSG), which is multiple sensing platform, we incorporates our proposed DL scheme into one single Airflow (AF) sensing signal (subset of PSG). Seventeen features have been extracted from AF and then fed into Deep Neural Networks to classify in two studies. First, we proposed a binary classifications which use the cutoff indices at AHI = 5, 15 and 30 events/hour. Second, the multiple Sleep Apnea-Hypopnea Syndrome (SAHS) severity classification was proposed to classify patients into 4 groups including no SAHS, mild SAHS, moderate SAHS, and severe SAHS. For methods evaluation, we used a higher number of patients than related works to accommodate more diversity which includes 520 AF records obtained from the MrOS sleep study (Visit 2) database. We then applied the 10-fold cross-validation technique to get the accuracy, sensitivity and specificity. Moreover, we compared the results from our main classifier with other two approaches which were used in previous researches including the Support Vector Machine (SVM) and the Adaboost-Classification and Regression Trees (AB-CART). From the binary classification, our proposed method provides significantly higher performance than other two approaches with the accuracy of 83.46 %, 85.39 % and 92.69 % in each cutoff, respectively. For the multiclass classification, it also returns a highest accuracy of all approaches with 63.70 %.