Detecting Hypopnea and Obstructive Apnea Events Using Convolutional Neural Networks on Wavelet Spectrograms of Nasal Airflow

• Published in: Advances in Knowledge Discovery and Data Mining Vol. 10937; pp. 361 - 372
• Main Authors: McCloskey, Stephen, Haidar, Rim, Koprinska, Irena, Jeffries, Bryn
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Convolutional Neural Networks; Obstructive Sleep Apnea; Sleep disorder; Supervised learning; Wavelets
• ISBN: 9783319930336; 3319930338
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-93034-3_29

We present a novel approach for detecting hypopnea and obstructive apnea events during sleep, using a single channel nasal airflow from polysomnography recordings, applying a Convolutional Neural Network (CNN) to a 2-D image wavelet spectrogram of the nasal signal. We compare this approach to directly training a 1-D CNN on the raw nasal airflow signal. The evaluation was conducted on a large dataset consisting of 69,264 examples from 1,507 subjects. Our results showed that both approaches achieved good accuracy, with the 2-D CNN outperforming the 1-D CNN. The higher accuracy and the less complex architecture of the 2-D CNN show that converting biological signals into spectrograms and using them in conjunction with CNNs is a promising method for sleep apnea recognition.