Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal
breathing. The severity of OSA can lead to many symptoms such as sudden cardiac
death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It
records many signals from the patient's body for at least one whole night and
calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or
hypopnea incidences per hour. This value is then used to classify patients into
OSA severity levels. However, it has many disadvantages and limitations.
Consequently, we proposed a novel methodology of OSA severity classification
using a Deep Learning approach. We focused on the classification between normal
subjects (AHI 30). The 15-second raw
ECG records with apnea or hypopnea events were used with a series of deep
learning models. The main advantages of our proposed method include easier data
acquisition, instantaneous OSA severity detection, and effective feature
extraction without domain knowledge from expertise. To evaluate our proposed
method, 545 subjects of which 364 were normal and 181 were severe OSA patients
obtained from the MrOS sleep study (Visit 1) database were used with the k-fold
cross-validation technique. The accuracy of 79.45\% for OSA severity
classification with sensitivity, specificity, and F-score was achieved. This is
significantly higher than the results from the SVM classifier with RR Intervals
and ECG derived respiration (EDR) signal feature extraction. The promising
result shows that this proposed method is a good start for the detection of OSA
severity from a single channel ECG which can be obtained from wearable devices
at home and can also be applied to near real-time alerting systems such as
before SCD occurs