7 research outputs found
Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach
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
Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification
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 %