Abstract:
Sleep apnea-hypopnea syndrome (SAHS) is widespread sleep and respiratory disorder which is
characterized by breaks in breathing or instances of superficial or uncommon breathing during
sleep. SAHS diagnosis is commonly performed using Polysomnography (PSG). However, this
technique is a very complex and time-consuming procedure due to the need of many
physiological variables and the use of multiple sensors attached to the patients the whole night.
Moreover, PSG is also inconvenient as an expert human observer is required to work overnight
and relies on a doctor‟s experience. Thus, the possibility of occurrence of the white coat effect,
as children take longer to adapt to the hospital environment and to fall asleep, a fact that will
affect the results. In order to improve the diagnosis efficiency, reduce the complexity and
diagnosis time and ensure a more accurate diagnosis, a quantitative and objective method is
required. Different biosignal features have been proposed for the detection of sleep apnea in the
literature. However, in most cases, only a single biosignal system alone is used with the only aim
of detection of sleep apnea. Using one signal alone may cause false positive or false negative
results due to different artifacts. Using two or more signals simultaneously increases the
reliability of the result. Sleep apnea syndrome detection based on a simultaneously recorded
electrocardiograph (ECG) and saturation of oxygen (SpO2) signals, individually and in
combination, has been proposed in this thesis. In addition, the automatic classification of sleep
apnea severity level has been incorporated to enhance the diagnosis and treatment procedure.
Various features from the RR intervals of ECG, and a number of statistical features from the
SpO2, were extracted as indicators of sleep apnea. The features were then fed to support vector
machine (SVM) for classification. An accuracy of 99.1%, specificity of 98.08% and sensitivity
of 100 % has been achieved using the simultaneously recorded combination of two biosignal
features and found to be better compared to other proposed techniques. Using the combined
features is inherently more robust, as in the event of either channel being poor quality, the system
can continue to make an analysis based on the other channel and achieve better accuracy
compared to using either signal alone.
