Single-channel EEG processing for sleep apnea detection and differentiation

Sleep apnea syndrome is a common sleep disorder. Detection of apnea and differentiation of its type: obstructive (OSA), central (CSA) or mixed is important in the context of treatment methods, however, it typically requires a great deal of technical and human resources. The aim of this research was to propose a quasi-optimal procedure for processing single-channel electroencephalograms (EEG) from overnight recordings, maximizing the accuracy of automatic apnea or hypopnea detection, as well as distinguishing between the OSA and CSA types. The proposed methodology consisted in processing the EEG signals divided into epochs, with the selection of the best methods at the stages of preprocessing, extraction and selection of features, and classification. Normal breathing was unmistakably distinguished from apnea by the k-nearest neighbors (kNN) and an artificial neural network (ANN), and with 99.98% accuracy by the support vector machine (SVM). The average accuracy of multinomial classification was: 82.29%, 83.26%, and 82.25% for the kNN, SVM and ANN, respectively. The sensitivity and precision of OSA and CSA detection ranged from 55 to 66%, and the misclassification cases concerned only the apnea type

A smart sleep apnea detection service

Over the last decades, sleep apnea has become one of the most prevalent healthcare problems. Diagnosis and treatment monitoring are key elements when it comes to addressing this public health crisis. A problem for diagnosis and treatment monitoring is a chronic lack of specialized lab facilities which results in long waiting times or the absence of such services. This can delay appropriate treatment which might prolong living with sleep apnea and thereby leading to health issues due to poor sleep. We address this problem with a smart sleep apnea detection service based on Heart Rate Variably (HRV) analysis. The service incorporates Internet of Medical Things (IoMT), mobile technology (MT), and advanced Artificial Intelligence (AI). The measured signals are relayed by a smart phone into a cloud server via IoMT protocols. Once the data is stored in the cloud server, a deep learning (DL) algorithm is used to detect sleep apnea events. Detecting these events can trigger a warning message which is sent to care givers. The smart sleep apnea detection service is beneficial for patients who find it difficult to access specialized lab facilities for diagnosis or treatment monitoring. Furthermore, the system prolongs the observation period, which can improve the diagnosis accuracy. The resource requirements for the proposed service are lower when compared to clinical facilities, this might lead to significant cost savings for healthcare providers