A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

Abstract

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.

Deep learning for healthcare applications based on physiological signals: A review

“…Nowadays, performance measures, such as classification accuracies [15,16], govern computer based analysis and classification of physiological signals for healthcare applications [17,18,19]. The goal is to design algorithms that outperform the established methods [20]. The design process is based on the idea of having an offline and online system, as shown in Figure 1 [21,22].…”

Performance Comparison Of Ann Classifiers For Sleep Apnea Detection Based On Ecg Signal Analysis Using Hilbert Transform

“…The cyclical repetitions of prominent symptoms of sleep apnea noted from the temporal (time intervals) and spectral characteristics of ECG signal are to be identified in order to detect sleep apnea. [14][15][16][17] An apnea (or hypopnea) event is said to occur under the following symptomatic conditions [21][22][23] that are used for computing AHI:…”

“…The performance comparison of the proposed method with different methods in the literature for sleep apnea detection based on ECG signal analysis are tabulated in Table 4 in terms of accuracy (Acc), sensitivity (Se) and specificity (Sp) as reported in literature [21]. The dataset used for experimentation of all these methods is the Physionet"s ECG apnea Database [30].…”

A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

“…Sleep is essential for physical and mental health. It is an active and regulated process that metabolizes energy, matures neural connections, and consolidates learning as well as memory [ 19 ]. Unfortunately, a faster paced rhythm of life and other detrimental lifestyle changes cause a higher prevalence of sleep disorders [ 20 ].…”