New model to identify sleep stages

10Feb - by aiuniverse - 0 - In Deep Learning


Helsinki: A new learning model for identifying sleep stages with exact precision as that of a physician has been identified by a recent study. The study was conducted by researchers of the University of Eastern Finland.

Sleep is manually classified into five stages, which are wake, rapid eye movement (REM) sleep and three stages of non-REM sleep. The study published in the IEEE Journal of Biomedical and Health Informatics opened up new avenues for the diagnostics and treatment of sleep disorders, including obstructive sleep apnoea (OSA).

OSA is a nocturnal breathing disorder that causes a major burden on public health care systems and national economies. It is estimated that up to one billion people worldwide suffer from OSA, and the number is expected to grow due to population ageing and increased prevalence of obesity. When untreated, OSA increases the risk of cardiovascular diseases and diabetes, among other severe health consequences.

Manual scoring of sleep stages is time-consuming, subjective and costly. To overcome these challenges researchers used polysomnographic recording data from healthy individuals and individuals with suspected OSA to develop an accurate deep learning model for automatic classification of sleep stages.

In addition, the team wanted to find out how the severity of OSA affects classification accuracy. Among healthy individuals, the model was able to identify sleep stages with 83.7 per cent accuracy when using a single frontal electroencephalography channel (EEG), and with 83.9 pc accuracy when supplemented with electrooculogram (EOG).

In patients with suspected OSA, the model achieved accuracies of 82.9 per cent (single EEG channel) and 83.8 per cent (EEG and EOG channels). The single-channel accuracies ranged from 84.5 per cent for individuals without OSA to 76.5 per cent for severe OSA patients.

The accuracies achieved by the model are equivalent to the correspondence between experienced physicians performing manual sleep scoring. However, the model has the benefit of being systematic and always following the same protocol, and conducting the scoring in a matter of seconds.

According to the researchers, deep learning enables automatic sleep staging for suspected OSA patients with high accuracy. The Sleep Technology and Analytics Group, STAG, at the University of Eastern Finland solves sleep diagnostics challenges by using a variety of different approaches.

The methods developed by the group are based on wearable, non-intrusive sensors, better diagnostic parameters and modern computational solutions that are based on artificial intelligence.

The new methods developed by the group are expected to significantly improve OSA severity assessment, promote individualised treatment planning and a more reliable prediction of OSA-related daytime symptoms and comorbidities.

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