Design and Analysis of an Effective and Intelligent Computing Method for Diagnosis of Sleep Disorders in Healthcare Monitoring
Keywords:
ANFUS, ANN, FCM, SVM, ECGAbstract
However, the standard diagnostic procedure for sleep disorders is polysomnography, which is expensive, involves human professionals, and is performed in specialised labs. Hence, a less invasive and cheaper method of detecting sleep problems is required. Although people with sleep difficulties are more likely to develop hypertension, cardiovascular disease, etc., delayed detection of these conditions increases the overall mortality rate. The detection of all these ailments has previously relied heavily on signal processing and pattern recognition methods, but to improve accuracy, researchers have used a wide range of intelligent approaches and procedures. As a result, we take a step in the right path by offering an intelligent computing method called ANFUS (an integration of ANN, FCM, and SVM) to diagnose sleep disorders such sleep apnea, insomnia, parasomnia, and snoring by analysing ECG data and clinical recommendations. With this in mind, the primary objective has been to develop an intelligent computing approach to the diagnosis of four sleep disorders: apnea, insomnia, parasomnia, and snoring. Yet, there is more than one source of difficulty. It may also contribute to the emergence of related illnesses. Thus, stopping the spread of sleep disorders makes accurate diagnosis a top goal. As a result, ANFUS can be useful not only for patients but also for healthcare providers and institutions.
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