Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2

Abstract

Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient’sneurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, and laborious process.Because  of  the  limits  of  human  sleep  stage  scoring,  there  is  a  greater  need  for  creating  automatic  sleep  stageclassification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleepand is an important step in assisting physicians in the diagnosis and treatment of associated sleep disorders. In thisresearch, we offer a unique method and a practical strategy to predict early onset of sleep disorders, such as restlessleg syndrome and insomnia, using the twin convolutional model FTC2, based on an algorithm composed of twomodules. To provide localized time-frequency information, 30-second-long epochs of electroencephalogram (EEG)recordings  are  subjected  to  a  fast  Fourier  transform,  and  a  deep  convolutional  long  short-term  networks  neuralnetwork is trained for sleep stage categorization. Automating sleep stage detection from EEG data offers a greatpotential to tackle sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification isproposed, which combines the best of signal processing and statistics. In this study, we used the PhysioNet SleepEuropean Data Format (EDF) database. The code evaluation showed impressive results, reaching an accuracy of90.43, precision of 77.76, recall of 93,32, F1 score of 89.12, and the final mean false error loss of 0.09. All the sourcecode is available at https://github.com/timothy102/eeg.