| dc.description.abstract | Background and objective: The goal of this project is to develop a new
framework and application that can automatically detect sleep phases and sleep problems.
We will achieve this by utilizing a machine learning method based on single-channel
EEG data. The electroencephalogram (EEG) is an essential tool for detecting sleep issues
since it detects and records brain activity. The electroencephalogram (EEG) is a vital
diagnostic tool for identifying sleep problems since it detects and records brain activity.
The accuracy of the data, as well as the stability of both the patient and the measuring
apparatus throughout sleep, are crucial factors in monitoring patients with sleep
disorders.
Methods: We use the CAP dataset, a subset of Physionet consisting of a
significant collection of sleep-related data. To extract the wave properties, we divide the
data into frequency ranges ranging from 0.5 to 30 Hz. Then the ANOVA techniques,
normalization, and collapse aid in processing the waves. We train the model using
processed waves after removing any background noise and patient movements from each
EEG. Using their sleep data, we internally design a user interface that allows users to
input, evaluate, and get predictions for illnesses linked to insufficient sleep, as well as
other research on sleep.
Outcome: The approach achieves two different goals. The first model evaluates
the many phases of sleep, while the second model categorizes sleep disorders. After
assessing the influence of various diseases on sleep phases, we ultimately chose the sleep
stages model, which consistently achieved 96% accuracy in classifying sleep phases.
Unlike the diseases, the model incorporates all the specific characteristics of each
condition, effectively classifying sleep disorders into multiple groups with a classification
accuracy exceeding 95%. After completing all these processes, we created a reliable and
user-friendly interface to assist medical professionals in eliminating their biases.
Moreover, it provides the foundation for therapy and prognosis for those who want to
evaluate their sleeping quality, as well as for patients receiving treatment. Conclusions: The study ultimately found that each person's unique brain
signaling characteristics limit the model's effectiveness, preventing the entire system
from fully synchronizing. The model serves as a diagnostic tool to accurately determine
the nature of the ailment. | en_US |
