Sleep Disorder Classification Method based on Logistic Regression with Apnea-ECG Dataset

Adequate sleep is significant for human to actively pursue daily activity. On the other hand, insomnia is directly proportional to aging and health deterioration. Sleep disorder classification is important for medical scientists as well as machine learning researchers. In the paper, we have developed a sleep disorder classification method for Electrocardiogram (ECG) data. The data set have two labels; sleep disorder or not, which can be categorized as binary output. As a result, logistic regression is used as classification technique. For initial experimentation, an existing data set is used. The data set consists of every 10 second data up to 6000 seconds for 35 patients. We have used 70% data for training, and 30% data for testing purpose. Our results from logistic regression show that the logistic regression method is efficient to detect sleep disorders. The prediction result is found to be 59%. The future research in this direction would be to take data from hospitals and use our developed algorithm for sleep disorder classification and prediction.

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