Sample run 2

In this sample run, a sample application was performed to analyze hypopnea. Although it is based on real data, the purpose of this example is simply to explain how PSGMiner could be used for testing a hypothesis, and it is not meant to suggest a clinical procedure for experiments of sleep apnea. In this sample study it is aimed to investigate differences which occur in the EEG features extracted from O1–A2 channel during 1 seconds before hypopnea occurrence and during 1 seconds following the onset of the apnea. In other words, this was done to see whether the before apnea period and onset of apnea could be classified using signals from O1 – A2 channel.The statistical verification of the study results for the sample run is shown in the user interface of Machine Learning in last figure. According to the results for the sample run, the accuracy rate is 76.52%. A careful study of the results indicates that there is no important distinction between before hypopnea period and onset of hypopnea. However, this is by no means a demanding study, and it was only carried out to illustrate how our software can be used in clinical events.
PSG data
PSG data that are used at the test run 2 were taken from two real patients, whose identification information is kept as secret. (Patient 1 age: 48, weight: 75 kg, height: 161 cm, neck circumference: 36 cm, sex: female, patient 2 age: 55 weight: 96kg, height: 173 cm, neck circumference: 43 cm, sex male). These patients were actual patients from the archive of a sleep laboratory. Recordings were used in retrospective manner. All patients were diagnosed with obstructive sleep apnea and underwent a one-night PSG for diagnosis.These PSG records are recorded with 44-channel polygraph (Compumedics 44E series, Avustralia) and these archive records are obtained through the use of complete PSG techniques in the Sleep Laboratory of the Faculty of Medicine, Trakya University.
Patient 1 Hypopnea count: 83
Patient 2 Hypopnea count: 48
Used channels and features for classification.
All available EEG features in PSGMiner
class {0,1}
class 0:Not Hypopnea
class 1:Hypopnea
Classification Results (PSGMiner)
Neural Network (Rprop)
Neuron Type : Sigmoid
Hidenlayer Count : 1
Hidenlayer 0 Neuron Count : 32
Learning Rate : 0.3
Maximum Iteration : 500
Epsilon : 1E-8
Weights Initial : 0.1
Weights Max : 50
Instances :230
Attributes :58
Accuracy :%76.522
Root mean squared error :0.439
Kappa Value :0.534
Sensitivity :0.765
Specificity :0.774
Precision :0.803
False Negative Rate :0.235
F1 score :0.759
ROC Area :0.77
Confusion Matrix 0 (Classified as)  1 (Classified as)
            0             71             47
            1             7              105
Click on [Create New Database] button
Enter database name
Enter Epoch Length
Click on [OK] button
Enter database values and then click on [Connect] button
Select features and click on [Event Based] radio button
Choose parameters and click on [Hypopnea] radio button
Click on [Create Analysis Table] Button
Click on [Feature Extraction] Button
Click on [Start] Button
After Finish click on [OK] button
Click on [Close] button
Click on [Remove Artifact] Button
Click on [AWAKE] item and click on [Remove Selected Epochs] Button
Click on [OK] button
Click on [Machine Learning] Button
Click on [Hypopnea] item and Click on [Output] Button
Click on [inputs you want] items and Click on [Input] Button and than Click on [Create Machine Learning Table] Button
Type [Table name] and click on [OK] button
Click on [Neural Network] Button
Setup Parameters and Click on [Start] Button
Classification Results
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