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Sensitive deep learning application on sleep stage scoring by using all PSG data

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Abstract

Polysomnography (PSG)-based sleep stage scoring is time-consuming and it suffers from variability in results. With the help of automated PSG scoring, which is based on deep learning techniques, it is possible to reduce labor costs and variability inherent to this task. Instead of using publicly available sleep databases, we created our database by using the Philips Alice clinic device, which employs 19 sensor channels connected to subjects. Since every sensor channel creates a huge amount of data, we limited our work to 50 patients and pre-processed this data. The deep learning sleep-based sleep stage classifier demonstrates excellent accuracy and agreement with the sleep expert’s scoring. Average accuracy, precision, recall, and F1-measure were defined as 91.6, 90.9, 91.6, and 90.7% respectively. The proposed work has novelty when it is compared with similar deep learning-based automatic sleep staging studies by using 19 channels as input rather than employing only EEG, EOG, or EMG data. The proposed model is scientifically created for making job quite similar to the sleep doctors during sleep stage scoring automatically instead of manual steps used by them. Furthermore, as explained, our database is created by using a local hospital, which has a sleep clinic, rather than using publicly available databases. The proposed work will allow a fully automated PSG scoring system by having its own database and employing all PSG inputs.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Architecture and Design, Kayseri University, 38000, Kayseri, Turkey

    Recep Sinan Arslan

  2. Department of Computer Engineering, Faculty of Engineering and Architecture, Bozok University, 66200, Yozgat, Turkey

    Hasan Ulutas, Ahmet Sertol Köksal & Mehmet Bakir

  3. Department of Chest Diseases, Faculty of Medicine, Yuksek Ihtisas University, Ankara, Turkey

    Bülent Çiftçi

Authors
  1. Recep Sinan Arslan
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  2. Hasan Ulutas
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  3. Ahmet Sertol Köksal
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  4. Mehmet Bakir
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  5. Bülent Çiftçi
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Correspondence to Recep Sinan Arslan.

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Appendix A: All classification results for 50 subjects

Appendix A: All classification results for 50 subjects

No

Accuracy

Precision

Recall

F1-measure

1

0.880

0.865

0.876

0.864

2

0.920

0.922

0.921

0.915

3

0.880

0.868

0.875

0.864

4

0.910

0.907

0.909

0.900

5

0.910

0.890

0.905

0.894

6

0.930

0.925

0.927

0.918

7

0.930

0.928

0.930

0.925

8

0.890

0.870

0.888

0.869

9

0.940

0.939

0.943

0.939

10

0.920

0.920

0.925

0.921

11

0.890

0.881

0.891

0.882

12

0.890

0.876

0.886

0.868

13

0.950

0.953

0.954

0.952

14

0.940

0.929

0.939

0.931

15

0.950

0.944

0.948

0.943

16

0.920

0.904

0.916

0.909

17

0.920

0.921

0.924

0.919

18

0.900

0.891

0.903

0.895

19

0.890

0.893

0.895

0.888

20

0.940

0.934

0.936

0.931

21

0.930

0.926

0.929

0.925

22

0.890

0.887

0.892

0.885

23

0.920

0.910

0.915

0.908

24

0.920

0.911

0.922

0.916

25

0.890

0.885

0.893

0.885

26

0.940

0.922

0.939

0.925

27

0.920

0.911

0.921

0.911

28

0.910

0.903

0.912

0.902

29

0.900

0.895

0.901

0.892

30

0.920

0.904

0.915

0.904

31

0.880

0.869

0.876

0.864

32

0.880

0.870

0.882

0.870

33

0.900

0.880

0.897

0.883

34

0.900

0.886

0.901

0.890

35

0.910

0.911

0.912

0.907

36

0.920

0.914

0.920

0.913

37

0.920

0.920

0.923

0.918

38

0.960

0.955

0.959

0.951

39

0.940

0.934

0.941

0.931

40

0.930

0.921

0.926

0.917

41

0.890

0.890

0.895

0.880

42

0.920

0.916

0.924

0.916

43

0.920

0.916

0.922

0.914

44

0.970

0.966

0.967

0.965

45

0.930

0.927

0.930

0.923

46

0.900

0.896

0.901

0.896

47

0.940

0.936

0.936

0.931

48

0.890

0.877

0.893

0.877

49

0.930

0.924

0.931

0.925

50

0.880

0.871

0.881

0.868

Avg

0.916

0.909

0.916

0.907

  1. Bold indicates average performance of the proposed model in all patients was obtained

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Arslan, R.S., Ulutas, H., Köksal, A.S. et al. Sensitive deep learning application on sleep stage scoring by using all PSG data. Neural Comput & Applic 35, 7495–7508 (2023). https://doi.org/10.1007/s00521-022-08037-z

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