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On Prediction of Epileptic Seizures by Means of Genetic Programming Artificial Features

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Annals of Biomedical Engineering Aims and scope Submit manuscript

A general-purpose, systematic algorithm is presented, consisting of a genetic programming module and a k-nearest neighbor classifier to automatically create artificial features—computer-crafted features possibly without a known physical meaning—directly from the reconstructed state-space trajectory of intracranial EEG signals that reveal predictive patterns of epileptic seizures. The algorithm was evaluated with IEEG data from seven patients, with prediction defined over a horizon of 1–5 min before unequivocal electrographic onset. A total of 59 baseline epochs (nonseizures) and 55 preictal epochs (preseizures) were used for validation purposes. Among the results, it is shown that 12 seizures out of 55 were missed while four baseline epochs were misclassified, yielding 79% sensitivity and 93% specificity.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA

    Hiram Firpi

  2. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. BioQuantix Corp., Atlanta, GA, USA

    Javier Echauz

  4. Department of Electrical and Computer Engineering, Georgia Institute of Technology, 777 Atlantic Dr., Atlanta, GA, 30332-0250, USA

    Hiram Firpi

Authors
  1. Hiram Firpi
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  2. Erik Goodman
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  3. Javier Echauz
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Corresponding author

Correspondence to Hiram Firpi.

APPENDIX

APPENDIX

This appendix shows the point-basis results for all the epochs available evaluated with the GPAF algorithm, for each patient. Small letters denote baseline epochs, whereas capital letters denote preictal epochs.

Epoch

GPAF

Patient A: Point-basis classification results for patient A

  b

61.54

  c

57.86

  d

57.19

  B

80.33

  C

69.23

  D

73.91

 Overall

66.68%

Patient B: Point-basis classification results for patient B

  b

82.61

  c

89.63

  d

86.96

  e

88.29

  f

81.61

  g

77.26

  B

85.62

  C

66.56

  D

94.65

  E

53.17

 Overall

80.64%

Epoch

GPAF

Patient C: Point-basis classification results for patient C

  b

100

  c

100

  d

40.8

  e

70.9

  f

99.0

  g

100

  h

100

  i

87.63

  j

100

  k

97.32

  A

100

  B

99.66

  C

87.63

  E

81.61

  F

99.0

  G

97.99

  H

96.99

  I

99.0

  J

96.66

  K

100

 Overall

92.71%

Patient D: Point-basis classification results for patient D

  a

96.66

  b

96.33

  d

47.16

  f

96.65

  g

91.30

  h

90.97

  i

45.82

  j

20.74

  k

51.84

  l

88.63

  B

0.33

  C

74.92

  D

8.03

  E

53.18

  F

86.29

  G

77.93

  H

86.96

  I

17.07

  J

86.29

  K

84.95

 Overall

65.10%

Patient E: Point-basis classification results for patient E

  a

26.85

  b

72.82

  c

100.0

  d

100.0

  e

70.47

  f

100.0

  h

100.0

  i

100.0

  j

57.05

  k

100.0

  l

100.0

  m

100.0

  n

100.0

  o

53.69

  p

74.5

Epoch

GPAF

  A

79.53

  B

93.29

  C

48.99

  D

92.95

  E

94.63

  F

92.28

  G

94.3

  H

94.97

  I

17.11

  J

97.32

  K

97.32

  L

98.32

  M

49.66

  N

78.52

  O

9.4

 Overall

79.8%

Patient F: Point-basis classification results for patient F

  b

83.56

  c

86.58

  d

66.56

  e

100.0

  f

100.0

  g

100.0

  h

100.0

  B

97.99

  C

99.33

  D

100.0

  E

100.0

  F

99.33

 Overall

94.45%

Patient G: Point-basis classification results for patient G

  b

60.2

  c

9.03

  d

26.76

  e

71.57

  f

59.2

  g

79.26

  i

94.65

  j

96.66

  B

29.77

  C

79.6

  D

90.97

  E

84.28

  F

51.17

  G

90.64

  H

89.97

  I

51.51

 Overall

66.58%

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Firpi, H., Goodman, E. & Echauz, J. On Prediction of Epileptic Seizures by Means of Genetic Programming Artificial Features. Ann Biomed Eng 34, 515–529 (2006). https://doi.org/10.1007/s10439-005-9039-7

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  • DOI: https://doi.org/10.1007/s10439-005-9039-7

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