1. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China 2. Xuanwu Hospital, Capital Medical University, Beijing 100053, China
A novel algorithm for seizure prediction based on patient specific manner was proposed to improve the accuracy of epileptic prediction, including feature extraction, pre-ictal period selection, feature selection and channel selection. Time-frequency features and spatial features were extracted from each channel by 2 s windows with 1 s overlap. A continuous 10 min data was selected as a valid positive sample of the pre-ictal period from segment before seizure onset, which achieved the maximum linear separability compared with the inter-ictal period. The effective features were selected by elastic net, then the selected effective features were used to select effective channels in greedy manner. The effective features of effective channels were input into classifier. The algorithm was tested on the scalp electroencephalogram (sEEG) from the MIT Physio database and the database collected in Xuanwu Hospital. The algorithm achieved a recall of 95.76% and a false positive rate of 0.1073 h?1 in MIT database, and a recall of 97.80% and a false positive rate of 0.0453 h?1 in Xuanwu Hospital database. Results show that the algorithm has high sensitivity and low false positive rate.
Ya-jing WANG,Qun WANG,Bo-wen LI,Zhi-wen LIU,Yuan-yuan PIAO,Tao YU. Seizure prediction based on pre-ictal period selection of EEG signal. Journal of ZheJiang University (Engineering Science), 2020, 54(11): 2258-2265.
Fig.2Matrixes of brain function connectivity network
编号
Ne
nh
R /%
FPR /h?1
1
6
5.6
94.44
0.0430
5
4
4.0
100.00
0.1311
6
10
9.5
95.00
0.1741
7
3
3.0
100.00
0.1717
8
5
4.4
88.00
0.0419
9
4
3.5
87.50
0.1177
10
7
6.5
93.88
0.0884
11
3
3.0
100.00
0.1387
14
6
6.0
100.00
0.1195
16
5
4.4
88.00
0.1814
18
4
3.7
93.75
0.0916
19
2
2.0
100.00
0.0056
20
6
6.0
100.00
0.0487
22
3
3.0
100.00
0.1483
平均
?
?
95.76
0.1073
Tab.1Prediction results based on MIT dataset
编号
T
Ne
nh
R /%
FPR /h?1
1
20
6
5.5
91.67
0.0412
2
22
6
4.8
80.56
0.0302
3
8
2
2.0
100.00
0.0412
4
16
3
3.0
100.00
0.1161
5
8
2
2.0
100.00
0.0527
6
11
4
4.0
100.00
0.0475
7
18
10
9.7
97.00
0.0280
8
8
2
2.0
100.00
0.0379
9
22
9
9.0
100.00
0.0726
10
19
6
6.0
100.00
0.0632
11
19
5
5.0
100.00
0.0485
12
17
4
4.0
100.00
0.0410
13
7
4
4.0
100.00
0.0465
14
13
4
4.0
100.00
0.0334
15
8
4
4.0
100.00
0.0489
16
16
7
7.0
100.00
0.0105
17
11
5
5.0
100.00
0.0324
18
27
9
8.0
88.89
0.0395
19
8
2
2.0
100.00
0.0289
平均
?
?
?
97.80
0.0453
Tab.2Prediction results based on dataset collected from Xuanwu Hospital
Fig.3Comparison of features between pre-ictal period and 10 minutes before onset
Fig.4Performance comparison of PPS and no-PPS on Recall
Fig.5Performance comparison of PPS and no-PPS on FPR
Fig.6Performance comparison of FCN and no-FCN on Recall
Fig.7Performance comparison of FCN and no-FCN on FPR
时间
方法
数据来源
发作次数
R /%
FPR /h?1
2016
文献[32]
MIT10位患者
31
77.00
0.1700
2013
文献[33]
MIT10位患者
51
88.20
?
2017
文献[34]
MIT13位患者
45
86.67
0.3670
2017
文献[35]
MIT24位患者
170
89.00
0.3900
2017
文献[36]
MIT13位患者
64
81.20
0.1600
本研究
MIT17位患者
75
95.76
0.1073
Tab.3Comparison of different algorithms based on MIT database
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