Here, S1 is the 2 network cascade system, S2 is the DYB3 system, and S3 is the DYB5 system. For the S1 and the S2 systems, the groundtruth for Non-AEP and AEP is marked yellow and green respectively while the predictions for Non-AEP and AEP are marked black and red. For the S3, the groundtruth follows the same pattern while the predictions are marked black, cyan, magenta, and red for 201, 202, 204, and 205 respectively.
File No. S1 S2 S3 M% MH% M% MH% M% MH% 12 19.34 18.02 14.76 12.35 5.6 11.05 30 1.5 11.8 3.56 11.8 0 0 36 1.02 10.28 4.07 0 1.02 0 65 4 58.24 3.05 36.15 2.54 10.56 89 8 48.02 7.63 52.01 2.54 39.95 95 0.5 41.05 3.56 22.03 3.05 0 160 7.6 11.09 8.14 6.39 1.02 0 34 0 100 1.53 0 0 100 93 0 100 3.05 0 0 100 194 0 100 2.54 0 0 100
Figure 6.1: File No. 12 Montage ID 11 showing numerous false negatives for S1
Figure 6.2: File No. 12 Montage ID 11 showing correct classification of Non-AEP with contex- tual information and false negatives for S1
Figure 6.3: File No. 65 Montage ID 10 show- ing correct classification of AEP with contextual information for S1
Figure 6.4: File No. 65 Montage ID 10 showing total classification results for S1
Figure 6.5: File No. 89 Montange ID 10 show- ing correct AEP classification and incorrect Non- AEP prediction for S1
Figure 6.6: File No. 65 Montage ID 10 showing total classification results for S2
Figure 6.7: File No. 194 Montage ID 10 showing misclassified true negatives for S2
Figure 6.8: File No. 95 Montange ID 9 show- ing correct Non-AEP classification and incorrect Non-AEP prediction for S2
Figure 6.9: File No. 36 Montage ID 10 showing total classification results for S3, with 0 detec- tions
0 5 10 15 20 25 30 Seconds(s) -150 -100 -50 0 50 100
Figure 6.10: File No. 93 Montage ID 10 showing perfect classification for S3
Figure 6.11: File No. 65 Montange ID 9 showing correct AEP Classification with contextual infor- mation for S3
Figure 6.12: File No. 65 Montage ID 10 showing total classification results for S3
6.4
Conclusion
The system validation returns a lot of false negatives and false positives. The individual phases on detection of Yellow-Boxes and classification of Yellow-Boxes yield a very high classification accuracy, while validating the system suffers with false predictions and failing to identify some AEP cases. From Table 6.2, it is evident that the cascade system works best for the overall system validation. DYB3 has a lot of false predictions, while DYB5 has the least performance values. Major reasons for success with the individual phases and failure with the overall system validation is that the individual annotated data is from different montages albeit it has the same parent EEG signal. Hence, validating on a single montage results in mis-classification and failure to classify, while learning and testing with different montages helps in individual phase performances. Hence, re-evaluating MH with multiple montages should give a better MR score.
File No. S1 S2 S3 MH% MH% MH% 12 81.08 78.38 57.66 30 69.62 61.36 36.58 36 28.79 24.27 21.59 65 78.29 56.94 39.86 89 72.80 76.63 49.04 95 76.31 66.46 55.38 160 93.62 81.70 66.38 Table 6.3: Re-Evaluated MH Ratio
Figure 6.13: File No. 160 Mon- tange ID 12 showing correct Non-AEP Classification with False Negatives for S1
Figure 6.14: File No. 160 Mon- tange ID 11 showing correct Non-AEP Classification for S1
Figure 6.15: File No. 160 Mon- tange ID 10 showing correct Non-AEP Classification with False Negatives for S1
Figure 6.16: File No. 95 Mon- tange ID 9 showing correct Non- AEP Classification with False Negatives for S1
Figure 6.17: File No. 95 Mon- tange ID 10 showing correct AEP for S1
Figure 6.18: File No. 36 Mon- tange ID 16 showing correct AEP Classification with False Negatives for S1
Figure 6.19: File No. 12 Mon- tage ID 10 showing incorrect Classification for S2
Figure 6.20: File No. 65 Mon- tange ID 10 showing correct AEP for S2
Figure 6.21: File No. 89 Mon- tange ID 10 showing correct AEP Classification with False Negatives for S2
Figure 6.22: File No. 65 Mon- tage ID 10 showing Correct 204 and 205 Classification for S3
Figure 6.23: File No. 89 Mon- tange ID 10 showing correct 204 classification and incorrect 205 detection for S3
Figure 6.24: File No. 160 Mon- tange ID 9 showing correct 201 and 202 classification for S3
Since, the training accuracy for CNN2 is always near 100% for all cases, models based on CNN2 should have a high MH score. The re-evaluation strategy accounts for all the montages that a signal is annotated in, giving the models higher accuracy in determining all the Yellow-Boxed events and classifying them. Even after re-evaluation the cascade system works best followed by DYB3 and DYB5.
Chapter 7
Conclusion and Future Research
This thesis proposes 1-D Convolution Neural Network models to detect and classify Epileptic Transients in EEG data. This is an end-to-end model, which by-passes the conventional feature extraction step. The highest accuracy in terms of detection of yellow boxes is 93.84% for the CNN2 architecture with K = 9 (Table 4.4), and for classification of yellow boxes, it is 95.23% for K = 7 (Table 5.4). The over-all system performance is largely effected by false positives and false negatives, while the system does a very good job in detecting true positives and negatives. The evaluation of system performance with multiple montages boosts the true positive and negative detection. Table 7.1 summarizes all the cases considered in detection of Yellow-Boxes, Classification of Yellow-Boxes and the over-all system validation.
While the results are promising, CNN1 and CNN2 architectures are quite shallow models. Typical deep learning CNNs have 5-10 convolution layers at a minimum. Deep learning algorithms take advantage of massive datasets, creating a layered, hierarchal architecture of learning and rep- resenting data. The reason for CNN1 and CNN2 to be only 2 or 3 layers deep, is the limited data available for this undertaking. A deeper neural network with limited data is prone to memo- rize/overfitt the input data, and it may not be possible to train a deep network which generalizes well. A shallower network architecture with normalization and dropout works quite well with limited data.