Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease
Abstract
:1. Introduction
2. Encoding-Based Wave2Vec Time Series Classifier
2.1. Signal Encoding
2.2. Sequence Classification
2.3. Wave Embedding and Wave Vector
2.4. Vector Operation for Prediction and Diagnosis
3. Experiments and Results
3.1. Data Description
3.2. Experimental Setting
3.3. Experiment 1: Searching for the Optimal Degree of Quantization
3.4. Experiment 2: Comparison with Other Deep Learning Classifiers
3.5. Experiment 3: Recognition and Identification of Effective Patterns through Model Visualization
4. Discussion
- The current proposed models did not employ any disease-specific context knowledge for enhancing analysis performance. The model is a general-purpose time series classifier, and its main goals are solving specific issues in analyzing bio-signal with conventional deep learning models, such as removing black boxes, reducing complexity, and recognizing and identifying important patterns. For real application to prediction or diagnosis of brain disease, such as dementia or alcoholism, more complicated knowledge and logic of the targeted disease, such as the relationship between the disease and the geometric information of EEG sensing spots, connections between sensing spots on the scalp, and democratic knowledge of the testing person, should be melted at the tokenizing phase.
- The analysis performance of the model, in terms of accuracy, is similar to those of conventional deep learning models and inferior to that of the deep neural network (DNN) if overfitting is ignored.
- Overfitting should be removed. As the number of epochs increases, the DNN tends to converge to a perfect status, which is overfitting. The process for data augmentation and the method of regularization, such as parameter tweaking, need to be studied.
- Unlike conventional deep learning models, the performance of the current approach is not affected directly, or affected inefficiently, by increasing the number of iterations of whole data training. This is caused by replacing large portions of the deep learning process for feature selection and classification with vector operations to reduce the number of black boxes and improve readability.
- A hybrid model, which includes both conventional deep learning modules and the proposed Wave2vec modules, should be considered because the most important values of classifiers, such as accuracy, transparency, readability, visibility, and high speed, will vary depending on the application areas.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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CNN-Based EEG Classifier | RNN (LSTM)-Based EEG Classifier | DNN-Based EEG Classifier |
---|---|---|
| | |
Training Time (Seconds per Epoch) | Testing Time (Seconds per Instance) | |||||
---|---|---|---|---|---|---|
Loading | Encoding | Learning | Loading | Encoding | Classifying | |
Wave2vec | 5.650 | 2.250 | 23.020 | 0.005 | 0.000 | 0.059 |
CNN | 5.650 | - | 194.030 | 0.005 | - | 0.390 |
RNN | 5.650 | - | 323.130 | 0.005 | - | 0.574 |
DNN | 5.650 | - | 206.394 | 0.005 | - | 0.480 |
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Kim, S.; Kim, J.; Chun, H.-W. Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease. Int. J. Environ. Res. Public Health 2018, 15, 1750. https://doi.org/10.3390/ijerph15081750
Kim S, Kim J, Chun H-W. Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease. International Journal of Environmental Research and Public Health. 2018; 15(8):1750. https://doi.org/10.3390/ijerph15081750
Chicago/Turabian StyleKim, Seonho, Jungjoon Kim, and Hong-Woo Chun. 2018. "Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease" International Journal of Environmental Research and Public Health 15, no. 8: 1750. https://doi.org/10.3390/ijerph15081750
APA StyleKim, S., Kim, J., & Chun, H.-W. (2018). Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease. International Journal of Environmental Research and Public Health, 15(8), 1750. https://doi.org/10.3390/ijerph15081750