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The Deep Neural Network (DNN) model is known for its high accuracy in classification tasks due to its intrinsic ability to learn the underlying patterns existing in a set of data. Hence it has gained momentum in seizure detection research, as in many other fields. However, its high performance is at the expense of an extensive training time. This is not appropriate for a real-time application such as seizure detection in which a swift reaction is required to save the life of the patient. This paper presents a novel Kriging-Bootstrapped Deep Neural Network hierarchical model for early seizure detection in which Kriging is first used to generate a well-correlated intermediate data set from the original input. The correlated data is then fed into the DNN for the final training. Experiments were carried out using electroencephalogram (EEG) data from both normal and epileptic patients. Results show that, with the same architecture and data size, the cumulative training time of the Krigging-Bootstrapped DNN is about 75% lower than that of the ordinary DNN without a compromise in performance as the proposed hybrid model shows a slightly better accuracy than the baseline DNN model.
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Distributed Kriging-Bootstrapped DNN Model for Fast, Accurate Seizure Detection from EEG Signals
The modeling of the brain as a three-dimensional spatial object, similar to a geographical landscape, has the paved way for the successful application of Kriging methods in solving the seizure detection problem with good performance but in cubic computational time complexity. The Deep Neural Network (DNN) has been widely used for seizure detection due to its effectiveness in classification tasks, although at the cost of a protracted training time. While Kriging exploits the spatial correlation between data locations, DNN relies on its capacity to learn intrinsic representations within the dataset from the basest unit parts. This paper presents a Distributed Kriging-Bootstrapped Deep Neural Network (DNN) model as a twofold solution for fast and accurate seizure detection using brain signals collected with the electroencephalogram (EEG) from healthy subjects and patients of epilepsy. The proposed model parallelizes the Kriging computation into different cores in a machine and then produces a strongly correlated, unified quasi-output data which serves as an input to the Deep Neural Network. Experimental results validate the proposed model as superior to conventional Kriging methods and DNN by training in 91% less time than the basic DNN and about three times as fast as the ordinary Kriging-Bootstrapped Deep Neural Network (DNN) model while maintaining good performance in terms of sensitivity, specificity and testing accuracy compared to other models and existing works.
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- Award ID(s):
- 1924112
- PAR ID:
- 10158121
- Date Published:
- Journal Name:
- Proceedings of the 19th IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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