An Investigation of Different Machine Learning Approaches for Epileptic Seizure Detection
| Autor: | Alex Barros, Denis Rosário, Eduardo Cerqueira, Paulo Resque |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry 010401 analytical chemistry Wearable computer Context (language use) 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences Random forest Support vector machine Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business computer Wearable technology 0105 earth and related environmental sciences |
| Zdroj: | IWCMC |
| DOI: | 10.1109/iwcmc.2019.8766652 |
| Popis: | Wearable devices increasing popularity provide convenient alternatives to healthcare services outside hospital premises. Wearables provide enhancements for automatic tools to assist physicians during patient diagnosis, treatment, and many other situations with limited costs and computing resources. In this context, in-device processing using machine learning algorithms can accelerate syndromes monitoring such as epilepsy detection and minimize risks of privacy disclosure due to extended data transmission to cloud servers. In this paper, we investigate the performance of five machine learning algorithms, i.e., Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Neural Network (NN), in terms of accuracy to diagnose a syndrome and the computational cost to embed it in a wearable device. We tested the algorithms in the classification of an Electroencephalography (EEG) sampled dataset available at the UCI machine learning repository. From the results, we concluded that SVM and RF have good accuracy in identifying epileptic seizures from the EEG dataset. Additionally, only RF fulfills the low computational cost required to embed such applications in-device. |
| Databáze: | OpenAIRE |
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