Abstract
Epilepsy affects over 70 million people worldwide, making it one of the most common serious neurological disorders in the world. The automated identification of seizures based on EEG signal is one of the most common methods but facing challenges such as the variability of seizures between individual patients and artifact generated during the measurement. In this work, we implement the multi-biosignals scheme for seizure detection by combing EEG, ECG and respiratory. We apply 1D and 2D convolutional neural network (CNN) on multi-biosignal epileptic seizure detection using the in-situ dataset with artifacts. The experimental results show that incorporating multi-biosignals outperforms than using EEG only. We also discovered that Conv2D model could achieve the best AUC of 65%, which is 7% better than the Conv1D model.
Original language | English |
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Title of host publication | Proceedings of the Australasian Computer Science Week Multiconference 2020, ACSW 2020 |
Editors | Abdur Forkan |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Number of pages | 8 |
ISBN (Electronic) | 9781450376976 |
DOIs | |
Publication status | Published - 4 Feb 2020 |
Event | Australasian Workshop on Health Informatics and Knowledge Management (HIKM) 2020 - Melbourne, Australia Duration: 3 Feb 2020 → 7 Feb 2020 Conference number: 13th |
Workshop
Workshop | Australasian Workshop on Health Informatics and Knowledge Management (HIKM) 2020 |
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Abbreviated title | HIKM 2020 |
Country/Territory | Australia |
City | Melbourne |
Period | 3/02/20 → 7/02/20 |
Keywords
- CNN
- Epilepsy
- Multi-Biosignal
- Multimodal learning
- Seizure detection