iSeizdiag: toward the framework development of epileptic seizure detection for healthcare
Ashish Sharma, Akshat Saxena, Mradul Agrawal, Kunal Kishor, Deepti Kaushik, Prateek Jain, Arvind R. Yadav, Manob Jyoti Saikia
TL;DR
This paper presents a machine learning framework for detecting epileptic seizures using EEG signals with high accuracy.
Contribution
The novel contribution is an optimized machine learning framework achieving high accuracy for epileptic seizure detection.
Findings
An average accuracy of 95% was achieved during training and validation.
A 97% accuracy was achieved after testing the optimized model.
Statistical parameters were calculated to validate the framework's performance.
Abstract
The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acquisition is the real-time module that records the electrical discharges produced by the brain cells. The electrical discharges are amplified and appear as a graph on electroencephalogram systems. Different neurological disorders are represented as different waves on EEG records. This paper involves the detection of Epilepsy which appears as rapid spiking on electroencephalogram signals, using feature extraction and machine learning techniques. Various models, such as the Support Vector Machine, K Nearest Neighbor, and random forest, have been trained, and accuracy has been analyzed to predict the seizure. An average accuracy of 95% has been claimed using the optimized model for epileptic seizure detection during training and validation. During the…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEEG and Brain-Computer Interfaces · ECG Monitoring and Analysis · Functional Brain Connectivity Studies