ArticleHealth information science and systems2019
Wavelet based deep learning approach for epilepsy detection.
Article in Health information science and systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.Computational intelligence and neuroscience · 2022Pooled it
- Lightweight deep learning model for nonconvulsive status epilepticus diagnosis using EEG time-frequency analysis.Scientific reports · 2026Article
- The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network.Frontiers in neuroinformatics · 2025Article
- A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.Neuroscience bulletin · 2024Article
- Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.Cureus · 2024Review
- One hundred years of neurosciences in the arts and humanities, a bibliometric review.Philosophy, ethics, and humanities in medicine : PEHM · 2023Review
- Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method.Biomedicines · 2023Article
- Epileptic Seizures Detection Using Deep Learning Techniques: A Review.International journal of environmental research and public health · 2021Review
- A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal.Brain sciences · 2021Review
- A space-frequency localized approach of spatial filtering for motor imagery classification.Health information science and systems · 2020Article
- Patient-specific seizure detection method using nonlinear mode decomposition for long-term EEG signals.Medical & biological engineering & computing · 2020Article
- Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform.Health information science and systems · 2020Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
Electroencephalogram (EEG) signal contains vital details regarding electrical actions performed by the brain. Analysis of these signals is important for epilepsy detection. However, analysis of these signals can be tricky in nature and requires human expertise. The human factor can result in subjective and possible erroneous epilepsy detection. To tackle this problem, Machine Learning (ML) algorithms were introduced, to remove the human factor. However, this approach is counterintuitive in nature as it involves using complex features for epilepsy detection. Hence to tackle this problem we have introduced a wavelet based deep learning approach which eliminates the need of feature extraction and also performs significantly better on smaller datasets compared to the present state of the art ML algorithms. To test the robustness of our model we have performed a binary (2-way) and ternary (3-way) classification using our model. It is found that the model is much more accurate than the present state of the art models and since it uses deep learning it also eliminates the need of feature extraction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.