Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis.
By Oliver Faust, Usha R. Acharya, Hojjat Adeli, Amir Adeli. Seizure, 2015
517 citations recorded by the source corpus.
Electroencephalography (EEG) is an important tool for studying the human brain activity and epileptic processes in particular. EEG signals provide important information about epileptogenic networks that must be analyzed and understood before the initiation of therapeutic procedures. Very small variations in EEG signals depict a definite type of brain abnormality. The challenge is to design and develop signal processing algorithms which extract this subtle information and use it for diagnosis, monitoring and treatment of patients with epilepsy. This paper presents a review of wavelet techniques for computer-aided seizure detection and epilepsy diagnosis with an emphasis on research reported during the past decade. A multiparadigm approach based on the integration of wavelets, nonlinear dynamics and chaos theory, and neural networks advanced by Adeli and associates is the most effective method for automated EEG-based diagnosis of epilepsy.
Subfields: Cognitive Neuroscience, Signal Processing
View Paper: https://www.semanticscholar.org/paper/94eb2a39f55c2e4956c7a243a44fa9465c077d0d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Apiwat Lek-uthai
- Poomipat Boonyakitanont
- Jitkomut Songsiri
- Krisnachai Chomtho
- Alexandre Campeau-Lecours
- U. Côté-Allard
- Clément M. Gosselin
- Alexandre Drouin
In Signal Processing
- Apiwat Lek-uthai
- Poomipat Boonyakitanont
- François Laviolette
- Jitkomut Songsiri
- Oliver Faust
- Krisnachai Chomtho
- Yuki Hagiwara
Citations in the corpus
- A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “A review mainly focusing on using the wavelet-based method for computer-aided seizure detection was described in [FAAA15].”
- Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “CWT features have been employed for electrocardiogram analysis [19], electroencephalography [20] and EMG signal analysis, but mainly for lower limbs [21], [22].”
- Deep learning for healthcare applications based on physiological signals: A review (2018, methodology): “The design process is based on the idea of having an offline and online system, as shown in Figure 1 [21, 22].”