Whole brain epileptic seizure detection using unsupervised classification

Belhadj, Sabrina; Attia, Abdelouahab; Adnane, Ahmed; Ahmed‐Foitih, Zoubir; Taleb, A Ahmed

doi:10.1109/icmic.2016.7804256

Cited by 12 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wijayanto et al [203] claimed to achieve 99.7% accuracy using the Bonn University dataset with five IMFs, FD, and SVM in combination with EMD. Belhadj [204] used the EMD tool and the rapid potential-based hierarchical agglomerative (PHA) clustering technique. The Euclidian, Batacharay, and Kolmogorov distances between the IMFs were calculated and fed into the PHA cluster to achieve an accuracy of 98.84% over the CHB-MIT datasets.…”

Section: Deep Learning-based Classification Of Brain Disorders From Eegmentioning

confidence: 99%

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

View full text Add to dashboard Cite

EEG is a common and safe test that uses small electrodes to record electrical signals from the brain. It has a broad range of applications in medical diagnosis, including diagnosis of epileptic seizure, Alzheimer's, brain tumors, head injury, sleep disorders, stroke, and other seizure and neurological disorders. EEG can also be used to help diagnose death in people who are in a persistent coma. The use of digital signal processing and machine learning to improve EEG analysis for medical diagnosis has gained traction in recent years. This is because EEG visual analysis can be complex and time-consuming, as it mostly involves high dimensions and consists of large datasets. The development of novel sensors for EEG recording, digital signal processing algorithms, feature engineering, and detection algorithms increases the need for efficient diagnostic systems. An extensive review of the recent approaches for EEG preprocessing, extraction of features, and diagnosis of brain disorders is provided. In this paper, the main focus is to identify reliable algorithms for preprocessing, feature engineering, and classification of EEG, applied to medical healthcare and diagnosis, providing practitioners with insights into the most effective strategies, as well as potential future directions for improving accuracy of the automatic diagnostic systems. The study of reliable feature extraction and classification algorithms is crucial for a more accurate analysis of EEG signals. This paper can provide valuable information to researchers and practitioners working in the fields of EEG analysis and machine learning, as it provides a summary of recent developments and highlights key areas for future research. This paper can help researchers and clinicians to stay up-to-date on the latest developments in this field.

show abstract

Section: Deep Learning-based Classification Of Brain Disorders From Eegmentioning

confidence: 99%

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Study [9], processes the epileptic EEG records using unsupervised Kohonen's Self-Organizing Maps, although the authors divide the signal into only two classes (epileptic and non-epileptic segments). Only two classes are made, for example, in studies [10] and [11]. The study [12] used five unsupervised algorithms (among other things K-means and K-medoid) for the automatic classification of childhood epileptic's EEGs.…”

Section: Introductionmentioning

confidence: 99%

Automatic Eeg Classification Using Density Based Algorithms Dbscan and Denclue

2019

View full text Add to dashboard Cite

Electroencephalograph (EEG) is a commonly used method in neurological practice. Automatic classifiers (algorithms) highlight signal sections with interesting activity and assist an expert with record scoring. Algorithm K-means is one of the most commonly used methods for EEG inspection. In this paper, we propose/apply a method based on density-oriented algorithms DBSCAN and DENCLUE. DBSCAN and DENCLUE separate the nested clusters against K-means. All three algorithms were validated on a testing dataset and after that adapted for a real EEG records classification. 24 dimensions EEG feature space were classified into 5 classes (physiological, epileptic, EOG, electrode, and EMG artefact). Modified DBSCAN and DENCLUE create more than two homogeneous classes of the epileptic EEG data. The results offer an opportunity for the EEG scoring in clinical practice. The big advantage of the proposed algorithms is the high homogeneity of the epileptic class.

show abstract

“…For further processing, they chose rst 4 levels and last, i.e. Other similar methods are explained in [58], [59], [60]. [61].…”

Section: Wavelet Domain (Timefrequency)mentioning

confidence: 99%

Epileptic seizures and sleep states detection using hybrid approaches

Paul¹

View full text Add to dashboard Cite

As the author of the doctoral dissertation, I agree to public disclosure of my doctoral dissertation after obtaining a doctoral degree in the storage of ELTE Digital Institutional Repository. I authorize Adina Kulcsar, the administrator of the ELTE Doctoral School of Informatics to upload the dissertation and the abstract to ELTE Digital Institutional Repository, and I authorize the administrator to ll all the declarations that are required in this procedure. I request in case the doctoral dissertation contains qualied data pertaining to national security, to disclose the doctoral dissertation publicly to the University Library and the ELTE Digital Institutional Repository ensuing the lapse of the period of the qualication process. I request to defer public disclosure to the University Library and the ELTE Digital Institutional Repository, in case there is a publishing contract concluded during the doctoral procedure or up until the award of the degree. However, the bibliographical data of the work shall be accessible to the public. If the publication of the doctoral dissertation will not be carried out within a year from the award of the degree subject to the publishing contract, I agree to the public disclosure of the doctoral dissertation and abstract to the University Library and the ELTE Digital Institutional Repository. As the author of the doctoral dissertation, I declare that the doctoral dissertation and abstract uploaded to the ELTE Digital Institutional Repository are entirely the result of my own intellectual work and as far as I know, I did not infringe anyone's intellectual property rights. The printed version of the doctoral dissertation and the abstract are identical with the doctoral dissertation les (texts and diagrams) submitted on electronic device. As the author of the doctoral dissertation, I agree to the inspection of the dissertation and the abstract by uploading them to a plagiarism checker software. Signature of dissertation author Contents called central focal seizures. These seizures fall into two classications: focal seizures without loss of awareness. called basic simple partial seizures, these seizures don't cause a deciency of awareness. They may adjust feelings or change the manner in which things look, smell, feel, taste or sound. They may likewise bring about compulsory jolting of a body part, like an arm or leg, and unconstrained tangible indications like shivering, dazedness and blazing lights. Focal seizures with impaired awareness. called complex partial seizures, these seizures include a change or loss of cognizance or mindfulness. During an intricate incomplete seizure, you may gaze into space and not react ordinarily to your current circumstance or perform tedious developments, for example, hand scouring, biting, gulping or strolling around and around. Seizures that seem to include all regions of the brain are called generalized seizures. Six kinds of generalized seizures exist.

show abstract

Whole brain epileptic seizure detection using unsupervised classification

Cited by 12 publications

References 31 publications

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Automatic Eeg Classification Using Density Based Algorithms Dbscan and Denclue

Epileptic seizures and sleep states detection using hybrid approaches

Contact Info

Product

Resources

About