Pilot study of a single-channel EEG seizure detection algorithm using machine learning

Ryu, Seungjun; Back, Seunghyeok; Lee, Seongju; Seo, Hyeon; Park, Chanki; Lee, Kyoobin; Kim, Dong-Seok

doi:10.1007/s00381-020-05011-9

Cited by 6 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although several research studies have included ML approaches for detecting neonatal seizures, our model showed a better performance than those described in most other studies. We compared our research with relevant studies that includes ML algorithms, for instance [ 9 , 10 , 11 , 12 , 13 ]. These are indicated with (✓).…”

Section: Resultsmentioning

confidence: 99%

“…The CNN model has been proven to be a robust tool for extracting the necessary features. A new seizure detection algorithm was proposed by Seungjun Ryu et al [ 12 ], which uses the principal component analysis (PCA) to extract features and compare them with other ML algorithms. Four prediction models were used, which included LR (logistic regression), dense trees, 2D-SVM (support vector machine), and cos-KNN (cosine k-nearest neighbor).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates

Mumenin

Biswas

Khan

et al. 2023

Sensors

View full text Add to dashboard Cite

Electroencephalography (EEG) is increasingly being used in pediatric neurology and provides opportunities to diagnose various brain illnesses more accurately and precisely. It is thought to be one of the most effective tools for identifying newborn seizures, especially in Neonatal Intensive Care Units (NICUs). However, EEG interpretation is time-consuming and requires specialists with extensive training. It can be challenging and time-consuming to distinguish between seizures since they might have a wide range of clinical characteristics and etiologies. Technological advancements such as the Machine Learning (ML) approach for the rapid and automated diagnosis of newborn seizures have increased in recent years. This work proposes a novel optimized ML framework to eradicate the constraints of conventional seizure detection techniques. Moreover, we modified a novel meta-heuristic optimization algorithm (MHOA), named Aquila Optimization (AO), to develop an optimized model to make our proposed framework more efficient and robust. To conduct a comparison-based study, we also examined the performance of our optimized model with that of other classifiers, including the Decision Tree (DT), Random Forest (RF), and Gradient Boosting Classifier (GBC). This framework was validated on a public dataset of Helsinki University Hospital, where EEG signals were collected from 79 neonates. Our proposed model acquired encouraging results showing a 93.38% Accuracy Score, 93.9% Area Under the Curve (AUC), 92.72% F1 score, 65.17% Kappa, 93.38% sensitivity, and 77.52% specificity. Thus, it outperforms most of the present shallow ML architectures by showing improvements in accuracy and AUC scores. We believe that these results indicate a major advance in the detection of newborn seizures, which will benefit the medical community by increasing the reliability of the detection process.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates

Mumenin

Biswas

Khan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Then, this 5-s EEG signal was used for feature extraction. Different signal fragments have been used in other research studies such as 0.1 s 30 , 2 s 31 , 32 , 4 s 33 , 5 s 23 , 34 , and 60 s 25 .…”

Section: Methodsmentioning

confidence: 99%

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Islam,

Basak

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

show abstract

“…Then, this 5s EEG signal was used for transform analysis to create an image. Different signal fragments have been used in other research studies such as 0.1s [ 28 ], 2s [ 29 , 30 ], 4s [ 31 ], 5s [ 4 , 32 ], and 60s [ 1 ].…”

Section: Methodsmentioning

confidence: 99%

Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models

Islam,

Basak,

Islam

et al. 2023

Heliyon

View full text Add to dashboard Cite

Pilot study of a single-channel EEG seizure detection algorithm using machine learning

Cited by 6 publications

References 9 publications

A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates

A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models

Contact Info

Product

Resources

About