Now showing 1 - 4 of 4
  • Publication
    Investigating the Need for Pediatric-Specific Machine Learning Approaches for Seizure Detection in EEG
    (IEEE, 2023-04-23) ;
    Approximately 1 in every 150 children is diagnosed with epilepsy during the first ten years of life. These children ex- perience seizures, which disrupt their lives and directly harm the developing brain. EEG is a key tool for the non-invasive recording of brain activity and the diagnosis of epilepsy. However, the interpretation of EEGs requires time-consuming expert analysis. Automated seizure detection can help to reduce the time required to annotate EEGs. Research on seizure detection methods mainly focuses on adult EEG; automated seizure detection methods in paediatric EEG has been limited. Research has shown that brain events in EEG change with ageing. Therefore, adult-based seizure detection methods maybe not be suitable for children. In this study, we present a random forest-based seizure detection method developed using TUH adult EEG. 4,449 adult EEG recordings were used to train the method, and 490 adult EEG recordings were used to validate the method. An additional 509 TUH adult EEG and 192 TUH pediatric EEG were used for independent testing of the method. The CHB-MIT pediatric EEG Database (N=668) was used as an external independent test set. Ten channels were selected, and twenty-two features were estimated from each channel to develop the method. The random forest-based method achieved 69.3% balanced accuracy on the independent test set of TUH adult EEG and 70.9% on the independent test set of TUH pediatric EEG. However, balanced accuracy on the paediatric CHB-MIT independent test set was only 50.8%. Additionally, specificity was very low on both the TUH pediatric and CHB-MIT independent test sets (49.8% and 10.3% respectively). These result shows that the adult-based seizure detection method is unsuitable for children. There is a need to develop seizure detection methods specifically for paediatric EEG.
      18
  • Publication
    Automatic detection and characterization of seizures and sleep spindles in electroencephalograms using machine learning
    (University College Dublin. School of Computer Science, 2022) ;
    0000-0002-7236-3965
    Electroencephalography (EEG) is an electrophysiological monitoring method used to measure tiny electrical changes of the brain, and it is commonly used in research involving neural engineering, neuroscience, and biomedical engineering. EEG is widely used to assist clinicians and researchers in analysing brain events, such as emotion recognition, sleep events identification, seizure detection, and Alzheimer’s classification. In this thesis, I describe the methods that I have developed for the detection of seizures and sleep spindle events in EEG recordings. Sixty-five million people worldwide suffer from epilepsy, and epilepsy-related disability, death, comorbidities, stigma and costs are the major burdens of epilepsy. Epilepsy is characterised by unpredictable seizures and can cause other health problems. In order to study disease development, understand disease mechanisms and evaluate the effects of anticonvulsant drugs and experimental treatments, EEG monitoring is also commonly used in rodent disease models of epilepsy. Increasingly, the field is moving toward identifying disease-modifying actions of drugs necessitating long-term recordings of EEG in rodents such as mice. Sleep spindles are significant transient oscillations in sleep stage N2; their developmental changes may be related to the maturation of thalamic cortical structures and are of considerable significance to the study of brain development in infants. However, manually identifying these brain events in EEG recordings is very time-consuming and typically requires highly trained experts. Automatic brain events detection would greatly facilitate this analysis. Research to date on automatic brain events detection methods in EEG data has been limited. The methods that I have developed have the potential to be beneficial in both experimental and clinical settings, greatly improving the speed, reliability and reproducibility of seizure and sleep spindles analysis in EEG data. Moreover, these methods were implemented as webservers that are made available for free academic use. This will assist researchers and clinicians in the automated analysis of seizures and sleep spindle events in EEG recordings.
      381
  • Publication
    XGboost-based Method for Seizure Detection in Mouse Models of Epilepsy
    Epilepsy is a chronic neurological disease which affects over 50 million people worldwide [1], caused by the disruption of the finely tuned inhibitory and excitatory balance in brain networks, manifesting clinically as seizures. Electroencephalographic (EEG) monitoring in rodent disease models of epilepsy is critical in the understanding of disease mechanisms and the development of anti-seizure drugs. However, the visual annotation of EEG traces is time-consuming, and is complicated by different models and seizure types. Automated annotation systems can help to solve these problems by reducing expert annotation time and increasing the throughput and reliability of seizure quantification. As machine learning is becoming increasingly popular for modelling sequential signals such as EEG, several researchers have tried machine learning to detect seizures in EEG traces from mouse models of epilepsy. Most existing work [2], [3] can only detect seizures in single mouse models of epilepsy and research on multiple mouse models has been limited to-date.
      79Scopus© Citations 1
  • Publication
    Epileptic Seizure Detection in Clinical EEGs Using an XGboost-based Method
    (IEEE, 2020-12-05) ;
    Epilepsy is one of the most common serious disorders of the brain, affecting about 50 million people worldwide. Electroencephalography (EEG) is an electrophysiological monitoring method which is used to measure tiny electrical changes of the brain, and it is frequently used to diagnose epilepsy. However, the visual annotation of EEG traces is time-consuming and typically requires experienced experts. Therefore, automatic seizure detection can help to reduce the time required to annotate EEGs. Automatic detection of seizures in clinical EEGs has been limited-to date. In this study, we present an XGBoost-based method to detect seizures in EEGs from the TUH-EEG Corpus. 4,597 EEG files were used to train the method, 1,013 EEGs were used as a validation set, and 1,026 EEG files were used to test the method. Sixty-four features were selected as the input to the training set, and Synthetic Minority Over-sampling Technique was used to balance the dataset. Our XGBoost-based method achieved sensitivity and false alarm/24 hours of 20.00% and 15.59, respectively, in the test set. The proposed XGBoost-based method has the potential to help researchers automatically analyse seizures in clinical EEG recordings.
      146Scopus© Citations 13