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Wei, Lan
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Wei, Lan
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Wei, Lan
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- PublicationAutomatic detection and characterization of seizures and sleep spindles in electroencephalograms using machine learningElectroencephalography (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.
87 - PublicationXGboost-based Method for Seizure Detection in Mouse Models of EpilepsyEpilepsy 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.
16 - PublicationRandom Forest-based Algorithm for Sleep Spindle Detection in Infant EEGSleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.
265Scopus© Citations 7 - PublicationEpileptic Seizure Detection in Clinical EEGs Using an XGboost-based MethodEpilepsy 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.
16Scopus© Citations 7