Lan attended the 2023 International Conference on Biomedical and Bioinformatics Engineering, which hold during November 9-12, 2023 in Ritsumeikan University, Kyoto, Japan.
Lan presented their work co-authored by Dr. Catherine and Dr. McHugh “Interictal Epileptiform Discharge Classification for the Prediction of Epilepsy Type in Children” and their work co-authored by Mercy, Dr Ommar, Prof. David and Dr. Catherine “Prediction of Epilepsy Phenotype in Intra-amygdala Kainic Acid Mouse Model of Epilepsy” . Being part of the live conference and engaging with fellow researchers in our field was an absolutely delightful experience!
Furthermore, Lan is thrilled to share that their presentation, “Interictal Epileptiform Discharge Classification for the Prediction of Epilepsy Type in Children”, received the Best Presentation award at ICBBE 2023. Immense thanks go out to all the co-authors for their incredible supports!
Epi-AI is a groundbreaking innovation poised to transform epilepsy research and treatment. It offers an automated, objective, and efficient approach to identifying seizures in animal models, accelerating drug discovery, and fostering global collaboration among researchers. Collaborative endeavours with esteemed institutions such as the Royal College of Surgeons in Ireland and the SFI FutureNeuro research center have aligned this project with national initiatives aimed at propelling epilepsy research into the future. Epi-AI, an innovative tool for seizure detection in mouse models of epilepsy, has had significant and far-reaching impacts on the field of epilepsy research and drug discovery:
Acceleration of Drug Discovery: Epi-AI has greatly accelerated the drug discovery process by reducing the time required for annotating seizures in mouse models. This acceleration expedites the translation of experimental findings into human trials. Additionally, it promotes global collaboration by providing a standardized methodology, enabling researchers worldwide to exchange data and insights seamlessly.
Industry Partnerships: Several companies have expressed interest in Epi-AI, with one prominent pharmaceutical company forming a partnership that goes beyond providing data and funding. This collaboration promises to enhance our understanding of epilepsy through extended analysis of mouse EEG data. It signifies Epi-AI’s essential contribution to research and therapeutic solutions. Moreover, it has fostered collaboration between academia and the industry, facilitating the practical implementation of machine learning models in real-world scenarios.
Global Collaboration: Epi-AI transcends geographical and institutional boundaries by offering a standardized and automated approach to seizure detection in mouse models. This innovation has facilitated global collaboration among researchers, giving them a common tool to exchange data, insights, and methodologies. This level of accessibility has energized a collective effort to address the intricate challenges of epilepsy, potentially accelerating progress toward effective treatments on a global scale. The tool’s international significance is evident in its widespread adoption, with a substantial number of page views and visits from individuals and institutions in numerous countries, underscoring its profound global impact.
This technology has significant industry implications, principally by accelerating vivo drug screening but also contributing to enhancing the understanding of epilepsy mechanisms and the potential for medical devices. It improves scientific rigour, comparability, and collaboration, ultimately advancing epilepsy research and treatment worldwide. This project has been shortlisted for the 2023 AI Awards in the Best Application of AI in a Student Project category.
In addition, Lan was super happy to meet her best friend in Sdyney! After the conference sessions concluded for the day, Iris and Lan embarked on a whirlwind exploration of Sydney. From the iconic Sydney Opera House to the picturesque Bondi Beach, we took in the sights and sounds of this vibrant city. As we strolled through the historic streets of The Rocks, indulged in delectable cuisine at local eateries, and shared stories under the starlit sky. It was amazing trip in Sydney!
Furthermore, Lan also saw the koala and kangaroo! They are sooooo cute!
As Lan bid farewell to Sydney, she carried back not just the knowledge gained from the conference but also a heart filled with gratitude for the experiences and connections that had enriched her journey. The trip reminded her that life is a tapestry woven from both professional accomplishments and cherished relationships, and it’s the combination of the two that makes the journey truly meaningful!
Lan presented her work co-authored by Dr. Catherine titled “Investigating the Need for Pediatric-Specific Machine Learning Approaches for Seizure Detection in EEG”. Attending the in-person conference and interacting with researchers who are working in a similar area is a delightful experience!
Lan presented a poster co-authored by Dr. Catherine titled “Investigating the Need for Pediatric-Specific Automatic Seizure Detection”. Approximately 1 in every 150 children is diagnosed with epilepsy during the first ten years of life. These children experience seizures, which disrupt their lives and directly harm the developing brain. Electroencephalography (EEG) is the main tool used clinically to diagnose seizures and epilepsy. However, the interpretation of EEGs requires time-consuming expert analysis. Automated detection systems are a powerful tool that can help address the issue by reducing expert annotation time.
Research on the automatic detection of seizures in pediatric EEG has been limited. Most seizure detection methods have been developed and tested using larger numbers of adult EEGs. However, research has shown that brain events in EEG change with ageing. Therefore, models trained on EEGs from adults may not be suitable for children. To test this hypothesis, we trained a seizure detection model on adult EEG and tested it on adult and pediatric EEG recordings. We find that a seizure detection model trained on adult EEGs is not suitable for children. Therefore, there is a need to develop a pediatric-specific method.
Today is Dr. Anna Markella Antoniadi’s last day in UCD. Huge thanks to Anna for being a remarkable member of the LiSDA research group! We have been lucky to have you as a PhD student and a post-doctoral researcher for the past five years. You are highly appreciated for being a wonderful researcher, colleague and friend! LiSDA will remember you with fond memories, and we wish you the best of luck for your new career in industry!
Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis. We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. The Deep-spindle system can reduce physicians’ workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.
Congratulations to Lan Wei who successfully passed her PhD Viva this morning.
Lan’s thesis title is “Automatic Detection and Characterization of Seizures and Sleep Spindles in Electroencephalograms Using Machine Learning”.
Many thanks to her external examiner, Prof. Lijuan Duan, and internal examiner, Dr Ruihai Dong. Many thanks also to Assoc. Prof. Brian Mac Namee for arranging and chairing the Viva via zoom, and to Lan’s co-supervisor, Prof. Madeleine Lowery, for her support and guidance of Lan over the last four years.Many thanks to Lan’s Research Studies Panel (Prof. Madeleine Lowery, Assoc. Prof. Gianluca Pollastri, and Dr Andrew Hines) for their advice during the past four years.
Finally, a big thank you to our collaborators, Soraia Ventura, Dr Gareth Morris, Prof. David C. Henshall, Dr Sean Mathieson, Prof. Geraldine B., Mary Anne Ryan, Halima Boutouil, Boylan Rogerio Gerbatin, Dr Cristina R Reschke, Dr Omar Mamad and Dr Mona Heiland for their assistance, patience, attention and all the advice throughout Lan’s PhD.
Epi-AI supports basic scientists to analyse EEG datasets by predicting if events are seizure events or not. Epi-AI supports single-channel EEG recordings in EDF, CSV, and PICKLE formats. If you upload an EEG in EDF format please choose which channel you want to analyse. If you upload a PICKLE or CSV file please choose which channel you want to analyse, the sampling frequency and the start time of your EEG recordings. Information about each detected seizure e.g. the number of seizures, start time, end time, duration, amplitude and corresponding spectrogram, will be available for download allowing for further analysis.
Spindle-AI allows the user to choose the sampling frequency of their data and submit a CSV file that contains a single-channel EEG signal. Spindle-AI will predict if the events are sleep spindle events or non-sleep spindle events. Spindle-AI then returns the start time, end time and the total number of the detected sleep spindle events.
Lan presented a full paper co-authored by Catherine, titled “Epileptic Seizure Detection in Clinical EEGs Using an XGboost-based Method”. 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.
Halima Boutouil presented a poster co-authored by L. Wei, R. Gerbatin, O. Mamad, H. Boutouil, C. Reschke, M. Lowery, D. Henshall, G. Morris and C. Mooney, titled “ XGboost-based Method for Seizure Detection in Mouse Models of Epilepsy ” at the 2020 IEEE Signal Processing in Medicine and Biology Symposium (IEEE-SPMB 2020), the conference of IEEE SPMB held virtually on December 5, 2020.
Epilepsy is a chronic neurological disease which affects over 50 million people worldwide. EEG monitoring in rodent disease models of epilepsy is critical in the understanding of disease mechanisms and drug development. However, the visual annotation of EEG traces is time-consuming, typically requires experienced experts and is subject to low inter-observer reproducibility. Therefore, automated seizure detection has been proposed recently to help to reduce the time required to annotate EEGs and improve reproducibility. Research on the automatic detection of seizures in mouse EEGs has been limited to date. In this study, we present a TKEO-based and an XGBoost-based method to detect seizures in EEGs from intra-amygdala kainic acid (IAKA) and Dravet syndrome (DS) mouse models of epilepsy. The TKEO-based method mimics how experts typically detect seizures in this type of mouse model of epilepsy and demonstrated 74.4% sensitivity and 97.5% specificity in the IAKA test set; and 25.0% sensitivity and 98.0% specificity in the DS test set. For the XGBoost-based method, one IAKA mouse and one DS mouse were used to train the method. 19 features were selected as the input and Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The sensitivity and specificity of the XGBoost-based method were 86.6% and 93.3% in the IAKA test set, and 98.5% and 98.8% in the DS test set. The proposed XGBoost-based method generalized well on two mouse models of epilepsy, and has the potential to assist researchers in the automated analysis of seizures in single-channel mouse EEG.
The 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society was offered virtually via the EMBS Learning Academy, July 20th – 24th, 2020.
Lan Wei presented a full paper entitled ‘Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG’. This paper co-authored by Dr. Catherine Mooney (UCD), Professor Madeleine Lowery (UCD), Soraia Ventura (UCC), Dr. Sean Mathieson(UCC), Professor Geraldine B. Boylan (UCC) as well as Mary Anne Ryan (UCC).
Sleep 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, which has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.
The conference was full of interesting talks and presentations, and it was my first time to attend a large virtual conference, which gave me a different experience.
The June Dublin Electrophysiology Research Clinic was held virtually on Fri 26th June, with a talk from Lan Wei from UCD entitled “An XGBoost-based Algorithm for Seizure Detection in Mouse Models of Epilepsy”.
Epilepsy is a chronic neurological disease which affects over 50 million people worldwide. EEG monitoring in rodent disease models of epilepsy is critical in the understanding of disease mechanisms and drug development. However, the visual annotation of EEG traces is time-consuming, typically requires highly trained experts and is subject to low inter-observer reproducibility. Therefore, automated seizure detection has been proposed recently to help to reduce the time required to annotate EEGs and improve reproducibility.
In this presentation, a TKEO-based and an XGBoost-based method were presented to detect seizures in EEGs from intra-amygdala kainic acid and Dravet syndrome mouse models of epilepsy, which has the potential to assist researchers in the automated analysis of seizures in single-channel mouse EEG.
It’s a great way to show the work to others and get valuable feedback. It’s also a great opportunity to gain online presentation experience in an informal environment.