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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
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Posts
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Blog Post number 1
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portfolio
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publications
Interpretable Classification of Early Stage Parkinson’s Disease from EEG
Published in AAAI Health Intelligence Workshop (pending publication in Computation Intelligence Springer Book Series), 2024
Detecting Parkinson’s Disease in its early stages using EEG data presents a significant challenge. This paper introduces a novel approach, representing EEG data as a 15-variate series of bandpower and peak frequency values/coefficients. The hypothesis is that this representation captures essential information from the noisy EEG signal, improving disease detection. Statistical features extracted from this representation are utilised as input for interpretable machine learning models, specifically Decision Tree and AdaBoost classifiers. Our classification pipeline is deployed within our proposed framework which enables high-importance data types and brain regions for classification to be identified. Interestingly, our analysis reveals that while there is no significant regional importance, the N1 sleep data type exhibits statistically significant predictive power (p < 0.01) for early-stage Parkinson’s Disease classification. AdaBoost classifiers trained on the N1 data type consistently outperform baseline models, achieving over 80% accuracy and recall. Our classification pipeline statistically significantly outperforms baseline models indicating that the model has acquired useful information. Paired with the interpretability (ability to view feature importance’s) of our pipeline this enables us to generate meaningful insights into the classification of early stage Parkinson’s with our N1 models. In Future, these models could be deployed in the real world - the results presented in this paper indicate that more than 3 in 4 early-stage Parkinson’s cases would be captured with our pipeline.
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Investigating Brain Connectivity and Regional Statistics from EEG for early stage Parkinson’s Classification
Published in IEEE International Workshop on Machine Learning for Signal Processing, 2024
We evaluate the effectiveness of combining brain connectivity metrics with signal statistics for early stage Parkinson’s Disease (PD) classification using electroencephalogram data (EEG). The data is from 5 arousal states - wakeful and four sleep stages (N1, N2, N3 and REM). Our pipeline uses an Ada Boost model for classification on a challenging early stage PD classification task with with only 30 participants (11 PD , 19 Healthy Control). Evaluating 9 brain connectivity metrics we find the best connectivity metric to be different for each arousal state with Phase Lag Index achieving the highest individual classification accuracy of 86% on N1 data. Further to this our pipeline using regional signal statistics achieves an accuracy of 78%, using brain connectivity only achieves an accuracy of 86% whereas combining the two achieves a best accuracy of 91%. This best performance is achieved on N1 data using Phase Lag Index (PLI) combined with statistics derived from the frequency characteristics of the EEG signal. This model also achieves a recall of 80% and precision of 96%. Furthermore we find that on data from each arousal state, combining PLI with regional signal statistics improves classification accuracy versus using signal statistics or brain connectivity alone. Thus we conclude that combining brain connectivity statistics with regional EEG statistics is optimal for classifier performance on early stage Parkinson’s. Additionally, we find outperformance of N1 EEG for classification of Parkinson’s and expect this could be due to disrupted N1 sleep in PD. This should be explored in future work.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Introduction to Machine Learning
Postgraduate course, University of Bristol, Computer Science Department, 2022
Teaching Assistant for MSc course covering Machine Learning methods across supervised learning (classification and regression), probabilistic methods (Bayesian networks and Markov decision processes) as well as genetic algorithms and multi-agent systems. Key responsibilities included running problem sheet classes and assessing final projects. Please find further information on the course here
Advanced Data Analytics
Postgraduate course, University of Bristol, Computer Science Department, 2023
Teaching assistant for postgraduate Advanced Data Analytics course. This course spans machine learning methods, data visualisation techniques and Natural Language Processing (NLP) methods. Machine learning methods covered range from dimensionality reduction and clustering to probablistic models and deep-learning neural networks. Data visualisation is primarily done via Python and Tableau. NLP methods covered include Long Short Term Memory networks, Recurrent Neural Networks and Transformers. Further information on the course can be found here
Machine Learning
Undergraduate course, University of Bristol, Computer Science Department, 2024
Teaching Assistant for final year undergraduate course covering Machine Learning methods across supervised learning (classification and regression), probabilistic methods and ensemble methods. Key responsibilities included running problem sheet classes and assessing final projects. Please find further information on the course here