Dr. Chima Oliugbo Profile

Dr. Chima Oliugbo

at Childrens National Health System

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Publications (2)

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Proceedings Article | 3 April 2024 Poster + Paper

Self-supervised learning for seizure classification using ECoG spectrograms

Van Lam, Chima Oliugbo, Abhijeet Parida, Marius George Linguraru, Syed Muhammad Anwar

Proceedings Volume 12927, 129272J (2024) https://doi.org/10.1117/12.3007431

KEYWORDS: Data modeling, Electroencephalography, Image classification, Machine learning, Transformers, Deep learning

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Epileptic seizure detection using deep learning (DL) are often focused on data recorded using scalp electroencephalography (EEG), cases with limited data such as intracranial EEG are indeed unexplored. Self-supervised learning (SSL) can serve as a pretext task to learn useful features before performing classification task using relatively smaller datasets. We herein introduce an SSL strategy for vision transformers utilizing group masked image modeling applied to EEG spectrograms for classifying seizure and non-seizure activity. Particularly, we leverage EEG spectrogram representation, which is robust to noise, and incorporates time and frequency information. By inducing corruption into each input spectrogram, our model recovers the lost information and, in the process, learns features, which enables the acquisition of significant characteristics essential for downstream classification task. Our experimental results show that longer spectrogram window sizes yielded higher accuracy in detecting seizure activity. Specifically, average classification using fully supervised model across four channels demonstrated optimal performance on 90s EEG segments with an accuracy of 88.65%, followed by 45s (88.49%) and 30s (84.54%) spectrograms. We further compared seizure classification performance using vision transformers with pretraining using spectrograms (SSL-ECoG), without pretraining, and with a pretraining strategy using non-EEG data (ImageNet-1K). Notably, we show that the classification task following SSL-ECoG pre-training attained highest accuracy of 94.32%, AUC-PR of 0.966, 96.47 % F-1 score, and AUROC of 0.977. These results highlight the potential of using SSL and vision transformers with pertinent data can achieve highly accurate classification, even with limited data sizes.

Proceedings Article | 8 March 2019 Paper

Heuristic-based optimal path planning for neurosurgical tumor ablation

Ajeet Wankhede, Likhita Madiraju, Dipam Patel, Kevin Cleary, Chima Oluigbo, Reza Monfaredi

Proceedings Volume 10951, 109512H (2019) https://doi.org/10.1117/12.2512352

KEYWORDS: Tumors, Surgery, Brain, Skull, MATLAB, Magnetic resonance imaging, Neuroimaging, Detection and tracking algorithms, 3D modeling

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In brain tumor ablation procedures, imaging for path planning and tumor ablation are performed in two different sessions. Using pre-operative MR images, the neurosurgeon determines an optimal ablation path to maximize tumor ablation in a single path ablation while avoiding critical structures in the brain. After pre-operative path planning the patient undergoes brain surgery. Manual planning for brain tumor ablation is time-intensive. In addition, the preoperative images may not precisely match the intra-operative images due to brain shift after opening the skull. Surgeons sometimes therefore adjust the path planned during the surgery, which leads to increased anaesthesia and operation time. In this paper, a new heuristic-based search algorithm is introduced to find an optimal ablation path for brain tumors, that can be used both pre- and intra-operatively. The algorithm is intended to maximize the safe ablation region with a single path ablation. Given the tumor location, healthy tissue locations, and a random start point on the skull from medical images, our proposed algorithm computes all plausible entry points on the skull and then searches for different ablation paths that intersect with the tumor, avoids the critical structures, and finds the optimal path. We implemented Breadth First Search (BFS), Dijkstra, and our proposed heuristic based algorithms. In this paper we report the results of a comparative study for these methods in terms of the search space explored and required computation time to find an optimal ablation path.

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