Abstract
The advancement in technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) has enabled the development of a variety of healthcare applications and services such as Remote Health Monitoring (RHM). Developing an RHM system requires integrating various medical sensors that generate a vast amount of data. The integration of AI and Machine Learning (ML) models in the RHM system for analysis of the medical data can enable the development of a decision support system that can reduce costs and save valuable time for clinicians and caregivers. This thesis is focused on leveraging ML and AI technique sto address the challenges in RHM with a specific emphasis on improving the performance of AI-based decision support systems. This is achieved by addressing limitations in current AI-based models and developing novel Random Neural Network (RNN)-based models for fall detection and epilepsy detection and prediction.Initially the thesis presents a literature review on state-of-the-art machine learning techniques used in remote healthcare solutions, automatic fall activity recognition for elderly populations, and epileptic seizure detection and prediction.
Furthermore, the study presents a novel fall detection method based on RNN. The results obtained from the proposed model are compared with traditional classification algorithms such as Artificial Neural Networks, Convolutional Neural Networks, and state-of-the-art fall detection schemes. The proposed RNN-based method demonstrates significant improvements in accuracy compared to other methods. The proposed scheme achieved an overall accuracy of 98% in detecting falls. Additionally, several other parameters such as precision, recall, specificity, and F-measure show that the proposed algorithm has better generalization capabilities when compared with other traditional machine learning schemes. Furthermore, the proposed RNN is also compared with a recent scheme and the obtained results demonstrate the superiority of the proposed scheme.
Additionally, the thesis presents two novel RNN-based classification schemes for detecting epileptic seizures. In the first scheme statistical features are extracted from raw EEG data, while the second method incorporates frequency analysis techniques such as Discrete Wavelet Transform (DWT) and Fast Fourier Transform. The performance of these models is evaluated and compared with other classification algorithms, demonstrating the effectiveness of RNN-based models for epilepsy detection.
Finally, a novel RNN-based epilepsy prediction model is proposed for classification of EEG data into three classes i.e. ictal, pre-ictal, and inter-ictal. The model pre-processes the EEG data by decomposing the signals into five frequency sub-bands using DWT. After pre-processing, six statistical features are extracted from each frequency sub-band which a reused as an input vector to train the RNN model. The proposed scheme achieves an overall accuracy of 95.66% in seizure prediction, outperforming other state-of-the-art models.
| Date of Award | 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Hadi Larijani (Supervisor), Ryan Gibson (Supervisor) & Dimitrios Liarokapis (Supervisor) |