This study explores the extraction of remote Photoplethysmography (rPPG) signals from images using various neural network architectures, addressing the challenge of accurate signal estimation in biomedical contexts. The objective is to evaluate the effectiveness of different models in capturing rPPG signals from dataset snapshots. Two training strategies were investigated: pre-training models with only the fully connected layer being fine-tuned and training the entire network from scratch. The analysis reveals that models trained from scratch consistently outperform their pre-trained counterparts in extracting rPPG signals. Among the architectures assessed, DenseNet121 demonstrated superior performance, offering the most reliable results in this context. These findings underscore the potential of neural networks in advancing rPPG signal extraction, which has promising applications in fields such as clinical monitoring and personalized medical care. This study contributes to the integration of advanced imaging techniques and neural network-based analysis in biomedical engineering, paving the way for more robust and efficient methodologies.

Accurate spectral estimation from irregularly sampled signals is a long-standing challenge in signal processing, particularly in biomedical applications. Traditional methods, such as the Fast Fourier Transform (FFT), rely on resampling, which introduces artefacts and computational inefficiencies. To address this, we propose DySpect-Net (Dynamic Spectrum Network), a neural network-based model that directly predicts spectral representations from non-uniformly sampled data, eliminating the need for resampling. Our approach is validated using synthetic datasets that mimic real-world sampling irregularities. Unlike conventional spectral estimation methods, DySpect-Net learns to recognise and reconstruct dominant frequency components dynamically. We introduce FAVE (Frequency Absolute Variance Error), a novel evaluation metric designed to quantify spectral peak alignment accuracy, outperforming conventional metrics such as Mean Squared Error (MSE). Comparative analysis demonstrates that DySpect-Net performs superior spectral estimation, particularly in low-sampling-rate conditions, with fewer distortions than FFT-based approaches. The proposed model has broad applications in biomedical signal analysis, e.g. remote photoplethysmography (rPPG), and other domains where sampling irregularities compromise spectral fidelity. Future research will focus on integrating FAVE as a loss function to enhance spectral reconstruction accuracy.

This study delves into the theoretical underpinnings and practical applications of advanced imaging techniques, specifically focusing on the diagnosis and treatment of brain aneurysms and related vascular pathologies. Beginning with an exploration of the theory behind intracranial aneurysms, we discuss their prevalence, characteristics, and the demographic factors influencing their occurrence. The utilization of diverse diagnostic methods, including CT scans, MRIs, and angiograms, is elucidated for effective aneurysm detection. A critical aspect of this research involves the comprehensive examination of cerebral angiography, a pioneering technique providing detailed images of blood vessels in and around the brain. Originating in 1927, its evolution is traced, highlighting its therapeutic applications and pivotal role in contemporary diagnostic protocols. We explore its superiority over less invasive methods, its role in immediate treatment interventions, and its significance in confirming brain death in certain jurisdictions. The study culminates in an exploration of the historical context of cerebral angiography and its adaptation in the modern era of neuroimaging. While newer technologies have supplanted its role in certain diagnostic scenarios, cerebral angiography persists as an indispensable tool for evaluating various vascular pathologies within the skull. This comprehensive examination sheds light on the intricate interplay between theoretical understanding, diagnostic advance-ments, and therapeutic applications in the domain of neurovas-cular imaging.