12 citations · 12 across the 11 of their papers we have counts for
15 papers
Context-Augmented, Session-Calibrated Blood Pressure Tracking from Photoplethysmography
Farouk Ganiyu Adewumi, Timothy Oladunni
Objective.To distinguish the contributions of acquisition context and photoplethysmography (PPG) to session-calibrated blood pressure (BP) tracking.Approach. The original compariso…
Representation Bias, Correction Transfer, and Resolution Sensitivity in Three-Dimensional Mitochondrial Morphometry
Farouk Ganiyu Adewumi, Timothy Oladunni
Quantitative imaging pipelines can produce precise but systematically different measurements of the same object. We present an empirical reliability assessment of three-dimensional…
Complementary Feature Domains: Information Preservation Does Not Imply Predictive-Contribution Preservation
Timothy Oladunni, Farouk Ganiyu-Adewumi
Complementary Feature Domains (CFD) theory characterizes predictive value as a context-indexed contribution system induced jointly by representations and their realization family.…
Property-Specific Recoverability from Contact PPG to Camera rPPG under Heterogeneous Observation Conditions
Timothy Oladunni, Farouk Ganiyu-Adewumi
Camera-derived remote photoplethysmography (rPPG) is commonly validated through endpoint accuracy, but endpoint performance does not establish whether other physiological propertie…
CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals
Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire +3
Cardiovascular AI models can classify clean elec- trocardiogram (ECG) signals, but real wearable signals change because of motion, breathing, posture, sensor contact, and true clin…
Attractor Domain Theory: A Mathematical Framework for Cardiovascular Attractor Analysis with Wearable Photoplethysmography (PPG) Validation
Timothy Oladunni, Farouk Ganiyu Adewumi
The cardiac attractor provides a phase-space representation of cardiovascular dynamics reconstructed from scalar observations under standard delay-embedding conditions. We introduc…
Attractor-Vascular Coupling Theory: Formal Grounding and Empirical Validation for AAMI-Standard Cuffless Blood Pressure Estimation from Smartphone Photoplethysmography
Timothy Oladunni, Farouk Ganiyu Adewumi
We present Attractor-Vascular Coupling Theory (AVCT), a theory-guided framework for deriving interpretable blood pressure (BP) predictors from photoplethysmography (PPG) attractor…
A Nonlinear Complexity Index for Wearable PPG Cardiovascular Stability: Multiscale Validation, Systematic Evaluation Correction, and Bayesian Parameter Optimization
Timothy Oladunni, Farouk Ganiyu Adewumi
Cardiovascular stability estimation from wearable photoplethysmography (PPG) requires a principled nonlinear framework, yet major gaps persist in heuristic parameter selection and…
Cardiac Stability Theory: An Axiomatically Grounded Framework for Continuous Cardiac Health Monitoring via Smartphone Photoplethysmography
Timothy Oladunni, Farouk Ganiyu Adewumi
We present Cardiac Stability Theory (CST), an axiomatically grounded framework formally defining cardiovascular health as a stability margin around a cardiac dynamical attractor. F…
Complementarity-Preserving Generative Theory for Multimodal ECG Synthesis: A Quantum-Inspired Approach
Timothy Oladunni, Farouk Ganiyu-Adewumi, Clyde Baidoo +1
Multimodal deep learning has substantially improved electrocardiogram (ECG) classification by jointly leveraging time, frequency, and time-frequency representations. However, exist…
When Should a Model Change Its Mind? An Energy-Based Theory and Regularizer for Concept Drift in Electrocardiogram (ECG) Signals
Timothy Oladunni, Blessing Ojeme, Kyndal Maclin +1
Models operating on dynamic physiologic signals must distinguish benign, label-preserving variability from true concept change. Existing concept-drift frameworks are largely distri…
Explainable Deep Neural Network for Multimodal ECG Signals: Intermediate vs Late Fusion
Timothy Oladunni, Ehimen Aneni
The limitations of unimodal deep learning models, particularly their tendency to overfit and limited generalizability, have renewed interest in multimodal fusion strategies. Multim…
Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification
Timothy Oladunni, Alex Wong
This study proposes a novel perspective on multimodal deep learning for biomedical signal classification, systematically analyzing how complementary feature domains impact model pe…
Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers
Staphord Bengesi, Hoda El-Sayed, Md Kamruzzaman Sarker +3
The launch of ChatGPT has garnered global attention, marking a significant milestone in the field of Generative Artificial Intelligence. While Generative AI has been in effect for…
Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)
Md Amimul Ehsan, Amir Shahirinia, Nian Zhang +1
Climate change is one of the most concerning issues of this century. Emission from electric power generation is a crucial factor that drives the concern to the next level. Renewabl…