8 papers
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 cardiovascular system evolves along a bounded trajectory in physiological state space that converges to a compact geometric object: the cardiac attractor. A wearable photopleth…
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…