A Nonlinear Complexity Index for Wearable PPG Cardiovascular Stability: Multiscale Validation, Systematic Evaluation Correction, and Bayesian Parameter Optimization
A new Stability-Constrained Cardiovascular Stability Index (SCSI) has been developed to improve the estimation of cardiovascular stability from wearable photoplethysmography (PPG). The study validates SCSI across multiple datasets and identifies evaluation artifacts that can inflate performance metrics. The corrected protocol aims to provide a reproducible benchmark for future wearable cardiovascular indices.
- ▪The SCSI was validated across 176,742 segments from four heterogeneous PPG datasets at three temporal scales.
- ▪Cross-dataset analysis showed a significant effect size and strong consistency across scales.
- ▪The study identified three evaluation artifacts that inflated heuristic AUC metrics, which were corrected through Bayesian optimization.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18802 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
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Electrical Engineering and Systems Science > Signal Processing arXiv:2605.18802 (eess) [Submitted on 11 May 2026] Title:A Nonlinear Complexity Index for Wearable PPG Cardiovascular Stability: Multiscale Validation, Systematic Evaluation Correction, and Bayesian Parameter Optimization Authors:Timothy Oladunni, Farouk Ganiyu Adewumi View a PDF of the paper titled A Nonlinear Complexity Index for Wearable PPG Cardiovascular Stability: Multiscale Validation, Systematic Evaluation Correction, and Bayesian Parameter Optimization, by Timothy Oladunni and Farouk Ganiyu Adewumi View PDF HTML (experimental) Abstract:Cardiovascular stability estimation from wearable photoplethysmography (PPG) requires a principled nonlinear framework, yet major gaps persist in heuristic parameter selection and…
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