Exploring the dynamic relationship: Changes in photoplethysmography features corresponding to intracranial pressure variations DOI Creative Commons
George R.E. Bradley, P. A. Kyriacou

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 98, P. 106759 - 106759

Published: Aug. 23, 2024

This study investigates the relationship between photoplethysmography (PPG) signals and intracranial pressure (ICP) through two primary hypotheses. Firstly, it examines whether alterations in PPG-derived features correspond to changes ICP levels. Secondly, explores these are more pronounced derived from "cerebral" long-distance near-infrared (NIR) PPG data compared "extracerebral" short-distance NIR-PPG data. A clinical dataset comprising synchronised measurements a non-invasive sensor an intra-parenchymal, invasive probe across 27 patients was compiled. From this dataset, distinct datasets were derived, short Within each 141 extracted for every one-minute window of data, including original, first derivative, second derivative features. Correlation analysis using Spearman's correlation non-parametric Kruskal–Wallis test range values conducted evaluate The results support both hypotheses, showing significant correlations Specifically, 77.30% 79.43% significantly correlated (p<0.05) with label distal proximal datasets, respectively. revealed that 81.56% 75.89% changed groups 0–10, 10–20, 20–39 mmHg. yielded meaningfully higher absolute average coefficient all in-comparison 25.76% 24.24% These findings indicate reflective variations ICP.

Language: Английский

Jump motion intention recognition and brain activity analysis based on EEG signals and Vision Transformer model DOI
Yanzheng Lu, Hong Wang, Jianye Niu

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 107001 - 107001

Published: Oct. 11, 2024

Language: Английский

Citations

4
Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms DOI Creative Commons
Gustavo Frigieri,

Sérgio Brasil,

Danilo Cardim

et al.

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 26, 2025

Abstract Noninvasive methods for intracranial pressure (ICP) monitoring have emerged, but none has successfully replaced invasive techniques. This observational study developed and tested a machine learning (ML) model to estimate ICP using waveforms from cranial extensometer device (brain4care [B4C] System). The explored multiple waveform parameters optimize mean estimation. Data 112 neurocritical patients with acute brain injuries were used, 92 randomly assigned training testing, 20 reserved independent validation. ML achieved absolute error of 3.00 mmHg, 95% confidence interval within ±7.5 mmHg. Approximately 72% estimates the validation sample 0-4 mmHg values. proof-of-concept demonstrates that noninvasive estimation via B4C System is feasible. Prospective studies are needed validate model’s clinical utility across diverse settings.

Language: Английский

Citations

0
A comprehensive survey of imaging-based methods of measuring intracranial pressure DOI Creative Commons
AZM Ehtesham Chowdhury, Graham J. Mann, William H. Morgan

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107854 - 107854

Published: March 20, 2025

Language: Английский

Citations

0
Exploring the dynamic relationship: Changes in photoplethysmography features corresponding to intracranial pressure variations DOI Creative Commons
George R.E. Bradley, P. A. Kyriacou

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 98, P. 106759 - 106759

Published: Aug. 23, 2024

This study investigates the relationship between photoplethysmography (PPG) signals and intracranial pressure (ICP) through two primary hypotheses. Firstly, it examines whether alterations in PPG-derived features correspond to changes ICP levels. Secondly, explores these are more pronounced derived from "cerebral" long-distance near-infrared (NIR) PPG data compared "extracerebral" short-distance NIR-PPG data. A clinical dataset comprising synchronised measurements a non-invasive sensor an intra-parenchymal, invasive probe across 27 patients was compiled. From this dataset, distinct datasets were derived, short Within each 141 extracted for every one-minute window of data, including original, first derivative, second derivative features. Correlation analysis using Spearman's correlation non-parametric Kruskal–Wallis test range values conducted evaluate The results support both hypotheses, showing significant correlations Specifically, 77.30% 79.43% significantly correlated (p<0.05) with label distal proximal datasets, respectively. revealed that 81.56% 75.89% changed groups 0–10, 10–20, 20–39 mmHg. yielded meaningfully higher absolute average coefficient all in-comparison 25.76% 24.24% These findings indicate reflective variations ICP.

Language: Английский

Citations

0