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, Год журнала: 2024, Номер 98, С. 106759 - 106759

Опубликована: Авг. 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.

Язык: Английский

Evaluating the effectiveness of non-invasive intracranial pressure monitoring via near-infrared photoplethysmography using classical machine learning methods DOI Creative Commons
George R.E. Bradley, P. A. Kyriacou

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 96, С. 106517 - 106517

Опубликована: Июнь 14, 2024

Язык: Английский

Процитировано

4
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation From Wearables DOI Creative Commons
Pranay Jain, Cheng Ding, Cynthia Rudin

и другие.

Harvard data science review, Год журнала: 2024, Номер 6(3)

Опубликована: Июль 1, 2024

Smartwatches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate aspects of cardiovascular health. However, PPG signals collected from such susceptible to corruption noise motion artifacts, resulting in inaccuracies during estimation. Conventional denoising methods filter or reconstruct ways that eliminate morphological information, even the clean segments signal should ideally be preserved. In this work, we develop an algorithm reconstructs corrupted parts signal, while preserving signal. Our novel framework relies on self-supervised training, where leverage a large database train autoencoder. As show, our reconstructed provide better estimates than leading estimation methods. Further experiments show improvement variability (HRV) using algorithm. We conclude denoises way can improve downstream analysis health metrics devices.

Язык: Английский

Процитировано

4
A hybrid denoising approach for PPG signals utilizing variational mode decomposition and improved wavelet thresholding DOI

Qinghua Hu,

Min Li,

Linwen Jiang

и другие.

Technology and Health Care, Год журнала: 2024, Номер 32(4), С. 2793 - 2814

Опубликована: Март 19, 2024

BACKGROUND: Photoplethysmography (PPG) signals are sensitive to motion-induced interference, leading the emergence of motion artifacts (MA) and baseline drift, which significantly affect accuracy PPG measurements. OBJECTIVE: The objective our study is effectively eliminate drift high-frequency noise from signals, ensuring that signal’s critical frequency components remain within range 1 ∼ 10 Hz. METHODS: This paper introduces a novel hybrid denoising method for integrating Variational Mode Decomposition (VMD) with an improved wavelet threshold function. initially employs VMD decompose into set narrowband intrinsic mode function (IMF) components, removing low-frequency drift. Subsequently, thresholding algorithm applied noise, resulting in denoised signals. effectiveness was rigorously assessed through comprehensive validation process. It tested on real-world measurements, generated by Fluke ProSim™ 8 Vital Signs Simulator synthesized extended MIMIC-III waveform database. RESULTS: application let substantial 11.47% increase signal-to-noise ratio (SNR) impressive 26.75% reduction root mean square error (RMSE) compared soft Furthermore, SNR 15.54% reduced RMSE 37.43% CONCLUSION: proposes effective based function, capable simultaneously eliminating while faithfully preserving their morphological characteristics. advancement establishes foundation time-domain feature extraction model development domain signal analysis.

Язык: Английский

Процитировано

1
Improving the quality of pulse rate variability derived from wearable devices using adaptive, spectrum and nonlinear filtering DOI Creative Commons
Monika A. Prucnal, Adam G. Polak, Przemysław Kazienko

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 102, С. 107336 - 107336

Опубликована: Дек. 15, 2024

Язык: Английский

Процитировано

1
An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method DOI

Ravi Pal,

Ákos Rudas, Sung‐Soo Kim

и другие.

Computer Methods and Programs in Biomedicine, Год журнала: 2024, Номер 254, С. 108283 - 108283

Опубликована: Июнь 12, 2024

Язык: Английский

Процитировано

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, Год журнала: 2024, Номер 98, С. 106759 - 106759

Опубликована: Авг. 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.

Язык: Английский

Процитировано

0