Identification of Cerebral Artery Stenosis Using Bilateral Photoplethysmography

Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study

Background: Intracranial photoplethysmography (PPG) signals can be measured from extracranial sites using wearable sensors and may enable long-term non-invasive monitoring of intracranial pressure (ICP). However, it is still unknown if ICP changes can lead to waveform changes in intracranial PPG signals.

Aim: To investigate the effect of ICP changes on the waveform of intracranial PPG signals of different cerebral perfusion territories.

Methods: Based on lump-parameter Windkessel models, we developed a computational model consisting three interactive parts: cardiocerebral artery network, ICP model, and PPG model. We simulated ICP and PPG signals of three perfusion territories [anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA), all left side] in three ages (20, 40, and 60 years) and four intracranial capacitance conditions (normal, 20% decrease, 50% decrease, and 75% decrease). We calculated following PPG waveform features: maximum, minimum, mean, amplitude, min-to-max time, pulsatility index (PI), resistive index (RI), and max-to-mean ratio (MMR).

Results: The simulated mean ICPs in normal condition were in the normal range (8.87–11.35 mm Hg), with larger PPG fluctuations in older subject and ACA/PCA territories. When intracranial capacitance decreased, the mean ICP increased above normal threshold (>20 mm Hg), with significant decreases in maximum, minimum, and mean; a minor decrease in amplitude; and no consistent change in min-to-max time, PI, RI, or MMR (maximal relative difference less than 2%) for PPG signals of all perfusion territories. There were significant effects of age and territory on all waveform features except age on mean.

Conclusion: ICP values could significantly change the value-relevant (maximum, minimum, and amplitude) waveform features of PPG signals measured from different cerebral perfusion territories, with negligible effect on shape-relevant features (min-to-max time, PI, RI, and MMR). Age and measurement site could also significantly influence intracranial PPG waveform.

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Dysregulation of miR-637 serves as a diagnostic biomarker in patients with carotid artery stenosis and predicts the occurrence of the cerebral ischemic event

The present research aims to explore the relationship between circulating microRNA and carotid artery stenosis (CAS). To evaluate the diagnostic significance of miR-637 in CAS patients and its potential predictive value for cerebral ischemia events through clinical studies. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to verify the differences in serum miR-637 between enrolled 97 CAS patients and 90 healthy individuals. Logistic regression analysis of the correlation between the level of miR-637 and the degree of carotid artery stenosis. The receiver operating characteristic (ROC) curve evaluated the diagnostic significance of miR-637 in identifying CAS patients from healthy individuals. Kaplan-Meier survival and Cox regression were used to evaluate the potential predictive ability of serum miR-637 levels during follow-up for cerebral ischemia events. Serum miR-637 of CAS patients was significantly reduced which was a good indicator of severe carotid stenosis (P < 0.001). Reduced miR-637 can identify CAS patients from healthy individuals, demonstrating strong diagnostic capabilities. Furthermore, Kaplan-Meier analysis confirmed that the lower miR-637 levels in CAS, the more cerebral ischemia events (log-rank, P = 0.035), and the Multivariate Cox regressions confirmed that miR-637 was an independent predictor of CAS patients (HR = 0.073, 95%CI = 0.017–0.313, P < 0.001). We confirmed that serum miR-637 in CAS patients was significantly reduced. And reduced miR-637 was not only a potentially reliable biomarker for the diagnosis of CAS but also a useful indicator for predicting future cerebral ischemic events.