Objective: Photoplethysmography utilizes a green-light-emitting diode to transmit light into a tissue. Reflected light from hemoglobin in dermal capillary red blood cells is received by a photo detector and is analyzed as light intensity along a frequency spectrum. This method of analysis allows for the removal of "noise" above (stray light and alternating current [AC]) and below (room vibrations and respiratory motion) the peak signal (1 to 2 Hz) and results in a means to distinguish between perfused and nonperfused tissues.
Methods: Twenty-two of 30 consecutive radial forearm free flap (RFFF) patients were enrolled in an approved human studies protocol to collect descriptive data for RFFFs that were perfused, arterial occluded, and venous occluded. The protocol was performed following completion of flap elevation and prior to pedicle ligation, flap inset, and microvascular anastomoses. Six 90-second measurements per flap were obtained (n = 132), processed by fast Fourier transform (FFT), and analyzed by blinded reviewers to determine their state of perfusion. Signal was collected 5 minutes after the onset or release of individual vessel occlusion.
Results: The reviewers' interpretations were compared with the status of the pedicle and analyzed for sensitivity (0.96), specificity (0.95), and positive predictive value (0.98).
Conclusions: FFT analysis of photoplethysmograms from RFFF patients provides an accurate and rapid means for determining RFFF pedicle vessel patency. Photoplethysmography may provide a clinically useful tool for postoperative perfusion monitoring of free flaps in the future.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/00005537-199809000-00013 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Detecting anomalies in smart wearables for hypertension: a deep learning mechanism.
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFValidation of a Fingertip Home Sleep Apnea Testing System Using Deep Learning AI and a Temporal Event Localization Analysis.
Sleep
January 2025
Courant Institute of Mathematical Sciences, New York University, New York, 10012, USA.
Study Objectives: This paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilizes PPG (red, infrared, and green channels) and accelerometer sensors to assess sleep apnea by the AI system.
View Article and Find Full Text PDFImpact of a brief series of soccer matches on vascular conditions in youth women.
Front Physiol
November 2024
Department of Biomedical Engineering, Toyo University, Kawagoe, Japan.
Background: Accumulative excessive physical load elevates central arterial stiffness and smooth muscular tone of peripheral vascular beds in endurance athletes. The aim of this study was to test the hypothesis that a brief series of soccer matches would increase central arterial stiffness and arterial wave reflection from the periphery in young female football players.
Methods: Fifteen subjects (17.
Raw photoplethysmogram waveforms versus peak-to-peak intervals for machine learning detection of atrial fibrillation: Does waveform matter?
Comput Methods Programs Biomed
November 2024
Laboratory of Cardiac Physiology, Department of Biomedical Sciences, University of Copenhagen, Denmark; Department of Internal Medicine, Eifelklinik St. Brigida GmbH & CO KG., Simmerath, Germany.
Background: Machine learning-based analysis can accurately detect atrial fibrillation (AF) from photoplethysmograms (PPGs), however the computational requirements for analyzing raw PPG waveforms can be significant. The analysis of PPG-derived peak-to-peak intervals may offer a more feasible solution for smartphone deployment, provided the diagnostic utility is comparable.
Aims: To compare raw PPG waveforms and PPG-derived peak-to-peak intervals as input signals for machine learning detection of AF.
Reliability of a portable toe pressure photoplethysmography device compared to a stationary laser doppler flowmeter.
Vascular
December 2024
Department of vascular surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Objective: The study aimed to investigate the reliability of a portable toe pressure (TP) photoplethysmography device (PPG) by comparing it to a stationary laser Doppler flowmeter (LD) used in the Helsinki University Hospital. The study evaluated if lower limb arterial circulation could be reliably evaluated with the portable PPG which is more affordable and mobile than the stationary LD.
Methods: TPs were measured from 102 toes of 54 patients in the vascular surgery outpatient clinic, vascular surgery ward and interventional radiology recovery ward of Helsinki University Hospital.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!