Evidence map›Paper›PMID 42420711›Full record

ReviewAdvances in experimental medicine and biology2026

Noninvasive Pulse Measurements for Cardiovascular Health Monitoring and Diagnoses.

Bingmei M Fu, Mohamed T Nur

Abstract readReview
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In one paragraph

Review in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Bingmei M FuDepartment of Biomedical Engineering, The City College of the City University of New York, New York, NY, USA. fu@ccny.cuny.edu.
Mohamed T NurDepartment of Biomedical Engineering, The City College of the City University of New York, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arterial pulses reflect the physiological and pathological conditions of the human body, especially cardiovascular conditions. Traditional Chinese Medicine (TCM) has used the pulse measurement at the radial artery for illness diagnoses over thousands of years although their technique by touch feelings for the pulses is too subjective. This chapter presents contemporary and more objective methods for pulse measurements and analyses. It first summarizes current noninvasive pulse measurement methods, including tonometry, photoplethysmography (PPG), ultrasound Doppler flowmetry and a variety of flexible pressure sensors. Then analyses for the collected pulse waveforms are described for extracting the characteristic parameters and how they are correlated to the cardiovascular conditions. The enhanced classification methods by AI/ML are also presented for efficiently analyzing the pulse waveform datasets obtained from healthy subjects and those with cardiovascular and other chronic diseases such as type 2 diabetes. Finally, mathematical models ranging from the lumped parameter model (0-dimensional, 0-D) to more complex 1-D and 3-D models are introduced to relate the pulse variables (e.g., pressure, velocity, displacement) to the arterial wall mechanical properties, blood density and viscosity, geometrical and structural distributions of artery trees in the cardiovascular system as well as cardiac outputs.

Indexed as

Cardiovascular DiseasesPulse Wave AnalysisHumansManometryMedicine, Chinese TraditionalModels, CardiovascularMonitoring, PhysiologicPhotoplethysmographyAI/MLCardiovascular diagnosesMathematical models for pulsesPPGPressure waveformsPulse analysisPulse measurements

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.