Evidence map›Paper›PMID 26516861›Full record

ArticleSensors (Basel, Switzerland)2015

Toward a Smartphone Application for Estimation of Pulse Transit Time.

He Liu, Kamen Ivanov, Yadong Wang, Lei Wang

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
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

12 citing papers in PubMed, 27 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Article
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

4 authors at 2 institutions in 1 country.

He LiuThe Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. he.liu.doc@gmail.com.
Kamen IvanovThe Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. kamen@siat.ac.cn.
Yadong WangThe Biomedical Engineering Department, Harbin Institute of Technology, Harbin150001, China. ydwang@hit.edu.cn.
Lei WangThe Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. wang.lei@siat.ac.cn.
Chinese Academy of Sciences · CNHarbin Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulse transit time (PTT) is an important physiological parameter that directly correlates with the elasticity and compliance of vascular walls and variations in blood pressure. This paper presents a PTT estimation method based on photoplethysmographic imaging (PPGi). The method utilizes two opposing cameras for simultaneous acquisition of PPGi waveform signals from the index fingertip and the forehead temple. An algorithm for the detection of maxima and minima in PPGi signals was developed, which includes technology for interpolation of the real positions of these points. We compared our PTT measurements with those obtained from the current methodological standards. Statistical results indicate that the PTT measured by our proposed method exhibits a good correlation with the established method. The proposed method is especially suitable for implementation in dual-camera-smartphones, which could facilitate PTT measurement among populations affected by cardiac complications.

Indexed as

SmartphoneAlgorithmsBlood Pressure DeterminationHumansPulse Wave Analysismicrocameraphotoplethysmographic imaging (PPGi)pulse transit time (PTT)signal processingsmartphone

Identifiers

PMID26516861
PMCPMC4634485
OpenAlexW1834258266

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.