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Record W3147907592 · doi:10.5772/22600

Pulse Wave Analysis

2011· book-chapter· en· W3147907592 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Biomedical Engineering · 2011
Typebook-chapter
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsMedicineCardiologyPulsatile flowPulse (music)Internal medicineArteryBlood flowCirculatory systemTunica intimaAdventitiaElastinBiomedical engineeringAnatomyPathologyCarotid arteriesEngineering

Abstract

fetched live from OpenAlex

Cardiovascular refers to the Cardio (heart) and vascular (blood vessels). The system has two major functional parts: central circulation system and systemic circulation system. Central circulation includes the pulmonary circulation and the heart from where the pulse wave is generated. Systemic circulation is the path that the blood goes from and to the heart. (Green 1984) Pulse wave is detected at arteries which include elastic arteries, medium muscular arteries, small arteries and arterioles. The typical muscular artery has three layers: tunica intima as inner layer, tunica media as middle layer, and tunica adventitia for the outer layer. (Kangasniemi & Opas 1997) The material properties of arteries are highly nonlinear. (langewouters et al. 1984) It depends on the contents of arterial wall: how collagen, elastin and protein are located in the arteries. Functional and structural changes in the arterial wall can be used as early marker for the hypertensive and cardiac diseases. Blood flow is the key to monitor the cardiovascular health condition since it is generated and restrict within such system. Currently the most widely used method for haemodynamic parameters detecting is invasive thermo-dilution method. Impedance-cardiography is the most commonly used non-invasive method nowadays; however, it is too complex for clinical routine check. Pulse wave analysis is an innovative method in the market to do fast and no burden testing (Zhang et al. 2008) Pulse is one of the most critical signals of human life. It comes directly from heart to the blood vessel system. As pulse transmitted, reflections will occur at different level of blood vessels. Other conditions such as resistance of blood flow, elastic of vessel wall, and blood viscosity have clear influence on pulse. Pathological changes affect pulse in different ways: the strength, reflection, and frequency. So pulse provides abundant and reliable information about cardiovascular system. Pulse can be recorded to a set of time series data and represented as a diagraph which is called pulse waveform or pulse wave for short. Gathering pulse at wrist by finger has been a major diagnosis method in China since 500 BC. Physicians used palpation of the pulse as a diagnostic tool during the examination. In 300AD, “Maijing” categoried pulse into 24 types and became the first systematic literature about the pulse. Grecian started to notice the rhythm, strength, and velocity at 400BC. Struthius described a method to watch the pulse wave by putting a leaf on the artery, which is considered as early stage of pulse wave monitoring. In 1860, Etienne Jules Mary invented a level based sphygmograph to measure the pulse rate. It is the first device can actually record the pulse wave. Frederick observed normal radial pressure wave and the carotid

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.193
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it