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Record W4251704144 · doi:10.1109/iembs.2006.4398723

Analysis of Temporal Pattern of Swallowing Mechanism

2006· article· en· W4251704144 on OpenAlexaff
Mohammad Aboofazeli, Zahra Moussavi

Bibliographic record

VenueConference proceedings · 2006
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMechanism (biology)Computer scienceSwallowingMedicinePhysicsDentistry

Abstract

fetched live from OpenAlex

This paper presents an objective method for analysis of temporal pattern of swallowing mechanism based on analysis of swallowing sounds and submental surface electromyogram (EMG). In this study, swallowing sound signal and submental EMG of 12 healthy subjects were recorded. Swallowing sound signals were divided into 25 millisecond segments each of which was represented by waveform fractal dimension (WED). Temporal pattern of swallowing sound signal was identified based on hidden Markov modeling (HMM) of the WED sequences. Submental muscle contraction was marked by thresholding the RMS values of the EMG signals. Duration of the swallowing sound phases, duration of the submental muscle contraction, and time difference between the onset of submental muscle contraction and the opening of cricopharynx were calculated. Experimental results suggest that the proposed method is efficient in the study of temporal pattern of swallowing mechanism and can provide an objective and accurate approach for swallowing mechanism analysis

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.364
Teacher spread0.320 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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