Evidence map›Paper›PMID 38803077›Full record

ArticleStatistics in medicine2024

Efficient matrix profile computation with Euclidean distance using Eigen transformation: Performance evaluation based on beat-to-beat interval (BBI) data.

James J Yang, Anne Buu

Abstract read
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In one paragraph

Article in Statistics in medicine, 2024. 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.

James J YangDepartment of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, Texas.ORCID 0000-0003-0331-9706
Anne BuuDepartment of Health Promotion and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, Texas.

Funding

Methodology and measurement for examining short-term and long-term effects of e-cigarette and polysubstance useR01DA049154 · NIDA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BUU, YUH-PEY ANNE, LIN, HSIEN-CHANG · 2019 to 2022
$2.6M
NIDA NIH HHS R01 DA049154NIH HHS R01 DA049154
6 · The paper itself

Abstract

The matrix profile serves as a fundamental tool to provide insights into similar patterns within time series. Existing matrix profile algorithms have been primarily developed for the normalized Euclidean distance, which may not be a proper distance measure in many settings. The methodology work of this paper was motivated by statistical analysis of beat-to-beat interval (BBI) data collected from smartwatches to monitor e-cigarette users' heart rate change patterns for which the original Euclidean distance (

Indexed as

AlgorithmsHeart RateData Interpretation, StatisticalElectronic Nicotine Delivery SystemsHumansModels, Statisticalbeat‐to‐beat intervalearly abandonEuclidean distancematrix profilesingular value decomposition

Identifiers

PMID38803077
PMCPMC11260257

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Registered trials

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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.