Evidence map›Paper›PMID 37761281›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Automatic Heart Rate Detection during Sleep Using Tracheal Audio Recordings from Wireless Acoustic Sensor.

Julia Zofia Tomaszewska, Marcel Młyńczak, Apostolos Georgakis, Christos Chousidis, Magdalena Ładogórska, Wojciech Kukwa

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Towards a Compositional Framework for Describing Human Phenotypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    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

6 authors.

Julia Zofia TomaszewskaSchool of Computing and Engineering, University of West London, London W5 5RF, UK.ORCID 0000-0002-9387-4350
Marcel MłyńczakInstitute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland.ORCID 0000-0001-8549-7973
Apostolos GeorgakisSchool of Computing and Engineering, University of West London, London W5 5RF, UK.
Christos ChousidisDepartment of Music and Media, Institute of Sound Recording, University of Surrey, Guildford GU2 7XH, UK.ORCID 0000-0003-3762-8208
Magdalena ŁadogórskaInstitute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland.
Wojciech KukwaDepartment of Otorhinolaryngology, Faculty of Medicine and Dentistry, Medical University of Warsaw, 02-091 Warsaw, Poland.ORCID 0000-0002-2982-4412

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart rate is an essential diagnostic parameter indicating a patient's condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)-a device monitoring the electrical activity of the heart using several electrodes attached to a patient's upper body-or photoplethysmography (PPG).

methodsThe following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired.

resultsThe presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman's plots and Pearson's Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM.

conclusionsThe results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process.

Indexed as

digital filteringheart ratepolysomnographysleep disordered breathingtracheal audio

Identifiers

PMID37761281
PMCPMC10529205

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.