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

Tomaszewska, Julia Z., Młyńczak, M., Georgakis, Apostolos, Chousidis, Christos ORCID:, Ładogórska, M. and Kukwa, W. (2022) Automatic Heart Rate Detection during Sleep Using Tracheal Audio Recordings from Wireless Acoustic Sensor. MDPI Diagnostics.

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Background: Heart 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). Methods: The 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. Results:
The 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. Conclusions: The 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.

Item Type: Article
Identifier: 10.3390/diagnostics13182914
Additional Information: Open Access journal
Subjects: Computing
Depositing User: Marc Forster
Date Deposited: 16 Mar 2024 08:30
Last Modified: 16 Mar 2024 08:30


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