ArticleJournal of healthcare engineering2018
A Novel Sleep Respiratory Rate Detection Method for Obstructive Sleep Apnea Based on Characteristic Moment Waveform.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 54 citations in OpenAlex.
- Respiratory Monitoring in Motion: An Overview of Wearable Methods and Algorithmic Approaches for Reliable Assessment.Biosensors · 2026Review
- For a clinical application of optical triangulation to assess respiratory rate using an RGB camera and a line laser.BMC medical imaging · 2024Article
- Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies.Biosensors · 2024Review
- Effects of low-pressure Valsalva maneuver on changes in cerebral arterial stiffness and pulse wave velocity.PloS one · 2024Article
- Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only.IEEE open journal of engineering in medicine and biology · 2024Article
- Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone.Biosensors · 2023Article
- Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare.Journal of healthcare engineering · 2023Article
- Respiratory Motion and Airflow Estimation During Sleep Using Tracheal Movement and Sound.Nature and science of sleep · 2022Article
- A Single-Center Validation of the Accuracy of a Photoplethysmography-Based Smartwatch for Screening Obstructive Sleep Apnea.Nature and science of sleep · 2021Article
- Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning.Sensors (Basel, Switzerland) · 2020Article
- Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obstructive sleep apnea (OSA) affecting human's health is a kind of major breathing-related sleep disorders and sometimes leads to nocturnal death. Respiratory rate (RR) of a sleep breathing sound signal is an important human vital sign for OSA monitoring during whole-night sleeping. A novel sleep respiratory rate detection with high computational speed based on characteristic moment waveform (CMW) method is proposed in this paper. A portable and wearable sound device is used to acquire the breathing sound signal. And the amplitude contrast decreasing has been done first. Then, the CMW is extracted with suitable time scale parameters, and the sleep RR value is calculated by the extreme points of CMW. Experiments of one OSA case and five healthy cases are tested to validate the efficiency of the proposed sleep RR detection method. According to manual counting, sleep RR can be detected accurately by the proposed method. In addition, the apnea sections can be detected by the sleep RR values with a given threshold, and the time duration of the segmentation of the breath can be calculated for detailed evaluation of the state of OSA. The proposed method is meaningful for continued research on the sleep breathing sound signal.
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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.