Using the Lomb periodogram for non-contact estimation of respiration rates

Vasu, V.; Fox, Niall; Heneghan, Conor; Sezer, Sakir

doi:10.1109/iembs.2010.5626125

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…In using Lomb-Scargle Periodogram, the corrupted signals, which are often caused by intermittent movements or motion artefacts, can be excluded by treating those portions as missing data segments. Results have demonstrated that the Lomb-Scargle Periodogram has been successfully used on evenly sampled data, with periods of missing data, to achieve an average error of less than 0.4 breath per minute and standard deviation of 0.3 breath per minute for respiratory rate estimation [90].…”

Section: Numerical Analysismentioning

confidence: 99%

Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review

Tran

Al-Jumaily

Islam

2019

BDCC

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Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage.

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Section: Numerical Analysismentioning

confidence: 99%

Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review

Tran

Al-Jumaily

Islam

2019

BDCC

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“…Alternative less obtrusive solutions were proposed in the past. See [5][6][7][8][9] for examples making use of inductive coupling [5], capacitive sensors [6], motion detectors [7], Doppler radar [8] and high-frequency reflections [9]. See [10] as well for a commercial product performing RR measurements by analysing the airflow sound using a sticker attached to the neck.…”

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confidence: 99%

Respiration rate estimation from channel state information in wireless body area networks

Tsouri¹,

Maimon²

2014

Electron. lett.

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Respiration rate is an important measure for assessing a patient's health and is typically measured using obtrusive devices. A non-obtrusive method of estimating respiration rate by taking advantage of data packets being sent from sensor nodes in a wireless body area network is proposed and investigated. In contradistinction to other methods, the proposed method does not require dedicated hardware and does not impose overheads on transmission power and throughput. Instead, it makes use of data packets already being sent to support some other physiological monitoring applications such as cardiac, temperature or motion monitoring. The method is demonstrated on a single subject and results are presented from a study including 40 measurements taken from eight subjects. The mean estimation error in the study was 0.58 breaths per minute. The proposed method can be used to augment wireless body monitoring applications with important respiration rate measurements without imposing overheads on the compact sensor nodes.Introduction: A wireless body area network (WBAN) is composed of compact sensor nodes placed on the body primarily for the purpose of physiological monitoring. The sensor nodes communicate wirelessly with an access point (AP) typically placed on the body and forming a star-topology network with the sensor nodes. WBANs are used to monitor cardiac activity, movement and overall well-being. They are expected to play a key role in addressing overloaded healthcare systems and were recently standardised in the IEEE 802.15.6 standard [1].Respiration rate (RR) is widely recognised as an important measure of a patient's health that is not measured often enough [2,3]. Traditionally, RR is measured using a capnometer [4] comprising a bulky device for measuring the carbon dioxide levels from tubes connected to the nostrils of the patient. Alternative less obtrusive solutions were proposed in the past. See [5][6][7][8][9] for examples making use of inductive coupling [5], capacitive sensors [6], motion detectors [7], Doppler radar [8] and high-frequency reflections [9]. See [10] as well for a commercial product performing RR measurements by analysing the airflow sound using a sticker attached to the neck. All the methods reported in the past rely on dedicated hardware or infrastructure. In this Letter, we propose and investigate a method of estimating RR using the existing wireless data transmission in a WBAN. The method is non-obtrusive, does not require dedicated hardware and does not impose overheads on system resources. We are unaware of a past attempt of RR estimation using a sensor node's existing data transmission.If a WBAN is already placed on a patient for monitoring purposes other than RR, the sensor nodes are already sending data packets to the AP. These packets are attenuated by the wireless channel thereby changing the received signal strength at the AP. While the patient is breathing, the positioning of the sensor nodes and their corresponding antennas change slightly in a periodic fashion following the bre...

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“…Doppler sensor modalities, including radio frequency transmitters/receivers [83][84][85][86][87][88] and ultrasound sensors [63,89,90], are placed with a line of sight to the subject so that reflected waves capture bodily motion. Audiovisual systems use video [91][92][93], or microphones [74].…”

Section: Sensor Modalitymentioning

confidence: 99%

Robust Ambient Multisensor Signal Fusion Towards Clinical Data Analytics

Holtzman¹

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As ambient systems proliferate, there is an increased need for data-level fusion methods that reflect the challenges of unknown environments, non-deterministic signal behaviours, and movement artifact. Cushioning between the sensor and bed occupant decreases signal to noise power and signal availability, while non-linear signals can masquerade as delayed and reversed signals. The main contributions of this thesis are to study how these challenges affect extraction of a breathing signal from bed-based sensors and to propose more robust fusion techniques.New trend analysis methods effectively corrected polarity reversals, increasing the number of good quality signals by 9% and reducing mean respiratory rate error by 24%. To fuse these signals, selection combining, weighted summation, and blind source separation methods were innovated and compared. None performed best all of the time; some were generally good with some weaknesses, while others had specialized strengths. Contextual ensemble fusion selected the best fusion method in degraded conditions in 55% of records, compared to 36% for the top individual fusion method, providing clinical applications with more reliable data. While sleep medicine is an important application, ambient monitoring is also suited to cognitive medicine and palliative care. Developed methods were applied to monitor patients in palliative care, marking the first long-term, continuous monitoring of this population. Breathing patterns observed in the last weeks of life included Cheyne-Stokes respiration and tachypnea, while breathing variability was associated with survival time.ii

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Using the Lomb periodogram for non-contact estimation of respiration rates

Cited by 7 publications

References 5 publications

Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review

Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review

Respiration rate estimation from channel state information in wireless body area networks

Robust Ambient Multisensor Signal Fusion Towards Clinical Data Analytics

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