Breathing Patterns

Tobin, Martin J.; Chadha, Tejvir S.; Jenouri, Gilbert; Birch, Stephen; Gazeroglu, Hacik; Sackner, Marvin A.

doi:10.1016/s0012-3692(15)33498-x

Cited by 80 publications

(24 citation statements)

References 9 publications

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“…[25] using Optoelectronic plethysmography with 83 healthy adult subjects, report that Ti/Ttot did not change with posture or sex which confirms our finding. Furthermore, actual values reported for expected Ti/Ttot in healthy adults are broadly similar to ours, with Tobin [22] and Parreira [25] reporting an average Ti/Ttot of approximately 0.42±0.03 and 0.39±0.04 respecitvely. Wilkens et.…”

Section: Respiratory Rate (Rr)supporting

confidence: 85%

Reference equations for tidal breathing parameters using structured light plethysmography

Motamedi-Fakhr¹,

Iles

Barker

et al. 2021

ERJ Open Res

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Tidal breathing measurements can be used to identify changes in respiratory status. Structured light plethysmography (SLP) is a non-contact tidal breathing measurement technique. Lack of reference equations for SLP parameters makes clinical decision-making difficult. We have developed a set of growth adjusted reference equations for seven clinically pertinent parameters of respiratory rate (RR), inspiratory time (Ti), expiratory time (Te), duty cycle (Ti/Total-breath-time), phase (thoraco-abdominal asynchrony or TAA), relative thoracic contribution (RTC) and IE50 (tidal inspiratory/expiratory flow at 50% volume).Reference equations were developed based on a cohort of 198 seated healthy subjects (age 2–75 years, height 82 cm–194 cm, 108 Male). We adopted the same methodological approach as the global lung function initiative (GLI) report on spirometric reference equations [1]. Five minutes of tidal breathing was recorded per subject. Parameters were summarised with their medians. The online supplement provided is an integral part of this work and a reference range calculator is also provided therein.We found predicted RR to decrease with age and height rapidly in the first 20 years and slowly thereafter. Expected Ti, Te and RTC followed the opposite trend. RTC was 6.7% higher in females. Duty cycle increased with age, peaked at 13 and decreased after. TAA was high and variable in early life and declined rapidly with age. Predicted IE50 was constant as it did not correlate with growth.These reference ranges for seven key measures ensure clinicians and researchers can identify tidal breathing patterns in disease and better understand and interpret SLP and tidal breathing data.

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Section: Respiratory Rate (Rr)supporting

confidence: 85%

Reference equations for tidal breathing parameters using structured light plethysmography

Motamedi-Fakhr¹,

Iles

Barker

et al. 2021

ERJ Open Res

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“…RR threshold values have been defined in different ranges depending on the acquisition method and age of a patient [29] and are considered as an indicator of various symptoms, such as cardiac arrest, coughing, decreased alertness, poor feeding, grunting, and fever [30–32]. The mean observed RR was 14.2 (±4.17 precision (SD)) breaths per minute for adults [29], which was less than the mean RR 15.1 (±4.05 SD) breaths per minute measured by respiratory inductive plethysmograph [32]. In this study, the normal RR for monitoring elderly and adult subjects (between the ages of 20 and 50 years) at rest was from 12 breaths per minute to 16 breaths per minute.…”

Section: Methodsmentioning

confidence: 99%

Design of a Remote Real-Time Monitoring System for Multiple Physiological Parameters Based on Smartphone

Al-Naggar

Al-Hammadi

Al-Fusail

et al. 2019

Journal of Healthcare Engineering

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Background Utilization of the widely used wearable sensor and smartphone technology for remote monitoring represents a healthcare breakthrough. This study aims to design a remote real-time monitoring system for multiple physiological parameters (electrocardiogram, heart rate, respiratory rate, blood oxygen saturation, and temperature) based on smartphones, considering high performance, autoalarm generation, warning transmission, and security through more than one method. Methods Data on monitoring parameters were acquired by the integrated circuits of wearable sensors and collected by an Arduino Mega 250 R3. The collected data were transmitted via a Wi-Fi interface to a smartphone. A patient application was developed to analyze, process, and display the data in numerical and graphical forms. The abnormality threshold values of parameters were identified and analyzed to generate an autoalarm in the system and transmitted with data to a doctor application via a third-generation (3G) mobile network and Wi-Fi. The performance of the proposed system was verified and evaluated. The proposed system was designed to meet main (sensing, processing, displaying, real-time transmission, autoalarm generation, and threshold value identification) and auxiliary requirements (compatibility, comfort, low power consumption and cost, small size, and suitability for ambulatory applications). Results System performance is reliable, with a sufficient average accuracy measurement (99.26%). The system demonstrates an average time delay of 14 s in transmitting data to a doctor application via Wi-Fi compared with an average time of 68 s via a 3G mobile network. The proposed system achieves low power consumption against time (4 h 21 m 30 s) and the main and auxiliary requirements for remotely monitoring multiple parameters simultaneously with secure data. Conclusions The proposed system can offer economic benefits for remotely monitoring patients living alone or in rural areas, thereby improving medical services, if manufactured in large quantities.

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“…In practice, this requires a few rounds of iterative refinement, and the overall algorithm is detailed below. To aid the interpretation of the filtering operations below, note that the 'normal' breathing rate in adults-from both subjects resting in a supine position and undergoing MRI scanning-is approximately 0.25 Hz [Tobin et al 1983;.…”

Section: Practical Considerations and Algorithmic Approachmentioning

confidence: 99%

A Hilbert-based method for processing respiratory timeseries

Harrison

Bianchi

Heinzle

et al. 2020

Preprint

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In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation will be publicly available as part of the PhysIO toolbox, which is distributed as part of the open-source TAPAS package (translationalneuromodeling.org/tapas).

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Breathing Patterns

Cited by 80 publications

References 9 publications

Reference equations for tidal breathing parameters using structured light plethysmography

Reference equations for tidal breathing parameters using structured light plethysmography

Design of a Remote Real-Time Monitoring System for Multiple Physiological Parameters Based on Smartphone

A Hilbert-based method for processing respiratory timeseries

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