Robust estimation of respiratory rate via ECG- and PPG-derived respiratory quality indices

Birrenkott, Drew A.; Pimentel, Marco A. F.; Watkinson, Peter; Clifton, David A.

doi:10.1109/embc.2016.7590792

Cited by 20 publications

(13 citation statements)

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“…It has been observed that this way of PPG segment selection based on an SQI significantly eliminates unreliable respiratory rate estimates which may result from invalid sensor data or highly corrupted signal by various motion interferences. A separately designed respiratory quality index (RQI), which is currently under further development [20], will be more effective to directly find those segments of PPG signals where reliable RR estimates could be produced.…”

Section: B Ppg-based Respiratory Rate Estimation 1) Signal Quality Imentioning

confidence: 99%

Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring

et al. 2018

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This paper evaluates the use of accelerometers for continuous monitoring of respiratory rate (RR), which is an important vital sign in post-intensive care patients or those inside the intensive care unit (ICU). The respiratory rate can be estimated from accelerometer and photoplethysmography (PPG) signals for patients following ICU discharge. Due to sensor faults, sensor detachment, and various artifacts arising from motion, RR estimates derived from accelerometry and PPG may not be sufficiently reliable for use with existing algorithms. This paper described a case study of 10 selected patients, for which fewer RR estimates have been obtained from PPG signals in comparison to those from accelerometry. We describe an algorithm for which we show a maximum mean absolute error between estimates derived from PPG and accelerometer of 2.56 breaths/min. Our results obtained using the 10 selected patients are highly promising for estimation of RR from accelerometers, where significant agreements have been observed with the PPG-based RR estimates in many segments and across various patients. We present this research as a step towards producing reliable RR monitoring systems using low-cost mobile accelerometers for monitoring patients inside the ICU or on the ward (post-ICU). Index Terms-Respiratory rate (RR), accelerometer, photoplethysmography (PPG), adaptive line enhancer (ALE), autoregressive (AR).

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Section: B Ppg-based Respiratory Rate Estimation 1) Signal Quality Imentioning

confidence: 99%

Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring

et al. 2018

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“…Several PPG-based RR algorithms have been reported in the literature with variable levels of accuracy, that is, mean error of approximately 1 to 6 breaths per minute (brpm), depending on which combination of signal processing methods is incorporated in the algorithm formulation [ 42 , 43 ]. Birrenkott et al demonstrated the importance of establishing proper signal qualification methods to achieve accurate RR estimations from PPG signal [ 44 ]. In this study, we present the validation of a PPG-based RR estimation algorithm that combines the frequency modulation and baseline wander methods along with threshold-based respiratory signal qualification.…”

Section: Introductionmentioning

confidence: 99%

Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder

Dur¹,

Rhoades²,

Ng³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundWearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient, and scalable way to collect personal health data remotely. The Wavelet Health platform and the Wavelet wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals, including resting heart rate (HR), heart rate variability (HRV), and respiration rate (RR).ObjectiveThis study aimed to evaluate the accuracy of the biometric estimates and signal quality of the wristband.MethodsMeasurements collected from 35 subjects using the Wavelet wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements.ResultsThe HR, HRV SD of normal-to-normal intervals, HRV root mean square of successive differences, and RR estimates matched within 0.7 beats per minute (SD 0.9), 7 milliseconds (SD 10), 11 milliseconds (SD 12), and 1 breaths per minute (SD 1) mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable with that obtained from measurements from a finger-clip plethysmography device.ConclusionsThe accuracy of the biometric estimates and high signal quality indicate that the wristband photoplethysmography device is suitable for performing pulse wave analysis and measuring vital signs.

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“…Such possible techniques include amplitude modulation, frequency modulation, baseline wander or variation, or bioimpedance. 23–25 While these methods work well under normal conditions, similar performance decrements are observed on the higher- and lower-ends of expected respiration. More research is planned to address the efficacy of respiration estimation through these means in simulated confined spaces.…”

Section: Wearable Devices and Data Management Architecturementioning

confidence: 73%

Transforming work through human sensing: a confined space monitoring application

Kiehl

Durkee

Halverson

et al. 2019

Structural Health Monitoring

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The use of objective data via sensors and subjective data via qualitative assessments from subject matter experts for the structural health monitoring of important assets is a long-standing and prudent practice. However, humans who build, maintain, or operate these systems are often not monitored with the same degree of importance. Although the reasons for this discrepancy may be numerous and sometimes well-intentioned (e.g. privacy), human monitoring systems are still relatively nascent when compared to their machine-based counterparts. Fortunately, the recent proliferation and miniaturization of wearable technology offers as an unprecedented opportunity in the realm of human monitoring systems. This article presents an example of how human sensing technology is transforming the nature of work in the aircraft maintenance domain by reducing operating costs and enhancing personnel safety. More specifically, this article describes a remote monitoring system for monitoring the health and safety of individuals working Occupational Safety and Health Administration–defined confined spaces, which are dangerous places to work for reasons such as small physical space or adverse atmospheric conditions. The described system consists of (1) wearable monitoring systems for assessing physiological data, postural and activity data, geographic location, and atmospheric data; (2) a body area network for transmitting data to a centralized, cloud-based server; (3) a customized alerting architecture for the generation and management of health and safety alerts; and (4) various user interfaces for depicting critical information to the appropriate parties. This article describes these components in detail along with a brief description of both the history of human monitoring and pitfalls that typically plague such initiatives. The specific initiative that supported the generation of this system is still under development; however, this article aims to provide a practical example of how a human monitoring system can augment safety and cost efficiency in a precarious environment.

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Robust estimation of respiratory rate via ECG- and PPG-derived respiratory quality indices

Cited by 20 publications

References 10 publications

Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring

Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring

Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder

Transforming work through human sensing: a confined space monitoring application

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