Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations

Dehkordi, Parastoo; Garde, Ainara; Molavi, Behnam; Ansermino, J. Mark; Dumont, Guy A.

doi:10.3389/fphys.2018.00948

Cited by 54 publications

(39 citation statements)

References 25 publications

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“…Another approach to reduce variability would be to assess the quality of the PPG signal and exclude windows with poor quality [ 10 , 15 ], though this would mean loss of information during, e.g., noisy periods. Nevertheless, the bias and standard deviation presented in this work fall in line with our previous findings [ 8 ] and perform better in terms of bias and 95% LoAs for respiratory rate in other studies employing larger datasets [ 10 , 16 , 17 , 18 , 19 ]. The high variability may limit applicability in the clinical setting, but the performance may be adequate for general long-term monitoring of breathing rate for day-to-day use.…”

Section: Discussionsupporting

confidence: 92%

Derivation of Respiratory Metrics in Health and Asthma

Prinable

Jones

Boland

et al. 2020

Sensors

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The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all p < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates.

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Section: Discussionsupporting

confidence: 92%

Derivation of Respiratory Metrics in Health and Asthma

Prinable

Jones

Boland

et al. 2020

Sensors

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“…Respiratory-induced variation in the photoplethysmography of pulse oximetry permits measurement of breathing frequency. 40 A bioacoustic monitor of air flow can reliably monitor breathing frequency. 41 Chest wall movement can be measured with plethysmography technology (ie, elastomeric, impedance, or inductive).…”

Section: Capnography and Other Measures Of Ventilationmentioning

confidence: 99%

Respiratory Monitoring in General Care Units

Lamberti

2020

Respir Care

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Respiratory compromise is a common and potentially dangerous complication of patients admitted to general care units of hospitals. There are several distinct and disparate pathophysiologic trajectories of respiratory deterioration that hospitalized patients may suffer. Obstructive sleep apnea and preexisting cardiopulmonary disease increase the risk of respiratory failure after major surgery. Patients in general care units of hospitals currently receive only intermittent monitoring of vital signs. Early warning systems that utilize analysis of intermittently collected vital signs may result in earlier recognition of clinical deterioration. Continuous monitoring of oximetry and capnography may allow the detection of pathophysiologic abnormalities earlier in patients in general care units, but the evidence for improved clinical outcomes remains weak. Increased monitoring may lead to increased monitor alarms that can have negative effects on patient care.

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“…Hence, most approaches are applying a highpass or band-pass filter to remove the low-frequency components and to limit the pulsatile signal in a constant boundary envelope. This filtering, however, prevents the option to analyze these frequency components which are associated with activity in the autonomic nervous system and particularly respiration [10,22,25]. The frequency spectrum is split up into four bands.…”

Section: Frequency Spectral Ratiomentioning

confidence: 99%

“…The very low frequency VLF band (0.0 to 0.167 Hz) predominantly contains random baseline wandering. The low LF band (0.167 to 0.667 Hz respective 10 to 40 bpm) mainly contains respiratory signals, but is overlapping with the intermediate IF band (0.5 to 3.0 Hz respective 30 to 180 bpm) which mainly contains the heartbeat signal [10,12]. The high frequency HF band (>3.0 Hz) is associated with noise, but can also contain higher harmonics of the heartbeat.…”

Section: Frequency Spectral Ratiomentioning

confidence: 99%

The quest for raw signals

Wolling

Laerhoven

2020

Proceedings of the Third Workshop on Data: Acquisition to Analysis

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Photoplethysmography is an optical measurement principle which is present in most modern wearable devices such as fitness trackers and smartwatches. As the analysis of physiological signals requires reliable but energy-efficient algorithms, suitable datasets are essential for their development, evaluation, and benchmark. A broad variety of clinical datasets is available with recordings from medical pulse oximeters which traditionally apply transmission mode photoplethysmography at the fingertip or earlobe. However, only few publicly available datasets utilize recent reflective mode sensors which are typically worn at the wrist and whose signals show different characteristics. Moreover, the recordings are often advertised as raw, but then turn out to be preprocessed and filtered while the applied parameters are not stated. In this way, the heart rate and its variability can be extracted, but interesting secondary information from the non-stationary signal is often lost. Consequently, the test of novel signal processing approaches for wearable devices usually implies the gathering of own or the use of inappropriate data. In this paper, we present a multi-varied method to analyze the suitability and applicability of presumably raw photoplethysmography signals. We present an analytical tool which applies 7 decision metrics to characterize 10 publicly available datasets with a focus on less or ideally unfiltered, raw signals. Besides the review, we finally provide a guideline for future datasets, to suit to and to be applicable in digital signal processing, to support the development and evaluation of algorithms for resource-limited wearable devices. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile devices; • Hardware → Digital signal processing; • Applied computing → Health informatics.

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Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations

Cited by 54 publications

References 25 publications

Derivation of Respiratory Metrics in Health and Asthma

Derivation of Respiratory Metrics in Health and Asthma

Respiratory Monitoring in General Care Units

The quest for raw signals

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