Using a recurrent neural network to derive tidal volume from a photoplethsmograph

Prinable, Joseph; Jones, Peter W.; Thamrin, Cindy; McEwan, Alistair

doi:10.1109/lsc.2017.8268182

Cited by 5 publications

(9 citation statements)

References 9 publications

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“…It remains to be seen if the minimum correlation is bound between derived and reference respiratory waveforms for a given population. The findings of this paper show that our previous network parameter was much larger than required [ 11 ].…”

Section: Discussionmentioning

confidence: 88%

“…Currently, LSTM training requires GPU-grade computational power. With current low-power Bluetooth low energy devices [ 11 , 24 , 25 ], it may be possible to acquire PPG data and stream real-time data to a cloud-based GPU server to run online training. Once the weights and biases of the LSTM architecture are found, it may also be possible for an embedded platform to perform the required processing to obtain real-time breathing metric predictions.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the three time series given by the Alice PDx system, a bandpassed PPG time series was generated by passing the original PPG signal through a sixth order Butterworth bandpass filter with a center frequency corresponding to the respiratory rate of the signal with a bandwidth of 0.002 Hz. This additional time series was included because our previous findings suggested that this feature could improve model prediction [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

“…We previously determined experimentally that the inclusion of SPO 2 and pulse rate values helped inform the network when decoupling between the pulse signal and respiratory signal occurs [ 11 ]. The exact underlying physiological mechanisms are unclear.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we sought to address this using machine learning. In a previous pilot study [ 11 ], we demonstrated how a long short-term memory (LSTM) approach could predict a respiratory waveform from which BR could be derived. LSTM is a type of a recurrent neural network that can capture long-term, time-based dependencies in data [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Prinable¹,

Jones²,

Boland³

et al. 2020

JMIR Mhealth Uhealth

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Background There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility. Objective In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics. Methods A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison. Results Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84). Conclusions A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation.

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Section: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Prinable¹,

Jones²,

Boland³

et al. 2020

JMIR Mhealth Uhealth

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View full text Add to dashboard Cite

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Recent development of respiratory rate measurement technologies

et al. 2019

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Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.

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RespNet: A deep learning model for extraction of respiration from photoplethysmogram

Ravichandran

Murugesan

Balakarthikeyan

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Respiratory ailments afflict a wide range of people and manifests itself through conditions like asthma and sleep apnea. Continuous monitoring of chronic respiratory ailments is seldom used outside the intensive care ward due to the large size and cost of the monitoring system. While Electrocardiogram (ECG) based respiration extraction is a validated approach, its adoption is limited by access to a suitable continuous ECG monitor. Recently, due to the widespread adoption of wearable smartwatches with in-built Photoplethysmogram (PPG) sensor, it is being considered as a viable candidate for continuous and unobtrusive respiration monitoring. Research in this domain, however, has been predominantly focussed on estimating respiration rate from PPG. In this work, a novel end-to-end deep learning network called RespNet is proposed to perform the task of extracting the respiration signal from a given input PPG as opposed to extracting respiration rate. The proposed network was trained and tested on two different datasets utilizing different modalities of reference respiration signal recordings. Also, the similarity and performance of the proposed network against two conventional signal processing approaches for extracting respiration signal were studied. The proposed method was tested on two independent datasets with a Mean Squared Error of 0.262 and 0.145. The cross-correlation coefficient of the respective datasets were found to be 0.933 and 0.931. The reported errors and similarity was found to be better than conventional approaches. The proposed approach would aid clinicians to provide comprehensive evaluation of sleeprelated respiratory conditions and chronic respiratory ailments while being comfortable and inexpensive for the patient.

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Using a recurrent neural network to derive tidal volume from a photoplethsmograph

Cited by 5 publications

References 9 publications

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Recent development of respiratory rate measurement technologies

RespNet: A deep learning model for extraction of respiration from photoplethysmogram

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