A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

Lampier, Lucas; Coelho, Yves Luduvico; Caldeira, Eliete; Bastos, Teodiano

doi:10.17163/ings.n27.2022.09

Cited by 5 publications

(5 citation statements)

References 20 publications

(11 reference statements)

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“…Jarchi et al [ 35 ] used only 10 subjects from BIDMC to estimate RR from PPG signals relative to the accelerometer with an MAE of 2.56 bpm. Lampier et al [ 41 ] extracted respiratory-induced intensity variation, respiratory-induced amplitude variation, and respiratory-induced frequency variation signals from PPG. These signals were then fed to a deep neural network to estimate RR.…”

Section: Resultsmentioning

confidence: 99%

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Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal

et al. 2022

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Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.

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Section: Resultsmentioning

confidence: 99%

“…They used a very deep model with six layers for this task, which makes it non-suitable for portable devices. Lampier et al [ 41 ] used deep neural networks that include convolution and long short-term memory (LSTM) layers to estimate RR from PPG.…”

Section: Introductionmentioning

confidence: 99%

Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal

et al. 2022

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“…Although it is true that the current StMary dataset and its model showed significant results, classifying and verifying high-quality PPG signals in the StMary dataset will give fair PPG signals more suitable for training. The signal quality index (SQI) [13], which is often used for scoring signal characteristics, will be exploited to classify signal quality in our future work. Lastly, racial PPG differences also have to be considered.…”

Section: Discussionmentioning

confidence: 99%

“…The use of a deep neural network (DNN) is considered an approach to solve some recent clinical issues and studies have introduced several artificial intelligence (AI) techniques to extract meaningful information, such as RR and BP, from PPGs [10][11][12][13]. Generally, DNNs are well-known solvers of imaging problems and show sufficient performance in predicting problems using time-series data.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a New Photoplethysmography Dataset for Real-time Respiratory Rate Prediction using a Deep Neural Network

Hwang¹,

Kim²,

Hyun³

et al. 2023

Preprint

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Respiratory rate is an important biomarker that indicates changes in the clinical condition of critically ill patients, so a surveillance tool that can accurately monitor the changing respiratory rate in real time is needed. Through investigating various pairs of machine learning models, we proposed new machine learning model for real-time respiratory rate estimation using photoplethysmogram. New photoplethysmogram-driven respiratory rate dataset(StMary) was collected from surgical intensive care unit of a tertiary referral hospital, using photoplethysmogram signal collector. For 50patients and 50healthy volunteers, 2-minute photoplethysmogram was collected for each subject twice. To evaluate the respiratory rate of subject, it was inputted into the deep neural network model we built, and dataset was splitted into training, validation, testing dataset, then 4-fold cross validation was exploited. Our deep neural network model trained with StMary and two public datasets(BIDMC and CapnoBase) individually, or selectively merged dataset had shown a low error rate in respiration rate measurements. Our model trained with StMary showed low mean absolute error score(1.0273±0.8965), and trained with 3 datasets(CapnoBase, BIDMC and StMary) showed a lower error rate(1.7359±1.6724) than the model trained with CapnoBase and BIDMC(1.9480±1.6751). We could verify the performance of model evaluating respiratory rate from photoplethysmogram, and our dataset could contribute as the clinical research data that supports artificial intelligence models evaluating respiratory rate and surveillance tools to test whether their monitoring function works properly.

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“…The predominant optimisation of convolutional neural networks (CNNs) characterised algorithms aiming to comprehend signal patterns for RR estimation, with various archi-tectures explored for signal learning and RR estimation. In the current study, a distinctive approach has been taken by introducing a Long Short-Term Memory (LSTM) layer to the architectural configuration [29]. Notably, the preceding studies had yet to investigate the performance of the LSTM layer in this context.…”

Section: Introductionmentioning

confidence: 99%

A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model

Chin,

Kwan,

Lim

et al. 2024

Diagnostics

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Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.

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A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

Cited by 5 publications

References 20 publications

Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal

Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal

Evaluation of a New Photoplethysmography Dataset for Real-time Respiratory Rate Prediction using a Deep Neural Network

A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model

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