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

Chowdhury, Moajjem Hossain; Shuzan, Md. Nazmul Islam; Chowdhury, Muhammad E. H.; Reaz, Mamun Bin Ibne; Mahmud, Sakib; Emadi, Nasser Al; Ayari, Mohamed Arselene; Ali, Sawal Hamid Md; Bakar, Ahmad Ashrif A; Rahman, Syed Mahfuzur; Khandakar, Amith

doi:10.3390/bioengineering9100558

Cited by 15 publications

(5 citation statements)

References 51 publications

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“…The performance of TransRR was evaluated in comparison with five popular DL-based RR prediction models known as of January 2023 ( Table 5 ). Among them, RespNet [ 18 ], ResNet [ 35 ], and ConvMixer [ 22 ] models are based on CNN, while BiLSTM [ 23 ] and BiLSTM + ATT [ 24 ] models are based on RNN. In BiLSTM models [ 23 ], the input was the features of PPG and ECG signals, while the input for the other models was the raw ECG or PPG signals (additional attributes of these models are compared in Table 5 ).…”

Section: Resultsmentioning

confidence: 99%

“…Leveraging a ResNet block, Bian et al [ 21 ] trained another CNN-based (convolutional-neural-network-based) model on both synthetic and real data, yielding a mean absolute error (MAE) of 2.5. A lightweight model was introduced by Chowdhury et al [ 22 ] employing a ConvMixter architecture to predict RR from PPG signals. This is also a CNN-based model and can be deployed on mobile devices for real-time monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model

Zhao,

Liu,

Song

et al. 2023

Bioengineering

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The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model

Zhao,

Liu,

Song

et al. 2023

Bioengineering

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“…The study described by [ 10 ] used the neural network to fetch RR from ECG and PPG. A ConvMixer network was proposed by [ 31 ] to fetch RR from PPG. A CNN was proposed in [ 12 ] to estimate RR from the PPG signal.…”

Section: Related Workmentioning

confidence: 99%

A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation

Rathore

Vijayarangan

Preejith

et al. 2023

Sensors

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Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model’s parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices.

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“…All blocks in their network were composed of 1-D convolution blocks, leading to a reduction in the network size. Chowdhury et al [ 20 ] also proposed a lightweight deep learning network for RR prediction. They added a projection layer at the front of the network to reduce the size of the input, followed by a residual module with depth-wise separable convolution blocks for a lightweight structure [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks

Yang,

Kang,

Charlton

et al. 2024

Sensors

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Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model—spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

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

Cited by 15 publications

References 51 publications

Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model

Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model

A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks

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