<i>i</i>-PRExT: Photoplethysmography Derived Respiration Signal Extraction and Respiratory Rate Tracking Using Neural Networks

Roy, Biplab; Roy, Arka; Chandra, Jayanta K.; Gupta, Rajarshi

doi:10.1109/tim.2020.3043506

Cited by 16 publications

(3 citation statements)

References 30 publications

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“…Figure 1 shows the pre-processing, signal analysis and post-processing steps of the proposed respiratory rate estimation algorithm. Wavelet transforms [25,26] Requires the selection of more than one parameter such as the mother wavelet function and the total number of decomposition levels Smart fusion [20] Adaptive estimations Adaptive respiratory rate estimators [23,27] Very sensitive to noise and results in very poor respiratory rate estimation if there are any motion artefacts in the signal Empirical mode decomposition (EMD) [28,29] Analytical methods Autoregression [30,31] Often requires a relatively long time to converge and give an accurate estimation of respiratory rate Artificial neural networks [32] Principal component analysis (PCA) [33] Complex demodulation [34], Independent component analysis (ICA) [35] 1 3…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…S1 to Fig. S9 shows the Bland-Altman plot of all the subjects using a window size of 10,20,30,32,45,60,64, 120 and best window in seconds. According to the United States Food and Drug Administration (FDA), repeated measurements through a device must lie within the allowed 3Ϭ (± 3* standard deviation) range to be classified as a Class II medical device [52].…”

Section: Appendixmentioning

confidence: 99%

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Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications

et al. 2022

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Purpose Respiratory rate can provide auxiliary information on the physiological changes within the human body, such as physical and emotional stress. In a clinical setup, the abnormal respiratory rate can be indicative of the deterioration of the patient's condition. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method. Methods This paper proposes a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. It combines selective windowing, preprocessing and signal conditioning, modified Welch filtering and postprocessing to achieve high accuracy and robustness to noise. Results The Mean Absolute Error and the Root Mean Square Error of the proposed algorithm, with the optimal signal window size, are determined to be 2.05 breaths count per minute and 2.47 breaths count per minute, respectively, when tested on a publicly available dataset. These results present a significant improvement in accuracy over previously reported methods. The proposed algorithm achieved comparable results to the existing algorithms in the literature on the BIDMC dataset (containing data of 53 subjects, each recorded for 8 min) for other signal window sizes. Conclusion The results endorse that integration of the proposed algorithm to a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting.

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Section: Proposed Algorithmmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications

et al. 2022

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“…Wang et al [23] evidenced that deep learning methods usually outperform FFT and wavelet based processing approaches; exploiting a fiber optic based sensor 2 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < and deep learning models, they found that 95.64% of BR values are within the 95% confidence interval (reference instrument: ventilator). Roy et al [24] exploited a multilayer perceptron neural network to extract BR from PPG signals, achieving a correlation of 90% and a normalized root mean squared error (NMRSE) of approximately 0.2. Moreover, vision-based sensors can be exploited to estimate BR; radar systems have been successfully used (e.g., Zhai et al [25] reported an accuracy >93%), as well as depth cameras [26] and visible and infrared sensors [27].…”

Section: Introductionmentioning

confidence: 99%

Indirect Estimation of Breathing Rate Using Wearable Devices

Cosoli,

Antognoli,

Panni

et al. 2024

IEEE Trans. Instrum. Meas.

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Wearable sensors can be exploited for the indirect estimation of physiological parameters, such as Breathing Rate (BR). Indeed, BR is a significative quantity for both general health status monitoring and diagnostic purposes; however, standard methods for its assessment are often uncomfortable and mainly used for punctual (or brief, anyway) measurements. This paper aims to perform an uncertainty analysis of BR indirect estimation made starting from electrocardiographic signals gathered through wearable sensors, namely a cardiac belt (Zephyr BioHarness 3.0) and a smartwatch (Samsung Galaxy Watch3). Three different estimation methods were employed, considering respiratory sinus arrhythmia (RSA), signal amplitude modulation (AM), and Machine Learning (ML)-based techniques. Finally, the Monte Carlo simulation method was exploited for the measurement uncertainty estimation, including both sensors (hardware) and algorithms (software) contributions in the measurement chain. The results show that both the considered sensors are quite accurate (almost null bias) and precise (±[3-5] bpm, depending on the estimation method) in the estimation of BR with the three different estimation algorithms. A slightly higher precision is obtained for the cardiac belt (a reduced 95% confidence interval is reported, with a maximum reduction of 4 bpm depending on the estimation algorithm), whose results are also more strongly correlated to the reference ones (Pearson's correlation coefficient ≥0.75 in all the three methods). The Monte Carlo simulation evidenced that the ML-based method is the most robust with respect to the sensors uncertainty (with no differences in the output uncertainty with respect to the sensors uncertainty in input); moreover, the higher precision of the cardiac belt with respect to the smartwatch was confirmed (-1 bpm in the output uncertainty) if RSA-and AM-based methods are considered.

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PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal

Shuzan,

Chowdhury,

Alam

et al. 2024

Phys Eng Sci Med

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i-PRExT: Photoplethysmography Derived Respiration Signal Extraction and Respiratory Rate Tracking Using Neural Networks

Cited by 16 publications

References 30 publications

Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications

Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications

Indirect Estimation of Breathing Rate Using Wearable Devices

PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal

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