Proceedings ArticleDOI
Estimation of Respiration Rate from Motion Corrupted Photoplethysmogram: A Combined Time and Frequency Domain Approach
Biplab Roy,Rajarshi Gupta,Jayanta K. Chandra +2 more
- pp 292-297
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TLDR
This paper proposes a method based on combination of variational mode decomposition (VMD) and ensemble empirical mode decompose (EEMD) to estimate the respiration rate (RR) from motion corrupted PPG which is better than the existing methods in terms of accuracy.Abstract:
Photoplethysmogram (PPG) signal reflects blood volume changes in peripheral vascular system and can be used to derive multitude of surrogate cardiovascular measurements, including respiration. Under ambulatory monitoring and stress-exercises, PPG signal is prone to corruption by motion artifacts (MA), leading to measurement inaccuracies. In this paper, we propose a method based on combination of variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) to estimate the respiration rate (RR) from motion corrupted PPG. The signal was decomposed using VMD to identify the various frequency components and heart rate, followed by extraction of amplitude, baseline and frequency modulation due to respiration using EEMD. Finally, the accurate estimation of RR was done from these three components. To test and validate, we used Multiparameter Intelligent Monitoring in Intensive Care (MIMIC)-II database and volunteers’ data collected at our laboratory. Results of our method showed mean absolute error (MAE) of 0.41 breaths/min for 10 subjects from volunteers’ data and 0.35 breaths/min over 53 subjects from MIMIC-II database which is better than the existing methods in terms of accuracy.read more
Citations
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Journal ArticleDOI
A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model
Md. Nazmul Islam Shuzan,Moajjem Hossain Chowdhury,Md. Shafayet Hossain,Muhammad E. H. Chowdhury,Mamun Bin Ibne Reaz,M. Monir Uddin,Amith Khandakar,Zaid Bin Mahbub,Sawal Hamid Md Ali +8 more
TL;DR: In this paper, a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features was described, where feature selection algorithms were used to reduce computational complexity and the chance of overfitting.
Journal ArticleDOI
i -PRExT: Photoplethysmography Derived Respiration Signal Extraction and Respiratory Rate Tracking Using Neural Networks
TL;DR: In this article, an ensemble empirical mode decomposition (EEMD) is used to select the appropriate intrinsic mode functions (IMFs) through filtering in the respiration band and reconstruct by a linear weighted sum to obtain the photoplethysmography derived respiration (PDR) signal.
Journal ArticleDOI
Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal
Moajjem Hossain Chowdhury,Md. Nazmul Islam Shuzan,Muhammad E. H. Chowdhury,Mamun Bin Ibne Reaz,Sakib Mahmud,Nasser Al Emadi,Mohamed Arselene Ayari,Sawal Hamid Md Ali,Ahmad Ashrif A Bakar,Syed Mahfuzur Rahman,Amith Khandakar +10 more
TL;DR: A deep-learning-based end-to-end solution for estimating Respiration rate (RR) directly from the PPG signal is proposed and a lightweight model, ConvMixer, outperformed all of the other deep neural networks.
Proceedings ArticleDOI
Attention-LRCN: Long-term Recurrent Convolutional Network for Stress Detection from Photoplethysmography
TL;DR: A novel deep learning algorithm based on long-term recurrent convolutional networks and an attention module, and named this as Attention-LRCN is proposed for stress detection of photoplethysmography signals.
Proceedings ArticleDOI
Attention-LRCN: Long-term Recurrent Convolutional Network for Stress Detection from Photoplethysmography
TL;DR: Wang et al. as discussed by the authors proposed a novel deep learning algorithm for stress detection, which is based on long-term recurrent convolutional networks (LRCN) and an attention module, and they used WESAD dataset which provides photoplethysmography (PPG) signals with normal and stress statuses for 15 subjects.
References
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Journal ArticleDOI
Variational Mode Decomposition
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Journal ArticleDOI
Photoplethysmography and its application in clinical physiological measurement.
TL;DR: Photoplethysmography is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue and is often used non-invasively to make measurements at the skin surface.
Journal ArticleDOI
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TL;DR: A view of the algorithm as a novel optimization method which combines desirable characteristics of both classical optimization and learning-based algorithms is provided and Mathematical results on conditions for uniqueness of sparse solutions are also given.
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Pulse oximetry
TL;DR: In this paper, a review on pulse oximetry that was published in 1999 in Critical Care was updated and a summary of the recently developed multi-wavelength pulse oximeters and their ability in detecting dyshemoglobins was provided.
Journal ArticleDOI
Multiparameter Respiratory Rate Estimation From the Photoplethysmogram
TL;DR: The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas and shows trends of improved estimation.