Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization

Lei, Ruisheng; Ling, Wing‐Kuen; Feng, Peng; Chen, Jinrong

doi:10.3390/s20113238

Cited by 21 publications

(13 citation statements)

References 34 publications

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“…For the best performance at detecting R-waves from noisy PPG signals collected with wearable technologies, future BrainBeats versions may provide alternative algorithms for these applications. Other promising algorithms include signal derivatives-based algorithms 77 , adaptive linear neuron artificial neural networks (used for ECG 78 ), or ensemble empirical mode decomposition 79 .…”

Section: Discussionmentioning

confidence: 99%

BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Cannard

Wahbeh

Delorme

2023

Preprint

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The link between the cortical and cardiovascular systems is garnering increased attention due to its potential to offer valuable insights into brain and heart function coupling. Current joint analysis methodologies largely involve invasive or high-cost neuroimaging methods. EEG and ECG/PPG, however, provide non-invasive, cost-effective, and portable alternatives enabling broader data collection in both laboratory and clinical settings. However, the analysis of these biosignals is challenging for scalable applications due to their complex nature. Existing research and tools often lack consensus in processing and statistical methodologies, easy-to-use user interface, or batch processing capacity of large datasets, impeding reproducibility. A further void exists in standardized methods for EEG and heart-rate variability (HRV) feature extraction, undermining clinical diagnostics or the robustness of machine learning models. We introduce the BrainBeats toolbox in response to these challenges, an open-source EEGLAB plugin providing an suite of signal processing and feature-extraction functions adhering to current guidelines and recommendations. The toolbox integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO); 2) EEG and HRV feature extraction; 3) Automated removal of heart artifacts from EEG signals. Accompanied by sample data and guidance, BrainBeats aims to facilitate brain-heart interplay research and reproducibility. This open-source toolbox offers a valuable resource for clinicians and researchers studying brain-heart interactions and can be tailored to unique research needs.

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Section: Discussionmentioning

confidence: 99%

BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Cannard

Wahbeh

Delorme

2023

Preprint

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“…DeepPhys [ 68 ] is a deep convolutional network, proposed for heart and breathing rate measurement, tested against four datasets. In [ 69 , 70 , 71 , 72 ], a time-domain algorithm was proposed for real-time detection of the PR from wearable devices. Pulse rate variability can be detected from the SDPTG as well [ 48 , 49 , 50 , 51 , 73 ].…”

Section: Diagnostic Features and Their Clinical Usagesmentioning

confidence: 99%

“…RR may differ if a person has a medical condition [ 109 ] and PPG waveform is also affected by respiration. Many methods were proposed to extract RR from PPG signal [ 70 , 71 , 110 , 111 ] and a real-time estimation of RR from PPG is presented in [ 70 ]. Extracted RR from finger PPG has better quality than extracted from ear PPG [ 110 ].…”

Section: Diagnostic Features and Their Clinical Usagesmentioning

confidence: 99%

“… Venn diagram representing an overview of the PPG healthcare applications [ 5 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 37 , 38 , 40 , 41 , 42 , 43 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , <...>…”

Section: Potential Applications Of Ppg Signal and Impacts On Healthcareunclassified

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Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

et al. 2022

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Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

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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%

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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Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization

Cited by 21 publications

References 34 publications

BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications

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