A principal component analysis based data fusion method for ECG-derived respiration from single-lead ECG
01 Mar 2018-Australasian Physical & Engineering Sciences in Medicine (Springer Netherlands)-Vol. 41, Iss: 1, pp 59-67
TL;DR: The statistically difference is significant among the PCA data fusion method and the EDR methods based on the RR intervals and the RS amplitudes, showing that PCAData fusion algorithm outperforms the others in the extraction of respiratory signals from single-lead ECGs.
Abstract: An ECG-derived respiration (EDR) algorithm based on principal component analysis (PCA) is presented and applied to derive the respiratory signals from single-lead ECG. The respiratory-induced variabilities of ECG features, P-peak amplitude, Q-peak amplitude, R-peak amplitude, S-peak amplitude, T-peak amplitude and RR-interval, are fused by PCA to yield a better surrogate respiratory signal than other methods. The method is evaluated on data from the MIT-BIH polysomnographic database and validated against a “gold standard” respiratory obtained from simultaneously recorded respiration data. The performance of fusion algorithm is assessed by comparing the EDR signals to a reference respiratory signal, using the quantitative evaluation indexes that include true positive (TP), false positive (FP), false negative (FN), sensitivity (SE) and positive predictivity (PP). The statistically difference is significant among the PCA data fusion method and the EDR methods based on the RR intervals and the RS amplitudes, showing that PCA data fusion algorithm outperforms the others in the extraction of respiratory signals from single-lead ECGs.
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TL;DR: An overview of the currently available contact-based methods for measuring respiratory rate is provided, based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity.
Abstract: There is an ever-growing demand for measuring respiratory variables during a variety of applications, including monitoring in clinical and occupational settings, and during sporting activities and exercise. Special attention is devoted to the monitoring of respiratory rate because it is a vital sign, which responds to a variety of stressors. There are different methods for measuring respiratory rate, which can be classed as contact-based or contactless. The present paper provides an overview of the currently available contact-based methods for measuring respiratory rate. For these methods, the sensing element (or part of the instrument containing it) is attached to the subject’s body. Methods based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity are presented. Working principles, metrological characteristics, and applications in the respiratory monitoring field are presented to explore potential development and applicability for each method.
248 citations
Cites methods from "A principal component analysis base..."
...Especially during exercise, poor quality of ECG signals can strongly affect the performance of the method for extracting accurate fR values [245]....
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TL;DR: In this article, actual interesting multi-sensor principles are described on the grounds of the own long-year experiences and many experiments to complement these methods and diminish the level of artifacts.
Abstract: Modern Holter devices are very trendy tools used in medicine, research, or sport. They monitor a variety of human physiological or pathophysiological signals. Nowadays, Holter devices have been developing very fast. New innovative products come to the market every day. They have become smaller, smarter, cheaper, have ultra-low power consumption, do not limit everyday life, and allow comfortable measurements of humans to be accomplished in a familiar and natural environment, without extreme fear from doctors. People can be informed about their health and 24/7 monitoring can sometimes easily detect specific diseases, which are normally passed during routine ambulance operation. However, there is a problem with the reliability, quality, and quantity of the collected data. In normal life, there may be a loss of signal recording, abnormal growth of artifacts, etc. At this point, there is a need for multiple sensors capturing single variables in parallel by different sensing methods to complement these methods and diminish the level of artifacts. We can also sense multiple different signals that are complementary and give us a coherent picture. In this article, we describe actual interesting multi-sensor principles on the grounds of our own long-year experiences and many experiments.
20 citations
TL;DR: In this article, the authors presented a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electro resistive respiration band (ERB), which was assumed as the reference.
Abstract: In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients' discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic sensors for monitoring multiple vital signs, which could improve the simultaneous recording of different physiological data. This study presents a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electroresistive respiration band (ERB), which was assumed as the reference. FCG is a novel technique that records the cardiac-induced vibrations of the chest wall via specific force sensors, which provide seismocardiogram-like information, along with a novel component that seems to be related to the ventricular volume variations. Simultaneous acquisitions were obtained from seven healthy subjects at rest, during both quiet breathing and forced respiration at higher and lower rates. The raw FCG sensor signals featured a large, low-frequency, respiratory component (R-FCG), in addition to the common FCG signal. Statistical analyses of R-FCG, EDR and ERB signals showed that FCG sensors ensure a more sensitive and precise detection of respiratory acts than EDR (sensitivity: 100% vs. 95.8%, positive predictive value: 98.9% vs. 92.5%), as well as a superior accuracy and precision in interbreath interval measurement (linear regression slopes and intercepts: 0.99, 0.026 s (R2 = 0.98) vs. 0.98, 0.11 s (R2 = 0.88), Bland-Altman limits of agreement: ±0.61 s vs. ±1.5 s). This study represents a first proof of concept for the simultaneous recording of respiration signals and forcecardiograms with a single, local, small, unobtrusive, cheap sensor. This would extend the scope of FCG to monitoring multiple vital signs, as well as to the analysis of cardiorespiratory interactions, also paving the way for the continuous, long-term monitoring of patients with heart and pulmonary diseases.
20 citations
TL;DR: The proposed EDR technique has the advantages over existing techniques in terms of the better agreement in the respiratory rates and specifically, it reduces the need for an extra step required for the detection of fiducial points in the ECG for the estimation of respiration which makes the process effective and less-complex.
Abstract: Monitoring of the respiration using the electrocardiogram (ECG) is desirable for the simultaneous study of cardiac activities and the respiration in the aspects of comfort, mobility, and cost of the healthcare system. This paper proposes a new approach for deriving the respiration from single-lead ECG based on the iterated Hilbert transform (IHT) and the Hilbert vibration decomposition (HVD). The ECG signal is first decomposed into the multicomponent sinusoidal signals using the IHT technique. Afterward, the lower order amplitude components obtained from the IHT are filtered using the HVD to extract the respiration information. Experiments are performed on the Fantasia and Apnea-ECG datasets. The performance of the proposed ECG-derived respiration (EDR) approach is compared with the existing techniques including the principal component analysis (PCA), R-peak amplitudes (RPA), respiratory sinus arrhythmia (RSA), slopes of the QRS complex, and R-wave angle. The proposed technique showed the higher median values of correlation (first and third quartile) for both the Fantasia and Apnea-ECG datasets as 0.699 (0.55, 0.82) and 0.57 (0.40, 0.73), respectively. Also, the proposed algorithm provided the lowest values of the mean absolute error and the average percentage error computed from the EDR and reference (recorded) respiration signals for both the Fantasia and Apnea-ECG datasets as 1.27 and 9.3%, and 1.35 and 10.2%, respectively. In the experiments performed over different age group subjects of the Fantasia dataset, the proposed algorithm provided effective results in the younger population but outperformed the existing techniques in the case of elderly subjects. The proposed EDR technique has the advantages over existing techniques in terms of the better agreement in the respiratory rates and specifically, it reduces the need for an extra step required for the detection of fiducial points in the ECG for the estimation of respiration which makes the process effective and less-complex. The above performance results obtained from two different datasets validate that the proposed approach can be used for monitoring of the respiration using single-lead ECG.
14 citations
Cites methods from "A principal component analysis base..."
...Recently, the respiratory related changes obtained from the various ECG features are fused together using the PCA technique to estimate the respiration from single-lead ECG [25]....
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01 Nov 2019
TL;DR: A sleep stage detection algorithm for Wake, Rapid eye movement (REM), and Non-Rapid eye movements (NREM) stages using Heart rate variability (HRV) and ECG-derived respiration (EDR) waveforms extracted from Electrocardiogram (ECG) signal is presented.
Abstract: In this paper, we present a sleep stage detection algorithm for Wake, Rapid eye movement (REM), and Non-Rapid eye movement (NREM) stages using Heart rate variability (HRV) and ECG-derived respiration (EDR) waveforms extracted from Electrocardiogram (ECG) signal. First, we remove the baseline wander of ECG signal and indicate its R-peaks to form HRV and EDR signals from 30-second ECG intervals. The EDR is extracted using a Neural-PCA based method. In the next step, several features in time and frequency domains are extracted from EDR and HRV signals. Moreover, several features are extracted from mutual information of HRV and EDR, which is derived from cross-spectral and magnitude-squared coherence of both signals. The extracted features were evaluated statistically using the nonparametric Kruskal-Wallis test, and the optimum number of features were selected by the Minimum Redundancy Maximum Relevance (mRMR) algorithm. The sleep stage classification has been done with a multi-class Support Vector Machine (SVM) classifier with Error-Correcting Out-put Codes (ECOC) extension and Radial Basis Function (RBF). The performance of the proposed method was evaluated using the MIT-BIH Polysomnographic database. The obtained accuracy for classification of two classes (Sleep vs. Wake) was up to S1.76%, with Specificity 92.35%, and for three classes, the accuracy was up to 76%, with Specificity 81.39%.
8 citations
References
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Book•
30 Sep 2006
TL;DR: The ECG and Its Contaminants, Visualization Methods, Knowledge Management and Emerging Methods, and Supervised and Unsupervised Classification.
Abstract: This cutting-edge resource provides you with a practical and theoretical understanding of state-of-the-art techniques for electrocardiogram (ECG) data analysis. Placing an emphasis on the fundamentals of signal etiology, acquisition, data selection, and testing, this comprehensive volume presents guidelines to help you design, implement, and evaluate algorithms used for the analysis of ECG and related data. Additionally, explanations of open source software and related databases for signal processing are given. The book focuses on the modeling, classification, and interpretation of features derived from advanced signal processing and artificial intelligence techniques. Key topics covered include physiological origin, hardware acquisition and filtering, time-frequency quantification of the ECG and derived signals (including heart rate variability and respiration), analysis of noise and artifact, models for ECG and RR interval processes, linear and nonlinear filtering techniques, and adaptive algorithms such as neural networks. Much of the book is devoted to deriving robust, clinically meaningful parameters such as the QRS axis, QT-interval, the ST-level, and T-wave alternan metrics. Methods for applying these metrics to clinical classification are also discussed, together with supervised and unsupervised classification techniques. Including over 190 illustrations, the book offers you a solid grounding in the relevant basics of physiology, data acquisition and database design, and addresses the practical issues of improving existing data analysis methods and developing new applications.
799 citations
"A principal component analysis base..." refers background in this paper
...However, such techniques are unmanageable in certain applications such as stress testing, ambulatory monitoring and sleep studies due to the use of some cumbersome devices that may interfere with natural breath [3]....
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...(2) The thoracic impedance variations caused by the volume changes in the lungs [3, 5]....
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01 Jan 2008
TL;DR: This paper presents an implementation of the Monte Carlo simulation of the response of the autonomic nervous system to treatment-side-of-the-articulating-vehicle errors.
Abstract: Massachusetts Institute of Technology, Cambridge, Mass., and Beth Israel Hospital, Boston, Mass., USA (*); Politecnico di Milano, Milan, Italy (+) This article originally appeared in Computers in Cardiology (Washington, DC: IEEE Computer Society Press). Please cite this publication when referencing this material. A sample implementation of the algorithm described in this paper is available in source form here.
343 citations
"A principal component analysis base..." refers methods in this paper
...Then some EDR methods based on the amplitude of ECG features like R peak [8, 9], RS [10], T peak [11] or the area under the QRS complex [12] were proposed....
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TL;DR: A polysomnographic database on CD‐ROM has values for researchers investigating clinical physiology or non‐linear dynamics during sleep apnea syndrome; for engineers developing a new algorithm for the computerized analysis of PSG data related to sleep apna syndrome; and for students learning sleep physiology.
Abstract: We have developed a polysomnographic database on CD-ROM. The data were obtained from 16 subjects with sleep apnea syndrome. The physiological signals include electroencephalogram, electromyogram, electrooculogram, invasive blood pressure, respiratory wave, oxygen saturation, and cardiac volume as measured by VEST method. The CD-ROM also include programs to analyze polysomnography (PSG) data. The CD-ROM has values: (i) for researchers investigating clinical physiology or non-linear dynamics during sleep apnea syndrome; (ii) for engineers developing a new algorithm for the computerized analysis of PSG data related to sleep apnea syndrome; (iii) for students learning sleep physiology.
226 citations
Additional excerpts
...The records are digitized at 250 Hz with 12 bit resolution and continue between 2 and 7 h [20]....
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TL;DR: Two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG is presented.
Abstract: The respiratory signal can be accurately evaluated by single-channel electrocardiogram (ECG) processing, as shown in recent literature. Indirect methods to derive the respiratory signal from ECG can benefit from a simultaneous study of both respiratory and cardiac activities. These methods lead to major advantages such as low cost, high efficiency, and continuous noninvasive respiratory monitoring. The aim of this paper is to reconstruct the waveform of the respiratory signal by processing single-channel ECG. To achieve these goals, two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis. The results highlight the main differences between them in terms of both theoretical foundations, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG are presented. The results also show that both algorithms are able to reconstruct the respiratory waveform, although the EMD is able to break down the original signal without a preselected basis function, as it is necessary for wavelet decomposition. The EMD outperforms the wavelet approach. Some results on experimental data are presented.
177 citations
TL;DR: An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs.
Abstract: An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs. The respiratory-induced variability of ECG features, P waves, QRS complexes, and T waves are described by the PCA. We assessed which ECG features and which principal components yielded the best surrogate for the respiratory signal. Twenty subjects performed controlled breathing for 180 s at 4, 6, 8, 10, 12, and 14 breaths per minute and normal breathing. ECG and breathing signals were recorded. Respiration was derived from the ECG by three algorithms: the PCA-based algorithm and two established algorithms, based on RR intervals and QRS amplitudes. ECG-derived respiration was compared to the recorded breathing signal by magnitude squared coherence and cross-correlation. The top ranking algorithm for both coherence and correlation was the PCA algorithm applied to QRS complexes. Coherence and correlation were significantly larger for this algorithm than the RR algorithm(p < 0.05 and p < 0.0001, respectively) but were not significantly different from the amplitude algorithm. PCA provides a novel algorithm for analysis of both respiratory and nonrespiratory related beat-to-beat changes in different ECG features.
169 citations
"A principal component analysis base..." refers background or methods in this paper
...The mechanism is that heart rate changes due to respiratory-induced changes to the autonomic nervous system [6]....
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...[6] introduced principal component analysis (PCA) to EDR algorithm....
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