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.

Contact-Based Methods for Measuring Respiratory Rate

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.

Application of Modern Multi-Sensor Holter in Diagnosis and Treatment.

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.

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Respiration Monitoring via Forcecardiography Sensors.

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.

ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition.

With the development of wearable health-monitoring technologies, a variety of textile electrodes have been produced and applied by researchers. However, there are no universal and effective methods even testing platforms for evaluating the skin–electrode electrochemical interface for textile electrodes because different human bodies have different skin characteristics. An electrochemical modeling and evaluation for textile electrodes to skin was proposed, and two electrochemical evaluation platforms (EEP) were set up based on two simulated skin models (SSM). First, skin–electrode electrochemical interface (SEEI) models for traditional wet electrodes and textile electrodes were analyzed. Based on the SEEI models and YY/T 0196-2005 (Chinese YY/T pharmaceutical industry standard for disposable ECG electrode), three skin–electrode electrochemical characteristics (SEEC), including skin–electrode static impedance (SESI), skin–electrode alternating current impedance (SEAI), and skin–electrode polarization voltage (SEPV), were proposed. Then, three electrochemical evaluation methods for textile electrodes to skin were proposed and analyzed, which were the correlation between SEEC and skin–electrode contact pressure (SECP), skin–electrode relative movement (SERM), and conduction loss of active signals (CLAS). Finally, an electrochemical evaluation platform was set up based on an active simulated skin model (ASSM) and passive simulated skin model (PSSM). 9 feature parameters based on the passive electrochemical evaluation platform (PEEP) and 11 feature parameters based on the active electrochemical evaluation platform (AEEP) were obtained for evaluating textile electrodes. And four kinds of textile electrode characteristics including SEEC, SECP, SERM, and CLAS were quantitatively measured based on the electrochemical evaluation platform, and the testing accuracy and range for these characteristics were measured separately. Finally, correlation between SEEC and SECP for 10 kinds of textile electrode samples was studied, and 14 electrochemical characteristics and four skin–electrode contact pressure characteristics were extracted. Experimental results showed that significant correlations were found between six SEEC characteristics and SECP characteristics, and the correlation coefficient between ACI_3 and USECP was the highest. And the polarization voltages of most dry electrode samples showed a downward trend with the increase of contact pressure. The electrochemical evaluation platform yielded effective experimental data and could provide strong support for the evaluation and application of textile electrodes, which was also effective in evaluating other bioelectric electrodes such as 3M electrode, stainless steel electrode, dry electrode and microneedle electrode.

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Electrochemical modeling and evaluation for textile electrodes to skin

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.

A principal component analysis based data fusion method for ECG-derived respiration from single-lead ECG

Several different lying positions, such as lying on the left side, supine, lying on the right side and prone position, existed when healthy people fell asleep. This article explored the influence of lying positions on the shape of ECG (electrocardiograph) waveform during sleep, and then lying position classification based on ECG waveform features and random forest was achieved. By means of de-noising the overnight sleep ECG data from ISRUC website dataset, as well as extracting the waveform features, we calculated a total of 30 ECG waveform features, including 2 newly proposed features, S/R and ∠QSR. The means and significant difference level of these features within different lying positions were calculated, respectively. Then 12 features were selected for three kinds of classification schemes. The lying positions had comparatively less effect on time-limit features. QT interval and RR interval were significantly lower than that in supine (
 $${\text{P}}\, \le \,0.01$$
 ). Significant differences appeared in most of the amplitude and double-direction features. When lying on the left side, the height of P wave and T wave, QRS area and T area, the QR potential difference and ∠QSR were significantly lower than those in supine (
 $${\text{P}}\, \le \,0.01$$
 ). However, S/R was significantly greater on left than those in supine (
 $${\text{P}}\, \le \,0.01$$
 ) and on right (
 $${\text{P}}\, \le \,0.05$$
 ). The height of T wave and area under T wave were significantly higher in supine than those on right (
 $${\text{P}}\, \le \,0.01$$
 ). For the subject specific classifier, a mean accuracy of 97.17% with Cohen’s kappa statistic κ of 0.91, and AUC > 0.97 were achieved. While the accuracy and κ dropped to 63.87% and 0.32, AUC > 0.66, respectively when the subject independent classifier was considered. When subjects were lying on the left side during sleep, due to the effect of gravity on heart, the position of heart changed, for example, turned and rotated, causing changes in the vectorcardiogram of frontal plane and horizontal plane, which lead to a change in ECG. When lying on the right side, the heart was upheld by the mediastinum, so that the degree of freedom was poor, and the ECG waveform was almost unchanged. The proposed method could be used as a technique for convenient lying position classification.

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Lying position classification based on ECG waveform and random forest during sleep in healthy people.

An ECG-derived respiration (EDR) algorithm based on signal reconstruction and filtering is presented and applied to derive the respiratory signals from single-lead ECG. The ECG features, R-peak amplitude, S-peak amplitude, and R-peak position are used to reconstruct the signal by cubic spline interpolation. The EDR signal is obtained by applying a Kaiser filter to the reconstructed signal at last. 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. Correlation coefficient (C) and magnitude-squared coherence coefficient (MSC) are used to assess the performance of the methods. The statistical difference is significant among the method presented in this study and the EDR methods based on wavelet and empirical mode decomposition (EMD), proving that the algorithm introduced in this article outperforms the others in the extraction of respiratory signals from single-lead ECGs.

An ECG-Derived Respiration Method Based on Signal Reconstruction of R, S Amplitudes and Filtering

The invention provides a method and device for acquiring respiration information. The method comprises the steps that electrocardiosignal samples are acquired; first feature points of the electrocardiosignal samples are obtained according to wavelet transform detection; waveform reconstruction is conducted on the electrocardiosignal samples according to the first feature points to generate a reconstructed waveform; the reconstructed waveform is filtered to obtain the respiration information. By obtaining a plurality of electrocardiosignal sequences in the electrocardiosignal samples, the first feature point on each sequence is obtained, and then corresponding processing is carried out on the first feature points to obtain the reconstructed waveform; the electrocardiogram information part included in the processed first feature points presents a high-frequency carrier frequency spectrum in the frequency spectrum of the reconstructed waveform, and the included respiration information part presents a low-frequency carrier frequency spectrum in the frequency spectrum of the reconstructed waveform, so that the high-frequency carrier frequency spectrum and the low-frequency carrier frequency spectrum of the frequency spectrum of the reconstructed waveform obtained through the above processing are obviously distinguished, then the constructed waveform is filtered, the high-frequency carrier frequency spectrum is filtered out, and the more accurate respiratory information is obtained.

Yue Gao

Papers

A principal component analysis based data fusion method for ECG-derived respiration from single-lead ECG

Lying position classification based on ECG waveform and random forest during sleep in healthy people.

An ECG-Derived Respiration Method Based on Signal Reconstruction of R, S Amplitudes and Filtering

Method and device for acquiring respiration information