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

The continuous and noninvasive blood pressure (BP) measurement based on pulse transit time (PTT) doesn't need cuff and could monitor BP in real time for a long period. However, PTT is just a time index derived from electrocardiogram (ECG) and photoplethysmogram (PPG), while BP-related information within the PPG waveform has seldom been taken into consideration. We hypothesized that PPG waveform feature might be useful for BP estimation. Nine healthy subjects took part in an exercise stress test, including baseline resting, exercise on bicycle ergometry and recovering resting. ECG of lead V5 and PPG from left finger were collected simultaneously, and systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded from a cuff sphygmometer on the right wrist. The correlation coefficients were obtained between BP (SBP, DBP and pulse pressure (PP)) and PPG morphological indices (total 15 indices in terms of waveform amplitude, time span and area ratio). Five PPG indices were correlated with both SBP and PP (absolute value of correlation coefficient |r| > 0.6) and were further tested for the capability to BP estimation, which were: (1) PTTA, time delay between the R peak of ECG and the foot point of PPG; (2) RSD, time ratio of systole to diastole; (3) RtArea, area ratio of systole to diastole; (4) TmBB, time span of PPG cycle; (5) TmCA, diastolic duration. Comparisons were made between the measured BP and the estimated BP by regression lines and quadratic curve fitting, respectively. As a result, the mean errors of SBP liner fitting with RSD, RtArea, TmBB and TmCA respectively were 5.5, 5.4, 5.2, 5.1 mmHg, which were smaller than that with PTTA of 5.8 mmHg. And the mean errors of SBP quadratic curve fitting with RSD, RtArea, TmBB and TmCA were all 5.1 mmHg, which were smaller than that with PTTA of 5.7 mmHg. The mean errors of multiple regression for SBP, PP and DBP was 4.7, 4.7, 3.5 mmHg respectively, which were more accurate than the regression with single PTTA of 5.8, 5.3, 5.2 mmHg respectively. However, PPG-based SBP and DBP could under estimate cuff pressure by 8 mmHg and over estimate by 10 mmHg respectively, which is a clinically significant error. In conclusion, the combination of time span (PTT, time ratio of systole to diastole, time span of PPG cycle and diastolic duration) and waveform morphology (area ratio of systole to diastole) could improve the performance of PPG-based BP estimation.

Characters available in photoplethysmogram for blood pressure estimation: beyond the pulse transit time.

The basis and reliability for timely diagnosis of cardiovascular diseases depend on the robust and accurate detection of QRS complexes along with the fiducial points in the electrocardiogram (ECG) signal. Despite, the several QRS detection algorithms reported in the literature, the development of an efficient QRS detector remains a challenge in the clinical environment. Therefore, this article summarizes the performance analysis of various QRS detection techniques depending upon three assessment factors which include robustness to noise, computational load, and sensitivity validated on the benchmark MIT-BIH arrhythmia database. Moreover, the limitations of these algorithms are discussed and compared with the standard signal processing algorithms, followed by the future suggestions to develop a reliable and efficient QRS methodology. Further, the suggested method can be implemented on suitable hardware platforms to develop smart health monitoring systems for continuous and long-term ECG assessment for real-time applications.

Development of robust, fast and efficient QRS complex detector: a methodological review

Surface bioelectric dry Electrodes: A review

Myocardial infarction (MI) is a major risk factor responsible for morbidity and mortality. Xinji′erkang (XJEK) has been clinically used as an effective medication in the treatment of coronary heart disease and myocarditis. The purpose of this study was to investigate the cardioprotective effect of Xinji′erkang on MI mice. Forty male mice were randomly assigned into four groups as follows (n = 10): sham, model, MI with administration of XJEK and fosinopril for four weeks. At the end of studies, hemodynamic parameters and electrocardiography (ECG) were recorded. Heart and body mass were measured and heart weight/body weight (HW/BW) ratio was calculated as index of hypertrophy. The hypertrophy of heart and aorta was examined using the hematoxylin and eosin (HE) staining, and the collagen deposition was evaluated using Van Gieson (VG) staining. Serum nitric oxide level (NO), superoxide dismutase (SOD) activity and malondialdehyde (MDA) concentration were assayed by colorimetric analysis. The expressions of endothelial NO synthetase (eNOS) expression in serum and cardiac tissues were determined using ELISA assay and immunohistochemistry. Angiotensin II (Ang II) in serum and cardiac tissues was measured using ELISA assay. Besides, tumor necrosis factor-α (TNF-α), interleukin1β (IL-1β) and interleukin10 (IL-10) were observed in cardiac tissues with ELISA assay as well. The administration of XJEK significantly improved cardiac dysfunction and abnormal ECG with reduced HW/BW ratio and ameliorated cardiomyocyte hypertrophy and collagen deposition compared to MI, which was partly due to the decreased SOD and increased MDA in serum. Moreover, XJEK treatment also improved endothelial dysfunction (ED) with not only enhanced eNOS activities in serum and cardiac tissues and elevated NO levels in serum, but also decreased Ang II content in serum and cardiac tissues. Finally, protein expressions of pro-inflammation cytokines, TNF-α and IL-1β in the cardiac tissues with XJEK treatment were significantly decreased compared to model. On the contrary, IL-10, an anti-inflammatory cytokine concentrated in cardiac tissues was significantly enhanced compared to model. Xinji′erkang exerts cardioprotective effect on myocardial infarction in mice, which may be due to the improvement of endothelial dysfunction and the reduction of oxidative stress and inflammation response.

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Protective effects of Xinji'erkang on myocardial infarction induced cardiac injury in mice.

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

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

Electrocardiogram (ECG) is an economic, convenient, and non-invasive detecting tool in myocardial ischemia (MI), and its clinical appearance is mainly exhibited by the changes in ST–T complex. Recently, QRS complex characters were proposed to analyze MI by more and more researchers. In this paper, various QRS complex characters were extracted in ECG signals, and their relationship was analyzed systematically. As a result, these characters were divided into two groups, and there existed good relationship among them for each group, while the poor relationship between the groups. Then these QRS complex characters were applied for statistical analysis on MI, and five characters had significant differences after ECG recording verification, which were: QRS upward and downward slopes, transient heart rate, angle R and angle Q. On the other hand, these QRS complex characters were analyzed in frequency domain. Experimental results showed that the frequency features of RR interval series (Heart Rate Variability, HRV), and QRS barycenter sequence had significant differences between MI states and normal states. Moreover, QRS barycenter sequence performed better.

Myocardial ischemia analysis based on electrocardiogram QRS complex

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.

Zhi Xu

Papers

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

Electrochemical modeling and evaluation for textile electrodes to skin

Myocardial ischemia analysis based on electrocardiogram QRS complex

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