Home Medicine Deriving respiration from high resolution 12-channel-ECG during cycling exercise
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Deriving respiration from high resolution 12-channel-ECG during cycling exercise

  • Andy Schumann EMAIL logo , Marcus Schmidt , Marco Herbsleb , Charlotte Semm , Georg Rose , Holger Gabriel and Karl-Jürgen Bär
Published/Copyright: September 30, 2016
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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

Monitoring of cardiac and respiratory activity, is essential in several clinical interventions like bicycle ergometries. The respiration signal can be derived from the ECG if it is not recorded itself (ECG derived respiration, EDR). In this study, we tried to reconstruct breathing rates (BR) from stress test high resolution 12-channel-ECGs in nine healthy subjects using higher order central moments. A mean absolute error per subjects of 2.9/min and relatively high correlation (rp = 0.85) and concordance coefficient (rc = 0.79) indicated a quite accurate reproduction of respiratory activity. The analysis of the different test stages revealed an increase of BR errors while subjects were effortful cycling compared to rest. During incremental cycling exercise test the mean absolute error per subjects was 3.4/min. Compared to the results reported in other studies at rest in supine position, this seems adequately accurate. In conclusion, our results indicate that EDR using higher order central moments is suited for monitoring BR during physical activity.

Keywords: central moments; EDR

1 Introduction

In several clinical interventions, monitoring of vital parameters, cardiac and respiratory activity, is essential to control patients’ safety. If the respiratory signal per se is not recorded, it can be extracted from the ECG (ECG derived respiration, EDR). Various approaches have been proposed in the past [1], [2], [3]. Recently, we introduced a new approach to quantify the decline of the ECG between R- and S-wave using higher order central moments [4]. Here, we applied this method to high resolution 12-channel-ECGs that were recorded during cycling exercise. Physical stress tests are common clinical diagnostic tools mainly for investigation of the cardiac status. The stress ECG is indispensable to unravel pathological changes of myocardial repolarization that are not visible at rest (e.g. in coronary artery disease or ischemic heart failure). Especially patients with cardiovascular impairments carry high risk of suffering from angina pectoris, ventricular arrhythmias and apnea during an exhausting exercise. But also in young healthy subjects the test might cause dyspnea or syncope. Therefore, it is essential to monitor health status of the participants to ensure an immediate termination of the test if indicated by vital signs. In general, heart rate and occurrence of arrhythmia are permanently controlled throughout the test. In this study, we try to estimate breathing rates from the ECG as second important parameter to be monitored.

2 Methods

2.1 Cycling exercise test

Exercise testing was performed in upright position with an electronically braked cycle ergometer (Ergometrics 900, Ergoline, Bitz, Germany) with its seat and handlebar heights correctly set for each subject. After a resting period of 5 min and unloaded pedaling for 3 min, the incremental bicycle protocol started with the subject pedaling at 15 W. The power output was then increased by 15 W for every minute until the subject reached his or her limit of tolerance. Degree of effort was subjectively estimated by Borg scale rating (6–20) and objectively by achieved maximum lactate levels. Borg rating of perceived exertion >16 were regarded as indicators of sufficient effort. We encouraged all subjects to aim to maintain a pedaling frequency of 70–80 revolutions per minute throughout the test session. After a patient’s individual limit of tolerance was reached, a cooling down phase ensued, consisting of 3 min pedaling at a slow rate (<40 revolutions/min) and a power output of 15 W. Afterwards, the participant remained seated for another 7 min without pedaling.

2.2 Data recording and preprocessing

High resolution 12-channel-ECG was acquired in nine young female healthy controls (age: 24.3 ± 3.4 years, BMI: 20.7 ± 1.7). Ten ECG leads, arranged in a standard scheme including six electrodes at the chest (V1–V6) and four at the extremities (left and right forearm and pelvis), were digitized at 4 kHz by CardioPart USB (AMEDTEC GmbH, Aue, Germany) and transformed to 12 channels (I, II, III, aVR, aVL, aVF, V1–V6). Input frequency range was 0–150 Hz for all leads. One channel with the least noise and artefacts was sampled down to 500 Hz and used for reconstruction of respiratory signals. ECG signal was bandpass filtered by a 45 Hz low-pass elliptic filter 3rd order and a 0.5 Hz high-pass elliptic filter 4th order. After filtering the ECG signal the baseline wander was removed as proposed by De Chazal et al. [5]. Gas exchange measurements were carried out throughout the test (METALIZER 3B, Cortex, Leipzig, Germany). Respiration rates were automatically detected by the software and exported in mean values per time window lasting 30 s (MetaSoft, Cortex, Leipzig, Germany). Heart rate was estimated by extracting R-waves automatically from the ECG, visual inspection and averaging over time [6].

2.3 EDR based on central moments

The movement of the chest due to filling and emptying of the lungs leads to a displacement of the ECG electrodes resulting in an impedance change that effects RS-distances. Another reason for a morphological change of QRS-complexes is the stretching of the heart’s apex towards the abdomen during inspiration. Additionally, the diaphragm is shifted downwards due to the filling lungs. During expiration the diaphragm is elevated in order to support emptying the lungs. Due to this effect, the apex of the heart is compressed towards the chest. The change of the apex’s position causes a rotation of the electrical heart axis and therefore a morphological change of QRS-complexes dependent on the phase of respiration [7].

In Schmidt et al. the quantification of RS-slopes by 4th order central moments for EDR estimation was introduced [4]. This statistical measure is defined as:

(1)m4=1L⁢∑i= 1L(xi−μ)4.

The vector X = [x1, x2, …, xN] is a segment of the ECG signal with the length L and μ is its mean value. 4th order central moments were calculated in a sliding window of L = 0.02s ⋅ fs with a step width of D = 1. For the QRS-complex the corresponding 4th order central moments show two peaks which represent the QR-slope and the RS-slope. For EDR-signal estimation RS-peaks were extracted. As illustrated in Figure 1A for every RS-interval the 4th order central moment was calculated (Figure 1B, black lines). The maximum per heart beat was extracted. These anchor points (Figure 1B, black circles) were cubic interpolated and band-pass filtered between 0.05 and 1 Hz (Figure 1C, gray curve).

Figure 1: Example of ECG derived respiration (EDR) estimation by central moments. A: ECG, B: central moments of 4th order in black with their maximum (black circles) interpolated by splines (gray curve), C: EDR curve (gray) and reference respiration signal (black).
Figure 1:

Example of ECG derived respiration (EDR) estimation by central moments. A: ECG, B: central moments of 4th order in black with their maximum (black circles) interpolated by splines (gray curve), C: EDR curve (gray) and reference respiration signal (black).

2.4 Comparison to reference respiration

For estimation of breathing rates inspiratory peaks were determined using an approach reported by Schäfer and Kratky [8]. This method is based on the evaluation of the vertical distance of inspiration and expiration extremes in the EDR-signal. A respiratory cycle was detected if the distance between respective maximum and minimum exceeded a threshold defined as the third quartile of the respiration signal weighted by a factor x = 0.25 [8]. Respiration rate in the current window was estimated by calculating the median of temporal differences of the maxima. The breathing rate BREDR estimated on EDR was compared to the reference respiration frequency BRRef. Therefore, absolute E and relative absolute error e of the respiration rates was calculated in the i-th window.

(2)E=1N⁢∑i= 1N|BRRef⁢(i)−BREDR⁢(i)|
(3)e=1N⁢∑i= 1N|BRRef⁢(i)−BREDR⁢(i)|BRRef⁢(i)⋅100%

2.5 Statistical analysis

For EDR rates’ linear dependence on the reference rate we calculated Pearson correlation coefficient rp, i.e. the quotient of covariance of X and Y (SXY) and the standard deviations of X and Y (SX, SY), with X = fEDR, Y = fRef.

(4)rp=SX⁢YSX⁢SY

In Lin et al. concordance coefficient rc (eq. 4) was used to assess degree of reproducibility, that is not sufficiently covered by Pearson correlation coefficient [8], [9].

(5)rc=2⁢SX⁢YSX2+SY2+(y¯+x¯)2

3 Results

Participants had a mean heart rate of 84.6 beats per min and breathed at 16 breaths per min at rest (see Table 1). Mean systolic to diastolic blood pressure was 120 to 81 mm Hg. Baseline lactate level of the group was 1.4 mmol/l. On average participants cycled until maximum load of 185 W was reached. Heart and breathing rate increased to a maximum of 182 and 44/min, respectively. Lactate level and subjective effort rating were 10 mmol/l and 19 on Borg scale.

Table 1:

Heart rate and breathing rate in different test states. Results at rest or maximum during recording are given in mean ± standard deviation.

ParameterMean ± standard deviation
HRbaseline (1/min)83.2 ± 13.4
BRbaseline (1/min)16.9 ± 4.1
SBPbaseline (mm Hg)120 ± 9.4
DBPbaseline (mm Hg)81.1 ± 7.4
Lactatebaseline (mmol/l)1.4 ± 0.6
HRmax (1/min)182.3 ± 9.7
BRmax (1/min)47.2 ± 6.5
Pmax (W)185.3 ± 34.6
Lactatemax (mmol/l)9.7 ± 1.9
Borgmax18.9 ± 1.2

HR, heart rate; BR, breathing rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; Pmax: maximum load

In Figure 2 breathing rates derived from ECG and reference spirometry results of one participant are depicted in different states of the testing. The breathing rate of the subject increased considerably during exercise (stage III) to compensate for the rising effort of cycling. Respiration frequency peaked at the point of the highest load (at about 44/min). Reconstructed BR matched the reference value quite well. There were no obvious differences between the several stages of the exercise test.

Figure 2: Reference (gray dashed line) and ECG derived (black solid line) breathing rate (BR) of one participant. Stage I: baseline, II: unloaded pedaling, III: cycling exercise, IV: recovery.
Figure 2:

Reference (gray dashed line) and ECG derived (black solid line) breathing rate (BR) of one participant. Stage I: baseline, II: unloaded pedaling, III: cycling exercise, IV: recovery.

Overall mean error of EDR rates was E = 2.91 ± 1.41/min (e = 13.47 ± 7.66%). Mean Pearson correlation coefficient of EDR results and BRRef was rp = 0.85 ± 0.09 (all p < 0.01) with a concordance of rc = 0.79 ± 0.12. t-Test did not reveal a mean error different from zero. BREDR was not correlated to reference BRRef (rRef = −0.01 ± 0.16, all p > 0.05). Thus, the accuracy of EDR was not dependent on actual BR.

In Table 2 errors of BR estimation by central moments at different stages of the recording are listed. During baseline the mean absolute error E = 1.78 ± 0.96/min of BR estimates was lowest (e = 10.50 ± 5.25%). During unloaded pedaling errors increased moderately (E = 2.91 ± 1.66/min, e = 14.92 ± 10.04%). While subjects started effortful cycling the accuracy of BR estimation decreased. The mean absolute error increased to 3.44 ± 2.13/min. Relative errors were only marginally changed from unloaded pedaling due to higher actual BR at exercise condition (15.03 ± 11.24%). During recovery errors of BR estimation were comparable to rest (E = 1.96 ± 1.17/min, e = 8.97 ± 6.47%).

Table 2:

Accuracy of breathing rate estimation by ECG derived respiration. Absolute error E [1/min] and relative error e [%] are given in mean ± standard deviation.

StageE [1/min]e [%]
Beseline1.78 ± 0.9610.50 ± 5.25
Unloaded pedaling2.91 ± 1.6614.92 ± 10.04
Exercise3.44 ± 2.1315.03 ± 11.24
Recovery1.96 ± 1.178.97 ± 6.47
All2.91 ± 1.4113.47 ± 7.66

4 Discussion

In this study we applied a new approach of deriving respiration from ECG recordings to stress test high resolution 12-channel-ECGs. Compared to spirometry results, the errors of breathing rate estimation were analyzed in the different stages of the test. The overall mean absolute error per subject was 2.91/min and was lowest at rest (1.78/min) and highest during exercise (3.44/min). A heart rate of 83/min demonstrate that participants are not as relaxed as during long resting state measurements in supine position. With a mean absolute error of 1.78/min at baseline the central moments approach appears quite precise. For example, Schäfer and Kratky achieved mean absolute errors of 1.14/min in supine position and cleaned 1 min analysis windows [8]. The fact, that accurary of BRs decline while subjects were pedaling seems to be logical, due to movement artefacts, arrhythmias and physiological noise contaminating the ECG. These distortions increase with exercise intensity during the test. Cardiorespiratory indices and levels of lactate and Borg scale indicated a very high individual effort to the limit of tolerance.

We found that EDR accuracy was strongly dependent on the correct detection of R-waves in the ECG. In segments with arrhythmias or artefacts preventing an adequate EDR reconstruction, produce outliers of estimated BRs. Therefore, we calculated the median BR in the analysis windows that is less susceptible to outliers. But in the future we want to test approaches for reconstructing the EDR signal also in distorted segments (e.g. by autoregressive modeling). In larger analysis windows the impact of outliers was even less, but our aim was to monitor breathing rate as vital sign during stress tests with sufficiently high temporal resolution.

One short-coming of our analysis is the automatic extraction of BR by the METALIZER software (MetaSoft, Cortex, Leipzig, Germany), that can not be adjusted in all detail. So differences of the detection of respiratory cycles may also contribute to the EDR errors in a systematic fashion. Additionally, a group consisting of nine young female volunteers is hardly large enough for statistical analysis and definitely not suited to draw generalized conclusions. Nevertheless, our results plead for the applicability of higher order central moments extracting BR from stress ECG recordings. Some methodological advantages of this approach are robustness against noise and drifts and no need for exact detection of characteristic peaks of the ECG except for R-waves. In conclusion, this was the first attempt to reconstruct respiratory activity from stress ECGs using using higher order central moments. Our results indicate that this EDR strategy is suited for monitoring BR with adequate accuracy and temporal resolution.

Author’s Statement

Research funding: The author state no funding involved. Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The research has been complied with all the relevant national regulations, institutional policies and was performed in accordance with the tenets of the Helsinki Declaration, and has been approved by the ethics committee of Jena University.

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Andy Schumann et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0039
Keywords for this article
central moments; EDR
Creative Commons
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  42. Vasomotor assessment by camera-based photoplethysmography
  43. Classification of morphologic changes in photoplethysmographic waveforms
  44. Novel computation of pulse transit time from multi-channel PPG signals by wavelet transform
  45. Efficient design of FIR filter based low-pass differentiators for biomedical signal processing
  46. Nonlinear causal influences assessed by mutual compression entropy
  47. Comparative study of methods for solving the correspondence problem in EMD applications
  48. fNIRS for future use in auditory diagnostics
  49. Semi-automated detection of fractional shortening in zebrafish embryo heart videos
  50. Blood pressure measurement on the cheek
  51. Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation
  52. Left cardiac atrioventricular delay and inter-ventricular delay in cardiac resynchronization therapy responder and non-responder
  53. An automatic systolic peak detector of blood pressure waveforms using 4th order cumulants
  54. Real-time QRS detection using integrated variance for ECG gated cardiac MRI
  55. Preprocessing of unipolar signals acquired by a novel intracardiac mapping system
  56. In-vitro experiments to characterize ventricular electromechanics
  57. Continuous non-invasive monitoring of blood pressure in the operating room: a cuffless optical technology at the fingertip
  58. Application of microwave sensor technology in cardiovascular disease for plaque detection
  59. Artificial blood circulatory and special Ultrasound Doppler probes for detecting and sizing gaseous embolism
  60. Detection of microsleep events in a car driving simulation study using electrocardiographic features
  61. A method to determine the kink resistance of stents and stent delivery systems according to international standards
  62. Comparison of stented bifurcation and straight vessel 3D-simulation with a prior simulated velocity profile inlet
  63. Transient Euler-Lagrange/DEM simulation of stent thrombosis
  64. Automated control of the laser welding process of heart valve scaffolds
  65. Automation of a test bench for accessing the bendability of electrospun vascular grafts
  66. Influence of storage conditions on the release of growth factors in platelet-rich blood derivatives
  67. Cryopreservation of cells using defined serum-free cryoprotective agents
  68. New bioreactor vessel for tissue engineering of human nasal septal chondrocytes
  69. Determination of the membrane hydraulic permeability of MSCs
  70. Climate retainment in carbon dioxide incubators
  71. Multiple factors influencing OR ventilation system effectiveness
  72. Evaluation of an app-based stress protocol
  73. Medication process in Styrian hospitals
  74. Control tower to surgical theater
  75. Development of a skull phantom for the assessment of implant X-ray visibility
  76. Surgical navigation with QR codes
  77. Investigation of the pressure gradient of embolic protection devices
  78. Computer assistance in femoral derotation osteotomy: a bottom-up approach
  79. Automatic depth scanning system for 3D infrared thermography
  80. A service for monitoring the quality of intraoperative cone beam CT images
  81. Resectoscope with an easy to use twist mechanism for improved handling
  82. In vitro simulation of distribution processes following intramuscular injection
  83. Adjusting inkjet printhead parameters to deposit drugs into micro-sized reservoirs
  84. A flexible standalone system with integrated sensor feedback for multi-pad electrode FES of the hand
  85. Smart control for functional electrical stimulation with optimal pulse intensity
  86. Tactile display on the remaining hand for unilateral hand amputees
  87. Effects of sustained electrical stimulation on spasticity assessed by the pendulum test
  88. An improved tracking framework for ultrasound probe localization in image-guided radiosurgery
  89. Improvement of a subviral particle tracker by the use of a LAP-Kalman-algorithm
  90. Learning discriminative classification models for grading anal intraepithelial neoplasia
  91. Regularization of EIT reconstruction based on multi-scales wavelet transforms
  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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