Influence of respiration on recording cardiac potentials

Flaherty, John T.; Blumenschein, Sarah D.; Alexander, Ann W.; Gentzler, Richard D.; Gallie, Thomas M.; Boineau, John P.; Spach, Madison S.

doi:10.1016/0002-9149(67)90106-3

Cited by 41 publications

(5 citation statements)

References 6 publications

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“…It is well known the volume conductivity of the thorax takes inequality and changes the electro cardiac potentials on the thorax’s surface with respiratory movements [1, 5]. …”

Section: Discussionmentioning

confidence: 99%

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Algorithm for the classification of multi-modulating signals on the electrocardiogram

Mita

2006

Med Bio Eng Comput

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This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a j (t), u j (t), o j (a j ) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a j (t), u j (t) change the source into the periodic signal and s j = o j (a j ) confines its harmonics into a few instantaneous components at s j being a common instant on two scales between t and s j . As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a j (t), a j+1 (t) and u j+1 (t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings.

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“…It is well known the volume conductivity of the thorax takes inequality and changes the electro cardiac potentials on the thorax’s surface with respiratory movements [1, 5]. …”

Section: Discussionmentioning

confidence: 99%

“…The influence of respiration on recording cardiac potentials was reported as early as in 1967; Flaherty et al [5] and Moody et al [13] were probably the first to discuss the possibility to derive the respiratory signal from the ECG. Since then several algorithms were proposed to reconstruct respiration by demodulating the ECG signal (e.g.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for the classification of multi-modulating signals on the electrocardiogram

Mita

2006

Med Bio Eng Comput

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“…This provides the feasibility and potentiality to design wearable ECG-based RR measurement devices. The first study on respiration-induced ECG variation was proposed by Einthoven et al [12]. Flaherty and Riekkinen further analysed the respiration influence on children and cardiac patients by isopotential surface-mapping and vectorcardiography (VCG) [13,14].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

Bao

Abdala

Kamavuako

2020

Sensors

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The respiratory rate (RR) is a vital physiological parameter in prediagnosis and daily monitoring. It can be obtained indirectly from Electrocardiogram (ECG) signals using ECG-derived respiration (EDR) techniques. As part of the study in designing an early cardiac screening system, this work aimed to study whether the accuracy of ECG derived RR depends on the auscultation sites. Experiments were conducted on 12 healthy subjects to obtain simultaneous ECG (at auscultation sites and Lead I as reference) and respiration signals from a microphone close to the nostril. Four EDR algorithms were tested on the data to estimate RR in both the time and frequency domain. Results reveal that: (1) The location of the ECG electrodes between auscultation sites does not impact the estimation of RR, (2) baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, (3) using frequency domain features to estimate RR can provide more accurate RR except when using the band-pass filter algorithm. These results pave the way for ECG-based RR estimation in miniaturised integrated cardiac screening device.

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“…The basic principle of this cardio-respiratory signal is known and used in intensive care units (1)(2)(3)(4)(5) but not yet for MR acquisitions where interference of the hostile MR environment on the ECG recording is often stronger than the modulation caused by respiratory motion. Recently, we proposed an optically coupled sensor yielding an almost artifact-free ECG signal (6).…”

Section: Introductionmentioning

confidence: 99%

Amplitude demodulation of the electrocardiogram signal (ECG) for respiration monitoring and compensation during MR examinations

Felblinger¹,

Boesch²

1997

Magnetic Resonance in Med

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A method for respiration monitoring during MR-sequences using the electrocardiogram signal (ECG) is presented. The basic principle is known in conventional patient monitoring and is based on the fact that the amplitude of the ECG-signal varies with respiratory motion. A demodulation of this "cardio-respiratory signal" yields a respiration curve that can be used for patient monitoring, triggering, or respiration compensation of MRI sequences. Since no drift is introduced by digital demodulation, this method is superior to analog signal processing. The proposed method was applied during MR examinations using an optically coupled ECG sensor that has the advantage of almost negligible interference due to the MR sequences (< < 10% of the main ECG signal). The respiration signal obtained is strongly correlated (r = 0.79 to 0.98) with the position of the diaphragm in inferior-superior direction measured during breath-holding MRI.

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Influence of respiration on recording cardiac potentials

Cited by 41 publications

References 6 publications

Algorithm for the classification of multi-modulating signals on the electrocardiogram

Algorithm for the classification of multi-modulating signals on the electrocardiogram

Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

Amplitude demodulation of the electrocardiogram signal (ECG) for respiration monitoring and compensation during MR examinations

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