scholarly journals QUANTIFICATION OF RESPIRATORY SINUS ARRHYTHMIA WITH HIGH-FRAMERATE ELECTRICAL IMPEDANCE TOMOGRAPHY

2013 ◽  
Vol 53 (6) ◽  
pp. 854-861
Author(s):  
Christoph Hoog Antink ◽  
Steffen Leonhardt
Keyword(s):  
Heart Rate ◽  
Quantitative Analysis ◽  
Tidal Volume ◽  
Electrical Impedance Tomography ◽  
Respiratory Sinus Arrhythmia ◽  
Electrical Impedance ◽  
Cardiac Activity ◽  
Patient Specific ◽  
Impedance Tomography ◽  
Sinus Arrhythmia

Respiratory Sinus Arrhythmia, the variation in the heart rate synchronized with the breathing cycle, forms an interconnection between cardiac-related and respiratory-related signals. It can be used by itself for diagnostic purposes, or by exploiting the redundancies it creates, for example by extracting respiratory rate from an electrocardiogram (ECG). To perform quantitative analysis and patient specific modeling, however, simultaneous information about ventilation as well as cardiac activity needs to be recorded and analyzed. The recent advent of medically approved Electrical Impedance Tomography (EIT) devices capable of recording up to 50 frames per second facilitates the application of this technology. This paper presents the automated selection of a cardiac-related signal from EIT data and quantitative analysis of this signal. It is demonstrated that beat-to-beat intervals can be extracted with a median absolute error below 20 ms. A comparison between ECG and EIT data shows a variation in peak delay time that requires further analysis. Finally, the known coupling of heart rate variability and tidal volume can be shown and quantified using global impedance as a surrogate for tidal volume.

scholarly journals A model-based analysis of autonomic nervous function in response to the Valsalva maneuver

2019 ◽  
Vol 127 (5) ◽  
pp. 1386-1402 ◽  
Author(s):  
E. Benjamin Randall ◽  
Anna Billeschou ◽  
Louise S. Brinth ◽  
Jesper Mehlsen ◽  
Mette S. Olufsen
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Respiratory Sinus Arrhythmia ◽  
Valsalva Maneuver ◽  
Patient Specific ◽  
Control Mechanisms ◽  
Sinus Arrhythmia ◽  
Control Subjects ◽  
Model Based ◽  
Model Based Analysis

The Valsalva maneuver (VM) is a diagnostic protocol examining sympathetic and parasympathetic activity in patients with autonomic dysfunction (AD) impacting cardiovascular control. Because direct measurement of these signals is costly and invasive, AD is typically assessed indirectly by analyzing heart rate and blood pressure response patterns. This study introduces a mathematical model that can predict sympathetic and parasympathetic dynamics. Our model-based analysis includes two control mechanisms: respiratory sinus arrhythmia (RSA) and the baroreceptor reflex (baroreflex). The RSA submodel integrates an electrocardiogram-derived respiratory signal with intrathoracic pressure, and the baroreflex submodel differentiates aortic and carotid baroreceptor regions. Patient-specific afferent and efferent signals are determined for 34 control subjects and 5 AD patients, estimating parameters fitting the model output to heart rate data. Results show that inclusion of RSA and distinguishing aortic/carotid regions are necessary to model the heart rate response to the VM. Comparing control subjects to patients shows that RSA and baroreflex responses are significantly diminished. This study compares estimated parameter values from the model-based predictions to indices used in clinical practice. Three indices are computed to determine adrenergic function from the slope of the systolic blood pressure in phase II [ α (a new index)], the baroreceptor sensitivity ( β), and the Valsalva ratio ( γ). Results show that these indices can distinguish between normal and abnormal states, but model-based analysis is needed to differentiate pathological signals. In summary, the model simulates various VM responses and, by combining indices and model predictions, we study the pathologies for 5 AD patients. NEW & NOTEWORTHY We introduce a patient-specific model analyzing heart rate and blood pressure during a Valsalva maneuver (VM). The model predicts autonomic function incorporating the baroreflex and respiratory sinus arrhythmia (RSA) control mechanisms. We introduce a novel index ( α) characterizing sympathetic activity, which can distinguish control and abnormal patients. However, we assert that modeling and parameter estimation are necessary to explain pathologies. Finally, we show that aortic baroreceptors contribute significantly to the VM and RSA affects early VM.

Quantitative analysis of respiratory sinus arrhythmia during heart rate fluctuations

1995 ◽  
Vol 56 (1-2) ◽  
pp. 142
Author(s):  
Ikuo Isogai ◽  
Shigeru Kageyama ◽  
Kazuo Aihara ◽  
Yukihide Isogai ◽  
Fusao Katoh
Keyword(s):  
Heart Rate ◽  
Quantitative Analysis ◽  
Respiratory Sinus Arrhythmia ◽  
Sinus Arrhythmia ◽  
Heart Rate Fluctuations

Cardiac eigen imaging: a novel method to isolate cardiac activity in thoracic electrical impedance tomography

2020 ◽  
Vol 41 (9) ◽  
pp. 095008
Author(s):  
Saaid H Arshad ◽  
Ethan K Murphy ◽  
Joshua M Callahan ◽  
James T DeVries ◽  
Kofi M Odame ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Cardiac Activity ◽  
Impedance Tomography ◽  
Thoracic Electrical Impedance ◽  
Novel Method

scholarly journals Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses

2017 ◽  
Vol 122 (4) ◽  
pp. 855-867 ◽  
Author(s):  
Christian J. Roth ◽  
Tobias Becher ◽  
Inéz Frerichs ◽  
Norbert Weiler ◽  
Wolfgang A. Wall
Keyword(s):  
Mechanical Ventilation ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Distress Syndrome ◽  
Lung Model ◽  
Patient Specific ◽  
Impedance Tomography

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel “virtual EIT” module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future.

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