Cardiac afferent signaling partially underlies premature ventricular contraction–induced cardiomyopathy

Hori, Yuichi; Temma, Taro; Wooten, Christian; Sobowale, Christopher O; Chan, Christopher; Swid, Mohammed Amer; Ajijola, Olujimi A.

doi:10.1016/j.hrthm.2021.04.004

Cited by 10 publications

(10 citation statements)

References 33 publications

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“…It should be noted that a subset of HF animals ( n = 6) underwent an intervention, epicardial application of resiniferatoxin (RTX) to study its effects on the progression of cardiomyopathy as a separate study. However no significant effect of RTX was noted in any of the echocardiographic, serum, physiological, and autonomic tests ( Hori et al, 2021 ). Hence, in this work, we combined RTX-treated HF animals with untreated HF animals.…”

Section: Methodsmentioning

confidence: 95%

“…The heart failure model was created with implanted pacemakers (Viva Cardiac Resynchronization Therapy-Pacemaker, Biotronik, Lake Oswego, OR), as previously described ( Hori et al, 2021 ), and summarized in Fig. 1D.…”

Section: Methodsmentioning

confidence: 99%

“…Heart failure model was created with implanted pacemakers (Viva Cardiac Resynchronization Therapy-Pacemaker, Biotronik, Lake Oswego, OR), as previously described [27], visualized in Figure 1D. After implantation, animals had a recovery period of 48 hours and chronic bigeminy pacing was initiated from the right ventricle.…”

Section: Animal Experimentsmentioning

confidence: 99%

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Metrics of High Cofluctuation and Entropy to Describe Control of Cardiac Function in the Stellate Ganglion

Gurel

Hadaya

et al. 2021

Preprint

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Neural control of the heart involves dynamic adaptation of mechanical and electrical indices to meet blood flow demands. The control system receives centrally-derived inputs to coordinate cardiac function on a beat-by-beat basis, producing 'functional' outputs such as the blood pressure waveform. Bilateral stellate ganglia (SG) are responsible for integration of multiple inputs and efferent cardiopulmonary sympathetic neurotransmission. In this work, we investigate network processing of cardiopulmonary transduction by SG neuronal populations in porcine with chronic pacing-induced heart failure and control subjects. We derive novel metrics to describe control of cardiac function by the SG during baseline and stressed states from in vivo extracellular microelectrode recordings. Network-level spatiotemporal dynamic signatures are found by quantifying state changes in coactive neuronal populations (i.e., cofluctuations). Differences in 'neural specificity' of SG network activity to specific phases of the cardiac cycle are studied using entropy estimation. Fundamental differences in information processing and cardiac control are evident in chronic heart failure where the SG exhibits: i) short-lived, high amplitude cofluctuations in baseline states, ii) greater variation in neural specificity to cardiac cycles, iii) limited sympathetic reserve during stressed states, and iv) neural network activity and cardiac control linkage that depends on disease state and cofluctuation magnitude. These findings indicate that spatiotemporal dynamics of stellate ganglion neuronal populations are altered in heart failure, and lay the groundwork for understanding dysfunction neuronal signaling reflective of cardiac sympathoexcitation.

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Section: Methodsmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

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Metrics of High Cofluctuation and Entropy to Describe Control of Cardiac Function in the Stellate Ganglion

Gurel

Hadaya

et al. 2021

Preprint

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“…The extracted statistical features comprise of the mean and standard deviation of the RR fragment. Also, it should be noticed that the percentage of differences among the neighboring RR intervals is greater than 10 ms or 50 ms (pRR10 and pRR50) [ 30 – 32 ]. An explanation of the time-domain features is demonstrated in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach

Sarshar

Mirzaei

2022

Journal of Healthcare Engineering

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Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and noninvasive tool employed to recognize the actual life threat related to the heart. Abnormal ECG heartbeat and arrhythmia are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is one of the most common arrhythmias which begins from the lower chamber of the heart and can cause cardiac arrest, palpitation, and other symptoms affecting all activities of a patient. Nowadays, computer-assisted techniques reduce doctors' burden to assess heart arrhythmia and heart disease automatically. In this study, we propose a PVC recognition based on a deep learning approach using the MIT-BIH arrhythmia database. Firstly, 10 heartbeat and statistical features including three morphological features (RS amplitude, QR amplitude, and QRS width) and seven statistical features are computed for each signal. The extraction process of these features is conducted for 20 s of ECG data that create a feature vector. Next, these features are fed into a convolutional neural network (CNN) to find unique patterns and classify them more effectively. The obtained results prove that our pipeline improves the diagnosis performance more effectively.

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“…The HF model was created with implanted pacemakers (Viva Cardiac Resynchronization Therapy–Pacemaker, Biotronik, Lake Oswego, OR), as previously described ( Hori et al, 2021 ), and summarized in Figure 6D . After implantation, animals had a recovery period of 48 hr and chronic bigeminy pacing was initiated from the right ventricle.…”

Section: Methodsmentioning

confidence: 99%

Metrics of high cofluctuation and entropy to describe control of cardiac function in the stellate ganglion

Gurel¹,

Sudarshan

Hadaya³

et al. 2022

eLife

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Stellate ganglia within the intrathoracic cardiac control system receive and integrate central, peripheral, and cardiopulmonary information to produce postganglionic cardiac sympathetic inputs. Pathological anatomical and structural remodeling occurs within the neurons of the stellate ganglion (SG) in the setting of heart failure. A large proportion of SG neurons function as interneurons whose networking capabilities are largely unknown. Current therapies are limited to targeting sympathetic activity at the cardiac level or surgical interventions such as stellectomy, to treat heart failure. Future therapies that target the stellate ganglion will require understanding of their networking capabilities to modify any pathological remodeling. We observe SG networking by examining cofluctuation and specificity of SG networked activity to cardiac cycle phases. We investigate network processing of cardiopulmonary transduction by SG neuronal populations in porcine with chronic pacing-induced heart failure and control subjects during extended in-vivo extracellular microelectrode recordings. We find that information processing and cardiac control in chronic heart failure by the SG, relative to controls, exhibits: i) more frequent, short-lived, high magnitude cofluctuations, ii) greater variation in neural specificity to cardiac cycles, and iii) neural network activity and cardiac control linkage that depends on disease state and cofluctuation magnitude.

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Cardiac afferent signaling partially underlies premature ventricular contraction–induced cardiomyopathy

Cited by 10 publications

References 33 publications

Metrics of High Cofluctuation and Entropy to Describe Control of Cardiac Function in the Stellate Ganglion

Metrics of High Cofluctuation and Entropy to Describe Control of Cardiac Function in the Stellate Ganglion

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach

Metrics of high cofluctuation and entropy to describe control of cardiac function in the stellate ganglion

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