Stroke and ventricular arrhythmias

Koppikar, Sahil; Baranchuk, Adrián; Guzmán, Juan C.; Morillo, Carlos A.

doi:10.1016/j.ijcard.2013.03.058

Cited by 48 publications

(42 citation statements)

References 75 publications

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“…Five features were considered for obtaining the disease prediction using the three methods RS-RMC, PPM-VA and PE-SSTD respectively. From the figure, the value of disease prediction rate achieved using the proposed RS-RMC framework is higher when compared to two other existing techniques namely, PPM-VA [1] and PE-SSTD [2]. Besides we can also observe that by increasing the number of patients who provide their disease features, the disease prediction rate is increased using all the methods.…”

Section: Disease Prediction Ratementioning

confidence: 78%

“…The RS-RMC framework is simulated using MATLAB. The experimental work is compared against the existing Prevention and Potential Management of Ventricular Arrhythmias (PPM-VA) [1] and Prediction of Events using Spatio Spectro Temporal Data (PE-SSTD) [2] to identify the effectiveness of RS-RMC framework. The performance of the RS-RMC framework is measured in terms of disease prediction rate, execution time and false positive rate on effective disease diagnosis and class accuracy.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Potential Management of Ventricular Arrhythmias (PPM-VA) [1] identified the stroke pattern and management of ventricular Arrhythmias that were largely related to stroke for the effective diagnosis of disease at an early stage. Prediction of Events using Spatio Spectro Temporal Data (PE-SSTD) [2] provided with a case study on stroke resulting in the accuracy of the disease being diagnosed.…”

Section: Introductionmentioning

confidence: 99%

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Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Prema¹,

Maheswari²

2016

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Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Naïve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-ofthe-art works.

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Section: Disease Prediction Ratementioning

confidence: 78%

Section: Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Prema¹,

Maheswari²

2016

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“…An inhibitory influence of these parietal regions on the insular cortex, which breaks away after ischemic injury and thus causes a sympathetic disinhibition, was proposed as a model [22]. On the cellular level, it is assumed that an unphysiologically high probability of open calcium channel occurs due to the overwhelming activation of ß-adrenoceptors by the increased release of cAMP [23][24][25]. The increased intracellular calcium concentration in turn causes a metabolic imbalance and a myocardial relaxation disorder leading up to cell death and myocytolysis.…”

Section: Tachycardic Arrhythmiasmentioning

confidence: 99%

“…The increased intracellular calcium concentration in turn causes a metabolic imbalance and a myocardial relaxation disorder leading up to cell death and myocytolysis. Furthermore, the increased sympathetic tone may cause electrolyte disturbances in the serum, in particular hypokalemia and hypomagnesemia, which in turn favor the occurrence of arrhythmias [25]. A prolongation of the QT time is already evident in the 12-lead ECG as a sign of an increased risk of ventricular arrhythmia in a significant section of the patients [26].…”

Section: Tachycardic Arrhythmiasmentioning

confidence: 99%

Cardiac Arrhythmia following Stroke

Kallmünzer

Schwab

2017

Neurology International Open

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The autonomous innervation of the heart is based on a complex interaction of central and peripheral mechanisms in sympathetic and parasympathetic parts of the autonomic nervous system (▶Fig. 1). An extrinsic cardiac innervation is distinguished from an intrinsic one. Extrinsic systemTo put it simply, a center of the efferent parasympathetic innervation of the heart is found in the nucleus of the medulla oblongata, especially in the nucleus dorsalis [1]. The preganglionic fibers leave the central nervous system along with the vagus nerve and are connected to the second neuron in cardiac ganglion cells. Sympath etic fibers, on the other hand, originate from the nucleus intermediolateralis of the upper thoracic cord [2]. They reach the plexus cardiacus by means of the so-called "cardiac nerves" (usually 4 right-sided and 3 left-sided nervi cardiaci), after interposition in the border ganglia, especially in the ganglion stellatum. For the control of cardiac functions, the profound part of the plexus cardiacus is decisive; it extends between the tracheal bifurcation and the aortic arch, divides into a right and left half and in its further course follows the right or left coronary artery. The function of the efferent limb is modified by afferents from various organ systems, including the gastrointestinal tract or the carotissinus, by means of multisynaptic reflex arcs (e. g., baroreflex) [1]. In addition, in the blood circulating catecholamine, the release of which in the adrenal system is also subject to the autonomic system, mediates beta-adrenergic effects on the myocardium. It is postulated that dysbalances in the extrinsic system, in particular the pathological disinhibition of sympathetic activity, are relevant to the development of cardiac arrhythmias [1,[3][4][5]. For example, extensive brain stem damage can lead to suppression of central parasympathetic functions and overcoming of sympathetic tone [6], thus promoting the occurrence of cardiac arrhythmias. Conversely, blockade of the ganglion stellatum or renal sympathetic denervation should reduce the risk of ventricular arrhythmias in patients with prolonged-QT syndromes [7]. It is noteworthy in this context that extensive cardiac denervation, which may be present after orthotopic heart transplantation, is associated with a lower risk of supra ventricular rhythm disturbances, especially atrial fibrillation [8]. In these cases, the denervation should have protective effects and protect the myocardium from the autonomic imbalances of the extrinsic system before transmission. It should be noted, however, that organ donors are usually much younger and the vascular damage to the donor organs is often less pronounced, which significantly reduces the risk of atrial fibrillation independent of cardiac denervation [9].

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Wounded brain, ailing heart: Central autonomic network disruption in acute stroke

Sörös

Hachinski

2017

Annals of Neurology

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Stroke and ventricular arrhythmias

Cited by 48 publications

References 75 publications

Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Cardiac Arrhythmia following Stroke

Wounded brain, ailing heart: Central autonomic network disruption in acute stroke

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Stroke and ventricular arrhythmias

Cited by 48 publications

References 75 publications

Multitude Classifier Using Rough Set Jelinek Mercer Na&#239;ve Bayes for Disease Diagnosis

Multitude Classifier Using Rough Set Jelinek Mercer Na&#239;ve Bayes for Disease Diagnosis

Cardiac Arrhythmia following Stroke

Wounded brain, ailing heart: Central autonomic network disruption in acute stroke

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Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis