Amirali Shayan AraniИсточник: http://roger.ucsd.edu/... Abstract of the thesisHeart activities are governed by the sympathetic and parasympathetic nervous system. Changes in these neural signals influence the development of arrhythmias, including ventricular tachyarrhythmias that often progress to sudden cardiac death (SDC). A simultaneous study of the cardio neural signals with the electrical heart activity can elucidate the mechanism of abnormal heart activity. In this work, we identify the locations to detect cardio neural signals. We first perform a high density electrode measurement to explore the cardio neural signals. We then focus on selected locations for longer period measurement. We monitor the changes in the heart rate following each sympathetic or vagal nerve activity. The neural signals are extracted from ECG by applying independent component analysis. The work provides a promising approach for studying the association of cardio neural signals with electrical heart activity. IntroductionAccording to American Heart Association heart diseases are America's number one killer. Cardioneural signal influence heart activities and rhythms. Damage to nerves extrinsic to the heart, such as the stellate ganglia, as well as to intrinsic cardiac nerves from diseases that may aspect nerves primarily, such as viral infections, or secondarily, from the disease that cause cardiac damage, may produce cardio neuropathy. Also, the nerve ber density and regional sympathetic hyperinnervation in the ventricles is correlated with the occurrence of spontaneous ventricular tachyarrythmia or Sudden Cardiac Death. In this work we proposed a comprehensive study for exploring and analyzing cardioneural signal to track, monitor and potentially prevent heart arrhythmias. In our current study, we explored the cardioneural signal from noninvasive measurements. Previously, Jung et al.[19] performed an invasive nerve study for monitoring the circadiane nerve activities in ambulatory dogs. Their study results motivated us, to develop an original research for exploring the cardioneural signal on human subjects. Hence, we performed both high density and portable noninvasive measurements to extract the cardioneural signal. We tried di®erent combinations of the activities and location to probe the cardioneural signal based on the heart rate variation. We applied ICA algorithm to decompose the cardioneural signal from mixture of ECG measurements. The qualitative promising results demonstrate the feasibility of this direction. First, we develop set of high density Electrocardiogram (ECG) measurements with around 100 electrodes for a comprehensive study of the ECG signal. Then we applied Independent Component Analysis algorithm as a blind source decomposition method for decomposing original ECG temporal and spatial sources. We analyzed the back projection of each source and its corresponding dipole vector on the body surface potential map to localize failure in the heart activity. Since 100 channels were redundant for the separation, we derived a methodology for the minimum possible electrode number and locations. We proposed the method for localizing each ECG source component dipole vector that can be used to track the activity of each source during the time and space domain. This approach facilitates the feasibility of the ICA study on the patients with moderate spatial resolution, since it requires significantly fewer leads for recording and easier diagnosis setup. Next, we explored cardioneural signal which is used for tracking and monitoring heart arrhythmias. We performed both high density and portable noninvasive measurements on human subject to localize the cardioneural signal. We extract cardioneural signal using ICA. Our method and experimental results show a promising method to extract the neural signals from ECG sources. We localized the cardioneural signals based on the changes in the heart rate and the symmetric SGNA. We applied ICA to decompose the cardioneural signals from the ECG mixture.
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