Analysis Of Electrocardiograms During Human Ventricular Arrhythmias For Optimizing Treatment Options

Cardiovascular diseases are diseases that arise from abnormal medical conditions of the heart and the circulation system. Ventricular arrhythmias are a subset that originates from rhythm disorders of the lower chambers (ventricles) of the heart. In spite of research and technology advancements, annually 350,000 sudden cardiac deaths are reported in North America (45,000 in Canada) most of which are ventricular fibrillation (VF) related. This serves as a strong motivation to improve upon or optimize the choice of current treatment options from an engineering perspective which could eventually help reduce the number of SCDs. The choice of the treatment vary in general based on the following two categories of affected population and the type of arrhythmia: (1) symptomatic patients who are prone to or have had arrhythmia occurrences and are currently under medical care and (2) people who suffer ventricular arrhythmias in an out-of-the-hospital environment. This thesis, by employing advanced signal analysis, attempts to improve the characterization of the ventricular arrhythmias, thereby providing better iscriminatory clues in assisting clinicians and emergency medical staff (EMS) to arrive at optimal treatments options for both the categories of affected population. In the study of symptomatic patients, the organizational structure of the arrhythmia was quantified using wavelet-singular value decomposition analysis, which lead to a novel sub-classification of the ventricular arrhythmia. Classification accuracies of 93.7% for ventricular tachycardia (VT)/non-VT classification and 80% for organized-VF /disorganized-VF classification were achieved. In the study of out-of-the-hospital arrhythmia instances, focal structural variations were analyzed using wavelets, which led to identifying a signal pattern that could serve as an important clue for the EMS personnel to improve the resuscitation outcomes. Using a database of 25 out-of-the hospital arrhythmia segments, the proposed analysis yielded a classification accuracy of 80%.

Analysis Of Electrocardiograms During Human Ventricular Arrhythmias For Optimizing Treatment Options

Cardiovascular diseases are diseases that arise from abnormal medical conditions of the heart and the circulation system. Ventricular arrhythmias are a subset that originates from rhythm disorders of the lower chambers (ventricles) of the heart. In spite of research and technology advancements, annually 350,000 sudden cardiac deaths are reported in North America (45,000 in Canada) most of which are ventricular fibrillation (VF) related. This serves as a strong motivation to improve upon or optimize the choice of current treatment options from an engineering perspective which could eventually help reduce the number of SCDs. The choice of the treatment vary in general based on the following two categories of affected population and the type of arrhythmia: (1) symptomatic patients who are prone to or have had arrhythmia occurrences and are currently under medical care and (2) people who suffer ventricular arrhythmias in an out-of-the-hospital environment. This thesis, by employing advanced signal analysis, attempts to improve the characterization of the ventricular arrhythmias, thereby providing better iscriminatory clues in assisting clinicians and emergency medical staff (EMS) to arrive at optimal treatments options for both the categories of affected population. In the study of symptomatic patients, the organizational structure of the arrhythmia was quantified using wavelet-singular value decomposition analysis, which lead to a novel sub-classification of the ventricular arrhythmia. Classification accuracies of 93.7% for ventricular tachycardia (VT)/non-VT classification and 80% for organized-VF /disorganized-VF classification were achieved. In the study of out-of-the-hospital arrhythmia instances, focal structural variations were analyzed using wavelets, which led to identifying a signal pattern that could serve as an important clue for the EMS personnel to improve the resuscitation outcomes. Using a database of 25 out-of-the hospital arrhythmia segments, the proposed analysis yielded a classification accuracy of 80%.

Relative contribution of Trend and Interannually-varying SST Anomalies to the 2018 Heat Waves in the Extratropical Northern Hemisphere

The northern extratropics—including regions in northern Europe, northeast Asia, and North America—experienced extremely prolonged heat waves during May–August 2018. Record-breaking surface temperatures, which caused numerous deaths, were observed in several cities. The 2018 heat waves exhibited a circumglobal characteristic owing to a circumpolar perturbation (CCP) in the middle–upper troposphere of the Northern Hemisphere (NH). The CCP had two parts: a wave-like perturbation and a hemispheric perturbation that was almost zonally symmetric. Singular value decomposition analysis revealed that the zonally symmetric perturbation was coupled to the SST warming trend, whereas the wave-like perturbation was primarily coupled to the interannually-varying SST anomaly (SSTA), particularly in the tropical North Pacific, which reached an extreme in 2018. Numerical experiments confirmed that the zonally symmetric component was primarily resulted from the SSTA associated with the warming trend, whereas the interannually-varying SSTAs in the NH contributed mostly to the wave-like perturbation. The warming trend component of SSTA, especially that in the tropics, compounded by the unusually large SSTAs in 2018, was hypothesized to have contributed to inducing the circumpolar circulation anomaly that caused the record-breaking heat waves in the extratropical NH in 2018.

Time-resolved spectroscopic analysis of the light-energy harvesting mechanism in carbazole-dendrimers with a blue-phosphorescent Ir-complex core

In order to understand the light energy-harvesting mechanism, singular value decomposition analysis was performed to classify the temporal and spectral species in transient absorption spectra.