Autonomic Nervous Regulation Of Atrial Fibrillation Based On Ecg Database

Atrial fibrillation (AF) is one of the most common sustained arrhythmias in clinical practice associated with serious morbidity and mortality. With increasing survival rates of cardiovascular diseases and aging of population, it is predicted that AF will be one of the most prevalent cardiovascular diseases in the next half century.Sinus rhythm restoration and overall rate control are two main treatments for AF and help to relieve clinical symptoms significantly. However, both of them have little progress in reducing the rate of recurrence or morbidity of AF, inclining to induce further arrhythmias, strokes, myocardial detriments or cardiac dysfunctions. Radio frequency cardiac ablation therapy showed improvements in AF in pilot clinical studies, but it has not been accepted and applied routinely with its low success rate and pending operation difficulties. Hemodynamic changes associated with AF have been extensively studied, but the neural changes resulting from this arrhythmia remain unclear. Although both animal experiments and clinical researches have demonstrated that autonomous nerve system (ANS) plays an important role in the occurrence and maintenance of AF, the exact mechanisms have not been fully understood. Therefore, a comprehension of the modulation of ANS on AF may shed light on the etiology behind the reduced survival in AF cases, thus effective preventions and therapies of AF.In aim to investigate the underlying ANS role in AF, this study comprised the following four main parts. The first part was to develop an ECG information database. The second one was to accomplish ECG signal preprocessing and QRS complex detection. The third was to study the complexity and irregularity and their circadian rhythm of ventricular response interval (VRI) dynamics in AF compared with those in sinus rhythm (SR). The forth was to introduce concepts and methods of data mining into ECG information processing based on the database developed in the second part. In part four, clustering was used for the potential prognosis factors of AF and the diagnosis of cardiovascular diseases.The main innovations of this study are:1. A scalable relational ECG information database was designed and developed to becompatible with the international commonly used ECG signal databases. It was designed with the "entity-relationship" data model, and included as many as 11 different entities (tables) and 113 attributes. This relational database allowed different types of data to be operated and shared effectively and external entities or models to be integrated conveniently.2. Rich information on clinical examinations and diagnosis, along with 1200 long time (24-hour) dynamic Holter ECG signals from 816 patients, made the developed database to achieve its capacity of more than 150 GB. With various kinds of cardiovascular diseases, records in the database contained abundant clinical...