| Summary: | 博士 === 國立清華大學 === 電機工程學系 === 99 === Abstract
In this dissertation, the realization of real-time analyzer (RTA) for heart rate variability (HRV) analysis and their applications to congestive heart failure (CHF) are investigated. Implementations include wireless electrocardiogram (ECG) and personal computer (PC) based virtual RTAs, and their applications to CHF are studied. Firstly, we present the realization of ECG. Using low-cost components to replace expensive instrumentation and communication ICs in the circuits, we can not only reduce the system cost, but also resolve the cable inconvenience problems of the data acquisition. In addition, our result is good in noise immune. Next, through the virtual architecture, and modify the circuits of PC, we realized various RTAs capable of filtering, interval detecting, wavelet analyzing, and detrended fluctuation analysis (DFA), respectively. Then, use of DFA, approximate entropy and complexity measure as features of the adaptive neuro-fuzzy inference system (ANFIS) to CHF diagnosis is described. Finally, based on the support vector machine (SVM) we present the application of DFA during circadian observation, and conclude that 7PM~9AM is the best timing to diagnose CHF patient via the α1 scaling exponent of DFA.
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