- 學位論文
基於心率變異度分析之心室早期收縮偵測
A Premature Ventricular Contraction (PVC) Detection Scheme Based on Heart Rate Variability (HRV) Statistics
摘要
隨著科技的發展，人們的生活水準提高，伴隨而來的是生活壓力也愈來愈大，身體在承受巨大壓力下，久而久之健康就亮起了紅燈。心律不整是身體健康亮起紅燈的重要症狀之一，要如何發現心律不整的方法及技術就是非常重要的課題。人體心臟受到自主神經和生理激素影響，心律會產生變化，有時忽高有時忽低，這就說明心律與生理參數相關。心率變異分析(Heart Rate Variability，簡稱HRV)，它可用來測量心臟跳動速率變化的程度，可藉由觀察心率變異的程度來判別自律神經的強弱。 本論文採用MIT-BIH心律不整資料庫來分析心電圖的資料，利用Cubic Spline內插法計算出HRV的時域與頻域的參數，再利用Statistical Product and Service Solutions (SPSS)將統計數據進行多元迴歸分析，分析HRV的參數與心室早期收縮之間的關係，可發現利用VLFP、LFP、HFP這三個參數可預測是否有心室早期收縮徵狀，接下來可利用So-and-chan演算法找出每筆心電圖中的RR區間，利用RR區間的變化的異常，可偵測出發生心室早期收縮(PVC)的時間點。
並列摘要
With the rapid development of science and technology, people get more pressure from their daily lives. An important sign of being unhealthy is having heart arrhythmia. Diagnosis for the arrhythmia is important technique to saving people's life. In particular, people's heart beats are affected by autonomic nervous system and physiological hormone. The Heart Rate Variability (HRV) is used to measure the changes of the heart beat rates, and we can study the strength of autonomic nervous system by analyzing it. The database of arrhythmia used in this thesis is from MIT-BIH. We also applied the Cubic Spline interpolation to derive the HRV parameters. The Statistical Product and Service Solutions (SPSS) is used to analyze the relation between parameters of HRV and a typical arrhythmia called Premature Ventricular Contraction (PVC). Results showed that the very low frequency power (VLFP), low frequency power(LFP), high frequency power(HFP) in HRV can be used to determine the PVC arrhythmia.