心電圖頻譜分析研究

Abstract

根據行政院衛生署統計，民國九十六年國人十大死因排行榜，心血管疾病死亡率位居第二順位。由於心臟病所引發的心臟血管疾病是無法預期，所以發展一套緊急的警告系統去即時偵測心臟疾病是很重要的，而利用心電圖(Electrocardiogram)來檢測心臟疾病為最簡便有效，且心電圖有價格便宜、即時偵測和容易取得…等優點，因此在臨床上被廣泛使用。 心臟藉由收縮與舒張的運動來傳送血液、氧氣、養份至身體不同的氣官，對人體的循環系統有重大的影嚮力。但隨著心理狀況的不同，心臟跳動的強度和頻率也會有所調整。若仔細觀察，即使人體處於休息狀態也會發現心跳與心跳時間間隔都有微小的差異，即稱為心律變異度。臨床上將心率變異度用來評估自主神經系統的活絡性，其中自主神經系統可以分為交感及副交感神經，影響人體內許多生理變化，故將其發展成為一種生理的指標，可以協助醫生在診斷時作一個參考依據。 傳統利用心率變異度來分析心電圖訊號時，必須先偵測出QRS複合波的位置，再計算兩兩R波峰值之間的時間間隔R-R interval，最後由時域的角度對R-R interval作更進一步的分析。而本文則從不同以往對R-R interval分析的觀點切入，選定既有部份訊號的邊界值，來預測函數後，將其連接起來，使其成為類週期函數，並且連接成質數週期重新取樣後，濾出旁瓣現象，來量測心電圖的真實頻率，我們可以藉由頻譜的倍頻及半高寬分析，來辨別出正常心律及心律不整的人頻譜變化。以及正常心律若出現問題時，會先從高頻的部份開始出現倍頻出現偏移及半高寬會延伸現象，頻譜未來將可以提供早期預測疾病的功能。將來在臨床疾病的治療方面，得知病情的輕重程度。未來將可以此結果建立成資料庫，作為辨別疾病的依據以作為早期治療的預測。

According to statistic of Department of Health, Executive Yuan, R.O.C. in 2007, cardiovascular disease has been listed as the second rank of the top ten leading causes of death. Since cardiovascular disease induced heart attack may often occur in unexpected situation, development of an emergent alarm system for early detection of heart dysfunction is important. One most efficient and easy way for detection of early heart dysfunction is the use of electrocardiogram (ECG). ECG has advantages of cheap, real-time detection, and easy to implement which has been widely used in clinics. Via the delivery of blood, heart transfers oxygen and nutrients to various organs and is thus highly influential for circulatory system. To adapt to the variation of physiological conditions, the intensity and frequency of heart beats change with time. Careful observation finds that the time intervals between heartbeats are often different even if the body is at rest. Such as heart rate variability (HRV) has been used to estimate the activity of the autonomic nervous system which can be divided into sympathetic and parasympathetic subsystems both of which can significantly affect the physiological indicator to assist doctors in making diagnostic decisions. cator to assist doctors in making diagnostic decisions. Many studies have used HRV to analyze the ECG signal via studying the QRS complex waveform to determine the time intervals between R-peaks and analyze the R-R intervals from time and frequency domains. Different from the conventional R-R interval based approach, this work introduces new feature variables by computing the spectrogram of the ECG signal waveform. We selected part of the original ECG waveform to get boundary condition and generate predicted function. The new signal is qusai-periodical signal. To clearly analyze the power spectrum, we used the new signal base as qusai-periodical signal to continue to increase the frequent resolution. So that we can clearly distinguish between normal sinus rhythm (NSR) and arrhythmia. The method is validated through experiments on the MIT–BIH databases. The result shows that we can apply this method to distinguish between NSR and arrhythmia. After we filter out sidelobe, now we can get actual frequency without disturbance.