Study Of Electrocardiography Power Spectrum
Date Issued
2016
Date
2016
Author(s)
Wang, Chih-Yuan
Abstract
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.
Subjects
Electrocardiogram(ECG)
Power spectrum
Predicted function
Boundary condition
Qusai-periodical signal
sidelobe
SDGs
Type
thesis