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心室纤颤预警时频特征算法的分析论文
Abstract
ABSTRACT
In recent years, the incidence of cardiovascular disease increases over the years,
seriously threatening the lives of people. The sudden cardiac death is the most serious
symptom of arrhythmia, if defibrillation is not timely, survival probability of the patient
will reduce with the time. We know that the sudden cardiac death results from
ventricular fibrillation (VF) or sustained ventricular tachycardia (VT) in most
conditions. VF is the most deadly, and VT will quickly turn into VF, which leads to
sudden death. Therefore, it is particularly important to detect ventricular fibrillation
rapidly and accurately.
The VF detection algorithms are analyzed in time and frequency domain in this
paper. We find that the TCSC algorithm does not consider the shape of the ECG signal,
for that reason it fails to separate VT from VF successfully. Meanwhile, the standard
exponential algorithm is simple in time domain, which searches the absolute maximum
value of the investigated sequence of the signal. It therefore forms a gradually
decreasing exponential curve, and we then count the number of crossing points of the
ECG signal with the exponential curve to classify different types of ECG signals. But its
result is not satisfactory. Then we analyze the Lempel-Ziv complexity algorithm, which
transforms the ECG signal into a binary sequence and searches for repeated patterns to
detect VT and VF. When the heart changes fast and then exhibits arrhythmia, the
corresponding complexity become higher, so it is difficult to detect VT and VF signals.
Last but not the least we study empirical mode decomposition which originates from the
frequency domain. Our r
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