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Research on Premature Ventricular Contraction Detection and Classification Technology of Electrocardiogram

Author: ZhuFei
Tutor: LvYangSheng
School: Tianjin University
Course: Biomedical Engineering
Keywords: Premature ventricular contractions Linear Neural Networks ECG waveform classification Fuzzy sets Membership Similarity
CLC: R541.7
Type: Master's thesis
Year: 2005
Downloads: 176
Quote: 6
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Abstract


Cardiovascular disease is one of the major diseases of today 's harm to human health , the ECG is an important method for the clinical diagnosis of cardiovascular disease , the use of computer automatic and accurate analysis of the ECG has been the subject of domestic and foreign scholars are keen . Premature ventricular contractions are the most common type of abnormal heart rhythms , premature ventricular contractions ECG characteristics both premature ventricular contractions abnormal ECG waveform detection method was designed and implemented . First , consider premature ventricular contraction ECG R-wave and S-wave characteristics, RS spacing volatility detection method proposed in this paper . This algorithm is based on the RS wave spacing and the RS wave amplitude of the ECG waveform classification and quantify the various classes of waveform , and then based on the waveform characteristics of each patient to identify the quantitative value of its normal waveform , thus achieving the abnormal heart electric waveform detection . The articles focus on a linear neural network based on the RR interval the average height of the R wave , R wave polarity and QRS duration characteristics premature ventricular contractions abnormal ECG common testing standards on the basis of ECG waveform detection algorithm , the linear neural network design and training process . Standard MIT / BIH arrhythmia database verification , these two methods for the detection of premature ventricular contractions waveform has a certain effect . Especially linear neural network detection algorithm based on the characteristics of patient ECG waveform difference parameter threshold selection can achieve satisfactory recognition effect . QRS complex automatic analysis of the ultimate goal is to let the computer instead of the doctors who have completed the classification of the QRS complex , and automatically make a diagnosis based on other clinical features . This paper introduces the concept of fuzzy set and membership function of the fuzzy mathematics , the slope of the ECG waveform fuzzy characterization , the degree of membership of each ECG waveform collection , so as to extract the characteristics of the ECG waveform . Based on the collection of membership , the paper proposes an ECG waveform similarity metrics , and metrics based on the fuzzy self- clustering of the ECG waveform . MIT / BIH the standards arrhythmia database , the experimental results show that this paper, the ECG waveform similarity definition has some reasonable the waveform classification algorithm practicable .

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CLC: > Medicine, health > Internal Medicine > Heart, blood vessels ( circulatory ) disease > Heart disease > Arrhythmia
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