Comput. Biol. Med. - Real-time CHF detection from ECG signals using a novel discretization method.

Tópicos

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Resumo

This study proposes a new method, equal frequency in amplitude and equal width in time (EFiA-EWiT) discretization, to discriminate between congestive heart failure (CHF) and normal sinus rhythm (NSR) patterns in ECG signals. The ECG unit pattern concept was introduced to represent the standard RR interval, and our method extracted certain features from the unit patterns to classify by a primitive classifier. The proposed method was tested on two classification experiments by using ECG records in Physiobank databases and the results were compared to those from several previous studies. In the first experiment, an off-line classification was performed with unit patterns selected from long ECG segments. The method was also used to detect CHF by real-time ECG waveform analysis. In addition to demonstrating the success of the proposed method, the results showed that some unit patterns in a long ECG segment from a heart patient were more suggestive of disease than the others. These results indicate that the proposed approach merits additional research.

Resumo Limpo

studi propos new method equal frequenc amplitud equal width time efiaewit discret discrimin congest heart failur chf normal sinus rhythm nsr pattern ecg signal ecg unit pattern concept introduc repres standard rr interv method extract certain featur unit pattern classifi primit classifi propos method test two classif experi use ecg record physiobank databas result compar sever previous studi first experi offlin classif perform unit pattern select long ecg segment method also use detect chf realtim ecg waveform analysi addit demonstr success propos method result show unit pattern long ecg segment heart patient suggest diseas other result indic propos approach merit addit research

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