Abstract
Delineating the crucial waves in electrocardiogram records is a paramount work for the automatic diagnosis system of heart diseases. In this paper, a novel method is described to determine the boundaries and the peaks of P waves, QRS complexes and T waves by utilizing twelve-lead electrocardiogram signals. It avoids the difficulty of setting the thresholds when determining the boundaries of crucial waves and also the trouble of selection of wavelet basis as the wavelet-based method does. The signals are first preprocessed by a bandpass filter. After that, the locations of QRS complexes are identified. And based on the QRS locations, adaptive search windows are set to detect the locations of P waves and T waves. Then, a method called local distance transform decides the wave boundary in each lead. Finally, the final boundary determination rule is applied to obtain reliable boundaries. We justify the performance of our algorithm on LUDB database. When the tolerance window interval is 40ms, the peak accuracies of P wave, QRS complex and T wave are all beyond 98% and their boundary accuracies are all above 96%. Compared with the derivative threshold method and the wavelet-based method where the tolerance window interval is 150ms, the algorithm shows a sensitivity and a positive predictive value of peaks and boundaries greater than or equal to 98.43% and 96.44% for the P wave, 99.89% and 99.86% for the QRS complex and 99.21% and 99.85% for the T wave. For the critera of average error and standard deviation, our method has the performance similar to those methods. In addition, our algorithm can also handle such several situations where the boundary determination of crucial waves is tough as high T wave, high noise and baseline wandering well.
Highlights
Detection of cardiac abnormalities becomes extremely important with the increasing number of people who die of heart diseases every year
We verified the availability of our algorithm on the LUDB database which records twelve-lead signals and has thorough annotations of 200 individuals with various different heart diseases
In this paper, an algorithm for delineating P wave, QRS complex, and T wave is developed by utilizing twelve-lead signals
Summary
Detection of cardiac abnormalities becomes extremely important with the increasing number of people who die of heart diseases every year. Electrocardiogram (ECG) as an effective non-invasive tool to record the health state of the heart presents heart electrical activities in a graphic form. Physicians analyze the ECG records of patients to judge whether they have a benign or unkind heart state. Researchers focused on the automatic diagnosis of cardiovascular diseases to reduce the labor cost of analyzing ECG. An automatic ECG diagnosis system includes such several procedures as preprocessing, feature extraction, feature selection, feature transform, and disease classification.
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