Abstract
Electrocardiogram(ECG) is an important physiological signal of the human body. It is widely used in identification and arrhythmia detection. The first step of ECG application is signal segmentation, that is, the QRS detection. An effective and real-time QRS detection algorithm is proposed in this paper. A differentiator with adjustable center frequency is used to capture the first derivative information of the frequency band of the electrocardiogram. Then Hilbert transform is used to generate the envelope of the first derivative. After that, a dual threshold method is introduced to decrease FP and FN. Finally, a more precise R wave position is determined based on derivative method. The detector is validated on MIT-BIH arrhythmia database. The result show that the proposed algorithm has a high Sensitivity of 99.87%, Specificity of 99.84%, and the detection error rate is 0.28%. The average execution time of a 30 minutes record is 2.45s.
Highlights
Introduction signals are decomposed intoIMF components, they usually need to be screened many times and spend a lot ofMany methods have been utilized in QRS detection
The most common methods are based on wavelet transform(WT) [1], the derivative [2], empirical mode decomposition(EMD) [3], mathematical morphology [4] and Artificial Neural Networks(ANN)
QRS complexes are detected using an algorithm based on the multiscale approach
Summary
Introduction signals are decomposed intoIMF components, they usually need to be screened many times and spend a lot ofMany methods have been utilized in QRS detection. QRS complexes are detected using an algorithm based on the multiscale approach. This algorithm searches across the scales for “maximum modulus lines” exceeding some thresholds at scales [1]. Because of the use of multiple scale signals, the algorithm is time-consuming and complex. The structuring element is updated based on the characteristics of the previously detected QRS complexes. The more the structuring element resembles the QRS complex in the ECG, the more accurate the feature signal. ANN needs training for a period at the beginning of a signal This method is more suitable to peopleoriented, and the generalization ability is weak. Sometimes the shape of QRS varies greatly with time
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