title: [A P-wave detection method based on multi-feature].
abstract: Generally, P-wave is the wave of low-frequency and low-amplitude, and it could be affected by baseline drift, electromyography (EMG) interference and other noises easily. Not every heart beat contains the P-wave, and it is also a major problem to determine the P-wave exist or not in a heart beat. In order to solve the limitation of suiting the diverse morphological P-wave using wavelet-amplitude-transform algorithm and the limitation of selecting the pseudo-P-wave sample using the wavelet transform and neural network, we presented new P-wave detecting method based on wave-amplitude threshold and using the multi-feature as the input of neural networks. Firstly, we removed the noise of ECG through the wavelet transform, then determined the position of the candidate P-wave by calculating modulus maxima of the wavelet transform, and then determine the P-wave exist or not by wave-amplitude threshold method initially. Finally we determined whether the P-wave existed or not by the neural networks. The method is validated based on the QT database which is supplied with manual labels made by physicians. We compared the detection effect of ECG P-waves, which was obtained with the method developed in the study, with the algorithm of wavelet threshold value and the method based on "wavelet-amplitude-slope", and verified the feasibility of the proposed algorithm. The detected ECG signal, which is recorded in the hospital ECG division, was consistent with the doctor's labels. Furthermore, after detecting the 13 sets of ECG which were 15 min long, the detection rate for the correct P-wave is 99.911%.
authors.forename: Lixin
authors.initials: L
authors.lastname: Song
authors.forename: Lili
authors.initials: L
authors.lastname: Guan
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.forename: Yuhong
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D004576:Electromyography; D006339:Heart Rate; D006801:Humans; D016571:Neural Networks, Computer; D058067:Wavelet Analysis