title: Pulse waveform classification using support vector machine with Gaussian time warp edit distance kernel.
abstract: Advances in signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis. However, because of the inevitable intraclass variations of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. Utilizing the new elastic metric, that is, time wrap edit distance (TWED), this paper proposes to address the problem under the support vector machines (SVM) framework by using the Gaussian TWED kernel function. The proposed method, SVM with GTWED kernel (GTWED-SVM), is evaluated on a dataset including 2470 pulse waveforms of five distinct patterns. The experimental results show that the proposed method achieves a lower average error rate than current pulse waveform classification methods.
authors.affiliation: Harbin Ice Flower Hospital, Harbin 150086, China.
authors.forename: Danbing
authors.identifier: 0000-0001-5611-2499
authors.initials: D
authors.lastname: Jia
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Dongyu
authors.initials: D
authors.lastname: Zhang
authors.affiliation: Harbin Ice Flower Hospital, Harbin 150086, China ; School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Naimin
authors.identifier: 0000-0003-1945-7089
authors.initials: N
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D004548:Elasticity; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine