title: Time series for blind biosignal classification model.
abstract: Biosignals such as electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG), are important noninvasive measurements useful for making diagnostic decisions. Recently, considerable research has been conducted in order to potentially automate signal classification for assisting in disease diagnosis. However, the biosignal type (ECG, EEG, EMG or other) needs to be known prior to the classification process. If the given biosignal is of an unknown type, none of the existing methodologies can be utilized. In this paper, a blind biosignal classification model (B(2)SC Model) is proposed in order to identify the source biosignal type automatically, and thus ultimately benefit the diagnostic decision. The approach employs time series algorithms for constructing the model. It uses a dynamic time warping (DTW) algorithm with clustering to discover the similarity between two biosignals, and consequently classifies disease without prior knowledge of the source signal type. The empirical experiments presented in this paper demonstrate the effectiveness of the method as well as the scalability of the approach.
authors.affiliation: Department of Computer and Information Science, University of Macau, Av. Padre Tomás Pereira Taipa, Macau S.A.R., China.
authors.forename: Derek F
authors.initials: DF
authors.lastname: Wong
authors.affiliation: Department of Computer and Information Science, University of Macau, Av. Padre Tomás Pereira Taipa, Macau S.A.R., China. Electronic address: lidiasc@umac.mo.
authors.forename: Lidia S
authors.initials: LS
authors.lastname: Chao
authors.affiliation: Department of Computer and Information Science, University of Macau, Av. Padre Tomás Pereira Taipa, Macau S.A.R., China.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Zeng
authors.affiliation: Department of Computer and Information Science, University of Macau, Av. Padre Tomás Pereira Taipa, Macau S.A.R., China.
authors.forename: Mang-I
authors.initials: MI
authors.lastname: Vai
authors.affiliation: Department of Computer and Information Science, University of Macau, Av. Padre Tomás Pereira Taipa, Macau S.A.R., China.
authors.forename: Heng-Leong
authors.initials: HL
authors.lastname: Lam
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004568:Electrodiagnosis / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted
keywords: Bioinformatics; Blind biosignal classification; Dynamic time warping (DTW); Machine learning; Time series clustering