Robust Signal Identification for Dynamic Pattern Classification

TitleRobust Signal Identification for Dynamic Pattern Classification
Publication TypeJournal Article
Year of Publication2016
AuthorsZhao, R, Schalk, G, Ji, Q
Journal2016 23rd International Conference on Pattern Recognition
Date PublishedDec
Keywordscomputational modeling, data models, Hidden Markov models, motion segmentation, robustness, testing, Time series analysis

This paper addresses the problem of identifying signals of interest from discrete-time sequences contaminated by erroneous segments, which we define as the part of time series whose dynamic patterns are inconsistent with that of the signals. Assuming the signals of interest consist of consecutive samples with arbitrary starting point, duration and following a stationary dynamic pattern, we propose a robust algorithm combining Random Sample Consensus (RANSAC) and Hidden Markov Model (HMM) to automatically identify the start and end of signals of interest from time series. To evaluate the identification quality, we perform a classification task, where the identified signals are used to train a classifier. A majority vote strategy is adopted to handle error contaminated testing sequences. Compared with manual selection approach and other unsupervised learning methods, the proposed method shows improvement in classification accuracy on both synthetic and real Electrocorticographic (ECoG) data.


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