Comput. Biol. Med. - Time series for blind biosignal classification model.

Tópicos

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Resumo

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.

Resumo Limpo

biosign electrocardiogram ecg electroencephalogram eeg electromyogram emg import noninvas measur use make diagnost decis recent consider research conduct order potenti autom signal classif assist diseas diagnosi howev biosign type ecg eeg emg need known prior classif process given biosign unknown type none exist methodolog can util paper blind biosign classif model bsc model propos order identifi sourc biosign type automat thus ultim benefit diagnost decis approach employ time seri algorithm construct model use dynam time warp dtw algorithm cluster discov similar two biosign consequ classifi diseas without prior knowledg sourc signal type empir experi present paper demonstr effect method well scalabl approach

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