Sung-Nien Yu

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In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute(More)
The muscle I2 is a smooth muscle from the buccal mass of the marine mollusc Aplysia californica whose neural control, in vivo kinematics, and behavioral role have been extensively analyzed. In this study, we measured the activation and contractile dynamics of the muscle in order to construct a Hill-type kinetic model of the muscle. This is the first study(More)
In this paper, we propose a novel independent components (ICs) arrangement strategy to cooperate with the independent component analysis (ICA) method used for ECG beat classification. The ICs calculated with a regular ICA algorithm are re-arranged according to the L2 norms of the rows of the de-mixing matrix. The validity of this ICs arrangement strategy is(More)
Feature selection plays an important role in pattern recognition systems. In this study, we explored the problem of selecting effective heart rate variability (HRV) features for recognizing congestive heart failure (CHF) based on mutual information (MI). The MI-based greedy feature selection approach proposed by Battiti was adopted in the study. The mutual(More)
OBJECTIVE This paper presents a noise-tolerant electrocardiogram (ECG) beat classification method based on higher order statistics (HOS) of subband components. METHODS AND MATERIAL Five levels of discrete wavelet transform (DWT) were applied to decompose the signal into six subband components. Higher order statistics proceeded to calculate four sets of(More)
Clustered microcalcifcations (MCs) in digitized mammograms has been widely recognized as an early sign of breast cancer in women. This work is devoted to developing a computer-aided diagnosis (CAD) system for the detection of MCs in digital mammograms. Such a task actually involves two key issues: detection of suspicious MCs and recognition of true MCs.(More)
A novel feature extraction method, which uses subband features calculated from higher order statistics, is proposed for ECG beat classification. Five levels of discrete wavelet transformation (DWT) are applied to decompose the signal into six subband signals with different frequency distribution. Higher order statistics proceeds to calculate valuable(More)
We propose a method that uses independent component analysis (ICA) and backpropagation neural network to classify electrocardiogram (ECG) signals. In this study, ICA is used to extract important features from ECG signals. A backpropagation neural network follows to classify the input ECG beats into one of eight beat types. The independent components are(More)