TY - GEN
T1 - A non-Gaussian approach for biosignal classification based on the Johnson SU translation system
AU - Hayashi, Hideaki
AU - Kurita, Yuichi
AU - Tsuji, Toshio
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/4/7
Y1 - 2016/4/7
N2 - This paper proposes a non-Gaussian approach for biosignal classification based on the Johnson SU translation system. The Johnson system is a normalizing translation that transforms data without normality to normal distribution using four parameters, thereby enabling the representation of a wide range of shapes for marginal distribution with skewness and kurtosis. In this study, a discriminative model based on the multivariate Johnson SU translation system is transformed into linear combinations of coefficients and input vectors using log-linearization, and is incorporated into a neural network structure, thereby allowing the determination of model parameters as weight coefficients of the network via backpropagation-based training. In the experiments, the classification performance of the proposed network is demonstrated using artificial data and electromyogram data.
AB - This paper proposes a non-Gaussian approach for biosignal classification based on the Johnson SU translation system. The Johnson system is a normalizing translation that transforms data without normality to normal distribution using four parameters, thereby enabling the representation of a wide range of shapes for marginal distribution with skewness and kurtosis. In this study, a discriminative model based on the multivariate Johnson SU translation system is transformed into linear combinations of coefficients and input vectors using log-linearization, and is incorporated into a neural network structure, thereby allowing the determination of model parameters as weight coefficients of the network via backpropagation-based training. In the experiments, the classification performance of the proposed network is demonstrated using artificial data and electromyogram data.
UR - http://www.scopus.com/inward/record.url?scp=84966592125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966592125&partnerID=8YFLogxK
U2 - 10.1109/IWCIA.2015.7449473
DO - 10.1109/IWCIA.2015.7449473
M3 - Conference contribution
AN - SCOPUS:84966592125
T3 - 2015 IEEE 8th International Workshop on Computational Intelligence and Applications, IWCIA 2015 - Proceedings
SP - 115
EP - 120
BT - 2015 IEEE 8th International Workshop on Computational Intelligence and Applications, IWCIA 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2015
Y2 - 6 November 2015 through 7 November 2015
ER -