TY - JOUR
T1 - A quasi-optimal channel selection method for bioelectric signal classification using a partial Kullback-Leibler information measure
AU - Shibanoki, Taro
AU - Shima, Keisuke
AU - Tsuji, Toshio
AU - Otsuka, Akira
AU - Chin, Takaaki
PY - 2013/3/1
Y1 - 2013/3/1
N2 - This paper proposes a novel variable selection method involving the use of a newly defined metric called the partial Kullback-Leibler (KL) information measure to evaluate the contribution of each variable (dimension) in the data. In this method, the probability density functions of recorded data are estimated through a multidimensional probabilistic neural network trained on the basis of KL information theory. The partial KL information measure is then defined as the ratio of the values before and after dimension elimination in the data. The effective dimensions for classification can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to channel selection with nine subjects (including an amputee), and effective channels were selected from all channels attached to each subject's forearm. The results showed that the number of channels was reduced by 54.3 ±19.1%, and the average classification rate for evaluation data using selected three or four channels was 96.6 ±2.8% (min: 92.1%, max: 100%). These outcomes indicate that the proposed method can be used to select effective channels (optimal or quasi-optimal) for accurate classification.
AB - This paper proposes a novel variable selection method involving the use of a newly defined metric called the partial Kullback-Leibler (KL) information measure to evaluate the contribution of each variable (dimension) in the data. In this method, the probability density functions of recorded data are estimated through a multidimensional probabilistic neural network trained on the basis of KL information theory. The partial KL information measure is then defined as the ratio of the values before and after dimension elimination in the data. The effective dimensions for classification can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to channel selection with nine subjects (including an amputee), and effective channels were selected from all channels attached to each subject's forearm. The results showed that the number of channels was reduced by 54.3 ±19.1%, and the average classification rate for evaluation data using selected three or four channels was 96.6 ±2.8% (min: 92.1%, max: 100%). These outcomes indicate that the proposed method can be used to select effective channels (optimal or quasi-optimal) for accurate classification.
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U2 - 10.1109/TBME.2012.2205990
DO - 10.1109/TBME.2012.2205990
M3 - Article
C2 - 22752103
AN - SCOPUS:84891433234
VL - 60
SP - 853
EP - 861
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
SN - 0018-9294
IS - 3
ER -