GDL

Journal from gdlhub / 2018-06-02 10:25:18
Oleh : Intan Nurma Yulita, Rudi Rosadi, Sri Purwani, Rolly Maulana Awangga, Kursor
Dibuat : 2018-06-02, dengan 1 file

Keyword : Deep Belief Networks, Sleep Apnea, Sleep Stage Classification, Shallow Classifier
Url : http://kursorjournal.org/index.php/kursor/article/view/97
Sumber pengambilan dokumen : WEB

In this research, it is proposed to use Deep Belief Networks (DBN) in shallow classifier for

the automatic sleep stage classification. The automatic classification is required to minimize

the evaluation of Polysomnography because it needs more than two days for analysis

manually. Thus the automatically mechanism is required. The Shallow classifier used in this

research includes Naïve Bayes (NB), Bayesian Networks (BN), Decision Tree (DT), Support

Vector Machines (SVM), and K-Nearest Neighbor (KNN). The analysis compared each

methods in shallow classifier before and after the classifier were combined with DBN. The

results shown that many combination by using the shallow classifiers and DBN had

increased. The experiments that have been done indicated a significant increase of Naive

Bayes after being combined with DBN. The high-level features generated by DBN are proven

to be useful in helping Naive Bayes' performance. On the other hand, the combination of

KNN with DBN shows a decrease because high-level features of DBN make it harder to find

neighbors that optimize the performance of KNN.

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