Development of a portable device for home monitoring of snoring

Hsu, Yeh‐Liang; Chen, Ming-Chou; Cheng, Chih‐Ming; Wu, Chang-Huei

doi:10.1109/icsmc.2005.1571511

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This type of device can provide various outputs as: total snoring count, medium number of snores per hour, and the number of irregular snoring. The success rate of this device was 85% in a lab environment and around 70% in a home (Hsu, 2005). CBR is able to utilize the exact knowledge of previously experienced, concrete problem cases.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Computing Techniques for the Detection of Sleep Disorders: A Review

KumarGarg¹,

Bansal²

2015

IJCA

View full text Add to dashboard Cite

Intelligent computing methods and knowledge based systems are well known techniques used for the detection of various medical disorders. This paper is based on the review of various intelligent computing methods that are used to detect sleep disorders. The main concern is based on the detection of sleep disorders such as sleep apnea, insomnia, parasomnia and snoring. The most common diagnostic methods used by many researchers are based on knowledge-based system (KBS), rule based reasoning (RBR), case based reasoning (CBR), fuzzy logic (FL), artificial neural network (ANN), support vector machine(SVM), multi-layer perceptron (MLP) neural network, genetic algorithm (GA), k-nearest neighbor (k-NN), hybrid neural network, bayesian network (BN), data mining (DM) and many other integrated approaches. In traditional approach questionnaire was used for the detection of various disorders that is now overcome with all above mentioned techniques to enhance the accuracy, sensitivity and specificity.

show abstract

Section: Introductionmentioning

confidence: 99%