Neuro-fuzzy recognition of K-complexes in sleep EEG signals

Pohl, V.; Fahr, Erwin

doi:10.1109/iembs.1995.575364

Cited by 13 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Each record was presented to the system for an Accept (as a K-complex) or Reject decision. This test was similar to the testing done in other reported work [3,4]. b. On-Line K Complex Detection.…”

Section: Work Course and Resultssupporting

confidence: 84%

See 1 more Smart Citation

Detection of K-complexes in sleep EEG using CD-HMM

Kam

Cohen

Geva

et al.

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

In the current paper a new approach for K-complex detection using a continuous density hidden Markov model (CD-HMM) is presented. The system performance was evaluated in two manners. First using three seconds long segments of K-complexes and of background EEG (classification problem). Second using a whole night record and detecting the K complexes (detection problem). The fist test achieved an equal error rate of 7%. In the second test the system performance was compared to four trained scores that scored the signal independently. The performance of the algorithm was within the variance of the human scorers.

show abstract

Section: Work Course and Resultssupporting

confidence: 84%

“…Some did not test the algorithm on real data [3] and some did not report quantitative results [4]. Bankman et al [5] used a neural network with 14 features extracted from the EEG as input.…”

Section: Introductionmentioning

confidence: 99%

Detection of K-complexes in sleep EEG using CD-HMM

Kam

Cohen

Geva

et al.

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…It is a powerful tool for dealing with uncertainty and widely used for analyzing electrical activity of neurons. It is widely used for analyzing the electrical activity of the neurons [17][18][19][20] . The adaptive neuro-fuzzy inference systems (ANFIS) method was successfully applied for electroencephalography signals with a high accuracy of the results obtained [17] .…”

Section: Algorithm 1: Anfis Modelmentioning

confidence: 99%

Spike discharge prediction based on neuro-fuzzy system

Zarei¹

2017

JUMD

View full text Add to dashboard Cite

How to cite this article: Zarei M. Spike discharge prediction based on neuro-fuzzy system. J Unexplored Med Data 2017;2:88-101. This paper presents the development and evaluation of different versions of neuro-fuzzy model for prediction of spike discharge patterns. The author aims to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, the author analyzed the cerebral cortex data of the cat. Adaptive neuro-fuzzy inference systems (ANFIS), Wang and Mendel, dynamic evolving neural-fuzzy inference system, hybrid neural fuzzy inference system, genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms. Key words:Spike discharge, spike latency, spike frequency, neuro-fuzzy system ABSTRACT Article history:

show abstract

“…Model training was mostly based on linear and non-linear classification such as linear discrimination analysis (Molinari et al, 1984), neural network (Schaltenbrand et al, 1993(Schaltenbrand et al, , 1996Roberts and Tarassenko, 1992;Shimada et al, 1998), Neuro-Fuzzy logic (Pohl and Fahr, 1995), tree classification (Kubat et al, 1994), and Hidden Markov models (Flexer et al, 2000). Most of these approaches were based on using the training data to build the models and did not take into consideration the expert knowledge with the exception of the case-based reasoning approach (Park et al, 2000).…”

Section: Overviewmentioning

confidence: 99%

Combining decision trees classifiers: a case study of automatic sleep stage scoring

Khasawneh

Conrad

Fraiwan

et al. 2012

IJKEDM

View full text Add to dashboard Cite

This paper presents a new approach of classification in which multiple decision trees are combined together for achieving better accuracy compared to that achieved by each of the individual constituent decision trees. A major unit of the proposed system is the combination unit for which we present two algorithms; one is based on pre-pruning and true positive rate and the other is based on maximum probability voting. In presenting this new method, we use the case study of sleep stage scoring as a basis of demonstration. For such a task, two tree classifications are utilised. WeCombining decision trees classifiers 61 performed a tree classification based on the training data and then combined the resulting model with another classification tree supplemented by the expert according to Rechtschaffen and Kale's sleep scoring rules. We applied this method to nine recordings, six of which were used to construct the training tree and the remaining three were used for testing. The experiments showed that the combination method has a 7% better accuracy over a single model.

show abstract

Neuro-fuzzy recognition of K-complexes in sleep EEG signals

Cited by 13 publications

References 2 publications

Detection of K-complexes in sleep EEG using CD-HMM

Detection of K-complexes in sleep EEG using CD-HMM

Spike discharge prediction based on neuro-fuzzy system

Combining decision trees classifiers: a case study of automatic sleep stage scoring

Contact Info

Product

Resources

About