Comparison of detection methods: application to K-complex detection in sleep EEG

Henry, Didier; Sauter, Dominique; Caspary, Olivier

doi:10.1109/iembs.1994.415401

Cited by 6 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

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: 82%

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: 82%

“…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

“…An average F-score of 0.70 and 0.57% for the detection of the sleep spindles and the k-complexes were achieved, respectively. Another study was presented by Henry et al (1994), in which the k-complexes were classified based on matched filtering. Each segment was decomposed into a set of orthonormal functions and wavelets analysis.…”

Section: Related Workmentioning

confidence: 99%

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

Al-Salman

Wen

2019

Front. Neuroinform.

View full text Add to dashboard Cite

K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. In this study, an efficient method is proposed to detect k-complexes from EEG signals based on fractal dimension (FD) of time frequency (T-F) images coupled with undirected graph features. Firstly, an EEG signal is partitioned into smaller segments using a sliding window technique. Each EEG segment is passed through a spectrogram of short time Fourier transform (STFT) to obtain the T-F images. Secondly, the box counting method is applied to each T-F image to discover the FDs in EEG signals. A vector of FD features are extracted from each T-F image and then mapped into an undirected graph. The structural properties of the graphs are used as the representative features of the original EEG signals for the input of a least square support vector machine (LS-SVM) classifier. Key graphic features are extracted from the undirected graphs. The extracted graph features are forwarded to the LS-SVM for classification. To investigate the classification ability of the proposed feature extraction combined with the LS-SVM classifier, the extracted features are also forwarded to a k-means classifier for comparison. The proposed method is compared with several existing k-complexes detection methods in which the same datasets were used. The findings of this study shows that the proposed method yields better classification results than other existing methods in the literature. An average accuracy of 97% for the detection of the k-complexes is obtained using the proposed method. The proposed method could lead to an efficient tool for the scoring of automatic sleep stages which could be useful for doctors and neurologists in the diagnosis and treatment of sleep disorders and for sleep research.

show abstract

“…Complex K may occur both spontaneously (unevoked) and under the influence of external stimuli. Complex K features a frequency support between 0.5-1.5 Hz for the first peak and 5-10 Hz for the second peak and distinguishes itself by the high amplitude (65μV) of the wave shape of the background electroencephalogram of stage 2 (2,3). The importance of detecting complex K is due to the significance it hast for prognosis and diagnosis…”

Section: Electroencephalogram (Eeg)mentioning

confidence: 99%

K - Complex Detection Using the Continuous Wavelet Transform

Dumitrescu

Carmen²,

Dan

et al. 2018

ARS Medica Tomitana

View full text Add to dashboard Cite

The wide variety of waveform in EEG signals and the high non-stationary nature of many of them is one of the main difficulties to develop automatic detection system for them. In sleep stage classification a relevant transient wave is the K-complex. This paper comprehend the developing of two algorithms in order to achieve an automatic K-complex detection from EEG raw data. These algorithms are based on a time-frequency analysis and two time-frequency techniques, the Short Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT), are tested in order to find out which one is the best for our purpose, being of two wavelet functions to measure the capability of them to detect K-complex and to choose one to be employed in the algorithms. The first algorithm is based on the energy distribution of the CWT detecting the spectral component of the K-complex. The second algorithm is focused on the morphology of the K-complex / sleep spindle waveform after the CWT. Evaluating the algorithms results reveals that a false K-complex detection is as important as real K-complex detection.

show abstract

Comparison of detection methods: application to K-complex detection in sleep EEG

Cited by 6 publications

References 5 publications

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

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

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

K - Complex Detection Using the Continuous Wavelet Transform

Contact Info

Product

Resources

About