AUTOMATIC SIGNAL SEGMENTATION BASED ON SINGULAR SPECTRUM ANALYSIS AND IMPERIALIST COMPETITIVE ALGORITHM

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

AAZAMI HAMED; Sanei Saeid

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE)
Year:2014 | Issue:4

Download Full-Text

View:

Download:

109

Information Seminar Paper

Title

AUTOMATIC SIGNAL SEGMENTATION BASED ON SINGULAR SPECTRUM ANALYSIS AND IMPERIALIST COMPETITIVE ALGORITHM

Author(s)

AAZAMI HAMED | Sanei Saeid

Keywords

SIGNAL SEGMENTATION

SINGULAR SPECTRUM ANALYSIS

FRACTAL DIMENSION

IMPERIALIST COMPETITIVE ALGORITHM

Abstract

ELECTROENCEPHALOGRAM (EEG) IS GENERALLY KNOWN AS A NON-STATIONARY SIGNAL. DIVIDING A SIGNAL INTO THE EPOCHS WITHIN WHICH THE SIGNALS CAN BE CONSIDERED STATIONARY IS THEREFORE VERY IMPORTANT IN MANY APPLICATIONS. NOISE OFTEN INFLUENCES THE PERFORMANCE OF AN AUTOMATIC SIGNAL SEGMENTATION SYSTEM. THEREFORE, IN THIS ARTICLE, A NEW APPROACH FOR SEGMENTATION OF THE EEG SIGNALS BASED ON SINGULAR SPECTRUM ANALYSIS (SSA) AND IMPERIALIST COMPETITIVE ALGORITHM (ICA) IS PROPOSED. AS THE FIRST STEP, SSA IS EMPLOYED TO REDUCE THE EFFECT OF VARIOUS NOISE SOURCES. THEN, FRACTAL DIMENSION (FD) OF THE SIGNAL IS ESTIMATED AND USED AS A FEATURE EXTRACTION FOR AUTOMATIC SEGMENTATION OF THE EEG. IN ORDER TO SELECT TWO ACCEPTABLE PARAMETERS RELATED TO THE FD, ICA THAT IS A MORE POWERFUL EVOLUTIONARY ALGORITHM THAN TRADITIONAL ONES IS APPLIED. BY USING SYNTHETIC AND REAL EEG SIGNALS, THE PROPOSED METHOD IS COMPARED WITH ORIGINAL APPROACH (I.E. WITHOUT USING SSA AND ICA). ALSO, SIMULATION RESULTS SHOW THAT THE SPEED OF SSA IS MUCH BETTER THAN THAT OF THE DISCRETE WAVELET TRANSFORM (DWT) WHICH HAS BEEN ONE OF THE MOST POPULAR PREPROCESSING FILTERS FOR SIGNAL SEGMENTATION. THE SIMULATION RESULTS INDICATE THE PERFORMANCE SUPERIORITY OF THE PROPOSED METHOD.

Cites

No record.

References

No record.

Cite

APA: Copy

AAZAMI, HAMED, & Sanei, Saeid. (2014). AUTOMATIC SIGNAL SEGMENTATION BASED ON SINGULAR SPECTRUM ANALYSIS AND IMPERIALIST COMPETITIVE ALGORITHM. INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE). SID. https://sid.ir/paper/926365/en

Vancouver: Copy

AAZAMI HAMED, Sanei Saeid. AUTOMATIC SIGNAL SEGMENTATION BASED ON SINGULAR SPECTRUM ANALYSIS AND IMPERIALIST COMPETITIVE ALGORITHM. 2014. Available from: https://sid.ir/paper/926365/en

IEEE: Copy

HAMED AAZAMI, and Saeid Sanei, “AUTOMATIC SIGNAL SEGMENTATION BASED ON SINGULAR SPECTRUM ANALYSIS AND IMPERIALIST COMPETITIVE ALGORITHM,” presented at the INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE). 2014, [Online]. Available: https://sid.ir/paper/926365/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops