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Seizure classification in EEG signals utilizing Hilbert-Huang transform.

Oweis RJ, Abdulhay EW - Biomed Eng Online (2011)

Bottom Line: The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly.Extra benefits of this proposed system include low cost, and ease of interface.All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan. oweis@just.edu.jo

ABSTRACT

Background: Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.

Method: Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering.

Results: The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use.

Conclusion: An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

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Unsupervised Seizure Identification Using MEMD. Figure legend text. The classification of the calculated weighted frequencies by a supervised clustering using the Euclidian distance. Every point holds the information about the weighted frequencies of the first four IMF of an MEMD processed EEG signal. Two different groups of points are illustrated; the first group (stars) indicates healthy EEG. The second group (circles) indicates seizure EEG. Asterisks indicate two different centres of group. The lower one is related to the seizure group.
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Figure 9: Unsupervised Seizure Identification Using MEMD. Figure legend text. The classification of the calculated weighted frequencies by a supervised clustering using the Euclidian distance. Every point holds the information about the weighted frequencies of the first four IMF of an MEMD processed EEG signal. Two different groups of points are illustrated; the first group (stars) indicates healthy EEG. The second group (circles) indicates seizure EEG. Asterisks indicate two different centres of group. The lower one is related to the seizure group.

Mentions: Figure 9 shows the classification by a supervised clustering using the Euclidian distance applied to MEMD. The groups are once again concentrated around two different centroids (healthy and seizure). Table 3 contains the values of the calculated weighted frequencies for the first four MEMD of 10 healthy signals and 10 seizure signals (for better illustrations, not all the values are shown). Table 4 indicates the classification performance based on 50 values (25 couples of healthy and seizure signals). The obtained accuracy is 80%, the specificity is 80%, and the sensitivity is 81%.


Seizure classification in EEG signals utilizing Hilbert-Huang transform.

Oweis RJ, Abdulhay EW - Biomed Eng Online (2011)

Unsupervised Seizure Identification Using MEMD. Figure legend text. The classification of the calculated weighted frequencies by a supervised clustering using the Euclidian distance. Every point holds the information about the weighted frequencies of the first four IMF of an MEMD processed EEG signal. Two different groups of points are illustrated; the first group (stars) indicates healthy EEG. The second group (circles) indicates seizure EEG. Asterisks indicate two different centres of group. The lower one is related to the seizure group.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3116477&req=5

Figure 9: Unsupervised Seizure Identification Using MEMD. Figure legend text. The classification of the calculated weighted frequencies by a supervised clustering using the Euclidian distance. Every point holds the information about the weighted frequencies of the first four IMF of an MEMD processed EEG signal. Two different groups of points are illustrated; the first group (stars) indicates healthy EEG. The second group (circles) indicates seizure EEG. Asterisks indicate two different centres of group. The lower one is related to the seizure group.
Mentions: Figure 9 shows the classification by a supervised clustering using the Euclidian distance applied to MEMD. The groups are once again concentrated around two different centroids (healthy and seizure). Table 3 contains the values of the calculated weighted frequencies for the first four MEMD of 10 healthy signals and 10 seizure signals (for better illustrations, not all the values are shown). Table 4 indicates the classification performance based on 50 values (25 couples of healthy and seizure signals). The obtained accuracy is 80%, the specificity is 80%, and the sensitivity is 81%.

Bottom Line: The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly.Extra benefits of this proposed system include low cost, and ease of interface.All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan. oweis@just.edu.jo

ABSTRACT

Background: Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.

Method: Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering.

Results: The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use.

Conclusion: An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

Show MeSH
Related in: MedlinePlus