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Ghaffari A.,Cardiovascular Research Group CVRG | Ebrahimi Orimi H.,K. N. Toosi University of Technology
Journal of Medical Engineering and Technology

Epileptic disease can be diagnosed by using intelligent methods on the Electroencephalograph (EEG) signals. In this paper, wavelet packet transform (WPT) was used in each of the frequency bands and wavelet coefficients were obtained, then the energy and entropy function was done on the wavelet coefficients and used as initial feature vectors. In the next step, eight and 15 features from 30 initial energy and entropy features were selected as the final features because their receiver operating characteristic (ROC) curve areas were higher than others. There were seven classifier inputs. These seven classifiers consisted of four artificial neural networks (ANN) with different structures, support vector machines (SVM), K-nearest neighbours (KNN) and a hybrid network. Each classifier was trained by 0.5, 0.8 and 0.9 EEG signals. After the training process, a fusion network based on a voting criteria was used to make the algorithm robust against the possible changes in each classifier and increase the classification accuracy. Finally, the algorithm was tested by other EEG signals. As a result, normal and epileptic classes were detected with total classification accuracy of 99-100%. © 2014 Informa UK Ltd. All rights reserved: reproduction in whole or part not permitted. Source

Ghaffari A.,K. N. Toosi University of Technology | Ghaffari A.,Cardiovascular Research Group CVRG | Homaeinezhad M.R.,K. N. Toosi University of Technology | Homaeinezhad M.R.,Cardiovascular Research Group CVRG | And 3 more authors.
Annals of Biomedical Engineering

In this study, a simple mathematical-statistical based metric called Multiple Higher Order Moments (MHOM) is introduced enabling the electrocardiogram (ECG) detection-delineation algorithm to yield acceptable results in the cases of ambulatory holter ECG including strong noise, motion artifacts, and severe arrhythmia(s). In the MHOM measure, important geometric characteristics such as maximum value to minimum value ratio, area, extent of smoothness or being impulsive and distribution skewness degree (asymmetry), occult. In the proposed method, first three leads of high resolution 24-h holter data are extracted and preprocessed using Discrete Wavelet Transform (DWT). Next, a sample to sample sliding window is applied to preprocessed sequence and in each slid, mean value, variance, skewness, and kurtosis of the excerpted segment are superimposed called MHOM. The MHOM metric is then used as decision statistic to detect and delineate ECG events. To show advantages of the presented method, it is applied to MIT-BIH Arrhythmia Database, QT Database, and T-Wave Alternans Database and as a result, the average values of sensitivity and positive predictivity Se = 99.95% and P+ = 99.94% are obtained for the detection of QRS complexes, with the average maximum delineation error of 6.1, 4.1, and 6.5 ms for P-wave, QRS complex, and T-wave, respectively showing marginal improvement of detection-delineation performance. In the next step, the proposed method is applied to DAY hospital high resolution holter data (more than 1,500,000 beats including Bundle Branch Blocks-BBB, Premature Ventricular Complex-PVC, and Premature Atrial Complex-PAC) and average values of Se = 99.97% and P+ = 99.95% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process, reliable robustness against strong noise, artifacts, and probable severe arrhythmia(s) of high resolution holter data can be mentioned as important merits and capabilities of the proposed algorithm. © 2010 Biomedical Engineering Society. Source

Sabouri S.,Islamic Azad University at Tehran | Sabouri S.,Cardiovascular Research Group CVRG | SadAbadi H.,Concordia University at Montreal | SadAbadi H.,Cardiovascular Research Group CVRG | And 2 more authors.
Computing in Cardiology

This paper is the follow-up of our previous work presented for CinC/PhysioNet Challenge 2007 on the "electrocardiographic imaging of myocardial infarction". We have presented an automatic method for MI location detection by Neural Network classification of BSPM data. Data used here contain BSPM signal of four patients and their actual infarcted segments (two training cases and two cases for test). By mapping Q-wave integral and QRS-complex integral on torso surface and applying four threshold-based rules, an abnormal area on the torso can be obtain. This detected abnormal area then is mapped to the heart segments. A NN classifier is used at final step. The results expressed by parameter OS (overlapped segment) which is a value between 0 and 1, where 1 is a perfect match. The results for two test cases are OS case#3=0.7 and OScase#4=0.4 shows this mathematically simple method can predict the location of MI reasonably. However further works is needed to improve the results. Source

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