Mert A.,Piri Reis University
Physiological Measurement | Year: 2016
It is a difficult process to detect abnormal heart beats, known as arrhythmia, in long-term ECG recording. Thus, computer-aided diagnosis systems have become a supportive tool for helping physicians improve the diagnostic accuracy of heartbeat detection. This paper explores the bandwidth properties of the modes obtained using variational mode decomposition (VMD) to classify arrhythmia electrocardiogram (ECG) beats. VMD is an enhanced version of the empirical mode decomposition (EMD) algorithm for analyzing non-linear and non-stationary signals. It decomposes the signal into a set of band-limited oscillations called modes. ECG signals from the MIT-BIH arrhythmia database are decomposed using VMD, and the amplitude modulation bandwidth B AM, the frequency modulation bandwidth B FM and the total bandwidth B of the modes are used as feature vectors to detect heartbeats such as normal (N), premature ventricular contraction (V), left bundle branch block (L), right bundle branch block (R), paced beat (P) and atrial premature beat (A). Bandwidth estimations based on the instantaneous frequency (IF) and amplitude (IA) spectra of the modes indicate that the proposed VMD-based features have sufficient class discrimination capability regarding ECG beats. Moreover, the extracted features using the bandwidths (B AM, B FM and B) of four modes are used to evaluate the diagnostic accuracy rates of several classifiers such as the k-nearest neighbor classifier (k-NN), the decision tree (DT), the artificial neural network (ANN), the bagged decision tree (BDT), the AdaBoost decision tree (ABDT) and random sub-spaced k-NN (RSNN) for N, R, L, V, P, and A beats. The performance of the proposed VMD-based feature extraction with a BDT classifier has accuracy rates of 99.06%, 99.00%, 99.40%, 99.51%, 98.72%, 98.71%, and 99.02% for overall, N-, R-, L-, V-, P-, and A-type ECG beats, respectively. © 2016 Institute of Physics and Engineering in Medicine.
Ozden M.T.,Piri Reis University
Eurasip Journal on Advances in Signal Processing | Year: 2013
A multichannel characterization for autoregressive moving average (ARMA) spectrum estimation in subbands is considered in this article. The fullband ARMA spectrum estimation can be realized in two-channels as a special form of this characterization. A complete orthogonalization of input multichannel data is accomplished using a modified form of sequential processing multichannel lattice stages. Matrix operations are avoided, only scalar operations are used, and a multichannel ARMA prediction filter with a highly modular and suitable structure for VLSI implementations is achieved. Lattice reflection coefficients for autoregressive (AR) and moving average (MA) parts are simultaneously computed. These coefficients are then converted to process parameters using a newly developed Levinson-Durbin type multichannel conversion algorithm. Hence, a novel method for spectrum estimation in subbands as well as in fullband is developed. The computational complexity is given in terms of model order parameters, and comparisons with the complexities of nonparametric methods are provided. In addition, the performance is visually and statistically compared against those of the nonparametric methods under both stationary and nonstationary conditions. © 2013 Ozden; licensee Springer.
Li Y.,National Renewable Energy Laboratory |
Calisal S.M.,University of British Columbia |
Calisal S.M.,Piri Reis University
Renewable Energy | Year: 2010
Three-dimensional effects in studying a vertical axis tidal current turbine are modeled using a newly developed vortex method. The effects on predicting power output and wake trajectory are analyzed in particular. The numerical results suggest that three-dimensional effects are not significant when the height of the turbine is more than seven times the turbine radius. Further discussions are presented focusing on the relationship between the turbine height and the angle of attack and the induced velocity on a blade of the turbine without arms. Besides the three-dimensional effects, arms effects are quantified with an analytical derivation of the polynomial formula of the relationship between arm effects and the tip speed ratio of the turbine. Such a formula provides a correction for existing numerical models to predict the power output of a turbine. Moreover, a series towing tank tests are conducted to study the three-dimensional effects as well as the arm effects. Good agreements are achieved between the results obtained with numerical calculations with the arm effects correction and the towing tank tests. Finally, three-dimensional effects are examined experimentally together with the arm effects by using an end-plate test, which suggests that the combinational effect is rather minimal. For turbine designers at the early design stage, we recommend that a two-dimensional model is acceptable considering the high cost of the three-dimensional model. © 2010 Elsevier Ltd.
Mert A.,Piri Reis University
Biomedical Engineering Letters | Year: 2014
Methods: A hybrid method is proposed using the independent component analysis (ICA) and the discrete wavelet transform (DWT) to reduce feature vectors of Wisconsin diagnostic breast cancer (WDBC) data set. Two independent components (ICs), and one approximation coefficient of the DWT are used as a reduced feature vector to classify breast cancer using PNN. Performance measures such as accuracy, sensitivity, specificity, Youden’s index and the receiver operating characteristics (ROC) curve are computed to indicate the advantages of the hybrid feature reduction.Results: The proposed feature reduction approach using ICA and DWT improves the diagnostic capability of the PNN classifier. The hybrid feature reduction has a higher diagnostic capability than the original thirty features using PNN as a classifier. Accuracy and sensitivity are 96.31% and 98.88%, while the results of the classification using the original thirty features are 92.09% and 95.52%.Conclusions: Feature reduction approach using ICA and DWT together increases the performance measures of breast cancer classification using PNN, while reducing computational complexity.Purpose: Early and correct diagnosis of a disease is vital for the success of treatment. Medical diagnostic decision support system can be used to improve the accuracy of the traditional diagnosis. As such, various pattern recognition methods are studied and applied to develop medical diagnostic decision support system. In this study, the effects of dimensionality reduction techniques with probabilistic neural network (PNN) on breast cancer classification are examined. © 2014, Korean Society of Medical and Biological Engineering and Springer.
Dedeoglu B.,Bogazici University |
Aviyente V.,Bogazici University |
Ozen A.S.,Piri Reis University
Journal of Physical Chemistry C | Year: 2014
Poly(silafluorene-phenylenedivinylene)s and poly((tetraphenyl)-silole- phenylenedivinylene)s are promising materials for use in explosives detection. Monomers and dimers of silafluorene- and silole-containing polymers for the detection of nitro-containing explosives are modeled with M062X/6-31G(d). The geometric features of silafluorene- and silole-containing dimers optimized with M062X/6-31G(d) agree well with experimental findings. The binding properties of explosive and nonexplosive materials have been differentiated by comparing the relative stabilities of their complexes with silafluorene- and silole-containing dimers. The interactions that promote binding in the complexation of silafluorene- and silole-containing polymers with explosives are studied with a small model to shed light on the origin of the stability of the complexes. The topology of the electron density was analyzed using the quantum theory of atoms in molecules (QTAIM) methodology to understand the nature of the noncovalent interactions that are responsible for analyte-polymer binding. The carbon and germanium analogues of silafluorene-containing dimers are modeled to better understand the role of silicon in these polymeric systems. The calculated HOMO-LUMO energy differences of the complexes of dimers with explosives correlate well with the stability of the complexes; both (HOMO-LUMO and stability) support the selectivities of silafluorene- and silole-containing polymers. The stabilities of the complexes have shown that silafluorene- containing polymer detects the analytes in the order of 2,4,6-trinitrotoluene (TNT) ∼ picric acid (PA) > 2,6-dinitrotoluene (DNT) > cyclotrimethylenetrinitramine (RDX) > nitrobenzene (NB), while the silole-containing polymer is able to detect the aromatic TNT but is not responsive to the nonaromatic RDX. © 2014 American Chemical Society.