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Publications (9)4.79 Total impact

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    ABSTRACT: The aim of this study is to detect Acute Hypotensive Episodes (AHE) and Mean Arterial Pressure Dropping Regimes (MAPDRs) using ECG signal and Arterial Blood Pressure waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative Modified Hilbert Transform-Based algorithms namely as ECGMHT and BPMHT. A new smoothing algorithm is next developed based on piecewise polynomial fitting to smooth the fast fluctuations observed in RR-tachogram, systolic blood pressure (SBP) and diastolic blood pressure (DBP) trends. Afterwards, in order to consider the mutual influence of parameters on the evaluation of shock probability, a Sugeno Adaptive Network-based Fuzzy Inference System-ANFIS is trained using Hasdai et al. (J Am Coll Cardiol, 35: 136–143, 2000) parameters as input, with appropriate membership functions for each parameter. Using this network, it will be possible to incorporate the possible mutual influences between risk parameters such as heart rate, SBP, DBP, ST-segment episodes, age, gender, weight and some miscellaneous factors to the calculation of shock occurrence probability. In the next step, the proposed algorithm is applied to 15 subjects of the MIMIC II Database and AHE and MAPDRs (MAP ≤ 60 mmHg with a period of 30 min or more) are identified. As a result of this study, for a sequence of MAPDRs as long as 20 min or more, there will exist a consequent high peak with the duration of 3–4 min in the corresponding probability of cardiogenic shock diagram.
    Cardiovascular Engineering 03/2010; 10(1):12-29. · 0.81 Impact Factor
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    ABSTRACT: A robust multi-lead ECG wave detection-delineation algorithm is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting QRS complex, P-wave and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). First, a window with a specific length is slid sample to sample on the selected scale and the curve length in each window is multiplied by the area under the absolute value of the curve. In the next step, a variable thresholding criterion is designed for the resulted signal. The presented algorithm is applied to various databases including MIT-BIH arrhythmia database, European ST-T Database, QT Database, CinC Challenge 2008 Database as well as high resolution Holter data of DAY Hospital. As a result, the average values of sensitivity and positive predictivity Se=99.84% and P+=99.80% were obtained for the detection of QRS complexes, with the average maximum delineation error of 13.7ms, 11.3ms and 14.0ms for P-wave, QRS complex and T-wave, respectively. The presented algorithm has considerable capability in cases of low signal-to-noise ratio, high baseline wander, and abnormal morphologies. Especially, the high capability of the algorithm in the detection of the critical points of the ECG signal, i.e. the beginning and end of T-wave and the end of the QRS complex was validated by cardiologists in DAY hospital and the maximum values of 16.4ms and 15.9ms were achieved as absolute offset error of localization, respectively.
    Medical Engineering & Physics 09/2009; 31(10):1219-27. · 1.78 Impact Factor
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    ABSTRACT: The aim of this study is to detect acute hypotensive episodes (AHE) and mean arterial pressure dropping regimes (MAPDRs) using electrocardiographic (ECG) signals and arterial blood pressure waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative modified Hilbert transform-based algorithms, namely ECGMHT and BPMHT. The resulting systolic and diastolic blood pressure pulses are then used to calculate the MAP trend. A new smoothing algorithm is developed, next based on piecewise polynomial fitting (PPF) to smooth the fast fluctuations observed in RR-tachogram and MAP trends. PPF algorithm operates by sequentially fitting N number of polynomials to the original signal and calculating the corresponding coefficients using the best linear unbiased estimation approach. In the next step, the proposed algorithm is applied to 15 subjects of the MIMIC II Database and AHE and MAPDRs (MAP ≤ 60 mmHg with a period of 30 min or more) are identified. As a result of this study, MAPDR is realised as a specific marker of cardiogenic shock, in that for a sequence of MAPDRs as long as 20 min or more, there will exist a consequent high peak with a duration of 3-4 min in the corresponding probability of cardiogenic shock diagram.
    Computer Methods in Biomechanics and Biomedical Engineering 08/2009; 13(2):197-213. · 1.39 Impact Factor
  • Ali Ghaffari, Mohammad Atarod, Masood Ghasemi
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    ABSTRACT: An innovative method was proposed on the basis of vectorcardiography to characterize the location and extent of moderate to large, relatively compact infarcts using ECG evidence. It is assumed that heart vector is proportional to relevant active depolarization area(s). The normal VCG was then used to examine our ideas based on the information of location, amplitude, and direction of heart vector at any instant that is included in it. The model-based comparison of cases under study and relevant normal VCGs gives region and extent of myocardial infarction. Three criteria were finally defined to evaluate the presented method based on Physionet database. EPD, which is the percentage discrepancy between the extent of the infarct as estimated from our proposed method and as determined from the gold standard. SO, which was defined as the overlap between the sets of infarct segments as estimated and as determined from the gold standard. And CED, which is the distance between the centroid (geometrical center) of the infarct as estimated from our method and as determined from the gold standard. Finally, we gained the values of EPD equal to 32, SO equal to 0.933 and CED equal to 1. The presented method is not applicable in cases of hypertrophy, Bundle Branch Block (BBB) and arrhythmia which can be a plan for future work.
    Cardiovascular Engineering 04/2009; 9(1):6-10. · 0.81 Impact Factor
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    ABSTRACT: In this study, we have introduced an open-source program that can be used for the detection and analysis of different waves in the ECG signal. The effect of noise is first reduced by applying an adaptive least-squares method to the signal using a sliding window. The maximums and minimums of the signal are determined, and the R-waves are then detected using a signal-slope test. Waves located between two consequent R-waves, are next classified based on their distance from the left R-wave. Then, using the hypothesis test the detected signal is divided into five equal segments from its peak to the base line. The presented program is capable of computing the arc length of each segment, calculating correlation coefficients and also performing other non-parametric tests. Correlation and FFT-based methods were finally applied to the TWA database of the CinC 2008 challenge and the results are represented.
    Computers in Cardiology, 2008; 10/2008
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    ABSTRACT: In this study, an innovative least-squares adaptive estimator is developed for heart fault diagnosis. The reference model of this study is the one developed by Ursino in 1998. Mitral valve function is studied in this paper based on a new method which uses invasive noisy blood pressure waveform observations of left ventricle, left atrium, and left pulmonary vein. To meet this end, an adaptive algorithm is designed for estimation of discontinuous time-variant parameter values and then Ursinopsilas model simulator is utilized for the evaluation of Mitral valve non-linear gain. The results obtained, indicate the high capability of the presented model in the estimation of different cardiovascular parameters and so fault diagnosis in heart valves.
    Computers in Cardiology, 2008; 10/2008
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    ABSTRACT: Objective: The aim of this study is to found how well one can characterize the location and extent of relatively compact infarcts using electrocardiographic evidence. Method: Here, we address a method base on behavior of some ECG's features, which are Q, amplitudes and ST dispersion. We call these Q and ST curves. At the first step, by plotting the variability of Q, amplitude and ST dispersion for nodes which lies in lines on torso plane, these curves are obtained. The behavior of the mentioned curves for normal lines both in horizontal and vertical line differ from the abnormal ones. A threshold method is used here to determine the infarcted area. Results and Conclusion: The method is evaluated on Challenge 2007 database. The results are EPD=8, SO=0.944, and CED=1. The method achieved the best EPD and CED scores and the second place for SO and overall ranked the highest scores (first rank) in CinC/PhysioNet Challenge 2007.
    Computers in Cardiology, 2007; 11/2007
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    ABSTRACT: Hypothesis/Objective: The aim of this study is to characterize the location and extent of moderate to large, relatively compact infarcts using ECG evidence. Method: In this paper, we proposed a method on the basis of vectorcardiography which assumes that heart vector is proportional to relevant active depolarization area(s). To examine our ideas, we used the normal VCG which includes the information of location, amplitude, and direction of heart vector at any instant. The model based comparison of cases under study and relevant normal VCGs gives region i.e. segment(s) and depth i.e. extent of myocardial infarction. Results and Conclusion: We evaluated the method on CinC/Physionet Challenge 2007 database. In our final entry the scores of EPD equal to 32 (ranked 3<sup>rd</sup>), SO equal to 0.933 (ranked 3<sup>rd</sup>) and CED equal to 1 (ranked 1<sup>st</sup>) are achieved. It also ranked the third among the other methods proposed to CinC/Physionet Challenge 2007.
    Computers in Cardiology, 2007; 11/2007
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    ABSTRACT: In this study, a new pattern discrimination method for the classiication of Mean Arterial Pressure (MAP) regimes in ICU via an appropriately regulated Radial Basis Function (RBF) Support Vector Machine (SVM) is described. The aim of this classiication is to detect hazardous cardiogenic shock situations to prevent probable fatal failure of organs. To this end, electrocardiogram (ECG) and Blood Pressure (BP) waveforms are processed via a Modiied Hilbert Transform (MHT), and QRS complexes (equivalently obtaining heart rate-HR trend) and pressure pulses (equivalently obtaining trends of systolic, diastolic and mean arterial pressures) are detected, respectively. In the next step, a RBF-SVM classiier is tuned using features obtained from the cardiogenic shock risk scoring model developed by Hasdai et al. (2000) to classify MAP regimes into three categories; survival (the status that will not fall into shock), critical (the transient status that may lead to shock or a return to the survival episode) and Acute Hypotensive Episode -AHE (meaning cardiogenic shock will certainly occur.) Then, the regulated RBF-SVM classiier is applied to 60 records of the Computers in Cardiology (CinC) Challenge 2009 and the values of Se = 92% and P + = 93% are obtained for sensitivity and positive predictivity, respectively. As some results of this study, the proposed classiication method recognized truly 15 subjects out of 15 normal (without shock episodes) subjects of the MIMICII database as belonging to the \survival class", while the algorithm could classify 24 subjects as \AHE", 3 subjects as of the \critical class" and 3 subjects as in the \survival" situation out of 30 shock containing records of the MIMICII database.