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Ghaffari A.,K. N. Toosi University of Technology | Homaeinezhad M.R.,K. N. Toosi University of Technology | Daevaeiha M.M.,Non Invasive Cardiac Electrophysiology Laboratory NICEL
Expert Systems with Applications | Year: 2011

The presented study describes a false-alarm probability-FAP bounded solution for detecting and quantifying Heart Rate Turbulence (HRT) major parameters including heart rate (HR) acceleration/deceleration, turbulence jump, compensatory pause value and HR recovery rate. To this end, first, high resolution multi-lead holter electrocardiogram (ECG) signal is appropriately pre-processed via Discrete Wavelet Transform (DWT) and then, a fixed sample size sliding window is moved on the pre-processed trend. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the ECG events detection- delineation decision statistic (DS). The ECG events detection-delineation algorithm was applied to various existing databases and as a result, the average values of sensitivity and positive predictivity Se = 99.95% and P+ = 99.92% were obtained for the detection of QRS complexes, with the average maximum delineation error of 7.4 msec, 4.2 msec and 8.3 msec for P-wave, QRS complex and T-wave, respectively. Because the heart-rate time series might include fast fluctuations which don't follow a premature ventricular contraction (PVC) causing high-level false alarm probability (false positive detections) of HRT detection, based on the binary two-dimensional Neyman-Pearson radius test (which is a FAP-bounded classifier), a new method for discrimination of PVCs from other beats using the geometrical-based features is proposed. The statistical performance of the proposed HRT detection-quantification algorithm was obtained as Se = 99.94% and P+ = 99.85% showing marginal improvement for the detection-quantification of this phenomenon. In summary, marginal performance improvement of ECG events detection-delineation process, high performance PVC detection and isolation from noisy holter data and reliable robustness against holter strong noise and artifacts can be mentioned as important merits and capabilities of the proposed HRT detection algorithm. © 2010 Elsevier Ltd. All rights reserved. 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 | Year: 2010

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


Homaeinezhad M.R.,K. N. Toosi University of Technology | Homaeinezhad M.R.,Non Invasive Cardiac Electrophysiology Laboratory NICEL | Ghaffari A.,K. N. Toosi University of Technology | Atyabi S.A.,K. N. Toosi University of Technology | Atyabi S.A.,Islamic Azad University at South Tehran
Biomedical Engineering Letters | Year: 2011

Purpose: Since ambulatory electrocardiogram (ECG) signal is always accompanied by strong noise, high amplitude baseline wandering, impulsive artifacts, arrhythmic outliers and some discontinuities, these effects reduce the accuracy of a computerized cardiac-originated events detection-delineation algorithm. The aim of this study is to describe a multi-aspect robust structure of a solution designed for detection-delineation of major events of the long-duration holter ECG signal. Methods: In this work, after application of appropriately adopted preprocessing steps, a uniform-length sliding window was moved sample to sample on the preprocessed signal. In each slid, six geometrical features of the excerpted segment were calculated aimed for generating the newly defined geometric index (GI) metric. Then, the α-level Neyman-Pearson classifier was designed and implemented to detect and delineate QRS events. Results: The presented method was applied to diverse number of databases and as a result, the average values of sensitivity Se = 99. 96% and positive predictivity P+ = 99. 96% were obtained for the detection of QRS complexes, with the average maximum delineation error 5. 7, 3. 8 and 6. 1 msec for P-wave, QRS complex and T-wave, respectively. Also, the proposed method was applied to DAY general hospital highresolution holter data and the average values of Se=99. 98% and P+=99. 97% were obtained for QRS detection. Conclusions: It is observed that the proposed method successfully detects and delineates the ECG events showing marginal improvement of the ECG events detection-delineation recent studies. © 2011 Korean Society of Medical and Biological Engineering and Springer. Source


Homaeinezhad M.R.,K. N. Toosi University of Technology | Ghaffari A.,K. N. Toosi University of Technology | Toosi Najjaran H.,K. N. Toosi University of Technology | Daevaeiha M.M.,Non Invasive Cardiac Electrophysiology Laboratory NICEL
Iranian Journal of Electrical and Electronic Engineering | Year: 2011

In this study, a new long-duration holter electrocardiogram (ECG) major events detection-delineation algorithm is described operating based on the false-alarm error bounded segmentation of a decision statistic with simple mathematical origin. To meet this end, first three-lead holter data is pre-processed by implementation of an appropriate bandpass finite-duration impulse response (FIR) filter and also by calculation of the Euclidean norm between corresponding samples of three leads. Then, á trous discrete wavelet transform (DWT) is applied to the resulted norm and an unscented synthetic measure is calculated between some obtained dyadic scales to magnify the effects of low- power waves such as P or T-waves during occurrence of arrhythmia(s). Afterwards, a uniform length window is slid sample to sample on the synthetic scale and in each slid, six features namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second order differentiation, curve length, area and variance of the excerpted segment are calculated. Then all feature trends are normalized and superimposed to yield the newly defined multiple-order derivative wavelet based measure (MDWM) for the detection and delineation of ECG events. In the next step, a a{script}-level Neyman-Pearson classifier (which is a false-alarm probability-FAP controlled tester) is implemented to detect and delineate QRS complexes. 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 = 99.96% and P+ = 99.96% are obtained for the detection of QRS complexes, with the average maximum delineation error of 5.7 msec, 3.8 msec and 6.1 msec 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=99.98% and P+=99.97% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection- delineation process in a widespread values of signal to noise ratio (SNR), reliable robustness against strong noise, artifacts and probable severe arrhythmia(s) of high resolution holter data and the processing speed 163,000 samples/sec can be mentioned as important merits and capabilities of the proposed algorithm. Source


Homaeinezhad M.R.,K. N. Toosi University of Technology | Ghaffari A.,K. N. Toosi University of Technology | Toosi H.N.,K. N. Toosi University of Technology | Daevaeiha M.M.,Non Invasive Cardiac Electrophysiology Laboratory NICEL
Cardiovascular Engineering | Year: 2010

The aim of this study is to develop and describe a new ambulatory holter electrocardiogram (ECG) events detection-delineation algorithm with the major focus on the bounded false-alarm probability (FAP) segmentation of an information-optimized decision statistic. After implementation of appropriate preprocessing methods to the discrete wavelet transform (DWT) of the original ECG data, a uniform length sliding window is applied to the obtained signal and in each slid, six feature vectors namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second order differentiation, curve length, area and variance of the excerpted segment are calculated to construct a newly proposed principal components analyzed geometric index (PCAGI) by application of a linear orthonormal projection. In the next step, the α-level Neyman-Pearson classifier (which is a FAP controlled tester) is implemented to detect and delineate QRS complexes. The presented method was 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 = 99.96% and P+ = 99.96% are obtained for the detection of QRS complexes, with the average maximum delineation error of 5.7, 3.8 and 6.1 m for P-wave, QRS complex and T-wave, respectively. Also, the proposed method was applied to DAY general 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 = 99.98% and P+ = 99.97% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process in a widespread values of signal to noise ratio (SNR), reliable robustness against strong noise, artifacts and probable severe arrhythmia(s) of high resolution holter data and the processing speed 155,000 samples/s can be mentioned as important merits and capabilities of the proposed algorithm. © 2010 Springer Science+Business Media, LLC. Source

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