No abstract

The aim of this study is to develop and describe a new ambulatory Holter electrocardiogram (ECG) events detection-delineation algorithm via segmentation of an information-optimized decision statistic. After implementation of appropriate pre-processing, a uniform length sliding window is applied to the pre-processed trend and in each slide, some geometrical features of the excerpted segment are calculated to construct a newly proposed Discriminant Analyzed Geometric Index (DAGI), by application of a nonlinear orthonormal projection. Then the α-level Neyman-Pearson classifier is implemented to detect and delineate QRS complexes. The presented method was applied to several databases and the average values of sensitivity and positive predictivity, Se = 99.96% and P+ = 99.96%, were obtained for the detection of QRS complexes, with an average maximum delineation error of 5.7 ms, 3.8 ms and 6.1 ms for P-wave, QRS complex and T-wave, respectively. Also the method was applied to DAY general hospital high resolution holter data (more than 1500,000 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% were obtained for QRS detection. High accuracy in a widespread SNR, high robustness and processing speed (146,000 samples/s) are important merits of the proposed algorithm.

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.

No abstract

The major focus of this study is to describe the structure of a solution designed for robustly detecting and delineating the arterial blood pressure (ABP) signal events. To meet this end, first, the original ABP signal is pre-processed by application of à trous discrete wavelet transform (DWT) for extracting several dyadic scales. Then, a fixed sample size sliding window is moved on the appropriately selected 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 utilized to construct a newly proposed principal components analyzed geometric index (PCAGI) (to be used as the segmentation decision statistic (DS)) by application of a linear orthonormal projection. After application of an adaptive-nonlinear transformation for making the DS baseline stationary, the histogram parameters of the enhanced DS are used to regulate the α-level Neyman–Pearson classifier for false alarm probability (FAP)-bounded delineation of the ABP events. In order to illustrate the capabilities of the presented algorithm, it was applied to all 18 subjects of the MIT-BIH Polysomnographic Database (359,000 beats) and the end-systolic and end-diastolic locations of the ABP signal as well as dicrotic notch pressure were extracted and values of sensitivity and positive predictivity Se = 99.86% and P+ = 99.95% were obtained for the detection of all ABP events. High robustness against measurement noises, acceptable detection-delineation accuracy of the ABP events in the presence of severe heart valvular and arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameters dependency to the acquisition sampling frequency can be mentioned as the important merits and capabilities of the proposed PCAGI-based ABP events detection-segmentation algorithm.