Iranian Journal of Electrical and Electronic Engineering

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The aim of this study is to introduce a new methodology for isolation of ectopic rhythms of ambulatory electrocardiogram (ECG) holter data via appropriate statistical analyses imposing reasonable computational burden. First, the events of the ECG signal are detected and delineated using a robust wavelet-based algorithm. Then, using Binary Neyman-Pearson Radius test, an appropriate classifier is designed to categorize ventricular complexes into "Normal + Premature Atrial Contraction (PAC)" and "Premature Ventricular Contraction (PVC)" beats. Afterwards, an innovative measure is defined based on wavelet transform of the delineated P-wave namely as P-Wave Strength Factor (PSF) used for the evaluation of the P-wave power. Finally, ventricular contractions pursuing weak P-waves are categorized as PAC complexes however, those ensuing strong P-waves are specified as normal complexes. The discriminant quality of the PSF-based feature space was evaluated by a modified learning vector quantization (MLVQ) classifier trained with the original QRS complexes and corresponding Discrete Wavelet Transform (DWT) dyadic scale. Also, performance of the proposed Neyman-Pearson Classifier (NPC) is compared with the MLVQ and Support Vector Machine (SVM) classifiers using a common feature space. The processing speed of the proposed algorithm is more than 176,000 samples/sec showing desirable heart arrhythmia classification performance. The performance of the proposed two-lead NPC algorithm is compared with MLVQ and SVM classifiers and the obtained results indicate the validity of the proposed method. To justify the newly defined feature space (σi1, σi2, PSFi), a NPC with the proposed feature space and a MLVQ classification algorithm trained with the original complex and its corresponding DWT as well as RR interval are considered and their performances were compared with each other. An accuracy difference about 0.15% indicates acceptable discriminant quality of the properly selected feature elements. The proposed algorithm was applied to holter data of the DAY general hospital (more than 1,500,000 beats) and the average values of Se = 99.73% and P+ = 99.58% were achieved for sensitivity and positive predictivity, respectively.

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In this study, a new long-duration holter electrocardiogram (ECG) major events detection-delineation algorithm is described which operates 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, a 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 α-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.