Iranian Journal of Electrical and Electronic Engineering

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The aim of this study is to address a new feature extraction method in the area of the heart arrhythmia classification based on a metric with simple mathematical calculation called Curve-Length Method (CLM). In the presented method, curve length of the under study excerpted segment of signal is considered as an informative feature in which the effect of important geometric parameters of the original signal can be found. To show merits of the presented method, first the original electrocardiogram (ECG) in lead I is pre-processed by removing its baseline wander then by scaling it in the [-1,1] interval. In the next step, using a trous method, discrete wavelet scales 23 and 24 and smoothing function scale 22 are extracted. Afterwards, segments including samples of the QRS complex, P and T waves are estimated via an approximation criterion and CLM is implemented to extract corresponding features from aforementioned scales, smoothing function and also from each original segment. The resulted feature vector (including 12 components) is used to tune an Adaptive Network Fuzzy Inference System (ANFIS) classifier. The presented strategy is applied to classify four categories found in the MIT-BIH Arrhythmia Database namely as Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC) and average values of Se = 99.81%, P+ = 99.80%, Sp = 99.81% and Acc = 99.72% are obtained for sensitivity, positive predictivity, specifity and accuracy respectively showing marginal improvement of the heart arrhythmia classification performance.

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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.