Daevaeiha M M, Homaeinezhad M R, Akraminia M, Ghaffari A, Atarod M. Detection and Classification of Heart Premature Contractions via α-Level Binary Neyman-Pearson Radius Test: A Comparative Study. IJEEE 2010; 6 (3) :129-148
URL:
http://ijeee.iust.ac.ir/article-1-321-en.html
Abstract: (21289 Views)
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.
Type of Study:
Research Paper |
Subject:
Evolutionary Computation Received: 2010/09/15 | Accepted: 2013/12/30