TIPOS DE MADERAS

In the past decades, a significant amount of research work has been conducted for au- tomatic classification of PVC beats based on different feature extraction and classifica-

tion methods. Feature extraction methods include QRS complex morphology [83, 84], interbeat R-R intervals [83], filter bank [85], Hermite functions [86], wavelet trans- form [13, 14, 87], higher order cumulant features [88], Karhunen-Loeve expansion of ECG morphology [89], and correlation dimension and largest Lyapunov expo- nent [90, 91]. Several classifiers have been used for heartbeats classification including decision trees [85], nearest neighbors [92], Linear Discriminant [83], self-organizing maps [86, 93], neural networks [94, 95, 14], fuzzy neural network [13], support vector machines [96], and Optimum-path forest [97]. Some of these methods have been devel- oped for the classification between PVC and normal beats while others are developed to identify several arrhythmias into different classes. As our proposed method for PVC beat classification is evaluated in the MIT-BIH Arrhythmia database [98] (The descrip- tion of the database is provided in Section 3.2), only some of the recent methods that evaluated on this database are reviewed.

A PVC classification method based on filter bank features and decision trees pre- sented by Wieben et al. [85] achieved a sensitivity of 85.3% and a positive predictivity of 85.2%. In [94] author developed a classifier based on principal component analysis and neural networks. The method achieved a sensitivity of 98.1% and a positive predic- tivity of 94.7% for 14 records of the MIT-BIH database. Lagerholm et al. [86] built a classifier by combining Hermite functions and self-organizing maps neural networks for clustering of QRS complexes into 25 groups. They achieved a very low degree of mis- classification (about 1.5% beats misclassified). A real time QRS complex classification method based on QRS complex morphology and Mahalanobis distance was developed in [84]. Using only 44 records of the MIT-BIH database, they achieved a sensitivity of 90.74% and a positive predictivity of 96.55%. In [95] authors introduced a classifier based on neural network classifier for classification of normal QRS complexes and PVC. Feeding the classifier by 26 parameters extracted from two ECG leads, they achieved

98.5% of correct detection of premature ventricular beats and 99.7% of normal beats for entire MIT-BIH database. Shyu et al. [13] presented a PVC detection method based on wavelet transform and fuzzy neural network. The algorithm achieved an accuracy of 99.79% for six records of the MIT-BIH database. An arrhythmia beat classification method based on the RR-interval features was proposed by Tsipouras et al. [99]. They achieved 98% of accuracy. In [14] authors presented a neural network-based PVC clas- sifier based on wavelet transform and timing interval features. The algorithm achieved an accuracy of 96.82% and 95.16% for 22 and 40 records of the MIT-BIH database, respectively. A method based on linear discriminants classifier and ECG morphology and heartbeat interval features was developed by De Chazal et al. [83]. They reported a specificity of 98.8% and a sensitivity of 77.7%. A technique based on a nonlinear trimmed moving average filter proposed in [100] achieved 99.7% of specificity and 97.8% of sensitivity for 34 records of the MIT-BIH database. In [101] authors pro- posed a PVC classification method based on QRS template matching using different complex features such as cross-correlation, frequency, and morphological of the QRS complex. The classifier was evaluated on 48 records of the MIT-BIH database, and the results were 98.4% of sensitivity and 98.86% of specificity. Lim [15] developed a PVC classifier based on the neural network with weighted fuzzy membership functions. Using the six records of the MIT-BIH database, the algorithm achieved a specificity of 99.93% and a sensitivity of 99.21%. Talbi and Charef [93] proposed a PVC detection method based on the QRS power spectrum and self-organizing maps. They achieved a specificity of 95.18% and a sensitivity of 82.20% for 46 records. A Real-time Cardiac Arrhythmia Classification based on layered hidden Markov model proposed in [102] ob- tained a sensitivity of 97.75% and a positive predictivity of 96.63% for 16 records of the MIT-BIH database. Daamouche et al. [103] developed an arrhythmia beat classification method based on Wavelet transform and particle swarm optimisation technique. Using

only 20 records of the MIT-BIH database, they achieved a sensitivity of 91.75% and a positive predictivity of 96.14%. A low-complexity data-adaptive approach for PVC identification based on template matching was developed by Li et al. [16]. The classifier was tested on 22 records of the MIT-BIH database, and the results were 93.1% of sen- sitivity and 81.4% of positive predictivity. In [104] authors developed a PVC detection method based on Lyapunov exponents and LVQ neural network. The algorithm achieved an accuracy of 98.90%, sensitivity of 90.26%, positive predictability of 92.31% for 20 records of the MIT-BIH database. Talbi and Ravier [105] developed a PVC classification method based on the fractional linear prediction. Using only 43 records of the MIT-BIH database, they evaluated several classifiers and achieved the maximum accuracy of 96% for Neural network trained by LevenbergMarquardt rule classifier with a Specificity of 98% and a sensitivity of 74%.

From the literature, it is observed that there are still some limitations in the PVC detection and classification methods. Most of the existing techniques are not suitable for online PVC detection as they employ many complicated mathematical tools such as wavelet transform and artificial neural network. The efficiency of these methods is mainly accompanied by high complexity and long computational time. Moreover, some of the existing high detection results are based on relatively small data sets or used overlapped training and testing data sets. The efficiency of these methods over a large number of records (patients) is a difficult problem to deal with. To overcome these problems, this study aims to introduce an online PVC detection method to detect PVC beasts accurately in an online manner.