J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals

Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 6791405, 11 pages https://doi.org/10.1155/2018/6791405 Research Article J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals Deng-ao Li , Jie Zhou , Jumin Zhao , and Xinyan Liu College of Information and Computer, Taiyuan University of Technology, Taiyuan, China Correspondence should be addressed to Deng-ao Li; Received 18 November 2017; Revised 16 March 2018; Accepted 12 April 2018; Published 31 May 2018 Academic Editor: Li Xu Copyright © 2018 Deng-ao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. As a new important index of the electrocardiogram (ECG) of ventricular bipolar play, J wave plays an increasingly significant role in the clinical diagnosis. The existence of J wave hints at potential crisis of fatal disease and even death. Nowadays, however, it can hardly meet the clinical needs where the diagnosis of J wave variation only depends on experience of clinicians. Therefore, a new technique which is capable of detecting J wave using analytic time-frequency flexible wavelet transformation (ATFFWT) is proposed in this paper. We have used ATFFWT to decompose the processed ECG signals into the desired subbands. Further, Fuzzy Entropy (FE) is computed from each subband to capture more hidden and meaningful information. Feature scoring method is applied to select optimal feature set. Finally, the extracted features are fed to Least Squares-Support Vector Machine (LS-SVM) classifier. The 10-fold cross validation is used to obtain reliable and stable performance and to avoid the overfitting of the model. Our proposed algorithm has achieved accuracy of 97.61% for Morlet Wavelet (MW) kernel in comparison to 97.56% for Radial Basis Function (RBF) kernel. The developed effective algorithm can be used to design an expert system to aid clinicians in their regular diagnosis. 1. Introduction Nowadays, cardiovascular diseases (CVDs) cause nearly onethird of all deaths worldwide. CVDs remain a leading cause of health loss for all regions of the world and a major barrier to long-term sustainable development of mankind [1]. Nearly 17 million people die due to cardiovascular diseases globally every year [2]. J wave is regarded as a new important index of the electrocardiogram (ECG) of ventricular bipolar play, and it plays an increasingly significant role in the clinical diagnosis of cardiovascular diseases. A series of diseases or conditions that can produce J waves in the ECG continues to rise [3]. The J wave, also referred to as an Osborn wave, is a deflection immediately following the QRS complex of the surface ECG, which is usually partially buried inside the QRS, often appearing as a J-point elevation; it represents the end of depolarization and the start of bipolarization [4]. The presence of J wave may lead to early repolarization syndrome (ERS), pericarditis, idiopathic ventricular fibrillation (IVF), Brugada syndrome (BrS), and even sudden unexplained nocturnal death syndrome [4]. From a mechanistic point of view, these syndromes should be referred to as the J wave syndromes [4]. J wave and J wave syndrome are high-risk early warning indicators of sudden cardiac death [5]. The appearances of prominent J waves in the ECG have long been reported in cases of hypothermia and hypercalcemia [6]. More recently, accentuation of the J wave has been associated with life-threatening ventricular arrhythmias. Although typical J waves usually are accentuated with bradycardia or long pauses, the opposite has also been described [5, 7]. J waves are often seen in young males with no apparent structural heart diseases, whereas intraventricular conduction delay is often observed in older individuals or those with a history of myocardial infarction or cardiomyopathy [5, 7]. J wave is mixed in the normal ST segment, coupled with the small amplitude, existence of noise, and baseline wander. Accordingly, the diagnosis of J wave variation and minute changes in the ECG signals only depends on the clinicians’ experience, which can not meet demand at present 2 Mathematical Problems in Engineering Raw ECG signals Noise and baseline wander removal Signal decomposition based on ATFFWT Computation of Fuzzy Entropy Classification based on LS-SVM Feature selection based on Feature Scoring Normal class J wave class Figure 1: Steps used for automatic J wave detection. clinical, and is also apt to be misdiagnosed. Therefore, it is pretty essential and necessary for us to analyze J wave from the perspective of computer aided method with advanced digital signal processing techniques and machine learning algorithms, which can help to capture the subtle and hidden information in the ECG signals and realize the accurate and automatic diagnosis of J wave. Such an automated system will provide tremendous assistance to the clinicians in their routine screening of cardiac patients [8–12]. In literature, although the method of processing ECG signals has been very mature, methods of detecting J wave signals from ECG signal were relatively less and the detection precision is undesirable. The authors in [13] proposed the method based on locating a break point in the descending limb of the terminal QRS. They added new logic to Glasgow ECG analysis program to automate the detection of J wave. A technique based on digital 12-lead electrocardiogram is used in [14]. ECG signals were automatically processed with the GE Marquette 12SL program 2001 version (GE Marquette, Milwaukee, WI). And the functional data analysis techniques were applied to the processed ECG signals. In [15], five features including three time-domain features and two wavelet-based features are defined; these features are found significantly different in discriminate J wave and normal classes. Thereafter, Principle Component Analysis (PCA) is used to reduce the dimension of these features. An approach for J wave autodetection based on SVM is proposed. Curving Fitting (CF) and wavelet transform (WT) are used for feature extraction from J wave and healthy ECG segments data in [16], which have shown effective variations for J wave and normal subjects. In [17], a novel J wave detection method based on massive ECG data and MapReduce is presented. The power spectrum and the cumulative probability of ECG signals are computed as features and Decision Tree (DT) is applied for classification. Compared with the existing methods of J wave detection, the main contributions of this work are listed as follows: (1) we built a J wave detection expert system with FE entropy on the coefficient of ATFFWT as feature and LS-SVM as a classifier, and it owned high accurate rate; (2) we use lower number of features because feature scoring method is applied (only entro (...truncated)