## 1 Introduction  
The obstruction in one of the coronary arteries of the heart causes the myocardial infarction (MI) disease [1, 2]. Typically, the MI is progressed in three phases [3]. These three phases are (a) ischemic phase, (b) acute phase, and (c) myocardial necrosis phase. The 12-lead ECG signal is used in the clinical study for the early detection and localization of MI pathology [4]. The ST-segment elevations, inverted T-waves, and pathological Q-waves are the morphological changes observed in the ECG signals of different leads in MI pathology [5]. The morphological changes in the ECG signals of the channels or leads,such as V1, V2, V3, and V4, are used to diagnose anterior MI (AMI) [6]. Similarly, inferior MI is diagnosed based on the variations in the morphologies of ECG signals for II, III, and aVF channels. Moreover, the morphological variations in the ECG signals of I, aVL, V5, and V6 channels are used to diagnose left lateral MI pathology [6]. In 12-lead ECG, no ECG lead capture the information about the diagnosis of posterior MI [6,7]. However, the reciprocal changes in the V1 and V2 channel ECG signals are used in the clinical study to diagnose posterior MI [8]. Vectorcardiogram (VCG) is an orthogonal three lead system which measures the heart's electrical activity along transverse, sagittal, and frontal planes, and it has been used for the detection of MI pathology [3,9]. The 12-lead ECG can be derived from the VCG signal using various transformation techniques [10]. In VCG, one of the orthogonal leads reveals the posterior view of the heart [11]. Hence, the method based on the analysis of VCG signal information is helpful to detect and localize MI pathology. The continuous recording and monitoring of VCG signal information for MI disease diagnosis is cumbersome, and hence automated approaches have been used for the accurate detection and localization of MI using VCG signals [3]. The development of novel approaches to detect and localize MI pathology using the VCG signals is challenging in clinical study. In recent years, various approaches have been developed to detect MI using VCG signals [12, 13, 14, 15]. The methods based on the evaluation of various VCG signal morphological features, such as difference in ST-T vector magnitude, area of ST-segment vector, and other T-wave features, have been used to detect MI disease [14, 16, 17, 18]. Similarly, in [13], authors have applied independent component analysis (ICA) and principal component analysis (PCA) for projecting VCG signal feature vector into a lower-dimensional space. They have extracted various morphological features from the VCG signal to formulate the feature vector. The neural network-based classifier has been used for the detection of MI using reduced dimension feature vector of VCG signal [13]. In [14], authors have computed octant and vector-based features from VCG signals and used a decision tree model to detect MI pathology. These methods require the detection of P, Q, R, S, T-onset points manually in the VCG signal to compute the morphological features [3]. In literature, various wavelet-based techniques, such as multi-scale recurrent quantification analysis (MRQA) [15], and complex wavelet sub-band features [3] have been used to detect MI using VCG signals. In [15], each channel of the VCG signal is decomposed into sub-band signals using discrete wavelet transform (DWT). From each sub-band signal, the recurrent quantification analysis (RQA) based non-linear features have been extracted, and Gaussian discriminant analysis (GDA) classifier is used for the detection of MI [15]. Moreover, in [3], the dual-tree complex wavelet transform (DT-CWT) has been used to decompose the VCG signal into sub-band signals along each channel. The entropy and L1-norm features have been extracted from each sub-band signal. The relevance vector machine (RVM) classifier has been used to detect MI from these VCG signals features [3].