## 1 Introduction
Along with cardiac signal processing, cardiac imaging today represents an important area of clinical research that has achieved excellent results in recent years, such as deep-learning approaches [19]; especially, this led to the development of computer-assisted tools capable of segmenting the whole heart [20,21], as well as identifying specific regions of interest [22]. In the wavelet-based approach, the pre-defined basis functions and the number of decomposition levels are used to compute sub-band signals from VCG signal [23]. Additionally, the mentioned VCG signal-based approaches have considered only for MI detection. The automated classification of various types of MI pathologies has not been considered using VCG signals. The existing VCG-based approaches have considered various feature extraction and machine learning methods to detect MI. In recent years, various deep learning-based approaches have been used to detect and localize MI using 12-lead ECG signals [2,24,25]. The deep learning-based MI detection and localization methods do not require extracting features from 12-lead ECG signals [2]. The deep learning-based methods have not been explored for the detection and localization of MI using VCG signals. Therefore, a deep learning-based approach can be developed to detect and localize MI using VCG signals. The multivariate variational mode decomposition (MVMD) is a recently proposed signal processing technique to decompose the multi-channel signals into components or modes [26]. This method is fully signal-driven and does not consider any basis functions and decomposition levels like DWT to obtain components of non-stationary signals. The univariate version of VMD has been used for the analysis of ECG signals for the detection of ventricular tachycardia and atrial fibrillation episodes [27,28]. The VCG is a multi-channel signal, and, therefore, the MVMD can be used to decompose the signal into modes. Moreover, deep learning-based methods have been used in the multi-scale or modal domain of ECG signals to detect cardiac ailments [29]. For VCG signal, the deep learning method has not been explored in the multi-scale domain to detect and localization of MI. The novelty of this work is to develop a multi-channel multi-scale deep learning-based framework to detect and localize MI using VCG signals. The important contributions of this work are given as follows:  
1. The MVMD is introduced to decompose the VCG signals into sub-band signals or modes;
2. A multi-channel multi-scale two-stage deep convolutional neural network (CNN) framework is proposed for the detection and localization of MI;
3. The MI types, such as AMI, IMI, ILMI, ALMI, ASMI, and IPLMI, are classified in the second stage of the proposed multi-channel multi-scale deep CNN (MMD-CNN) model;
4. The multi-channel multi-scale two-stage deep CNN performance is evaluated using hold-out and 10-fold cross-validation (CV) schemes. The remaining sections of this paper are written as follows. The explanation regarding the VCG signal database is written in Section 2. In Section 3, the proposed approach for MI detection and localization is described. Section 4 presents the results and discussion of the proposed approach. In Section 5, conclusions of this paper is summarized.