## 1 Introduction  
Electrocardiogram (ECG) signals provide detailed information related to the abnormal behavior of the heart [1]. An expert cardiologist analyzes [2] these signal patterns during diagnosis. In the last two decades, a good number of intelligent systems have been developed to analyze ECG and TMT-ECG signals of the heart, to assist health care experts. These systems are primarily based on single-lead [3, 4] (Figure 1a) and 12-lead ECG signals [5, 6, 7, 8, 9](Figure 1b). Coronary artery disease (CAD) pertains to constriction or blocking of the coronary arteries caused by plaque formation. There are few literature studies related to CAD due to the lack of data and its complexity [10]. CAD is commonly identified and diagnosed based on different tests, such as ECG, treadmill ECG, echocardiogram (ECHO), and angiography. The intelligent systems use different neural architectures, such as the convolutional neural network CNN [11], recurrent neural network (RNN) [12], CNN with RNN [13; 14], deep belief network (DBN) [3; 15], and the fully-connected neural network (FC) [11] to predict electrocardiogram (ECG)-related issues, such as arrhythmia [16; 17; 18; 19; 20], atrial fibrillation (AF) [21; 22; 23], myocardial infarction (MI) [24; 25], ST elevation [21], CAD [10; 26; 27; 28], etc. [PERSON], [PERSON]; [PERSON], [PERSON] et al. [10] implemented a long short-term memory (LSTM) network with the convolutional neural network (CNN) to automatically diagnose CAD based on ECG signals. [PERSON], [PERSON], and [PERSON] et al. [26] compared various feature selection models utilizing binary particle swarm optimization and a genetic algorithm using a support vector machine classifier. [PERSON], [PERSON], and [PERSON] et al. [27] worked on limited repository data using the wavelet transform method for a CAD-related diagnosis. [PERSON], [PERSON], [PERSON], [PERSON] et al. [28] carried out a comparative study on various types of classifiers related to CAD.  
In view of the shortcomings related to the nature of data, the size of the data, and the lack of clinical data, which were encountered in earlier works, in the present study, we aimed to develop a reliable artificial intelligence-based prediction system for CAD. The  
Figure 1: (**a**) Single-lead ECG with segmentation; (**b**) 12-lead TMT-ECG.  
neural model is unique in terms of exhaustive datasets of live patients, a higher frequency of sampling of feature data, and a unique combination of activation functions across layers of the neural networks. Such a system will assist the healthcare expert in accurately predicting the presence or absence of CAD among the patients during periodic tests.  
The majority of ECG research use data from publicly accessible archives [29; 30; 31]. Archived datasets from the repository are readily available with noise cancellation and features extracted; moreover, they are in proper data formats, unlike clinical lab data. However, in the present case, a large dataset of 550 patients was considered, with a high sampling rate of 815 pixel points/sec, which is far higher than any other work reported thus far. No researcher has attempted to apply different activation functions across different layers. In the present study, different activation functions were adopted for different layers of the CNN model, optimally, resulting in highly accurate prediction capabilities for the model.