## 1 Introduction
Feature extraction was applied to the ECG signal in the spectral domain, and diagnosis was performed using a frequency-based compressive neural network. [PERSON] [20] detected ECG arrhythmia using a large Choi-Williams time-frequency feature set. The Choi-Williams time-frequency transform was used for feature extraction, and several algorithms such as SVM (support vector machine) and k-NN (k-nearest neighbor) were compared. [PERSON] [21] studied a joint time-frequency domain based coronary artery disease sensing system using ECG signals. The signal was converted to a time-frequency representation using an improved eigenvalue decomposition of the Hankel matrix and Hilbert transform. The time-frequency based features were computed, and these features were classified using the random tree and J48 decision tree. [PERSON] [22] studied noise rejection for wearable ECGs using the modified frequency slice wavelet transform and convolutional neural networks. The modified frequency slice wavelet transform was used to generate spectrograms, which were classified using a convolutional neural network (CNN). [PERSON] [23] evaluated the frequency, time-frequency, and wavelet analysis of an ECG signal. The Fourier transform, autoregressive moving average, multiple signal classification, short term Fourier transform, Choi-Williams, Wigner-Ville, and wavelets were considered to compare the segmentation of the QRS complex which is the combination of three of the graphical deflections seen on a typical ECG. The deep learning network is a neural network that has several to several hundred hidden layers, in contrast to one or two hidden layers in existing neural networks. The many hidden layersenable the neural network to solve problems of various complexities, which cannot be solved using only a few hidden layers. A CNN is a deep learning network that has shown good performance in image based applications. A CNN is a neural network that combines both feature extraction and classification. Several ECG biometrics based on the CNN have been studied. [PERSON] [24] studied single arm ECG biometric human identification using deep learning. Images projected from the trajectory of the ECG were used to train the CNN. [PERSON] [25] evaluated the deep learning of off-the-person heart biometrics representations. Two CNNs were trained using the raw signal and its spectrogram for feature extraction, where both features were classified by the distance measure. Finally, the scores were fused using the fusion rule. [PERSON] [26] studied ECG based biometric human identification using CNNs in smart health applications. Fiducial points were detected from a raw signal, and the distances between these points were calculated. The distances were input in the SVM, k-NN, and CNN, and their performances were compared. [PERSON] [27] studied personal identity verification based on a CNN. An ECG signal composed of 3600 samples in 10 s was converted to a \(60\times 60\) grayscale image, which was used to train a CNN. Various other studies have focused on the ECG biometrics using CNNs. As the ECG is noise-sensitive, various studies have applied time-frequency transformations that are robust to noise, with the CNNs achieving a good performance in image classification. Various studies have applied time-frequency representations of 1D signals to CNNs using transforms such as MFCC (mel frequency cepstrum coefficient), spectrogram, log spectrogram, mel spectrogram, and scalogram. CNNs have various pre-configured models such as VGGNet [28], Xception [29], ResNet [30], and DenseNet [31]. These time-frequency representations are normally used in signal processing such as voice and sound and have reported improvement of performance such as in a noisy environment. The signal is changed to a 2D representation by time-frequency analysis, and the 2D representation is considered as an image. Among recent literature works, the CNNs has reported good performance in image classification.