## 2 Related Work  
This section discusses the research so that our computational system can be compared to similar works. In the study by [PERSON] et al. (2019), the time and frequency information of the raw data, learned from the DWT feature, was referred to as temporal-frequency autoencoding. This approach achieved high accuracies in identifying multiple heartbeats: 98.87% on raw electrocardiogram (ECG) data from ECG-ID (two-recording) [4], 92.30% on ECG-ID (All-recording) and 96.82% on MIT-BIH-AHA [5] databases. This study [15] is based on information extracted from four distinct 15-minute-long RFII recordings, each related to a different subject. The above system achieved recognition rates of up to 99.9% using the CWT approach and rate increases of up to 100% using the DWT approach. The ECG data was collected by repeatedly recording 30 healthy individuals performing six regular everyday activities at a sample rate of 1000 Hz. The QRS segment was extracted using discrete wavelet transform (DWT) algorithms, including the first three Daubechies (dB) discrete wavelets and the Cardioid graph. To validate the matching process, classification was performed using the multilayer perceptron (MLP) technique. In [16], even when the data file was compressed to 73.33%, the recognition rate reached 96.46%. The study by [PERSON] et al. [17] was used to identify 80 individuals from four publicly available ECG databases (MIT-BIH [5], ST-T, NSR [4], PTB) and an electrocardiogram dataset collected from 20 students at Paris Est Institution. For feature extraction, the discrete wavelet transform (DWT) was used. The random forest framework presented for the ECG signal authentication was accurate and achieved a low error rate and 100% subject detection accuracy for healthy individuals using a reduced number of features. In this attempt, the feature vector from [1] was extracted by first extracting the heartbeat and then employing the discrete wavelet transform (DWT) to retrieve the wavelet coefficients. The method was evaluated on public datasets such as the MIT-BIH/arrhythmia (MIT-BIH [5]), MIT-BIH-Normal Sinus Rhythm (NSRDB) [4] and the ECG-ID database (ECG-IDDB) [4], with achieved accuracies of 93.1%, 99.4%, 82.3% and 94.4%, respectively. For this study [18], BIOPAC mechanisms were used to collect ECG signal data. The SS2L electrode Lead Set and body surface electrodes were used to collect ECG data. The dataset includes ECG recordings from fourteen subjects at rest, eight men and six women. Each ECG recording was sampled at 1 kHz. To remove artifacts or extract regions of focus from raw ECG signals, empirical mode decomposition (EMD) was used, and the features were extracted by obtaining indicative attributes from the frequency, time and statistical domains. SVM with a cubic kernel achieved the highest accuracy of 98.7%, sensitivity of 100% and specificity of 98.8% for the successful identification of fourteen subjects. The system is sensitive to the local distinction among heartbeats from different subjects, according to the author [19], because it utilizes principal component analysis filters as the convolution kernel. Finally, linear (SVM) is applied to address the identification for faster classification and training [20]. With only five heartbeats from 12 subjects, the proposed strategy achieves greater subject identification accuracy (94.4%) on ECG signals using the ECG-ID [4] Database. The author of [21] employs a convolutional neural network to acquire complicated patterns in data directly from electrocardiogram image representations, eliminating the requirement for extensive extracting features. The detection rate from two datasets was 98.4% (one-arm ECG) or 91.1% (two-arm ECG) (ear ECG). A deep learning-based 1D-CNN was used in this [22] attempt to categorize ECG data for biometrics. The NSRDB and ECG-ID [4] databases are used in the experiments. The accuracies of the NSRDB and ECG-ID databases are 96.93% and 100%, respectively.