## 1 Introduction
As a result of using the data acquired from 26 subjects and the PTB DB from 51 people, 99.94% was identified in the data acquired and 99.39% was identified in the PTB DB. [PERSON] et al. [18] acquired data from 56 subjects from the NSR, MIT-BIH arrhythmia database, and PTB DB, and selected the features using LDA in order to perform human recognition using ECG signals. Thereafter, nearest neighbor (NN) was used to classify the features. As a result, the beat identification rate was 96.2% and the subject identification rate was 96.42%, indicating a relatively high accuracy. [PERSON] et al. [19] suggested a non-fiducial feature extraction framework using a distance measuring set that includes the wavelet transform distance. Data was collected from 50 subjects by using a button electrode between the thumb and index finger. The result of 89% accuracy was shown by applying the wavelet transform distance method. [PERSON] et al. [20] suggested an individual identification method by using a Euclidean classifier and a 512 point feature extraction by applying a wavelet transform. The suggested method demonstrated a 100% recognition rate on 35 subjects, with a normal signal and an 81% recognition rate on 10 subjects with arrhythmia. [PERSON] et al. [21] proposed a biometric recognition system using a spectrum coefficient obtained through linear predictive coding (LPC). Data was acquired from 15 subjects by using one electrode. In the acquired data, features were extracted by using an LPC spectrum. The beat identification rate was 99.52% and the subject identification rate was 100% by using the neural network classifier. The non-fiducial approach-based method extracts features based on the time interval of the window. Other unnecessary types of signals may have been included other than those of the normal ECG in the window leading to decreased overall recognition rates. Thus, most of the previous studies including those mentioned above, performed with or without using data containing abnormal signals, showed poor results during ECG identification. In particular, non-fiducial-based studies, such as those of [PERSON] et al. [12], [PERSON] et al. [14], and [PERSON] et al. [18] can extract features using a window of time intervals. In the identification process using a window, a window is set at specific time intervals in the ECG signal, and classification is performed on all of the windows. Based on the results of the window identification rate, the subject class is finally determined by window majority voting. When setting the window of the time interval, a window may be generated, which interferes with the identification performance because a disease signal or a distorted signal may be present. As a result, this can lead to a decrease in the overall identification rate as well as a decrease in the window identification rate. This problem can be solved by examining all of the generated windows and removing the ones that are not recognizable or those that contain other unnecessary signals. In applying discrete cosine transform (DCT) to the windows that are generated at specific time intervals, spectrum differences may have occurred between the windows that contained the normal ECG and those with the distorted signals, causing differences in the energy distribution of the signals [18]. Therefore, in this study, it is possible to increase the overall identification rate by applying DCT to all of the generated windows and then removing the unnecessary windows that do not fit the condition, by applying the window removal method using the threshold setting. In addition to the above-mentioned problem, most of the previous studies performed identification on small sample sizes. Experiments with small sample sizes may be insufficient to evaluate an algorithm's effectiveness. In addition, individual identification has effects on the resulting classification, such as signal processing and pattern recognition.