## 4 Experiments and Results
### Experiments and Results  
To perform the experiments, this study used a computer equipped with an Intel(r) (Santa Clara, CA, USA) Core(TM) i7-10700 CPU @ 2.9 GHz, Windows 10 64 bit, 8 GB RAM (Random Access Memory), an NVIDIA GeForce RTX 2060, and Matlab 2023b. To train and validate the data from the same session, the PTB dataset was utilized and the Pan-Tompkins algorithm was used to detect the R-peaks. Subsequently, the segments centered around these detected R-peaks were extracted to ensure equal sizes before and after each peak. For ECG signals with a sample length of 2000, each segment was constructed to include 1000 samples before and after the R-peak. Data for which the number of detected R-peaks did not meet a certain threshold were excluded. A total of 239 individuals were included, with 70 samples allocated for training and 30 for validation for each individual. The training dataset consisted of 16,730 samples (239 subjects \(\times\) 70 samples) and the validation dataset consisted of 7170 samples (239 subjects \(\times\) 30 samples). To train and validate the data from the different sessions, only data from individuals who had at least two sessions available were selected from the PTB dataset. The Pan-Tompkins algorithm was employed to detect the R-peaks and ensure the segments centered around the detected R-peaks of equal sizes before and after each peak was extracted. For the ECG signals with a sample length of 2000, each segment consisted of 1000 samples before and after the R-peak. Data for which the number of detected R-peaks did not meet a certain threshold were excluded. A total of 105 individuals were selected, with 70 and 30 samples allocated for training and validation, respectively, per individual. The training dataset consisted of 7350 samples (105 subjects \(\times\) 70 samples), and the validation dataset consisted of 3150 samples (105 subjects \(\times\) 30 samples). Table 1 presents the experimental results of the training and validation of data from the same session. The ECG signals were transformed into time-frequency representations using a continuous wavelet transform, and the pretrained ResNet-101 was trained. The mini-batch size was 30, with a maximum of 20 epochs, and an initial learning rate of 0.0001, with no data augmentation being used. Adam optimization was employed. Single 1-channel ECG signals from the PTB dataset were used in this study. For a noise reduction comparison between pre- and post-processing, high-frequency noise was filtered using SGF with the parameters set to polynomial order 0 and frame length 15. Baseline wandering was addressed using BWPF, with parameters configured for 30 segments. Figure 6: Diagnostic categories of the PTB-ECG dataset. Table 2 presents the experimental results when training and validating with data from different sessions. The ECG signals were transformed into time-frequency representations using a continuous wavelet transform, and the pretrained ResNet-101 was trained. The mini-batch size was 30, with a maximum of 20 epochs, and an initial learning rate of 0.0001, with no data augmentation being used. Adam optimization was employed. The ECG signal had a sample length of 800, and the experimental results are shown for the input data from the 1-channel, 2-channel, and 3-channel ECG signals from the PTB dataset. Table 3 shows the experimental results of training and validation with data from different sessions without cutting the ECG signals precisely at the R-peak positions, but instead finely overlapping segments and training the neural network models regardless of the starting point of the data. The ECG signals were transformed into Mel-spectrograms, and a CNN-LSTM neural network model was trained. For the Mel-spectrogram transformation, a window length of 256 and hop length of 64 were applied, with 128 bands. The CNN-LSTM architecture consists of three sets of Convolution-BatchNorm-ReLU-MaxPool layers and one LSTM layer. The convolution filters were sized three, with 64, 64, and 128 filters applied. The LSTM had 256 hidden nodes.