# Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features
## 4 Discussion  
We put forth a novel method to generate a GLCM and a GLRLM from one-dimensional signals and have extracted features from the GLCM and the GLRLM. The proposed method is based on the key idea that the GLCM and the GLRLM can extract statistical features from and measure the heterogeneity in images or other two or three-dimensional data. Previous studies have demonstrated the successful application of texture features, such as the GLCM and the GLRLM, in medical fields. [PERSON] [28] obtained GLCM and GLRLM features from diffusion and conventional MRI to identify atypical primary central nervous system lymphomas (PCNSLs) mimicking glioblastomas. [PERSON] [23] extracted texture biomarkers from fused FDG-PET/MRI scans to predict lung metastases in soft-tissue  
Figure 4: Accuracy comparison among the six machine learning algorithms. Qnt stands for quantization and the following number indicates the quantization level. For example, Qnt-32 means that the preprocessed ECG data were quantized with 32 levels. Each quantization level is represented in shades. Figure 5: Top 10 most important features out of the 396 features using XGBoost. The letters in brackets denote ECG lead names: lead I, lead II, lead III, aVL, aVR, aVF, V1, V2, V3, V4, V5, V6. DIS, dissimilarity; CON, contrast; LRE, long run emphasis; RLN, run-length nonuniformity; RLV, run-length variance. sarcoma (STS) cancer. [PERSON] [29] obtained texture features from CT images of intratumoral and peritumoral lung parenchyma to predict lymph node metastasis in clinical stage T1 peripheral lung adenocarcinoma patients. [PERSON] [30] extracted GLCM features from pre- and post-neoadjuvant chemotherapy (NACT) two-dimensional dynamic contrast-enhanced MRI (DCE-MRI) image slices to evaluate the NACT response. We believe that GLCM and GLRLM features obtained from one-dimensional signals can produce reasonable results because the main idea of generating a GLCM and a GLRLM from two or three-dimensional data is identical and the proposed method showed reasonable performance in this study. In general, hyperparameters have a critical effect on performance. In this study, the quantization level and the GLCM offset distance were set as hyperparameters for feature extraction. Based on Figure 4, the offset distance had a significant effect on the accuracy, whereas the quantization level did not have a significant effect. The best accuracy was obtained when the offset distance was small, that is, when the two signal values were close in time. This means that the morphological information between temporally close signal values plays an important role in classifying ECG signals. There are two interesting aspects of the feature importance results. The first is that 5 of the top 10 features were dissimilarity features of the GLCM. As noted in Table 2, the dissimilarity feature is a measure of the local intensity variation defined as the mean absolute difference between the neighboring pairs. In other words, the dissimilarity feature gives importance to the relationship between occurrences of pairs with similar values and occurrences of pairs with differing values. Therefore, it is considered that local intensity variation between neighboring pairs plays an important role in classifying ECG signals. Another interesting point is that first-order features were not included in the top 10 most important features. This means that the importance of the first-order features is lower than that of GLCM or GLRLM features, which are morphological features. Although the proposed method showed reasonable results, it still has several limitations. First, the proposed feature extraction method has two hyperparameters (quantization level and offset distance). From Figure 4, the effect of the quantization level was negligible and the offset distance showed better results with a small value, such as 1 or 2. As a result, it seems that it should not be difficult to tune the hyperparameters. However, there is no guarantee that these patterns will apply to other one-dimensional data.