## 5 Conclusions  
This paper tackled ECG signal classification with a new deep learning approach. The raw time-series ECG signal is transformed into a visual representation called a spectrogram using STFT. This spectrogram shows how the frequencies within the signal change over time. To capture deeper insights from the spectrogram, two techniques are applied: HOG, which analyzes the direction and intensity of changes (gradients) within small regions of the spectrogram, and LBP, which focuses on how individual pixels in the spectrogram relate to their neighbors. This creates a code representing the local texture around each pixel, highlighting textural variations within the spectrogram. Three images are generated: the original spectrogram, along with the corresponding HOG and LBP images. These are then fed into a 2D convolutional neural network (CNN). A CNN is particularly adept at recognizing patterns within images. In this case, the CNN learns to identify specific patterns in the spectrograms, HOG, and LBP images that are indicative of different ECG classes. To further enhance the model's accuracy, a gated recurrent unit (GRU) is added after the CNN. GRUs are a type of recurrent neural network (RNN) that excels at handling sequential data like ECG signals. The GRU takes the combined outputs from the three-channel CNN and analyzes them over time, potentially capturing even more subtle features that contribute to classification. The model was rigorously tested on two datasets containing data representing three and five different classes. The results were impressive, demonstrating that the combination of spectrogram representation, feature fusion, and the CNN-GRU model outperforms existing algorithms, as shown in the last table.  
This suggests that the proposed method has significant potential for accurate ECG classification, which is crucial for diagnosing and treating various cardiovascular diseases. By effectively identifying patterns in ECG signals, this approach could become a valuable tool in cardiology. This also reflects the growing sophistication of models and their ability to handle ECG data with greater accuracy and reliability, indicating that newer methods are leveraging both the richness of these datasets and advancements in deep learning techniques to push the boundaries of performance.  
Conceptualization, A.E.; data curation, A.E.; formal analysis, F.B.; funding acquisition, A.E. and G.E.; investigation, A.E., F.B. and G.E.; methodology, A.E., F.B. and G.E.; project administration, A.E.; resources, G.E.; software, F.B.; supervision, A.E.; validation, F.B.; visualization, F.B.; writing--original draft, F.B. and G.E.; writing--review and editing, A.E. and G.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are openly available in [[https://physionet.org/about/database/](https://physionet.org/about/database/)]([https://physionet.org/about/database/](https://physionet.org/about/database/)) at [[https://doi.org/10.13026/C2F305](https://doi.org/10.13026/C2F305)]([https://doi.org/10.13026/C2F305](https://doi.org/10.13026/C2F305)) (accessed on 5 July 2024).  
The authors declare no conflicts of interest.