OICC Press Majlesi Journal of Electrical Engineering 2345-3796 19 2 2025 06 01 Hybrid Attention-based Deep Learning Network For Emotion Recognition by ECG Signal 10.57647/j.mjee.2025.1902.29 EN Mahtab Vaezi Department of Biomedical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University Isfahan, Iran https://orcid.org/0000-0001-9049-5681 Mehdi Nasri Department of Biomedical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran; Efficiency and Smartization of Energy Systems Research Center, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran https://orcid.org/0000-0002-9254-3584 Farhad Azimifar Department of Biomedical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University Isfahan, Iran https://orcid.org/0000-0002-1827-7695 Mahdi Mosleh Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University Isfahan, Iran https://orcid.org/0000-0003-0419-1142 Journal Article 2025 06 01 Emotions play an important role in our daily activities, decision-making, and artificial intelligence needs to identify emotions to interact constructively with its audience. In this paper, an intelligent method for two-dimensional emotion recognition is proposed. The ECG signal available in the DREAMER database has been used to recognize emotions because of the high correlation of this signal with emotions and easy recording. First step for valence and arousal recognition, the ECG signal is entered into the deep learning network, which is a combination of CNN and LSTM. CNN performs feature extraction and LSTM performs data classification. The attention mechanism aims to optimize the weights and improve the performance of the network, overseeing the proposed deep learning network. Using the proposed method, valence and emanation were identified with 95% and 94% accuracy, respectively. The proposed hybrid network is very suitable for high-dimensional data, and the use of the attention mechanism helps to improve the performance of the network by preventing overfit and getting stuck in local optimal.