Dream: A Novel Explainable Neural Network for Detecting Sleep Apnea Using Single-Lead ECG Signals
Published in Biomedical Signal Processing and Control, Elsevier (Journal), 2025
Alhamdulillah! This paper introduces Dream, an explainable neural network architecture specifically designed for sleep apnea detection using single-lead ECG signals. The model provides both high accuracy and interpretability, making it suitable for clinical applications where understanding model decisions is crucial for medical diagnosis.
Published in: Biomedical Signal Processing and Control (Elsevier)
Impact Factor: 4.9 | CiteScore: 11.5 | Q1 Journal
Ms. Ref. No.: BSPC-D-25-03525R1
Accepted: November 29, 2025
Heartfelt congratulations to the team, especially Sanjida Akter, Sanzida Islam Promi, and Akmol Masud Ayon for their exceptional hard work. Sincere thanks to Prof. Mohammad Abu Yousuf Sir and Mohammad Ali Moni Sir for their guidance and support.
Research Area: Medical AI, Explainable AI, Sleep Medicine, ECG Analysis
Recommended citation: Akter, S., Masud, M. A., Promi, M. S. I., Sultana, N., Ahmed, M., Rahman, M. M., Yousuf, M. A., Aloteibi, S., & Moni, M. A. (2025). Dream: A Novel Explainable Neural Network for Detecting Sleep Apnea Using Single-Lead ECG Signals. Biomedical Signal Processing and Control.
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