Publication

Journal

A. Jabbar, M. Hassanuzzaman et al., “Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion,” Computers in Biology and Medicine, vol. 197, no. Pt A, pp. 110993–110993, Sep. 2025, doi: https://doi.org/10.1016/j.compbiomed.2025.110993.
M. Hassanuzzaman et al., "Classification of Short Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network," in IEEE Access, 2025.
Sarker, R.A.; Hassanuzzaman, M.; Biswas, P.; Dadon, S.H.; Imam, T. An Efficient Surface Map Creation and Tracking Using Smartphone Sensors and Crowdsourcing. Sensors 2021, 21, 6969.
Islam, T.T.; Ahmed, M.S.; Hassanuzzaman, M.; Amir, S.A.B.; Rahman, T. Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning. Appl. Sci. 2021, 11, 618.

Hassanuzzaman, M., Hasan, N. A., Al Mamun, M. A., Ahmed, K. I., Khandoker, A. H., & Mostafa, R. A Deep Learning Model for Recognizing Pediatric Congenital Heart Diseases Using Phonocardiogram Signals. Computing in Cardiology (CinC). IEEE, 2023.
Hassanuzzaman, M., Hasan, N. A., Al Mamun, M. A., Alkhodari, M., Ahmed, K. I., Khandoker, A. H., & Mostafa, R. (2023). Recognition of Pediatric Congenital Heart Diseases by Using Phonocardiogram Signals and Transformer-Based Neural Networks. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE.
Hassanuzzaman, Md, Purnendu Biswas, and Tanzilur Rahman. "End to End Solution for Continuous Monitoring and Real-Time Analysis of Vital Signs From ECG Signal." 2019 IEEE R10 Humanitarian Technology Conference (R10-HTC)(47129). IEEE, 2019.

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