An Embedding Multitask Neural Network for Efficient Arrhythmia Detection
Resumo
This study addresses the critical need for prompt detection of life-threatening ventricular arrhythmias. We explore the application of neural networks within the constraints of Implantable Cardioverter Defibrillators to improve early arrhythmia detection. Our proposed neural network methodology leverages multitask learning, aiming to enhance detection efficiency by concurrently learning to identify ventricular arrhythmias and estimate RR intervals from intracardiac electrograms. Implemented on the NUCLEO-L432KC board, with limited memory and processing capacity, our approach achieved an Fβ score of 0.88, with a low inference latency of 59.96 ms. These results demonstrate the feasibility of integrating advanced neural network capabilities within Implantable Cardioverter Defibrillators (ICDs).
Referências
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Caruana, R. (1997). Multitask learning. In Machine Learning Proceedings 1997, pages 41–48. Elsevier.
DiMarco, J. P. (2003). Implantable cardioverter–defibrillators. New England Journal of Medicine, 349(19):1836–1847.
Geng, Q., Liu, H., Gao, T., Liu, R., Chen, C., Zhu, Q., and Shu, M. (2023). An ecg classification method based on multi-task learning and cot attention mechanism. In Healthcare, volume 11, page 1000. MDPI.
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine, 25(1):65–69. Publisher: Nature Publishing Group.
Jia, Z., Li, D., Liu, C., Liao, L., Xu, X., Ping, L., and Shi, Y. (2023). Tinyml design contest for life-threatening ventricular arrhythmia detection. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
Luz, E. J. d. S., Schwartz, W. R., Cámara-Chávez, G., and Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine, 127:144–164. Publisher: Elsevier.
Madhavan, M. and Friedman, P. A. (2013). Optimal programming of implantable cardiacdefibrillators. Circulation, 128(6):659–672.
Medhi, J. K., Ren, P., Hu, M., and Chen, X. (2023). A deep multi-task learning approach for bioelectrical signal analysis. Mathematics, 11(22):4566.
Mirowski, M. (1985). The automatic implantable cardioverter-defibrillator: an overview. Journal of the American College of Cardiology, 6(2):461–466.
Mousavi, S. and Afghah, F. (2019). Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1308–1312. IEEE.
Myerburg, R. J., Kessler, K. M., and Castellanos, A. (1992). Ventricular fibrillation and sudden cardiac death. New England Journal of Medicine, 326(11):741–747.
Pan, J. and Tompkins, W. J. (1985). A real-time qrs detection algorithm. IEEE transactions on biomedical engineering, pages 230–236.
Physiopedia (2022). Ventricular fibrillation. [link]. Accessed: March 19 of 2024.
Ruder, S. (2017). An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098.
Soto, J. T. and Ashley, E. (2020). Deepbeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices. arXiv preprint arXiv:2001.00155.
STMicroelectronics (2020). Ultra-low-power with fpu arm cortex-m4 mcu 80 mhz with 256 kbytes of flash memory, usb. [Online]. Available: [link].
Suzhou Singular Medical Co., Ltd. (2023). Singular medical. [link]. [Online]. Available.
Torres-Soto, J. and Ashley, E. A. (2020). Multi-task deep learning for cardiac rhythm detection in wearable devices. NPJ digital medicine, 3(1):116.
Zanker, N., Schuster, D., Gilkerson, J., and Stein, K. (2016). Tachycardia detection in icds by boston scientific: algorithms, pearls, and pitfalls. Herzschrittmachertherapie & Elektrophysiologie, 27(3):186.
Zipes, D. P. and Wellens, H. J. J. (1998). Sudden cardiac death. Circulation, 98(21):2334–2351.
Caruana, R. (1997). Multitask learning. In Machine Learning Proceedings 1997, pages 41–48. Elsevier.
DiMarco, J. P. (2003). Implantable cardioverter–defibrillators. New England Journal of Medicine, 349(19):1836–1847.
Geng, Q., Liu, H., Gao, T., Liu, R., Chen, C., Zhu, Q., and Shu, M. (2023). An ecg classification method based on multi-task learning and cot attention mechanism. In Healthcare, volume 11, page 1000. MDPI.
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine, 25(1):65–69. Publisher: Nature Publishing Group.
Jia, Z., Li, D., Liu, C., Liao, L., Xu, X., Ping, L., and Shi, Y. (2023). Tinyml design contest for life-threatening ventricular arrhythmia detection. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
Luz, E. J. d. S., Schwartz, W. R., Cámara-Chávez, G., and Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine, 127:144–164. Publisher: Elsevier.
Madhavan, M. and Friedman, P. A. (2013). Optimal programming of implantable cardiacdefibrillators. Circulation, 128(6):659–672.
Medhi, J. K., Ren, P., Hu, M., and Chen, X. (2023). A deep multi-task learning approach for bioelectrical signal analysis. Mathematics, 11(22):4566.
Mirowski, M. (1985). The automatic implantable cardioverter-defibrillator: an overview. Journal of the American College of Cardiology, 6(2):461–466.
Mousavi, S. and Afghah, F. (2019). Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1308–1312. IEEE.
Myerburg, R. J., Kessler, K. M., and Castellanos, A. (1992). Ventricular fibrillation and sudden cardiac death. New England Journal of Medicine, 326(11):741–747.
Pan, J. and Tompkins, W. J. (1985). A real-time qrs detection algorithm. IEEE transactions on biomedical engineering, pages 230–236.
Physiopedia (2022). Ventricular fibrillation. [link]. Accessed: March 19 of 2024.
Ruder, S. (2017). An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098.
Soto, J. T. and Ashley, E. (2020). Deepbeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices. arXiv preprint arXiv:2001.00155.
STMicroelectronics (2020). Ultra-low-power with fpu arm cortex-m4 mcu 80 mhz with 256 kbytes of flash memory, usb. [Online]. Available: [link].
Suzhou Singular Medical Co., Ltd. (2023). Singular medical. [link]. [Online]. Available.
Torres-Soto, J. and Ashley, E. A. (2020). Multi-task deep learning for cardiac rhythm detection in wearable devices. NPJ digital medicine, 3(1):116.
Zanker, N., Schuster, D., Gilkerson, J., and Stein, K. (2016). Tachycardia detection in icds by boston scientific: algorithms, pearls, and pitfalls. Herzschrittmachertherapie & Elektrophysiologie, 27(3):186.
Zipes, D. P. and Wellens, H. J. J. (1998). Sudden cardiac death. Circulation, 98(21):2334–2351.
Publicado
25/06/2024
Como Citar
SILVA, Guilherme; NEGRÃO, Arthur; MOREIRA, Gladston; LUZ, Eduardo; SILVA, Pedro.
An Embedding Multitask Neural Network for Efficient Arrhythmia Detection. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 412-423.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2024.2378.
