지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.11 No.3
- 발행연도
- 2022.6
- 수록면
- 169 - 173 (5page)
- DOI
- 10.5573/IEIESPC.2022.11.3.169
이용수
초록· 키워드
오류제보하기
Patients with hypertensive blood pressure (BP) needs a round-the-clock BP monitoring and must take precautions to prevent emergencies such as stroke or heart failure. This paper suggests a deep neural network (DNNs–based BP estimation approach using electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals. The proposed approach consists of a one-dimensional convolutional neural network (1D CNN) followed by the attention mechanism known as Luong attention. Estimations under the proposed model yield mean absolute error (MAE) of 3.299±2.419 for systolic and 2.69±1.821 for diastolic BP. The algorithm can effectively predict BP without a recurrent neural network (RNNs), which is a typical DNNs model for processing sequential data. Additionally, the proposed approach is preferable owing to its ability to explain the model.
목차
Abstract
1. Introduction
2. Data Acquisition
3. Results
4. Conclusion
References
참고문헌 (13)
참고문헌 신청
Wang, R., Jia, W., Mao, Z. H., Sclabassi, R. J., & Sun, M. (2014, October). Cuff-free blood pressure estimation using pulse transit time and heart rate. In 2014 12th international conference on signal processing (ICSP) (pp. 115-118). IEEE. Article (CrossRef Link)
Wong, M. Y. M., Poon, C. C. Y., & Zhang, Y. T. (2009). An evaluation of the cuffless blood pressure estimation based on pulse transit time technique: a half year study on normotensive subjects. Cardiovascular Engineering, 9(1), 32-38 Article (CrossRef Link)
Chan, K. W., Hung, K., & Zhang, Y. T. (2001, October). Noninvasive and cuffless measurements of blood pressure for telemedicine. In 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Vol. 4, pp. 3592-3593). IEEE. Article (CrossRef Link)
Simjanoska, M., Gjoreski, M., Gams, M., & Madevska Bogdanova, A. (2018). Non-invasive blood pressure estimation from ECG using machine learning techniques. Sensors, 18(4), 1160. Article (CrossRef Link)
Chowdhury, M. H., Shuzan, M. N. I., Chowdhury, M. E., Mahbub, Z. B., Uddin, M. M., Khandakar, A., & Reaz, M. B. I. (2020). Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors, 20(11), 3127.Article (CrossRef Link)
Wong, M. Y. M., Poon, C. C. Y., & Zhang, Y. T. (2009). An evaluation of the cuffless blood pressure estimation based on pulse transit time technique: a half year study on normotensive subjects. Cardiovascular Engineering, 9(1), 32-38 Article (CrossRef Link)
Chan, K. W., Hung, K., & Zhang, Y. T. (2001, October). Noninvasive and cuffless measurements of blood pressure for telemedicine. In 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Vol. 4, pp. 3592-3593). IEEE. Article (CrossRef Link)
Simjanoska, M., Gjoreski, M., Gams, M., & Madevska Bogdanova, A. (2018). Non-invasive blood pressure estimation from ECG using machine learning techniques. Sensors, 18(4), 1160. Article (CrossRef Link)
Chowdhury, M. H., Shuzan, M. N. I., Chowdhury, M. E., Mahbub, Z. B., Uddin, M. M., Khandakar, A., & Reaz, M. B. I. (2020). Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors, 20(11), 3127.Article (CrossRef Link)
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