1D Convolutional Neural Network Impact on Heart Rate Metrics for ECG and BCG Signals
Author:
Funder
Agencia Nacional de Investigación y Desarrollo
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40846-024-00872-w.pdf
Reference18 articles.
1. Yazdi, D., Sridan, S., Smith, S., Centen, C., Patel, S., Wilson, E., Gillon, L., Kapur, S., Tracy, J. A., Lewin, K., SystromJr, D. M., & MacRae, C. A. (2021). Noninvasive scale measurement of stroke volume and cardiac output compared with the direct fick method: A feasibility study. Journal of the American Heart Association, 10, 2. https://doi.org/10.1161/JAHA.121.021893
2. Pino, E. J., Chávez, J. A. P., & Aqueveque, P. (2017). BCG algorithm for unobtrusive heart rate monitoring. In 2017 IEEE healthcare innovations and point of care technologies (HI-POCT) (pp. 180–183). https://doi.org/10.1109/HIC.2017.8227614.
3. Postolache, O. A., Silva, P. M. B., Mendes, J., Pinheiro, E. C., & Postolache, G. (2010). Physiological parameters measurement based on wheelchair embedded sensors and advanced signal processing. IEEE Transactions on Instrumentation and Measurement, 59, 2564–2574. https://doi.org/10.1109/TIM.2010.2057590
4. Lin, C. L., Sun, Z. T., & Chen, Y. Y. (2022). Air-mattress system for ballistocardiogram-based heart rate and breathing rate estimation. Heliyon, 9, 2–3. https://doi.org/10.1016/j.heliyon.2022.e12717
5. Linschman, O., Revander, C., Leonhardt, S., & Lueken, M. (2022). Motion artifact detection and classification for unobtrusive cardiorespiratory signals using machine learning. Paper presented at the CinC 2022 Conference, Tampere University, Tampere, 4–7 September 2022.
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