摘要(英) |
Compare to healthy ones, patients with Heart Failure (HF) have more awful ability on Cardiac Output. If we want to discover the problem at early age, monitoring cardiac output is an imperative work. Nevertheless, the medical devices those which could monitor precisely have some inevitable problems. No matter using invasive or non-invasive monitor techniques, both of them will face some challenges.
This study will develop a wearable and non-invasive Electrocardiac-Stethoscope System. By using a digital stethoscope, we measure the single lead ECG signal and PCG signal with capacitive microphone. After filtering, sampling and encoding the both signal through A-D Converter (ADC), the signals are transmitted to microcontroller (MCU). Then, the signal will simultaneously save in microSD card and transmit to APP on smartphone by Bluetooth Low-Energy (BLE) wireless technique. At the end, align S1 peak with R-peak for synchronizing the time series. By calculating time delay between peaks, we will get two important parameter for cardiac output, Pre-Ejection Period (PEP) and Left Ventricular Time (LVET). What’s more, the device can measure lung sound, vascular sound as well, which is able to monitor cardiac and pulmonary function.
The system has not only utilize a non-invasive method, but is also low-cost, small size and wearable. It will also provide lots of merits clinically and personally. |
參考文獻 |
參考文獻
[1] Otto, C.M., Textbook of clinical echocardiography. 2013: Elsevier Health Sciences.
[2] Ihlen, H., et al., Determination of cardiac output by Doppler echocardiography. Heart, 1984. 51(1): p. 54-60.
[3] Meek, S. and F. Morris, ABC of clinical electrocardiography: Introduction. I—Leads, rate, rhythm, and cardiac axis. BMJ: British Medical Journal, 2002. 324(7334): p. 415.
[4] Rangayyan, R.M. and R.J. Lehner, Phonocardiogram signal analysis: a review. Critical reviews in biomedical engineering, 1987. 15(3): p. 211-236.
[5] Choudhary, T., L. Sharma, and M.K. Bhuyan, Heart sound extraction from sternal seismocardiographic signal. IEEE Signal Processing Letters, 2018. 25(4): p. 482-486.
[6] Kannel, W.B. and A.J. Belanger, Epidemiology of heart failure. American heart journal, 1991. 121(3): p. 951-957.
[7] Raherison, C. and P. Girodet, Epidemiology of COPD. European Respiratory Review, 2009. 18(114): p. 213-221.
[8] Lin, Y.-J., et al. An intelligent stethoscope with ECG and heart sound synchronous display. in 2019 IEEE international symposium on circuits and systems (ISCAS). 2019. IEEE.
[9] Geselowitz, D.B., On the theory of the electrocardiogram. Proceedings of the IEEE, 1989. 77(6): p. 857-876.
[10] Malmivuo, J. and R. Plonsey, Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. 1995: Oxford University Press.
[11] Algarni, A.D., et al., Encryption of ECG signals for telemedicine applications. Multimedia Tools and Applications, 2021. 80: p. 10679-10703.
[12] Altay, Y.A. and A.S. Kremlev. Comparative analysis of ECG signal processing methods in the time-frequency domain. in 2018 IEEE conference of Russian young researchers in electrical and electronic engineering (EIConRus). 2018. IEEE.
[13] Tereshchenko, L.G. and M.E. Josephson, Frequency content and characteristics of ventricular conduction. Journal of electrocardiology, 2015. 48(6): p. 933-937.
[14] Chakrabarti, T., et al. Phonocardiogram signal analysis-practices, trends and challenges: A critical review. in 2015 international conference and workshop on computing and communication (IEMCON). 2015. IEEE.
[15] Khaled, S., M. Fakhry, and A.S. Mubarak. Classification of pcg signals using
a nonlinear autoregressive network with exogenous inputs (narx). in 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). 2020. IEEE.
[16] Vásquez, P., Acoustical Signal Processing of Arterio-Venous Fistula Bruits. 2012: Department of Electrical and Information Technology, Lund University.
[17] Sung, P.-H., et al., Hemodialysis vascular access stenosis detection using auditory spectro-temporal features of phonoangiography. Medical & biological engineering & computing, 2015. 53: p. 393-403.
[18] Loudon, R. and R.L. Murphy Jr, Lung sounds. American Review of Respiratory Disease, 1984. 130(4): p. 663-673.
[19] Fraiwan, L., et al., Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers. Biocybernetics and Biomedical Engineering, 2021. 41(1): p. 1-14.
[20] Gupta, P., et al., Detection of pathological mechano-acoustic signatures using precision accelerometer contact microphones in patients with pulmonary disorders. Scientific Reports, 2021. 11(1): p. 13427.
[21] Landau, H., Sampling, data transmission, and the Nyquist rate. Proceedings of the IEEE, 1967. 55(10): p. 1701-1706. |