| 摘要(英) |
In this study, we propose an integrated system for the analysis of electrocardiogram (ECG) and heart rate variability (HRV), aiming to improve the early detection and prognostic assessment of cardiovascular diseases. The system employs machine learning techniques, specifically 1D convolutional neural network and long short-term memory (1D-CNN-LSTM), to automatically detect, classify, and diagnose ECG signals. Additionally, the system preprocesses ECG signals, including denoising, signal and baseline correction, as well as HRV analysis and graphical representation.
We utilize the MIT-BIH Arrhythmia Database in this study, which contains ECG records of various types of arrhythmias. Our approach effectively classifies and detects arrhythmias and provides a detailed analysis of HRV. The system performs excellently in automatically identifying arrhythmias and assessing heart rate variability, contributing to enhanced diagnostic accuracy and patient management.
In terms of clinical applications, the system can serve as a real-time monitoring tool, assisting physicians in quickly evaluating patients′ cardiovascular risk and formulating personalized treatment plans based on HRV analysis results. Moreover, the system can be applied to remote monitoring, providing timely medical assistance and alerts for home-based patients.
Future research directions include: 1) expanding the dataset, encompassing more types of arrhythmias and patient backgrounds, to improve the model′s generalizability; 2) incorporating other biomedical signals (such as pulse wave, blood pressure, etc.) into the system, achieving a more comprehensive assessment of cardiovascular health; 3) developing more efficient machine learning algorithms, enhancing diagnostic accuracy and computational efficiency; 4) investigating the interrelations between HRV and other physiological indicators to reveal additional clinical information and disease mechanisms; 5) exploring how to integrate the system with mobile healthcare devices, allowing patients to self-monitor and obtain professional advice anytime and anywhere.
In summary, the ECG and HRV analysis system proposed in this study demonstrates significant potential in the detection and assessment of cardiovascular diseases. The application of machine learning techniques enables the system to automatically identify and classify arrhythmias while providing a detailed analysis of heart rate variability, offering patients more accurate diagnoses and personalized treatment recommendations. Future research will focus on improving the system′s generalizability, integrating other biomedical signals, and developing more efficient machine learning algorithms and mobile healthcare applications to provide better care and improve the quality of life for cardiovascular disease patients. |
| 參考文獻 |
[1]
George B. Moody. “MIT-BIH Arrhythmia Database Directory.” PhysioNet, 24 May 1997, https://physionet.org/physiobank/database/html/mitdbdir/intro.htm.
[2]
George Moody, and Roger Mark. “MIT-BIH Arrhythmia Database.” PhysioNet, 24 Feb. 2005, MIT-BIH Arrhythmia Database.
[3]
O. EL B’CHARRI*, R. LATIF, A. ABENAOU, W. JENKAL “An efficient wavelet-based feature extraction scheme for electrocardiogram signals.” DOI:10.1109/WITS.2017.7934670 April 2017
[4]
European Heart Journal, Volume 17, Issue 3, March 1996, Pages 354–381, https://doi.org/10.1093/oxfordjournals.eurheartj.a014868
[5]
“Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use.” Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology (1996): 1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043.
[6]
Van Hiep Phung, and Eun Joo Rhee. “A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets. ” Oct. 2019, doi:DOI:10.3390/app9214500.
[7]
Amin Ullah. “A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.” Healthcare Monitoring and Management with Artificial Intelligence 2021. https://doi.org/10.3390/s21030951.
[8]
Mahmoud Abdallah, Nhien An Le Khac, Hamed Jahromi, Anca Delia Jurcut.“A Hybrid CNN-LSTM Based Approach for Anomaly Detection Systems in SDNs.” August 2021
[9]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
[10]
Taiyong Li, Min Zhou. “ECG Classification Using Wavelet Packet Entropy and Random Forests” DOI:10.3390/e18080285 August 2016Entropy 18(8):285
[11]
Raman Yadav, Sharda Vashisth, Ashok K Salhan. “Real Time Acquisition and Analysis of ECG signals using MATLAB.” ISSN: 2319-1120 January 2010
[12]
J. Pan, and W. J. Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Trans. Biomed. Eng., Vol. 32, pp. 230-236, 1985.
[13]
Pham, T., Lau, Z. J., Chen, S. H. A., & Makowski, D. (2021). Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. Sensors, 21(12), 3998. https://doi.org/10.3390/s21123998
[14]
Zhengmin Kong, Yande Cui, Zhou Xia, and He Lv. “Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics.” Applied Sciences 9, 19 (2019), 4156.
[15]
Vidushi Mishra, SMT. MANISHA AGARWAL, NEHA PURI. “COMPREHENSIVE AND COMPARATIVE ANALYSIS OF NEURAL NETWORK” DOI:10.26808/rs.ca.i8v2.15 January 2018
[16]
Fawcett, T. “An Introduction to ROC Analysis. Pattern Recogn.” Lett. 2006,27, 861–874. doi:10.1016/j.patrec.2005.10.010.
[17]
M. M. Rodríguez-Hernández, Rosa Eva Pruneda, Juan M. Rodríguez-Díaz. “Statistical Analysis of the Evolutive Effects of Language Development in the Resolution of Mathematical Problems in Primary School Education” DOI:10.3390/math9101081 May 2021
[18]
Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms.Association for the Advancement of Medical Instrumentation: Arlington, VA, USA, 1998
[19]
Streamlit’s open-source app framework [Online]. Available: https://github.com/streamlit/streamlit.git 2023 Snowflake Inc.
[20]
Carreiras C, Alves AP, Lourenço A, Canento F, Silva H, Fred A, et al. BioSPPy - Biosignal Processing in Python, 2015-, [Online]. Available: https://github.com/PIA-Group/BioSPPy/
[21]
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., ... & SciPy 1.0 Contributors. (2020). SciPy 1.0: fundamental algorithms for scientific computing in python. Nature methods, 17(3), 261-272.
[22]
Tensorflow [Online]. Available: https://www.tensorflow.org/ |
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