Real-Time Signal Processing for Biomedical Applications Using MATLAB
DOI:
https://doi.org/10.65405/.v10i37.617الكلمات المفتاحية:
Artificial Intelligence, MATLAB, Biomedical Signals, Real-Time Processing, ECG, EEG, Signal Filteringالملخص
Artificial Intelligence (AI) and advanced computational tools such as MATLAB have become
essential in biomedical research and clinical practice. Real-time biomedical signal processing
plays a critical role in improving patient monitoring, early diagnosis, and therapeutic interventions.
Biomedical signals such as electrocardiograms (ECG), electroencephalograms (EEG), and
electromyograms (EMG) contain rich diagnostic information, but they are often contaminated by
various sources of noise including power line interference, muscle artifacts, and baseline wander.
Real-time signal processing ensures that physicians and automated systems can interpret these
signals accurately and without delay, enabling prompt medical decisions
This paper presents an overview of assisted real-time biomedical signal processing, wherein the
stress is particular on ECG analysis. MATLAB offers an integrated environment with specialty
toolboxes such as Signal Processing Toolbox and DSP System Toolbox, where a researcher can
design, simulate, and implement algorithms in a real-time setting. MATLAB supports fast
prototyping and testing of biomedical systems by the use of heavy filtering techniques and an
active real-time visualization tool. With this case study, the ECG signals are filtered through a
fourth-order Butterworth bandpass filter within the range of 0.5–40 Hz. With baseline drift and
high-frequency noise being filtered out, the focus is so much on the clarity of the QRS complex
and other important features of the ECG waveform. MATLAB allows for the continuous acquisition
of signals, instantaneous signal processing, and coordination with clinical monitoring systems for
displaying both raw and filtered signals. Hence, the results show that good filtering could reduce
false alarms and ensure early detection of arrhythmias, thereby largely improving diagnostic
accuracy and patient safety
التنزيلات
المراجع
Acharya, U. R., Fujita, H., Lih, O. S., Hagiwara, Y., Tan, J. H., & Adam, M.
(2017). Automated detection of arrhythmias using different intervals of tachycardia
ECG segments with convolutional neural network. Information Sciences, 405, 81–
90.
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). A deep
convolutional neural network model to classify heartbeats. Computers in Biology and
Medicine, 89, 389–396.
Clifford, G. D., Azuaje, F., & McSharry, P. (2014). Advanced methods and tools for
ECG data analysis. Artech House.
Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark,
R. G., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet:
components of a new research resource for complex physiologic signals.
Circulation, 101(23), e215–e220.
Hamilton, P. S. (2002). Open source ECG analysis. Computers in Cardiology, 29,
101–104.
Li, T., Zhou, M., & Lu, C. (2020). Arrhythmia classification based on multi-domain
feature extraction for imbalanced ECG datasets. IEEE Transactions on
Instrumentation and Measurement, 69(7), 4242–4251.
Martínez, J. P., Almeida, R., Olmos, S., Rocha, A. P., & Laguna, P. (2004). A
wavelet-based ECG delineator: evaluation on standard databases. IEEE
Transactions on Biomedical Engineering, 51(4), 570–581.
MathWorks. (2024). MATLAB and Simulink for biomedical signal processing. The
MathWorks, Inc.
Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH Arrhythmia
Database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45–50.
Rajpurkar, P., Hannun, A. Y., Haghpanahi, M., Bourn, C., & Ng, A. Y. (2017).
Cardiologist-level arrhythmia detection with convolutional neural networks. Nature
Medicine, 13(12), 240–246.
Sameni, R., & Clifford, G. D. (2010). A review of filtering techniques for noise
reduction in cardiac signals. Physiological Measurement, 31(1), R1–R33.
Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). Real-time signal qualityaware ECG telemetry system for IoT-based health care monitoring. IEEE Internet of
Things Journal, 5(3), 1469–1476.
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