Machine Learning in Biomedical Signal Processing
( 25 Modules )

Module #1
Introduction to Biomedical Signal Processing
Overview of biomedical signal processing, types of biomedical signals, and importance of machine learning in biomedical signal processing.

Module #2
Machine Learning Fundamentals
Introduction to machine learning, types of machine learning, supervised and unsupervised learning, and model evaluation metrics.

Module #3
Biomedical Signal Acquisition and Preprocessing
Methods for acquiring biomedical signals, preprocessing techniques, and importance of preprocessing in machine learning.

Module #4
Time Domain Analysis of Biomedical Signals
Time domain analysis techniques, including filtering, segmentation, and feature extraction.

Module #5
Frequency Domain Analysis of Biomedical Signals
Frequency domain analysis techniques, including Fourier transform, power spectral density, and filter banks.

Module #6
Time-Frequency Analysis of Biomedical Signals
Time-frequency analysis techniques, including short-time Fourier transform, continuous wavelet transform, and Stockwell transform.

Module #7
Machine Learning for Biomedical Signal Classification
Introduction to classification algorithms, including k-NN, decision trees, and random forests, and their application to biomedical signal classification.

Module #8
Machine Learning for Biomedical Signal Regression
Introduction to regression algorithms, including linear regression, ridge regression, and support vector regression, and their application to biomedical signal regression.

Module #9
Deep Learning for Biomedical Signal Processing
Introduction to deep learning, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their application to biomedical signal processing.

Module #10
EEG Signal Processing and Analysis
Introduction to EEG signals, preprocessing techniques, and machine learning applications for EEG signal analysis.

Module #11
ECG Signal Processing and Analysis
Introduction to ECG signals, preprocessing techniques, and machine learning applications for ECG signal analysis.

Module #12
EMG Signal Processing and Analysis
Introduction to EMG signals, preprocessing techniques, and machine learning applications for EMG signal analysis.

Module #13
Machine Learning for Biomedical Image Analysis
Introduction to biomedical image analysis, including image preprocessing, feature extraction, and machine learning applications for image classification and segmentation.

Module #14
Medical Image Segmentation using Machine Learning
Machine learning approaches for medical image segmentation, including thresholding, edge detection, and deep learning-based methods.

Module #15
Clinical Decision Support Systems using Machine Learning
Introduction to clinical decision support systems, including data preprocessing, feature selection, and machine learning models for clinical decision-making.

Module #16
Machine Learning for Biomedical Time Series Analysis
Introduction to biomedical time series analysis, including time series preprocessing, feature extraction, and machine learning models for time series forecasting and anomaly detection.

Module #17
Explainability and Interpretability in Biomedical Machine Learning
Importance of explainability and interpretability in biomedical machine learning, including techniques for model explanation and feature importance analysis.

Module #18
Ethical and Regulatory Considerations in Biomedical Machine Learning
Ethical and regulatory considerations in biomedical machine learning, including bias, fairness, and transparency.

Module #19
Case Studies in Biomedical Signal Processing using Machine Learning
Real-world case studies of machine learning applications in biomedical signal processing, including EEG, ECG, and EMG signal analysis.

Module #20
Case Studies in Biomedical Image Analysis using Machine Learning
Real-world case studies of machine learning applications in biomedical image analysis, including image classification, segmentation, and detection.

Module #21
Machine Learning for Personalized Medicine
Introduction to personalized medicine, including machine learning approaches for personalized diagnosis, treatment, and prognosis.

Module #22
Machine Learning for Wearable Sensor Data Analysis
Introduction to wearable sensor data analysis, including data preprocessing, feature extraction, and machine learning models for activity recognition and health monitoring.

Module #23
Machine Learning for Biomedical Data Fusion
Introduction to biomedical data fusion, including fusion of heterogeneous data sources, and machine learning approaches for data integration and analysis.

Module #24
Machine Learning for Biomedical Signal Quality Analysis
Introduction to biomedical signal quality analysis, including machine learning approaches for signal quality assessment and artifact detection.

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning in Biomedical Signal Processing career

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Machine Learning in Biomedical Signal Processing ( 25 Modules )

Machine Learning in Biomedical Signal Processing
( 25 Modules )