Unit 2074 - Biomedical Signal Processing

1. Introduction to Biomedical Signal Processing

• Importance of signal processing in biomedical applications
• Types of biomedical signals (ECG, EMG, EEG, etc.)
• Challenges in processing biomedical signals (noise, artifacts, variability)
• Significance of signal processing techniques in healthcare diagnostics and monitoring

2. Basics of Digital Signal Processing (DSP)

• Fundamental concepts: sampling theorem, quantization
• Digital filters: FIR and IIR filters
• Fourier analysis: Fourier transform, FFT
• Applications of DSP in biomedical signal analysis

3. Biomedical Signal Acquisition

• Overview of acquisition techniques
• Electrocardiography (ECG): signal characteristics and challenges
• Electromyography (EMG): physiological basis and acquisition
• Electroencephalography (EEG): signal properties and technical considerations
• Instrumentation and sensors for biomedical signal acquisition

4. Signal Filtering in Biomedical Applications

• Importance of preprocessing biomedical signals
• Noise removal techniques: types of noise and filtering methods
• Baseline drift correction methods
• Artifact removal (e.g., motion artifacts, powerline interference)
• Design and implementation of digital filters for signal enhancement

5. Feature Extraction and Selection

• Time-domain analysis: statistical and morphological features
• Frequency-domain analysis: spectral features, power spectral density
• Time-frequency analysis: wavelet transform, short-time Fourier transform
• Feature selection methods: dimensionality reduction and relevance evaluation
• Importance of feature selection for computational efficiency and accuracy

6. Biomedical Signal Classification

• Overview of classification algorithms
• Supervised learning methods: k-NN, SVM, decision trees, neural networks
• Unsupervised learning methods: clustering techniques
• Pattern recognition in biomedical signals
• Applications in healthcare diagnostics and decision support

7. Signal Reconstruction and Compression

• Signal reconstruction concepts
• Data compression techniques: lossless vs lossy compression
• Wavelet transform for compression and reconstruction
• Implications for telemedicine and remote patient monitoring

8. Biomedical Image Processing

• Introduction to medical image acquisition modalities (MRI, CT, Ultrasound)
• Image enhancement techniques
• Image segmentation methods
• Image registration and fusion
• Analysis of medical images for diagnosis and treatment monitoring

9. Signal Processing in Bioinformatics

• Integration of signal processing with biological data analysis
• Processing DNA sequences and gene expression data
• Protein structure analysis using signal processing techniques
• Pattern discovery and relationship detection in biological datasets
• Applications in biomedical research and personalized medicine

10. Emerging Trends in Biomedical Signal Processing

• Wearable biomedical devices and sensors
• Internet of Things (IoT) in healthcare
• Big data analytics in biomedical signal processing
• Artificial intelligence and deep learning applications
• Future directions and innovative healthcare solutions