Biomedical Signal Analysis

Would you like a deeper dive into any specific signal type (ECG, EEG, EMG) or algorithm (wavelets, ICA, deep learning for biosignals)?

| Challenge | Example | Mitigation | |-----------|---------|-------------| | | EEG changes with mental state | Segmentation + time-frequency analysis | | Low SNR | Fetal ECG buried in maternal signal | Adaptive noise cancellation, blind source separation (e.g., ICA, CCA) | | Inter-patient variability | Different QRS morphologies | Robust features, patient-specific training | | Limited labeled data | Rare arrhythmias | Synthetic data, transfer learning, semi-supervised methods | | Real-time constraints | ICU alarms | Lightweight algorithms, embedded processing | Biomedical Signal Analysis

Convolutional Neural Networks (CNNs) and transformers have revolutionized signal classification Would you like a deeper dive into any

The final step involves making a decision. Is the signal normal or pathological? Traditionally, this was done via thresholding. Today, Machine Learning (ML) algorithms are increasingly used to Traditionally, this was done via thresholding

CNNs can learn hierarchical features directly from spectrograms (visual representations of signals). For instance, a CNN trained on thousands of ECG strips can now detect Atrial Fibrillation with greater accuracy than junior cardiologists.