Deep Learning for physiological signal analysis

Deep learning for physiological signal analysis employs neural networks to automatically extract meaningful features and patterns from complex biological signals such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), and photoplethysmography (PPG) data. These advanced AI models can process raw physiological signals without requiring manual feature engineering, automatically learning optimal representations for specific diagnostic tasks. Convolutional neural networks (CNNs) are particularly effective for analyzing time-series physiological data, while recurrent neural networks (RNNs) and transformer models excel at capturing temporal dependencies in long-term monitoring scenarios. Deep learning applications include automated arrhythmia detection from ECG signals, sleep stage classification from EEG data, stress level assessment from multiple physiological indicators, and respiratory pattern analysis from chest movement sensors.

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These systems can achieve diagnostic accuracy comparable to or exceeding human experts while providing consistent, objective assessments. The integration of attention mechanisms allows models to highlight the most relevant signal components for specific diagnoses, enhancing interpretability and clinical utility.

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