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.
To understand the analysis, one must first understand the source:
Biomedical Signal Analysis sits at the intersection of engineering, computer science, and medicine. It is the silent algorithm behind the beeping monitor in the ICU, the step count on your wrist, and the AI that spots a hidden murmur.
Biomedical signal analysis involves the acquisition and preprocessing of physiological signals to extract meaningful patterns for clinical diagnosis and monitoring. These signals, such as electrical activity from the heart (ECG) or brain (EEG), serve as essential indicators of a living organism's health status.
Since biomedical signals are sequences (time series), RNNs (LSTMs) are natural fits. They are used for:
Using the extracted features, algorithms classify the state of the subject.
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.
To understand the analysis, one must first understand the source: Biomedical Signal Analysis
Biomedical Signal Analysis sits at the intersection of engineering, computer science, and medicine. It is the silent algorithm behind the beeping monitor in the ICU, the step count on your wrist, and the AI that spots a hidden murmur. It is the silent algorithm behind the beeping
Biomedical signal analysis involves the acquisition and preprocessing of physiological signals to extract meaningful patterns for clinical diagnosis and monitoring. These signals, such as electrical activity from the heart (ECG) or brain (EEG), serve as essential indicators of a living organism's health status. Since biomedical signals are sequences (time series), RNNs
Since biomedical signals are sequences (time series), RNNs (LSTMs) are natural fits. They are used for:
Using the extracted features, algorithms classify the state of the subject.