Biomedical
Denoise, decompose, segment, and classify ECG, EEG, EMG, and physiological signals using signal processing layers, signal and wavelet features, and deep learning models.
Related Information
Featured Examples
Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals.
- Since R2022b
- Open Live Script
Human Activity Recognition Using Signal Feature Extraction and Machine Learning
Extract features from smartphone sensor signals and use them to classify human activity.
- Since R2021b
- Open Live Script
Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.
- Since R2021b
- Open Live Script
Classify ECG Signals Using Long Short-Term Memory Networks
Classify heartbeat electrocardiogram data using deep learning and signal processing.
Graph Signal Processing and Brain Signal Analysis
Perform graph signal processing to analyze brain activity by decomposing brain signals into aligned and liberal components.
- Since R2024a
- Open Live Script
Detect Anomalies in Signals Using deepSignalAnomalyDetector
Use autoencoders to detect abnormal points or segments in time-series data.
- Since R2023a
- Open Live Script
Waveform Segmentation Using Deep Learning
Segment human electrocardiogram signals using time-frequency analysis and deep learning.
Classify Arm Motions Using EMG Signals and Deep Learning
Classify arm motions using labeled EMG signals and a long short-term memory network.
- Since R2022a
- Open Live Script
Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression.
- Since R2021b
- Open Live Script
Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources.
- Since R2022b
- Open Live Script
Extract Classification Features from Physiological Signals
Quantify interstride time intervals and measure walking-pattern similarity; construct a feature vector to classify signals.
Classify Time Series Using Wavelet Analysis and Deep Learning
Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network.
Wavelet Packet Harmonic Interference Removal
Use wavelet packets to remove harmonic interference from an electrocardiogram (ECG) signal.
(Wavelet Toolbox)
- Since R2021b
Wavelet Time Scattering Classification of Phonocardiogram Data
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
(Wavelet Toolbox)
Time-Frequency Convolutional Network for EEG Data Classification
Classify electroencephalographic (EEG) time series from persons with and without epilepsy.
(Wavelet Toolbox)
- Since R2023a
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