生物医学
R2026a使用信号处理层、信号与小波特征以及深度学习模型，对 ECG、EEG、EMG 和生理信号进行去噪、分解、分割和分类。
精选示例
Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals.
Human Activity Recognition Using Signal Feature Extraction and Machine Learning
Extract features from smartphone sensor signals and use them to classify human activity.
Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.
使用长短期记忆网络对 ECG 信号进行分类
使用深度学习和信号处理对心跳心电图数据进行分类。
Graph Signal Processing and Brain Signal Analysis
Perform graph signal processing to analyze brain activity by decomposing brain signals into aligned and liberal components.
Detect Anomalies in Signals Using deepSignalAnomalyDetector
Use autoencoders to detect abnormal points or segments in time series data.
使用深度学习进行波形分割
使用时频分析和深度学习对人体心电图信号进行分割。
Classify Arm Motions Using EMG Signals and Deep Learning
Classify arm motions using labeled EMG signals and a long short-term memory network.
Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression.
Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources.
从生理信号中提取分类特征
量化步幅间时间间隔并测量行走模式相似性；构造特征向量来对信号进行分类。
使用小波分析和深度学习对时间序列分类
使用连续小波变换和深度卷积神经网络对 ECG 信号进行分类。
Wavelet Packet Harmonic Interference Removal
Use wavelet packets to remove harmonic interference from an electrocardiogram (ECG) signal.
Wavelet Time Scattering Classification of Phonocardiogram Data
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
Time-Frequency Convolutional Network for EEG Data Classification
Classify electroencephalographic (EEG) time series from persons with and without epilepsy.
