![]() The example uses 162 ECG recordings from three PhysioNet databases: MIT-BIH Arrhythmia Database, MIT-BIH Normal Sinus Rhythm Database, and The BIDMC Congestive Heart Failure Database. This example uses ECG data obtained from three groups, or classes, of people: persons with cardiac arrhythmia, persons with congestive heart failure, and persons with normal sinus rhythms. You can find a deep learning approach to this classification problem in this example Classify Time Series Using Wavelet Analysis and Deep Learning and a machine learning approach in this example Signal Classification Using Wavelet-Based Features and Support Vector Machines.Ī note on terminology: In the context of wavelet scattering, the term "time windows" refers to the number of samples obtained after downsampling the output of the smoothing operation. The data used in this example are publicly available from PhysioNet. You must have the Wavelet Toolbox™ and the Statistics and Machine Learning Toolbox™ to run this example. Wavelet time scattering yields signal representations insensitive to shifts in the input signal without sacrificing class discriminability. ![]() In wavelet scattering, data is propagated through a series of wavelet transforms, nonlinearities, and averaging to produce low-variance representations of time series. This example shows how to classify human electrocardiogram (ECG) signals using wavelet time scattering and a support vector machine (SVM) classifier.
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