The electrical activity in the human heart can be measured as a sequence of amplitudes away from a baseline signal. The segmentation of these regions of ECG waveforms can provide the basis for measurements useful for assessing the overall health of the human heart and the presence of abnormalities [2]. Manually annotating each region of the ECG signal can be a tedious and time-consuming task. Signal processing and deep learning methods potentially can help streamline and automate region-of-interest annotation. This example uses ECG signals from the publicly available QT Database [3] [4].

You can accelerate the simulation of your algorithms in Simulink by using different execution environments. By using support packages, you can also generate and deploy C/C and CUDA code on target hardware. Simulate and generate code for deep learning models in Simulink using MATLAB function blocks. Simulate and generate code for deep learning models in Simulink using library blocks. This example shows how to develop a CUDA application from a Simulink model that performs lane and vehicle detection using convolutional neural networks (CNN).

Perform supervised machine learning by supplying input data and known responses to the data. With this data, you can train a model that generates predictions for the response to new data and see the validated model results. You can automatically train a selection of or all classifiers, compare validation results, and choose the best model that works for your classification problem.

I'd like to introduce Frantz Bouchereau, development manager for Signal Processing Toolbox who is going to dive deep into insights on deep learning for signal processing, including the complete deep... read more

I'd like to introduce Frantz Bouchereau, development manager for Signal Processing Toolbox who is going to dive deep into insights on deep learning for signal processing, including the complete deep... read more

Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty.

You can use a support vector machine (SVM) when your data has exactly two classes. An SVM classifies data by finding the best hyperplane that separates all data points of one class from those of the other class. The best hyperplane for an SVM means the one with the largest margin between the two classes. Margin means the maximal width of the slab parallel to the hyperplane that has no interior data points. The support vectors are the data points that are closest to the separating hyperplane; these points are on the boundary of the slab.

Sponsored by MathWorks, the National Institutes of Health (NINDS), the American Epilepsy Society, and the University of Melbourne, the competition attracted 478 teams and 646 competitors from around the world. The algorithms I developed in MATLAB scored highest among individual participants and third highest in the competition overall. The EEG data came from a long-term study conducted by the University of Melbourne. In this study, intracranial EEG recordings were collected from 15 epileptic patients via 16 surgically implanted electrodes sampled at 400 Hz for several months. In the original study, researchers were unable to reliably predict seizures for about 50% of the test subjects.

Use neighborhood component analysis (NCA) to choose features for machine learning models. Manipulate and analyze data that is too big to fit in memory. Perform support vector machine (SVM) and Naive Bayes classification, create bags of decision trees, and fit lasso regression on out-of-memory data. Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters. Manipulate, compare, and store text data efficiently .

How does deep learning attain such impressive results? Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images. Deep learning applications are used in industries from automated driving to medical devices.