Machine and Deep Learning for Medical Applications


The use of AI techniques on signals and time-series data is growing in popularity across different industries for a variety of applications, including many in the medical and healthcare areas such as digital health, physiological signal analysis, and patient monitoring applications.  In this interactive technical session, we'll use real-world EMG and ECG datasets to demonstrate various machine learning and deep learning approaches in MATLAB to show classification and prediction frameworks for 1-D signals. Some of the tasks we'll explore in developing advanced predictive models include:

Workshop Highlights: (Limited to 50paxs)

  • Importing labeled signal datasets, and efficiently labeling signal datasets 

  • Applying time-frequency transformations and wavelets to pre-process signals, and extract signal features, and automating deep feature extraction

  • Exploring and evaluating machine learning models, designing new deep networks, optimizing hyperparameters, and exchanging models with deep learning frameworks via ONNX


Seats are limited,  Register now!



                    Intan Nuralisa Mat Dali

                      Application Engineer

Intan Nuralisa is an Application Engineer at TechSource Systems, Malaysia. She works to improve the adoption of MATLAB and toolboxes in universities around Southeast Asia. Engaging with customers to comprehend their technical and business challenges.

Previously, she worked as Research Assistant at Universiti Teknologi Malaysia that focuses on signal processing, water treatment and desalination processes. She graduated from Universiti Teknologi Malaysia with a Master of Philosophy (MPhil) in Chemical Engineering and a Bachelor Engineering in Chemical-Gas Engineering.

She has built her forte in data analysis, deep learning for communications, statistical analytic with MATLAB. Currently her main interest is in control systems, predictive maintenance, and robotic systems.

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