Biomedical Data Analysis Using MATLAB and Simulink

Design, simulate, and build AI-based next-generation medical devices while complying with regulations

MATLAB® and Simulink® enable biomedical engineers to analyze large volumes of multimodal biomedical data sets. They also enable engineers to develop smart algorithms that can help build next-generation medical devices to aid the management of chronic diseases and improve overall quality of life.

With MATLAB and Simulink you can:

  • Analyze, visualize, and preprocess large volumes of biomedical signals, images, and text data
  • Build interpretable predictive AI models using automated machine learning (AutoML) and deep learning architectures
  • Automate generation of C/C++ or GPU code for embedded medical Internet of Things (IoT) and high-performance applications
  • Trace requirements to architecture, design, tests, and code
  • Automate reporting to prove and accelerate compliance with FDA/CE regulations and industry standards such as IEC 62304

“MATLAB enables us to rapidly develop, debug, and test sound-processing algorithms, and MATLAB Coder simplifies the process of implementing those algorithms in C. There’s no other environment or programming language that we could use to produce similar results in the same amount of time.”

Yulya Goryachev, Respiri

Using MATLAB and Simulink for 
Biomedical Data Analysis

Biomedical Data Preprocessing and Visualization

With MATLAB and Simulink, you can analyze and preprocess large volumes of physiological signals, medical imaging, and biomedical text and literature data sets. You can interface with hardware equipment to acquire physiological signals. For instance, with Raspberry Pi™ and Arduino® Support Packages, you can interface with embedded boards like Raspberry Pi, Arduino, and EKGShield to collect data from these sensors. You can also access and analyze signals stored in files such as EDF, Excel®, and MAT-files.

As a biomedical engineer or researcher, you can:

  • Automate the acquisition and analysis of images, videos, and signals from hardware
  • Prepare and automate labeling of biomedical signals, images, and text data using apps
  • Generate physiological data sets, such as ECG, through simulations

AutoML and Deep Learning

Using MATLAB, you can prototype and develop medical devices with machine learning applications. You can build predictive models using AI techniques such as machine learning and deep learning, and develop advanced algorithms for patient monitoring, hearing aids, and therapeutic applications.

With MATLAB and Simulink you can:

  • Train and compare models with point-and-click apps
  • Use advanced signal and image processing and automatic feature extraction techniques
  • Integrate with Simulink as native or MATLAB function blocks for embedded deployment or simulations
  • Use interpretable machine learning to overcome the black-box nature of most machine learning algorithms
  • Collaborate with peers using frameworks like TensorFlow™, PyTorch, and MxNet
  • Use tall arrays to train machine learning models with data sets too large to fit in memory, with minimal changes to your code

Code Generation and Simulink Integration

Deploy statistics and machine learning models to embedded systems and generate readable C/C++ code for your entire machine learning algorithm, including preprocessing and postprocessing steps. Speed up verification and validation of your high-fidelity simulations using machine learning models through MATLAB function blocks and native blocks in Simulink. You can also deploy your trained models on embedded systems, enterprise systems, FPGA devices, or in the cloud. MATLAB supports automatic CUDA® code generation for the trained network as well as for preprocessing and postprocessing to specifically target the latest NVIDIA® GPUs.


Verification and Validation – Compliance with FDA Regulations and Standards

You can validate MathWorks tools for use in FDA/CE-regulated workflows and to meet harmonized standards such as IEC 62304. Using MATLAB and Simulink during the medical device development process can help reduce the regulatory burden and speed up the submission timelines by automating the creation of many engineering reports.