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Saint Petersburg State University

The Development of Mobile Health Monitoring Systems

Saint Petersburg State University via Coursera

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This course created by SPSU and ETU includes five modules dedicated to different stages of system development. Its modules represent several widely separated fields of biomedical engineering. We interconnect them by applying their knowledge to a common task – the development of a prototype of a mHealth ECG system with built-in data-driven signal processing and analysis. Working on this task throughout the course, you will learn how these branches of science, including electronics, mathematics, data science, and programming, are applied together in an actual project. Pieces of hardware and software and the data sets that we utilize in this course are the same components that we use in our work developing prototypes of devices and algorithms for our tasks in science and engineering.
The goal of the course is to give you some valuable skills on implementing them in practical tasks using the MATLAB environment, rather than to dive deep into fundamental knowledge on any of the topics highlighted in the modules.
Note. All enrolled learners are provided with a trial MATLAB license on the period sufficient to complete all the assignments.


  • Remote health monitoring system hardware
    • Welcome to Module 1! Medical systems for remote monitoring of patients have become extremely popular in recent years. Most of them have a similar structure, which will be discussed in detail in this module using the example of an electrocardiogram signal registration device. We will talk about the hardware part of modern ECG recorders and the problems connected with the processing of biomedical signals.
  • Data Exchange Between Device and Personal Computer
    • Welcome to Module 2! Implementing the protocol for transferring data from a patient’s wearable device to a computer is essential in developing a telemedicine system. This module will consider the most accessible and affordable wired data transfer method using the RS-232 interface, virtual Com-ports, and the MATLAB software environment.
  • Preprocessing of Biomedical Signals
    • Welcome to Module 3! As you may know, a significant amount of noise corrupts biomedical signals. So, noise removal is necessary to increase signal quality. We will discuss the primary method to prepare your signal for future analysis. In the programming part of the module, we will learn how to evaluate and analyze the ECG-signal spectrum and create a digital filter using MATLAB.
  • Event Detection in Biomedical Signals
    • Welcome to Module 4! In most cases, the biomedical signal analysis includes finding some reference or essential events in the signal. It can be QRS-complexes (for ECG), breaths (for spirogram), eyes movements (for EEG), or steps (for accelerometric signal). We will look closely at this task in the context of ECG analysis. You will learn different QRS-detection algorithms and create QRS-detector using MATLAB.
  • Developing Data-Driven Recommendation System
    • Welcome to Module 5! In this module, you will further develop your mobile-based health monitoring system. How to deal with extracted features and how they can help you create recommendations are the primary questions for this module. It is a vast topic involving methods from statistical analysis, machine learning, and medical practice. We will study a practical approach to use these methods in developing monitoring systems on the example, which is, in our case, a recognition of noisy ECG complexes and their removal.

Taught by

Evgenii Pustozerov, Yuliya Zhivolupova and Aleksei Anisimov


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