Medical IoT paradigm and biomedical systems

Monday, September 16, 15:00-16:30
Anton Popov, PhD, SMIEEE

The complex infrastructure of interconnected medical devices and systems had evolved over the last several decades to provide accurate, continuous, and reliable support of human health and wellbeing. The healthcare IoT monitors, consults, and delivers the service to people at different scales, from wristbands counting the steps and calories, to multimodal visualization for cancer therapy.

In this tutorial, the general approach to medical IoT network will be presented, and the main toolboxes will be described. A brief overview of biosignals will be given to demonstrate the variety of the input data fro medical systems. The classification of biomedical systems will be provided with examples of recent developments in biosystems for diagnosis, monitoring, visualization, and others. Machine learning approach to the diagnosis will be discussed in the second part of the tutorial, and the signal analysis methods for feature engineering will be briefly summarized.

About Speaker

Anton Popov is an associate professor of Electronic Engineering Department of Igor Sikorsky Kyiv Polytechnic Institute (Ukraine), He is a lecturer for the courses “Theory of signals”, “Biomedical Electronic Systems”, “Digital processing and analysis of biomedical signals and images”. He is also the head of the Biomedical Electronics and Signal Analysis Group of Kyiv Polytechnic Institute, AI/Deep Learning technical lead in the Ciklum company (UK), and IEEE Senior Member.

Anton Popov authored 150 publications in peer-reviewed journals and conference proceedings and was a supervisor for more than 60 bachelor, master, and PhD students.

His research interests include applications of signal processing methods to the analysis and interpretation of biomedical signals and images. Currently, his group is working on epileptic seizure prediction based on electroencephalograms and cardiorythmograms, techniques for quantification of cognitive workload and emotions, recognition of imaginary movements, muscle synergies detection, stabilography, as well as other biosignals and medical images for the diagnosis of diseases and evaluating the person’s physical conditions.