Apparatus and method for artificial intelligence-based cardiovascular health assessment using hybrid stimulus test


Research proposing a new deep neural network model using virtual reality that can be used in cardiovascular health monitoring.

The Invention

This research makes use of virtual reality (VR) as it proposes a novel deep neural network (DNN) model that can detect severe cardiovascular diseases using electrocardiogram (ECG) signals. The proposed DNN-based model is trained and tested using the CPSC 2018 dataset. It is designed to achieve high accuracy of detecting nine classes of rhythms, including ST-segment abnormalities, which are difficult to be classified out from ECG signals using the traditional DNN models.

Key Benefits

  • More accurate than current DNN models
  • Detect rhythms that current DNN models have difficulty detecting improved convolution layers


The proposed method in the research paper can potentially be applied in the field of cardiovascular health monitoring using ECG signals. Specifically, it aims to improve the accuracy of detecting various types of abnormal ECG rhythms, including ST-segment abnormalities, which are associated with severe cardiovascular diseases such as sudden cardiac arrest and myocardial ischemia. The proposed method is expected to become a viable solution for cardiovascular health monitoring, especially with the greater adoption of VR and increasing availability of portable and home-based ECG devices.

Market Opportunity

The interventional cardiology & Peripheral Vascular Devices Market size exceeded USD 22 billion in 2022 and the industry is expected to register more than 7.5% CAGR from 2023 thru 2032. On top of that, The global cardiovascular digital solutions market size was valued at USD 102.2 billion in 2022 and is estimated to expand at a compound annual growth rate (CAGR) of 4.8% from 2023 to 2030”.

An increase in cardiovascular disease diagnosis, the geriatric population, and prevalence of obesity has increased the need of cardiovascular health monitoring. Moreover, it is imperative to have an accurate diagnosis based on cardiac abnormalities.

Development and Intellectual Property Status

  • Bench Prototype
Patent Information:
For Information, Contact:
Zachary Miles
Associate Vice President for Technology & Partnerships
University of Nevada, Las Vegas
shengjie zhai
Yingtao Jiang
Jian Ni
Computer Science & Software
Life Science - Health
Med Devices, Diagnostics & Healthtech
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