Improving and Decentralizing Cardiac Healthcare

The world’s first mobile device that produces clinical-standard ECGs.

300+ mln

ECGs are taken every year


first gets cardiac symptoms


could have been prevented

The problem

The main Cardiac Diagnostic device, the Electrocardiogram (ECG) Machine, is large, stationary, and user-unfriendly. ECG’s are currently made in hospitals by trained professionals, which are time-consuming, expensive, and are a single point in time diagnosis. One in three deaths in the Netherlands are due to cardiovascular problems. Most of these deaths (90%) occur out of the hospital.

Our solution

1. ECG at home

With the HeartEye device and supporting app, you or your medical professional can take clinical-standard ECGs without any wires.

2. Pocket size device

The main Cardiac Diagnostic device, the Electrocardiogram (ECG) Machine, is large, stationary, and user-unfriendly. Our solution offers a pocket size device.

3. Deep neural networks

Our algorithm can identify deviant cardiac patterns. The results will be divided into either acute, subacute or normal.

4. Medical grade

Diagnoses are made earlier and more accurate, decreasing cardiovascular mortality. Our patented device gives medical standard results.

How it works


The user scans

Hold the device against your chest and scan for a minute.


The app analyzes

The HeartEye app analyzes the heart rhythm and creates an ECG.


Results are given

The app will give advice based on your cardiac health.

Where to place?

The device needs to be placed at the center of the chest for around 30 seconds. The app will make a scan and analyses it afterwards to formulate an advice.

In depth


Although home-monitoring devices for rhythm disorders are getting more common, there is no multi-lead electrocardiography device available to detect acute ischemic heart disease in the home setting. The device can be used by patients at home, but also by the GP or in hospital settings where the standard 12-lead ECG is not readily available. The deep learning algorithm will be trained and optimized to process the output without human interaction and will provide the user with immediate feedback. With this device, patients will be able to keep track of their cardiac health and if required data can be send to a cardiologist for further analysis.

We have already developed a smart phone sized proof of principle device that can acquire a multi-lead ECG within 30 seconds at the push of button without the need for ECG-patches or professional equipment.

For the further development of this device we will closely work together with software partners experienced in medical software development and implementation. The final product will be a CE-ready standalone device, combined with software running on an external device that will analyze the data.

Potential market

The total CVD Expenditure for the Netherlands in 2019 was ~€10.5B. Electro medical device sales in the Netherlands made up €432M, of which ECG devices accounted for €145M. At an average price of €10,000, this means 14,500 ECG devices are sold annually. In 2019, 3 million ECG’s were made in hospitals and an unknown amount, estimated at 100.000, made with private physicians and GP’s. ECG’s are compensated at €50 by insurers, which makes it a €155M market. ECG device sales and ECG compensations therefore made up 3,0% of the total CVD expenditure.

Meet the team

To learn more about the team members behind the project, click the button below.

About us
Competitors’ devices differ in three topics:
  1. They have a wearable but ECG’s are 1-lead, 3-lead or 6-lead, making them non-medical grade;
  2. Have not been tested in clinical studies;
  3. Competing companies focus on B2C rather than B2B.

In short, HeartEye is the only company with a medical grade ECG device that can service the B2B market.


In the future, improvements on the device should be performed:

  1. PCB redesign & battery consumption improvements;
  2. Include more sensors and feedback to users;
  3. Improve user experience with the App.
  4. Prepare device for use outside the clinic

Future plans

Our journey

August 2018

Proof of concept completed, establishment of miniECG 1.0

December 2019

Patent received with patent nr. 2021115

April 2020

Establishment of miniECG 2.0

March 2021

Incorporation of HeartEye B.V.

December 2021

Launch website and start industrialising miniECG

The next step?

Always looking for customer feedback, please reach out ↓


This project is endorsed by

    Get in touch

    We need your help! Do you want to be part of this revolutionary journey and support novel medical technology that can greatly improve the health care system?

    Don’t hesitate to shoot us a message.


    Relevant research

    Van De Leur, R. R., Taha, K., Bos, M. N., Van Der Heijden, J. F., Gupta, D., Cramer, M. J., Hassink, R. J., Van Der Harst, P., Doevendans, P. A., Asselbergs, F. W., & Van Es, R. (2021). Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers. Circulation: Arrhythmia and Electrophysiology.

    Van de Leur, R. R., Blom, L. J., Gavves, E., Hof, I. E., van der Heijden, J. F., Clappers, N. C., Doevendans, P. A., Hassink, R. J., & van Es, R. (2020). Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks. Journal of the American Heart Association, 9(10), e015138.

    Bos, M. N., Van De Leur, R. R., Vranken, J. F., Gupta, D. K., Van Der Harst, P., Doevendans, P. A., & Van Es, R. (2020). Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks. Computing in Cardiology, 2020-Septe, 2–5.

    Vranken, J. F., van de Leur, R. R., Gupta, D. K., Juarez Orozco, L. E., Hassink, R. J., van der Harst, P., Doevendans, P. A., Gulshad, S., & van Es, R. (2021). Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms. European Heart Journal – Digital Health, 1–41.

    Rutger R van de Leur, Machteld J Boonstra, Ayoub Bagheri, Rob W Roudijk, Arjan Sammani, Karim Taha, Pieter AFM Doevendans, Pim van der Harst, Peter M van Dam, Rutger J Hassink, René van Es, Folkert W Asselbergs, Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology. Arrhythmia Electrophysiol Rev. 2020;9(3):146–54.