R&D Project: AI-Powered Interpretation of Electrocardiograms

A team of engineers Waverley works with developed an innovative tool for electrocardiograms annotation and interpretation. Compatible with various kinds of ECG and involving artificial intelligence and deep learning, this product streamlines cardiac patient monitoring and diagnostics.

The Product

is an off-the-shelf software for remote patient monitoring and clinical diagnostics to be applied for constant cardiac monitoring and patient health surveillance. The tool works with digital health devices and apps to interpret and annotate electrocardiograms.

Project Analysis

Since the product’s main tasks are focused on healthcare, the main requirements were:

  • HIPAA compliance and correspondence to the quality and performance standards for medical equipment
  • Accuracy, providing near real-time accurate processing
  • Reliability, working in the remote and difficult locations

The Solution

The solution is an unprecedented tool to enhance and streamline the interpretation of electro cardiograms. Leveraging deep learning, it detects and annotates a wide range of cardiac events according to HL7® aECG standard. This highly adaptive solution can be integrated into an EHR system or work with a mobile health device. The proprietary zero-knowledge system developed for this system to store Protected Health Information is securely encrypted.

How It Works

The product is an all-encompassing system integrating with health assets such as electrocardiographs, Holters, hospital EHR systems, etc. It is very flexible, enabling patient supervision in any location, from the regular checkup to cardiac monitoring during rehab.

  • The electrocardiogram is obtained through a compatible digital recorder via API or a web-based platform and then automatically transferred to the system for interpretation. The team is constantly working to add new ECG formats to the list of supported.
  • The data is then analyzed to recognize patterns and detect cardiac events.
  • The software automatically calculates the amplitudes and intervals.
  • The reporting is made in charts, diagrams and tables, with marked points of interest.


Deep Learning and Artificial Intelligence

Pattern recognition lies at the core of this innovative solution. The team applied deep learning and big data approaches, analyzing the data from a ten-year population study. All records were labeled by the patent pending technology and then verified by the qualified cardiologists. These labels were used to train the system with the help of artificial neural networks.


As a result, the team of engineers managed to create a system that detects patterns so subtle that the doctors themselves can often miss, transforming patient monitoring and care.

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