Office of Technology Transfer – University of Michigan

Electrocardiogram Data Analysis Method For Rapid Diagnosis

Technology #6257

With an expected market size of over US$4 billion, electrocardiograms are the most utilized diagnostic tool in the medical field and are generating huge amount of data. Dealing with these data requires efficient data management systems, signal processing, and data analysis methods. To meet these needs, the data management system market has been growing steadily in the developed world and is expected to reach US$200 million in Europe by 2016. The purpose of the technology herein is to revolutionize the representation of ECD data, enabling rapid diagnosis and more efficient use of physicians' time. Continuous monitoring of patients by ECG, common in hospitals and for monitoring rare cardiac events, records over 100,000 heart beats over the course of a day. Sifting through all these data is tedious and error prone, especially when physicians are looking for minor changes in the beating pattern. By utilizing modern signal processing methods, this technology transforms formidably long data sets into an easily absorbed pictorial representation, utilizing the same information, allowing for quicker pattern recognition.

Details of Electrocardiogram Data Analysis Method For Rapid Diagnosis

The ECG data analysis technology presented works by plotting traditionally 2-dimensional data in 3-dimentions. Expanding to this additional axis allows for much more information to be shown without sacrificing data clarity and meaning. The method relies on windowing the ECG strip chart and aligning the main features in each window. Representing the data as time vs. window number allows for rapid assessment of the patterns in the data and a variable window size allows multiple levels of comparison to be made. Current competing products on the market show a side-on view of the 3D data, this can prove overwhelming and difficult to navigate. Instead, this technology utilizes a topologic approach showing the data on a 2D grid and utilizing color to represent height. With its unique representation, the data are more approachable and easier to absorb, increasing the efficiency of ECG analysis.

Applications

  • Method for ECG data analysis and representation
  • Streamline ECG interpretation process, quickly highlighting arrhythmic patterns
  • Applicable to Respiration tracking and other cyclical data sets

Advantages

  • Provides added value to existing Data Management Systems, increasing efficiency of ECG analysis
  • Clear and compact data representation as opposed to bulky 3D views
  • Smartly processes data – reducing work needed to recognize slight pattern deviations