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Automatic Electrocardiogram Detection of Suspected Hypertrophic Cardiomyopathy: Application to Wearable Heart

Max Denis1,2, Mulatu Bachoro1,2, Winta Gebreslassie1,2

  • 1Department of Mechanical Engineering, University of the District of Columbia, Washington, DC 20008 USA.

IEEE Sensors Letters
|October 31, 2022
PubMed

Insights

An automatic algorithm can detect hypertrophic cardiomyopathy (HCM) using single-lead ECG data. It identifies left ventricular hypertrophy (LVH) by analyzing S-wave amplitude and ST-segment differences in patients versus healthy volunteers.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Medical Diagnostics

Background:

  • Hypertrophic cardiomyopathy (HCM) is a primary clinical abnormality characterized by left ventricular hypertrophy (LVH).
  • Accurate and early detection of LVH is crucial for managing HCM.
  • Current diagnostic methods may require complex or invasive procedures.

Purpose of the Study:

  • To present an automatic detection algorithm for hypertrophic cardiomyopathy (HCM).
  • To evaluate the algorithm's capability in differentiating HCM subjects with LVH from healthy individuals using single-lead ECG.
  • To identify specific ECG parameters indicative of LVH.

Main Methods:

  • An automatic detection algorithm was developed and applied to single-lead ECG datasets.
  • The study included 43 human subjects: 22 with LVH and 21 healthy volunteers.
  • Statistical analysis was performed to compare ECG parameters between groups.

Main Results:

  • The algorithm successfully differentiated between LVH patients and healthy volunteers.
  • Significant differences (p-value 0.01) were observed in S-wave amplitude.
  • Significant differences (p-value 0.04) were noted in the ST-segment between the groups.

Conclusions:

  • The developed automatic algorithm shows promise for detecting LVH, a key indicator of HCM, from single-lead ECG.
  • S-wave amplitude and ST-segment are significant ECG parameters for differentiating LVH patients from healthy individuals.
  • This non-invasive approach could aid in the early detection and management of HCM.

Keywords:
Sensor applicationsautomatic ECG feature extractionelectrocardiogram (ECG)hypertrophic cardiomyopathy (HCM)sensor applicationswearable heart monitor.

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