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Arrhythmia Detection With Feature Delineation and Machine Learning

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Summary

USPTO published patent application US20260108198A1 on April 23, 2026, covering arrhythmia detection techniques that combine feature-based delineation and machine learning to classify cardiac arrhythmia in patients. The application (No. 19425615) was filed December 18, 2025, with eight named inventors including Niranjan Chakravarthy and Rodolphe Katra. The technology enables a computing device to receive cardiac electrogram data, obtain a first classification via feature delineation, apply a machine learning model for a second classification, and output a report identifying arrhythmia episodes and coinciding cardiac features.

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GovPing monitors USPTO Patent Applications - Health Informatics (G16H) for new healthcare & life sciences regulatory changes. Every update since tracking began is archived, classified, and available as free RSS or email alerts — 169 changes logged to date.

What changed

USPTO published patent application US20260108198A1 titled 'Arrhythmia Detection With Feature Delineation and Machine Learning' on April 23, 2026. The application covers techniques for detecting cardiac arrhythmia using a dual-classification approach: feature-based delineation of cardiac electrogram data and machine learning model analysis. The computing device uses both classifications to determine whether an arrhythmia episode has occurred or to verify one classification against the other.

Affected parties include medical device manufacturers developing arrhythmia detection systems, healthcare technology companies implementing cardiac monitoring solutions, and clinical researchers working with cardiac electrogram data. The published application indicates active patent prosecution in this technology space, which may be relevant for freedom-to-operate analyses for competing cardiac monitoring and diagnostic systems.

Archived snapshot

Apr 24, 2026

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← USPTO Patent Applications

ARRHYTHMIA DETECTION WITH FEATURE DELINEATION AND MACHINE LEARNING

Application US20260108198A1 Kind: A1 Apr 23, 2026

Inventors

Niranjan Chakravarthy, Siddharth Dani, Tarek D. Haddad, Donald R. Musgrove, Andrew Radtke, Eduardo N. Warman, Rodolphe Katra, Lindsay A. Pedalty

Abstract

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

CPC Classifications

A61B 5/349 A61B 5/316 G16H 10/60 A61B 2560/0214

Filing Date

2025-12-18

Application No.

19425615

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Last updated

Classification

Agency
USPTO
Published
April 23rd, 2026
Instrument
Notice
Branch
Executive
Legal weight
Non-binding
Stage
Draft
Change scope
Minor

Who this affects

Applies to
Medical device makers
Industry sector
3345 Medical Device Manufacturing
Activity scope
Patent application filing Medical device research
Geographic scope
United States US

Taxonomy

Primary area
Intellectual Property
Operational domain
Legal
Topics
Healthcare Artificial Intelligence

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