MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641099935 A) filed by Dr. Kavitha Elumalai; Prabakaran Subramanian; and Anusha Ramadoss on August 19, 2026, for Ai-Powered Real-Time Ecg Analysis System Detects Cardiac Abnormalities Faster.
Inventors include Dr. Kavitha Elumalai; Prabakaran Subramanian; and Anusha Ramadoss.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: AI-Powered Real-Time ECG Analysis System Detects Cardiac Abnormalities Faster ABSTRACT The present invention discloses an AI-powered real-time ECG analysis system configured to detect cardiac abnormalities faster and support timely clinical intervention. The system includes one or more ECG sensors for acquiring cardiac electrical signals from a patient, a signal acquisition module for digitizing the signals, a preprocessing unit for removing noise, motion artifacts, baseline wander, and signal distortions, and an artificial intelligence analysis engine for evaluating ECG waveform patterns in real time. The AI engine applies trained machine learning or deep learning models to identify abnormal events, including arrhythmias, atrial fibrillation, ventricular tachycardia, ventricular fibrillation, bradycardia, tachycardia, conduction abnormalities, ischemic changes, ST-segment deviations, and QT interval abnormalities. A cardiac abnormality detection module classifies detected conditions according to type, confidence level, and severity. An alert generation module provides immediate visual, audible, tactile, or electronic notifications to clinicians, patients, caregivers, emergency responders, or telemedicine platforms. The system may further include explainable decision support, patient history integration, cloud connectivity, wearable monitoring, clinician dashboards, and secure data storage. By combining continuous ECG monitoring with AI-based signal interpretation, the invention reduces diagnostic delay, improves abnormality detection accuracy, prioritizes urgent cases, and enhances patient safety in hospitals, ambulances, remote healthcare settings, and wearable cardiac monitoring applications.
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