MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641050615 A) filed by Bvrit Hyderabad College Of Engineering For Women; M. Kalpana Chowdary; Y Alekhya; and G Purnachandrarao, Hyderabad, Telangana, on April 21, for 'a low-power vlsi architecture for ecg arrhythmia detection using shiftbased neural network computation.'

Inventor(s) include M. Kalpana Chowdary; Y Alekhya; and G Purnachandrarao.

The application for the patent was published on May 1, under issue no. 18/2026.

According to the abstract released by the Intellectual Property India: "The present invention discloses a low-power VLSI architecture for electrocardiogram (ECG) arrhythmia detection using shift-based neural network computation. The system is designed to enable efficient and real-time classification of cardiac signals in resource-constrained environments such as wearable and portable healthcare devices. ECG signals are initially processed through a preprocessing module that performs noise removal, segmentation, and normalization to ensure reliable input for classification. The processed signals are provided to a data shifting neural network (DSNN), wherein conventional multiplication operations are replaced with shift-and-add computations to significantly reduce computational complexity, power consumption, and silicon area. The neural network operates using fixed-point representation, enabling efficient mapping onto hardware. The architecture is implemented using register transfer level design and synthesized using electronic design automation tools to generate an optimized hardware layout. The proposed system achieves accurate classification of normal and abnormal heart rhythms while maintaining low power usage and reduced hardware complexity. The elimination of multipliers and adoption of shift-based operations contribute to improved efficiency and scalability. The invention is suitable for real-time deployment in wearable monitoring systems, embedded biomedical devices, and remote healthcare platforms, providing an energy-efficient and reliable solution for continuous cardiac monitoring and early detection of arrhythmias."

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