MUMBAI, India, Jan. 23 -- Intellectual Property India has published a patent application (202641001523 A) filed by Madhankumar C; Ms Reshma N; Sasikala M; Kavinaya G; Shalini M; and Vaishnavi Devi J, Pollachi, Tamil Nadu, on Jan. 7, for 'edge-computing enabled hardware architecture for intelligent cardiac risk detection.'
Inventor(s) include Ms Reshma N; Sasikala M; Kavinaya G; Shalini M; and Vaishnavi Devi J.
The application for the patent was published on Jan. 23, under issue no. 04/2026.
According to the abstract released by the Intellectual Property India: "Edge-computing enabled hardware architecture for intelligent cardiac risk detection ABSTRACT Cardiovascular diseases (CVDs) continue to be one of the leading causes of mortality worldwide, posing a major challenge to global healthcare systems. The increasing prevalence of heart-related disorders highlights the critical need for reliable, continuous, and intelligent cardiac monitoring solutions that enable early detection and timely intervention. Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals are widely recognized as key physiological indicators of cardiac health. However, traditional cardiac monitoring systems often rely on bulky equipment, centralized hospital infrastructure, or cloud -based processing mechanisms. These approaches introduce significant limitations such as increased latency, high power consumption, continuous internet dependency, data privacy concerns, and limited suitability for real-time and long-term monitoring. To address these challenges, this project presents an Edge-Computing Enabled Hardware Architecture for Intelligent Cardiac Risk Detection, designed to acquire, process, and analyze cardiac signals in real time at the edge of the network. The proposed system integrates non-invasive biomedical sensors, specifically ECG and PPG sensors, to continuously monitor physiological parameters associated with heart activity. These sensors capture real-time cardiac signals, which are then passed through a signal conditioning stage involving amplification, filtering, and normalization. This preprocessing step ensures the removal of noise and artifacts, resulting in accurate and reliable signal acquisition suitable for further analysis. Unlike conventional systems that transmit raw physiological data to cloud servers for processing, the proposed architecture performs computational tasks locally using an edge computing device. This edge-based approach enables real-time feature extraction and intelligent assessment of cardiac risk without relying on continuous cloud connectivity. By shifting computation closer to the data source, the system significantly reduces response time, minimizes bandwidth usage, and enhances overall system reliability. The use of local processing also plays a crucial role in preserving patient data privacy, as sensitive medical information is analyzed and stored locally rather than being continuously transmitted over external networks. The intelligent analysis framework incorporated in the proposed system utilizes analytical techniques and machine learning-based algorithms to evaluate cardiac conditions. Extracted features from ECG and PPG signals are analyzed to identify abnormal patterns that may indicate potential cardiac risk. The system is capable of distinguishing between normal and abnormal cardiac behavior, enabling early detection of risk conditions such as irregular heart rhythms. The edge-based intelligence ensures that critical decisions are made promptly, which is essential for effective cardiac risk management and emergency response scenarios. In addition to real-time analysis, the system provides a user-friendly interface that displays analyzed results in an understandable format. Detected abnormalities and risk indicators can be communicated wirelessly to external devices such as smartphones, monitoring dashboards, or healthcare platforms. This capability supports remote monitoring and enables healthcare professionals to access patient data for further evaluation and clinical decision-making. By transmitting only relevant results and alerts instead of raw data, the system further optimizes network usage and reduces unnecessary data traffic. The proposed hardware architecture is designed to be portable, scalable, and energy-efficient, making it suitable for continuous and long-term cardiac monitoring applications. Its modular design allows easy integration with additional biomedical sensors and supports future system enhancements. The low-power operation of the system makes it ideal for wearable and home- based healthcare solutions, enabling patients to monitor their cardiac health outside traditional clinical environments. This contributes to improved patient comfort, reduced hospital visits, and enhanced quality of life. From a broader healthcare perspective, the proposed edge-computing based solution supports a shift from reactive to proactive cardiac care. By enabling early detection and continuous monitoring, the system helps in preventing severe cardiac events and reduces the burden on healthcare infrastructure. The combination of biomedical sensing, edge computing, and intelligent analysis provides a reliable foundation for next-generation smart healthcare systems. In conclusion, this project demonstrates an efficient and practical approach to intelligent cardiac risk detection using edge computing and hardware-based architecture. The proposed system achieves low latency, enhanced data privacy, reduced power consumption, and reliable real-time performance. Its applications extend to wearable health monitoring devices, hospital-based patient monitoring systems, remote healthcare services, and personalized healthcare platforms. The project contributes to the advancement of intelligent healthcare technologies and offers a scalable solution for continuous cardiac monitoring and early risk prediction."
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