AI Wearable ECG Skin Patch for Sudden Cardiac Arrest Detection
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Solution Overview
Problem
Existing ECG devices are bulky, expensive, and lack AI capabilities for real-time detection of sudden cardiac arrest, leading to misdiagnosis and unavailability of emergency care, especially for patients with pre-existing cardiac abnormalities.
Innovation Solution
A wearable ECG skin patch using flexible printed electronics, IoT connectivity, and AI/ML pipelines for real-time detection of cardiac arrhythmias, capable of capturing electrical signals, processing data, and predicting sudden cardiac arrest through a cloud-based system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ECG devices are used for cardiac monitoring, then measurement precision is maintained, but device complexity and cost increase, making them unsuitable for continuous ambulatory use
Solution Approach 1:
The patent extracts the essential ECG monitoring function from complex hospital-grade equipment and implements it in a simplified wearable patch. The system uses a minimal set of electrodes and signal processing components to capture and analyze cardiac electrical activity, eliminating unnecessary complexity while maintaining diagnostic reliability through AI-based arrhythmia detection algorithms
Solution Approach 2:
The patent replaces traditional mechanical and electronic signal processing systems with AI-based computational models. Machine learning algorithms process the ECG signals to detect arrhythmias, substituting complex hardware-based analysis with software-based intelligent detection that reduces device complexity while improving monitoring reliability
2Measurement precision
If AI capabilities are added to ECG devices for real-time detection, then diagnostic accuracy improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the diagnostic function into two parts: a simple wearable device that collects and pre-processes ECG signals, and a cloud-based AI system that performs complex arrhythmia analysis. This segmentation allows the wearable device to remain simple while achieving high diagnostic accuracy through remote AI processing of the captured cardiac signals
Solution Approach 2:
The patent introduces a cloud-based processing intermediary that bridges the simple wearable device and the complex AI analysis. The wearable patch transmits raw or pre-processed ECG data to cloud servers where sophisticated machine learning models detect arrhythmias, allowing high measurement precision without increasing the complexity of the wearable device itself
3Reliability
If continuous monitoring is implemented, then detection reliability improves, but energy consumption increases
Solution Approach 1:
The patent implements periodic sampling of ECG signals rather than continuous high-rate acquisition. The wearable device captures cardiac electrical activity at intervals sufficient for arrhythmia detection, reducing energy consumption while maintaining reliable continuous monitoring capability. The system activates sensors and transmission only when needed based on detected signal characteristics
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables cost-effective, continuous monitoring and accurate prediction of sudden cardiac arrest, reducing the burden on healthcare systems and providing timely alerts to patients and caregivers.
Implementation Method 1
The present invention works on conceptualization and development of the product prototype based on the principles of ECG
Data Source
AI summary
There is described an artificial intelligence wearable ECG skin patch (400) to detect sudden cardiac arrest. The wearable ECG monitoring patch (400) with AI based predictive analytics and remote based cardiac monitoring (615) system that can detect cardiac arrhythmias automatically in real-time and make a diagnosis with AI models trained with acquired data. The wearable skin has a biocompatible polymer patch (400) which captures the electrical signal through a flexible printed electronic technology based conducting ink and a substrate. The microcontroller controls (201), store and transmit the data packets. The IoT connected signal transmission is capable of recording and transferring the data packets through wireless communication. The AI engine is capable of analysing, evaluating, testing and providing the data packets of sudden cardiac arrest through a peak detector algorithm. The ECG skin patch (400) to detect and measure the sudden cardiac arrest with the R-R interval time series to obtain heart rate variability.