AI Wearable Defibrillator for Accurate Shock Detection
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Solution Overview
Problem
Current wearable cardioverter defibrillators (WCDs) rely solely on electrocardiogram (ECG) signals for decision-making, which can be corrupted by noise, leading to false alarms and inefficient shock/no shock determinations, and lack integration of advanced technologies like AI for real-time patient monitoring and device optimization.
Innovation Solution
Integration of AI technology into WCDs for real-time analysis of ECG, patient data, and environmental factors, along with machine learning algorithms to improve garment fit, detect imminent device failures, adjust alarm thresholds, enhance patient compliance, and implement intelligent voice recognition, enabling more accurate and personalized treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If WCDs rely solely on ECG signals for decision-making, then the device structure remains simple, but the accuracy of shock/no shock determinations deteriorates due to noise corruption
Solution Approach 1:
The patent combines multiple sensing modalities (ECG, accelerometer, microphone, temperature sensor) into an integrated monitoring system. The processor fuses data from these diverse sources to make shock determination decisions, merging previously separate functions into a unified system that overcomes the limitations of ECG-only monitoring.
Solution Approach 2:
The patent introduces an intermediary processing layer that filters and interprets sensor data before making clinical decisions. The processor acts as an intermediary between raw sensor signals and shock determination, using algorithms to distinguish true cardiac events from noise generated by motion or environmental factors.
2Measurement precision
If multiple sensors are integrated for comprehensive monitoring, then measurement accuracy improves, but device complexity and energy consumption increase
Solution Approach 1:
The system employs periodic sampling of sensor data rather than continuous monitoring. The processor activates sensors and performs analysis at intervals appropriate for detecting cardiac events, reducing energy consumption while maintaining detection accuracy. Motion sensors trigger ECG acquisition only when significant movement is detected.
Solution Approach 2:
The monitoring system dynamically adjusts its operation based on detected conditions. During periods of low activity or normal cardiac rhythm, the system reduces sampling rates and sensor activation. When arrhythmia or significant motion is detected, the system increases monitoring intensity, creating a dynamic energy management strategy.
3Measurement precision
If AI algorithms are implemented for real-time data analysis, then diagnostic accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary processing and filtering of sensor data before applying complex AI algorithms. Basic signal conditioning, noise filtering, and feature extraction are done in real-time at the sensor level, preparing data for more intensive analysis. This preliminary action reduces the computational burden on the AI processor.
Solution Approach 2:
The data processing pipeline is segmented into multiple stages: initial signal acquisition and filtering, feature extraction, AI-based pattern recognition, and final decision-making. Each stage processes data independently and passes results to the next stage, allowing parallel processing and reducing overall computation time.
Data Source
AI summary
“Artificial Intelligence” or “AI” technology can be applied to Wearable Cardioverter Defibrillators (“WCDs”) and other wearable medical equipment in various ways, including: garment fitting and adjustment; analyzing electrocardiogam (“ECG”), other sensor data and/or other patient data (e.g., age, gender, previous medical conditions, etc.) in real time to detect/assess the patient's present condition and/or need for treatment for cardiac and other conditions (e.g., stroke, coughing, apnea, etc.); detecting imminent failure of the wearable medical device components; capturing and reporting data collected from the patient for presenting to clinicians; adjusting thresholds for alarms and notifications based on patient's responses; improving patient compliance based on the patient's past non-compliant behavior and actions that resulted in the patient becoming compliant; providing tests to the patient (e.g., grip test, dexterity tests, balance tests, etc.) and learning the patient's responses to detect/assess the patient's present condition and/or need for treatment; learning the patient's voice, activity, posture, time of day, etc. for implementing intelligent voice recognition/activation of the medical device.