Wearable Defibrillator Machine Learning Tachyarrhythmia Detection
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Solution Overview
Problem
Current wearable automated external defibrillators (WAEDs) rely on conventional arrhythmia detection algorithms that analyze only recent electrocardiogram (ECG) data, failing to consider other types of sensor data and often resulting in inaccurate detections and unnecessary therapies.
Innovation Solution
The implementation of a machine learning algorithm in a WAED system that uses a GPU and CPU architecture to probabilistically determine a patient's state by analyzing diverse data sources, including ECG signals and other physiological or environmental signals, allowing for more accurate detection of tachyarrhythmia and predictive capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AED arrhythmia detection algorithms are used that analyze only recent ECG data, then the device complexity is low and ease of operation is maintained, but the measurement precision of tachyarrhythmia detection is insufficient and false alarms occur
Solution Approach 1:
The patent combines multiple data sources (ECG data, respiratory data, activity data) that were previously analyzed separately into a unified machine learning model. This integration allows the system to consider diverse physiological and environmental factors simultaneously, improving detection accuracy while managing complexity through consolidated processing architecture.
Solution Approach 2:
The machine learning algorithm is designed to perform multiple functions: detecting tachyarrhythmia, distinguishing cardiac events from noise, and analyzing various sensor data types. This multi-functional approach replaces multiple specialized algorithms with a single versatile system that improves measurement precision across different detection scenarios.
2Reliability
If conventional arrhythmia detection algorithms analyzing only seconds of ECG data are used, then the loss of information is minimal and processing speed is maintained, but the reliability of patient state determination is insufficient
Solution Approach 1:
The patent transitions from analyzing only temporal data (seconds of ECG) to incorporating multi-dimensional data sources including respiratory patterns, activity levels, and environmental factors. This dimensional expansion provides a more comprehensive view of patient state, improving reliability by considering physiological and environmental context beyond cardiac electrical activity alone.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning algorithm continuously processes incoming sensor data and updates patient state determinations. Historical sensor data is incorporated into the analysis, allowing the system to learn from patterns over time and improve reliability through cumulative information processing rather than isolated momentary assessments.
3Productivity
If conventional AED algorithms are used that do not consider other sensor data, then the device complexity is low and power consumption is reduced, but the productivity of accurate tachyarrhythmia detection is insufficient
Solution Approach 1:
The patent replaces traditional mechanical signal processing algorithms with a machine learning-based computational system. This substitution enables the processing of diverse sensor data types and complex pattern recognition that would be impractical with conventional algorithms, thereby improving detection productivity through advanced computational methods.
Solution Approach 2:
The system changes the parameters of data analysis by incorporating multiple sensor modalities (ECG, respiratory, activity) and using machine learning models that can process variable data types. This parameter expansion allows the system to extract more information from the same hardware infrastructure, improving detection productivity without proportionally increasing device complexity.
Data Source
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AI summary
An apparatus is disclosed that is configured to be worn by a patient for cardiac defibrillation comprises sensing electrodes configured to sense a cardiac signal of the patient, defibrillation electrodes, therapy delivery circuitry configured to deliver defibrillation therapy to the patient via the defibrillation electrodes, communication circuitry configured to receive data of at least one physiological signal of the patient from at least one sensing device separate from the apparatus, a memory configured to store the data, the cardiac signal, and a machine learning algorithm, and processing circuitry configured to apply the machine learning algorithm to the data and the cardiac signal to probabilistically-determine at least one state of the patient and determine whether to control delivery of the defibrillation therapy based on the at least one probabilistically-determined patient state.