Adaptive ECG Power Modes in Wearable Cardioverter Defibrillators
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Solution Overview
Problem
Battery-powered wearable medical devices (WMDs) face challenges in maintaining continuous operations while being power-efficient, particularly in tasks like ECG acquisition and analysis for arrhythmia detection, which require varying levels of processing power based on events and inputs.
Innovation Solution
The WMD systems employ adaptive techniques to dynamically adjust power consumption by alternating between higher and lower performance monitoring states, using hardware and software components to optimize power usage without requiring special-purpose processors or complex software schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the WMD continuously performs high-fidelity ECG acquisition and complex arrhythmia detection analysis, then the detection accuracy and response capability are improved, but the power consumption increases significantly
Solution Approach 1:
The patent implements dynamic adjustment of processing performance levels. The system transitions between high-fidelity mode (for comprehensive ECG acquisition and complex analysis) and low-fidelity mode (for basic monitoring), allowing the processor to adapt its operating characteristics based on current operational requirements and power availability.
Solution Approach 2:
The system changes operational parameters such as sampling rate, processing complexity, and analysis depth based on detected events and power status. When battery power is abundant or critical events are detected, the system operates at high parameters; when power is constrained, it reduces parameters to extend battery life.
2Reliability
If the WMD operates in high-performance monitoring state continuously, then the ability to detect and respond to events is improved, but the battery life is reduced
Solution Approach 1:
The system employs periodic monitoring at different intensity levels. Instead of continuous high-performance operation, it alternates between periodic high-fidelity monitoring and low-fidelity monitoring, maintaining reliable event detection while extending battery life through duty cycling of the high-power operations.
Solution Approach 2:
The system autonomously manages its own power consumption by self-assessing battery status and automatically adjusting processing performance levels without external intervention. The processor monitors power levels and event detection needs to autonomously select appropriate operating modes.
3Use of energy by moving object
If the WMD uses simple arrhythmia detection analysis, then the power consumption is reduced, but the detection accuracy and false positive rate are worsened
Solution Approach 1:
The system applies partial analysis using simpler algorithms during low-power modes, accepting reduced accuracy, and applies comprehensive high-fidelity analysis only when necessary (e.g., when events are detected or battery power is abundant). This partial action approach balances power consumption with detection accuracy by applying full analysis only when needed.
Data Source
AI summary
A wearable cardioverter defibrillator (WCD) comprises a plurality of electrocardiography (ECG) electrodes and a plurality of defibrillator electrodes to contact the patient's skin when the WCD is delivering therapy to the patient, a preamplifier coupled to the ECG electrodes to obtain ECG data from the patient. a processor to receive the ECG data from the preamplifier, and a high voltage subsystem to provide a defibrillation voltage to the patient through the plurality of defibrillator electrodes in response to a shock signal received from the processor. In a first power mode of a range of power modes the preamplifier is configured to perform low-fidelity ECG acquisition and the processor is configured to perform simple arrythmia detection analysis, and in a second mode of the range of power modes the preamplifier is configured to perform high-fidelity ECG acquisition and the processor is configured to perform complex arrythmia detection analysis.