Smart ECG Patch Encoding for Arrhythmia Detection
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Solution Overview
Problem
Existing wearable ECG sensors face challenges in real-time arrhythmia detection due to high power consumption and the need for extensive data transmission, which shortens battery life and limits their use in continuous, long-term monitoring.
Innovation Solution
The Smart ECG Patch (SEP) encodes ECG signals into 14 integer parameters per cycle, using a deep learning system called ACES for real-time arrhythmia classification, reducing data transmission and power consumption by focusing on morphological changes in the QRS complex and instantaneous heart rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ECG signals are transmitted in real-time for arrhythmia detection, then detection accuracy is improved, but power consumption increases and battery life decreases
Solution Approach 1:
The patent extracts only the essential features from complete ECG signals for transmission. Specifically, it identifies and transmits only P-wave, QRS complex, and T-wave components that are critical for arrhythmia detection, while discarding redundant signal portions. This extraction approach maintains detection accuracy by preserving diagnostically important information while significantly reducing data transmission volume and associated power consumption.
Solution Approach 2:
The patent applies local quality by differentiating transmission strategies for different ECG signal components. Critical features like QRS complex morphology and R-R interval variations are transmitted with high fidelity for accurate arrhythmia detection, while less critical portions are either compressed or omitted. This selective quality approach ensures diagnostic accuracy where needed while reducing overall power consumption.
2Reliability
If complete ECG signals are transmitted for analysis, then arrhythmia detection reliability is improved, but data transmission requirements increase
Solution Approach 1:
The system extracts and transmits only the diagnostically essential ECG features including P-wave presence and morphology, QRS complex characteristics, and T-wave features. By identifying and transmitting only these critical components rather than complete ECG waveforms, the system maintains arrhythmia detection reliability while reducing data transmission volume by a significant margin.
Solution Approach 2:
The ECG signal is segmented into functionally distinct components (P-wave, QRS complex, T-wave) with only the necessary segments transmitted for arrhythmia detection. This segmentation allows the system to send discrete feature parameters rather than continuous waveform data, thereby maintaining detection reliability while minimizing data quantity.
3Reliability
If continuous monitoring is implemented for real-time arrhythmia detection, then patient safety is improved, but battery life is reduced
Solution Approach 1:
The system continuously monitors ECG signals by extracting and transmitting only essential arrhythmia-related features in real-time. This continuous extraction and transmission of critical parameters enables ongoing patient safety monitoring while consuming significantly less power than continuous transmission of complete ECG waveforms, thereby extending battery life.
Solution Approach 2:
The system implements periodic transmission of extracted ECG features at optimized intervals rather than continuous transmission. By determining when arrhythmia events occur and transmitting feature data periodically at these events, the system maintains reliable continuous monitoring capability while reducing average power consumption to extend battery operation.
Data Source
AI summary
An electrocardiogram sensing system, comprising: an input port configured to receive an electrocardiogramal; at least one automated processor, configured to: process a representation of the electrocardiographic signal to determine an electrocardiographic waveform for a single heartbeat; and encode a set of quantitative parameters from the electrocardiographic waveform, dependent on geometric relationships, e.g., amplitude, width and relative spacing of components of the electrocardiographic waveform; and a wireless communication device, configured to communicate the encoded set of quantitative parameters.