ECG Shock Detection Using Impedance Spectrograms During CPR
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Solution Overview
Problem
Existing electrocardiogram analysis systems for determining the need for an electric shock during cardiopulmonary resuscitation (CPR) suffer from diagnostic inaccuracies and require interruptions in chest compressions, leading to decreased patient survival rates due to noise contamination from compressions.
Innovation Solution
An electrocardiogram analysis system that utilizes a convolutional neural network (CNN) to process body surface ECG and transthoracic impedance signals, transforming them into spectrograms for real-time shock determination by self-learning from sample data, reducing noise interference and improving accuracy to 99.5% or higher.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ECG diagnosis is performed by visual checking or automatic diagnosis during CPR, then diagnostic accuracy can be achieved, but chest compressions must be interrupted causing noise contamination and decreased survival rate
Solution Approach 1:
The patent introduces impedance signal as an intermediary to assess chest compression status. By monitoring changes in transthoracic impedance, the system can detect whether chest compressions are occurring without requiring ECG quality assessment interruptions. This mediator allows continuous monitoring while maintaining compression continuity.
Solution Approach 2:
The patent replaces the mechanical interruption-based diagnosis method with a digital signal processing approach. Instead of physically stopping compressions to get a clean ECG, the system uses digital filtering and impedance monitoring to continuously analyze ECG signals during compressions, substituting mechanical interruption with electronic signal processing.
2Measurement precision
If noise filtering is applied to ECG signals during chest compressions, then diagnostic accuracy can be improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the ECG signal analysis into multiple components: impedance signal processing, ECG signal processing, and their combination. By dividing the complex task of accurate diagnosis during compressions into separate manageable segments, each can be optimized independently, reducing overall computational burden while maintaining accuracy.
Solution Approach 2:
The patent changes the parameter space from raw ECG signals to impedance signals and spectrograms. By transforming the data into different parameter representations (time-domain to frequency-domain via spectrograms), the system can identify diagnostic features more efficiently, reducing computational complexity while improving accuracy.
3Ease of operation
If traditional ECG analysis methods are used during CPR, then diagnostic capability is maintained, but survival rate decreases due to compression interruptions and noise
Solution Approach 1:
The patent enables continuous diagnostic action during CPR by simultaneously monitoring both ECG and impedance signals. The system continuously determines shockability without requiring interruptions to chest compressions, maintaining the continuity of life-saving compressions while providing ongoing diagnostic capability through parallel signal processing.
Solution Approach 2:
The patent creates a composite diagnostic approach by combining ECG signal analysis with impedance signal analysis. This composite method leverages the strengths of both signals: ECG provides cardiac electrical information while impedance provides mechanical compression status information, together achieving both diagnostic capability and continuous operation during CPR.
Data Source
AI summary
To provide an electrocardiogram analysis system capable of determining the need for an electric shock to a patient undergoing cardiopulmonary resuscitation (CPR) with a higher accuracy. An electrocardiogram analysis system includes electrocardiogram (ECG) signal acquiring means 11, ECG signal sampling means 12, ECG spectrogram transforming means 13, impedance signal acquiring means 21, impedance signal sampling means 22, impedance spectrogram transforming means 23, a convolutional neural network (CNN) 4 including an input layer 4I, an output layer 4O, sample data accumulation means 4L, and sample data input means 4T, and electric shock indication reporting means 5. The CNN is a priori provided with sample data including sample ECG spectrograms and sample impedance spectrograms obtained from a large number of subjects, and sample response data on the need for an electric shock, and is optimized by self-learning the sample data.