ECG Waveform Peak Distances for Non-Invasive Blood Glucose Estimation
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Solution Overview
Problem
Conventional blood glucose measurement methods are invasive, causing discomfort and are not suitable for continuous monitoring, and existing non-invasive methods using ECG signals lack accuracy and stability in estimating blood glucose values.
Innovation Solution
A method for non-invasively estimating blood glucose using a computing device that processes electrocardiogram (ECG) waveforms by extracting features such as P-waves, Q-waves, R-waves, S-waves, and T-waves, calculating peak distances, and employing machine learning models to estimate blood glucose values based on these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional invasive blood glucose measurement methods are used, then measurement precision is improved, but user comfort and ease of operation deteriorate due to physical pain and discomfort
Solution Approach 1:
The patent replaces the mechanical needle insertion system with an electrical signal-based measurement system. Instead of using a physical needle to extract blood for glucose measurement, the system uses electrodes to capture ECG signals and processes these electrical signals to estimate blood glucose levels, thereby eliminating physical pain and discomfort while maintaining measurement capability
Solution Approach 2:
The patent introduces ECG signals as an intermediary medium to indirectly measure blood glucose levels. Rather than directly measuring glucose in the blood through needle insertion, the system uses ECG signals (which are influenced by blood glucose levels) as a mediator to estimate glucose concentration, thus avoiding direct blood contact and physical discomfort
2Ease of operation
If conventional non-invasive ECG-based blood glucose estimation is used, then ease of operation is improved, but measurement precision and reliability deteriorate due to insufficient signal quality and inability to achieve continuous monitoring
Solution Approach 1:
The patent implements continuous ECG signal acquisition and processing to enable ongoing blood glucose estimation. The system continuously captures ECG signals, extracts features in real-time, and updates glucose estimates without interruption, thereby achieving continuous monitoring capability that was lacking in previous non-invasive methods
Solution Approach 2:
The patent incorporates feedback mechanisms where the extracted ECG features (such as QT interval, ST interval, PR interval) are continuously analyzed and used to adjust and refine the blood glucose estimation. The system uses statistical analysis and machine learning models that learn from the relationship between ECG waveform characteristics and blood glucose levels, improving estimation accuracy through iterative feedback
3Ease of operation
If ECG signal-based blood glucose estimation is used, then ease of operation is improved, but measurement precision deteriorates due to extremely high requirements on signal quality and difficulty in accurately detecting wave features
Solution Approach 1:
The patent performs preliminary processing of ECG signals including filtering, baseline correction, and waveform identification before feature extraction. By preprocessing the signals to enhance quality and reduce noise, the system lowers the threshold for acceptable signal quality while maintaining the ability to accurately detect P-waves, Q-waves, R-waves, S-waves, and T-waves
Solution Approach 2:
The patent transforms the raw ECG signals into derived parameters such as QT interval, ST interval, PR interval, and other morphological features. By changing from direct signal analysis to parameter-based analysis, the system reduces sensitivity to signal quality variations and makes the measurement more robust to noise and artifacts
4Reliability
If morphological feature extraction from ECG waveforms is used, then reliability is improved compared to direct ECG signal usage, but measurement precision deteriorates because existing methods only show correlation relationships rather than enabling actual blood glucose value estimation
Solution Approach 1:
The patent uses morphological features of ECG waveforms (QT interval, ST interval, PR interval) as intermediary parameters that mediate between the raw ECG signals and the blood glucose estimation. These features serve as stable intermediaries that capture the relationship between cardiac electrical activity and blood glucose levels, enabling actual quantitative estimation rather than just correlation analysis
Solution Approach 2:
The patent replaces the insufficient direct ECG signal analysis method with an enhanced feature-based estimation system. By substituting direct signal usage with carefully selected morphological features and applying statistical analysis and machine learning models, the system achieves both reliability through stable feature extraction and precision through accurate blood glucose value estimation
Data Source
AI summary
A method for non-invasively estimating blood glucose for estimating a blood glucose value of a user by a computing device. The method includes receiving a plurality of electrocardiogram (ECG) waveforms of the user, extracting at least two first ECG features from each of the plurality of ECG waveforms of the user, respectively determining a first feature peak position corresponding to each of the first ECG features, calculating at least one peak distance between the plurality of the first feature peak positions, and estimating the blood glucose value of the user based on the peak distance. The first ECG features are selected from the group consisting of a P-wave, a Q-wave, an R-wave, an S-wave, a T-wave, and a U-wave. Furthermore, a computing device for non-invasively estimating blood glucose, a device for non-invasively measuring ECG signal, and a non-transitory computer readable storage medium are utilized for the method.