A method and system for AED defibrillation rhythm classification and decision-making based on RF-SVM classification model
By combining the RF-SVM classification model and the random forest RF algorithm, the problem of long time consumption and low sensitivity in existing AED defibrillation rhythm classification is solved, and fast and high-precision rhythm recognition and classification are achieved.
CN116186584BActive Publication Date: 2026-04-03XINHUO MEDICAL EQUIP (CHONGQING) CO LTD
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Patent Information
- Application Number
- CN202310174994.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-02-28
- Publication Date
- 2026-04-03
- Estimated Expiration
- 2043-02-28
AI Technical Summary
Technical Problem
Existing AED defibrillation rhythm classification methods are time-consuming and have low sensitivity, making it difficult to achieve fast and accurate rhythm identification and classification.
Method used
A method based on the RF-SVM classification model is adopted. The random forest (RF) algorithm is used to quickly optimize the input feature variables of the SVM classifier. The Butterworth filter is used for preprocessing, and the RF-SVM classification model is used for training and optimization to improve the classification speed and accuracy.
Benefits of technology
It achieves fast and high-precision defibrillation rhythm classification, improving decision time and classification specificity.
✦ Generated by Eureka AI based on patent content.
Abstract
This invention discloses an AED defibrillation rhythm classification decision method and system based on an RF-SVM classification model, comprising the following steps: S1: Acquire ECG data to be processed and sort the ECG data into N waveform types; S2: Divide the N waveform types into defibrillable waveforms and non-defibrillable waveforms; S3: Preprocess the defibrillable and non-defibrillable waveforms respectively to obtain data to be extracted, and then extract features from the data to be extracted; S4: Input the features extracted in S3 into the constructed RF-SVM classification model for training, and complete the optimization of the RF-SVM classification model; S5: Use the optimized RF-SVM classification model to build a classification inference function and output the ECG rhythm classification decision.
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