17621665. ELECTROCARDIOGRAM DATA CLASSIFICATION METHOD, COMPUTER DEVICE, AND STORAGE MEDIUM simplified abstract (BOE TECHNOLOGY GROUP CO., LTD.)
ELECTROCARDIOGRAM DATA CLASSIFICATION METHOD, COMPUTER DEVICE, AND STORAGE MEDIUM
Organization Name
BOE TECHNOLOGY GROUP CO., LTD.
Inventor(s)
ELECTROCARDIOGRAM DATA CLASSIFICATION METHOD, COMPUTER DEVICE, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 17621665 titled 'ELECTROCARDIOGRAM DATA CLASSIFICATION METHOD, COMPUTER DEVICE, AND STORAGE MEDIUM
Simplified Explanation
The abstract describes a method for classifying electrocardiogram (ECG) data using a combination of time and frequency domain features. Here is a simplified explanation of the abstract:
- The method starts by obtaining multiple pieces of heartbeat data from the ECG data.
- These pieces of data are then processed through a convolution and pooling process to extract a first feature vector.
- Additionally, a second feature vector is obtained by analyzing the frequency and time domain characteristics of the heartbeat data.
- The first and second feature vectors are fused together to generate a fusion feature vector.
- Finally, the classification information of the ECG data is obtained based on the fused feature vector.
Potential Applications:
- Medical Diagnosis: This method can be used in the field of cardiology for the classification and diagnosis of various heart conditions based on ECG data.
- Remote Monitoring: The technology can be applied to remote monitoring systems, allowing healthcare professionals to analyze ECG data remotely and provide timely feedback to patients.
- Wearable Devices: The method can be integrated into wearable ECG devices, enabling real-time monitoring and early detection of heart abnormalities.
Problems Solved:
- Accurate Classification: The method addresses the challenge of accurately classifying ECG data by combining both time and frequency domain features, which can provide a more comprehensive understanding of the heart's electrical activity.
- Efficient Analysis: By using convolution and pooling techniques, the method simplifies the analysis process and reduces computational complexity, allowing for faster and more efficient classification of ECG data.
Benefits:
- Improved Accuracy: The fusion of time and frequency domain features enhances the accuracy of ECG data classification, leading to more reliable diagnoses.
- Real-time Monitoring: The method enables real-time monitoring of ECG data, allowing for immediate detection of abnormalities and timely intervention.
- Cost-effective: By utilizing existing ECG data and processing techniques, the method offers a cost-effective solution for ECG classification without the need for additional hardware or complex algorithms.
Original Abstract Submitted
An electrocardiogram data classification method includes: obtaining a plurality of pieces of heartbeat data according to electrocardiogram data; convolving and pooling the plurality of pieces of heartbeat data to obtain a first feature vector; obtaining a second feature vector, the second feature vector representing frequency domain feature data of the plurality of pieces of heartbeat data and time domain feature data of the plurality of pieces of heartbeat data; fusing the first feature vector and the second feature vector to generate a fusion feature vector; and obtaining classification information of the electrocardiogram data according to the fused feature vector.
