ECG Heartbeat Feature Fusion for Accurate Arrhythmia Classification
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Solution Overview
Problem
Existing methods for electrocardiogram data analysis are inefficient in accurately and automatically detecting and classifying heart diseases due to the complexity of heart disease manifestations in heartbeat patterns over time.
Innovation Solution
A method involving convolution and pooling of heartbeat data to generate a first feature vector, combining it with frequency and time domain feature vectors to create a fusion feature vector, which is then used to obtain classification information using machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrocardiogram data analysis methods are used, then the analysis process is simple, but the accuracy and reliability of heart disease detection is insufficient
Solution Approach 1:
The patent segments electrocardiogram data into multiple heartbeat cycles and extracts features from each segment. The analysis is divided into time-domain feature extraction, frequency-domain feature extraction, and spatial feature extraction, allowing comprehensive analysis while maintaining manageable complexity through systematic decomposition of the detection process
Solution Approach 2:
The patent transforms electrocardiogram analysis from traditional single-dimensional time-domain analysis to multi-dimensional analysis by incorporating frequency domain (spectral analysis), time domain (temporal patterns), and spatial domain (lead relationships) features. This dimensional expansion enables more accurate detection of heart conditions that cannot be identified through conventional methods
2Measurement precision
If multi-dimensional feature extraction is implemented, then the classification accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary feature extraction and dimensionality reduction before final classification. By pre-processing the electrocardiogram data to extract essential time-domain, frequency-domain, and spatial features in advance, the system reduces the computational burden during real-time classification while maintaining high accuracy
Solution Approach 2:
The patent merges multiple feature extraction methods (time-domain, frequency-domain, spatial) into a unified feature vector that is fed into a single classification model. This consolidation allows the system to leverage complementary information from different domains while avoiding the need for multiple separate computational pipelines
Data Source
AI summary
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.