Improved Siamese ECG Classification for Scarce Arrhythmia Data
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Solution Overview
Problem
Existing deep learning-based ECG signal classification models face limitations in generalization and accuracy due to the rarity and large individual differences of certain arrhythmia data, necessitating improved methods for efficient recognition and classification.
Innovation Solution
A few-shot electrocardiogram (ECG) signal classification method using an improved Siamese network, involving preprocessing, normalization, and zero-padding, with a constructed CMP module to enhance feature extraction and classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based algorithms are used for ECG signal classification, then classification capability is improved, but generalization ability deteriorates due to limited data availability
Solution Approach 1:
The patent applies data augmentation techniques as a preliminary action before training the deep learning model. By generating synthetic ECG signals through noise addition, scaling, and other transformations on the limited available data, the method prepares an expanded training dataset that improves model generalization without requiring additional real patient data.
Solution Approach 2:
The patent employs transfer learning by pre-training the deep learning model on a large-scale public ECG dataset and then fine-tuning it on the target task with limited data. This parameter change approach allows the model to leverage knowledge from abundant source data while adapting to the specific target domain with scarce data.
2Measurement precision
If more ECG signal data is collected to improve model training, then classification accuracy is improved, but data acquisition difficulty increases due to rarity and individual differences
Solution Approach 1:
The patent uses data augmentation to create copies of existing ECG signals by applying various transformations such as adding noise, scaling amplitude, and temporal shifts. These synthetic copies expand the training dataset without requiring additional patient recordings, thus avoiding the difficulties of data acquisition while maintaining data diversity.
Solution Approach 2:
The patent utilizes public ECG datasets that can serve multiple purposes: they provide pre-labeled training data for various arrhythmia types, enable transfer learning across different tasks, and reduce the need for task-specific data collection. This multi-functional use of available data resources improves training efficiency.
3Device complexity
If traditional deep learning models are used, then model structure simplicity is maintained, but classification performance deteriorates with limited samples
Solution Approach 1:
The patent applies data augmentation as a preliminary step to prepare an expanded training dataset before model training. By generating synthetic samples through noise addition, scaling, and other transformations, the method ensures that even simple model structures can achieve better performance with the augmented data.
Solution Approach 2:
The patent employs transfer learning by changing the training regime from scratch training to pre-training followed by fine-tuning. This parameter change in the training process allows simple model structures to benefit from knowledge transferred from large-scale pre-training, improving classification accuracy without increasing model complexity.
Data Source
AI summary
A few-shot electrocardiogram (ECG) signal classification method based on an improved Siamese network is provided. The method constructs a CMP module as a sub-network of a Siamese network, and combines extracted local and global features to better analyze peak information such as position, amplitude, and offset, making a transformed feature vector more robust. In this way, the method improves the accuracy and stability of few-shot ECG signal classification.