Unsupervised Feature Selection-Driven Active Learning for Semi-Supervised Automatic ECG Analysis

Automatic analysis methods of electrocardiograms (ECGs) usually required large-scale annotated training data, but the annotation process is extremely time-consuming. While semi-supervised learning can leverage unlabeled data, its performance depends heavily on the quality of the initial labeled subset. Active learning has been used to identify the most informative samples for annotation, but conventional approaches face three critical limitations: (1) dependency on manual intervention for iterative query design, (2) prohibitive computational costs during sample selection, and (3) limited compatibility with semi-supervised learning frameworks. To address these limitations, we proposed an Unsupervised Active Feature-selective Semi-Supervised Learning (UAFSSL) framework for ECG analysis, including an unsupervised feature selection-based active learning module and a semi-supervised learning module. UAFSSL captures latent data distributions via unsupervised feature extraction, selects diverse and representative samples using pseudo-label clustering, and integrates seamlessly with semi-supervised learning to eliminate human intervention. We validated our algorithm on an ECG waveform segmentation task and an atrial fibrillation detection task. In the waveform segmentation task, our method improved the F1-score for P-wave delineation by 2. 4% compared to random sampling, using only 5% of labeled samples. For the atrial fibrillation detection task, we evaluated our method on both the AFDB and a 24-hour dataset collected from 500 atrial fibrillation patients. Using only 200 labeled samples for model training, our method achieved AUC improvements of 2. 5% and 2. 2% over random sampling in five-fold cross validation. This is the first study to integrate unsupervised active learning with semi-supervised learning for automatic ECG analysis, offering a robust, automated solution to reduce annotation costs while enhancing clinical applicability.

Authors

• Xiao Li Nanjing University
• Yongkang Zhou Shanghai Jiao Tong University Shanghai Innovation Institute
• Songyang An
• Yu Zeng University of Science and Technology of China
• Xinqi Zhang
• Jun Wang
• Yizhe Huang
• Fan Lin
• Peng Zhang

Keywords

• Electrocardiography
• Active learning
• Annotations
• Semisupervised learning
• Feature extraction
• Training
• Atrial fibrillation
• Manuals
• Data models
• Iterative methods
• Automatic Analyzer
• Electrocardiogram Analysis
• Waveform
• F1 Score
• Unsupervised Learning
• Segmentation Task
• Unlabeled Data
• Semi-supervised Learning
• Unsupervised Feature
• Annotated Samples
• Atrial Fibrillation Detection
• Semi-supervised Learning Framework
• Model Performance
• Training Set
• Convolutional Layers
• Classification Task
• Gaussian Noise
• Random Selection
• Student Model
• Active Learning Methods
• Labeled Data Set
• Active Learning Strategies
• Teacher Model
• Consistency Regularization
• Electrocardiogram Signals
• Private Dataset
• Dataset Characteristics
• Electrocardiogram
• semi-supervised
• ECG delineation