Generalization challenges in electrocardiogram deep learning: insights from dataset characteristics and attention mechanism

Abstract

Aim: Deep learning’s widespread use prompts heightened scrutiny, particularly in the biomedical fields, with a specific focus on model generalizability. This study delves into the influence of training data characteristics on the generalization performance of models, specifically in cardiac abnormality detection. Materials & methods: Leveraging diverse electrocardiogram datasets, models are trained on subsets with varying characteristics and subsequently compared for performance. Additionally, the introduction of the attention mechanism aims to improve generalizability. Results: Experiments reveal that using a balanced dataset, just 1% of a large dataset, leads to equal performance in generalization tasks, notably in detecting cardiology abnormalities. Conclusion: This balanced training data notably enhances model generalizability, while the integration of the attention mechanism further refines the model’s ability to generalize effectively.

Plain language summary

This study tackles a common problem for deep learning models: they often struggle when faced with new, unfamiliar data that they have not been trained on. This phenomenon is also known as performance drop in out-of-distribution generalization. This reduced performance on out-of-distribution generalization is a key focus of the research, aiming to improve the models’ ability to handle diverse data sets beyond their training data.

The study examines how the characteristics of the dataset used to train deep learning models affect their ability to detect abnormal heart activities when applied to new, unseen data. Researchers trained these models using various sets of electrocardiogram (ECG) data and then evaluated their performance in identifying abnormalities. They also introduced an attention mechanism to enhance the models’ learning capabilities. The attention mechanism in deep learning is like a spotlight that helps the model focus on important information while ignoring less relevant details.

The findings were particularly noteworthy. Despite being trained on a small, well-balanced subset of a larger dataset, the models excelled in detecting heart abnormalities in new, unfamiliar data. This training method significantly improved the models’ generalization and performance with unseen data. Furthermore, integrating the attention mechanism substantially enhanced the models’ ability to generalize effectively on new information.

Article highlights

Objective

• Investigate the impact of training data characteristics and attention mechanism on deep learning model generalizability in cardiac abnormality detection.

Findings

• Balanced dataset (1% of the total) improves model performance in generalization tasks, especially in detecting cardiology abnormalities.

• The attention mechanism further enhances the model’s capacity to comprehend and utilize out-of-distribution data effectively.

Methodology

• Utilized multiple electrocardiogram datasets for the study.

• Trained models on subsets with varying characteristics and evaluated performance.

• Added attention mechanism to enhance learning capabilities.

Implications

• Balanced training data significantly enhances model generalizability.

• Attention mechanism improves the model’s ability to generalize on out-of-distribution data.

Limitations & future research

• Lack of clinical user information in datasets due to privacy and ethical considerations.

• Future research may consider patient-specific models for improved generalization in biomedical machine learning.

Conclusion

• Balanced and curated datasets are crucial for training high-performing models in cardiac abnormality detection using deep learning.

• Attention mechanisms show promise in enhancing model accuracy and generalization.

Keywords: :

• attention mechanism
• cardiac abnormality detection
• dataset characteristics
• deep learning
• electrocardiogram
• generalization

Acknowledgments

Z Huang would like to acknowledge the support of the Research Training Program provided by the Australian Government.

Author contributions

Z Huang: writing (lead), conceptualization (equal), data curation (equal), formal analysis (equal), investigation (equal), methodology (equal), software (equal), validation (equal), visualization (lead); S MacLachlan: conceptualization (equal), data curation (equal), formal analysis (equal), investigation (equal), methodology (equal), software (equal), validation (equal); L Yu: writing (support); LFH Contreras: visualization (support); ND Truong: supervision (support); AH Ribeiro: supervision (support); O Kavehei: supervision (lead).

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Code availability

You can access the attention model code through this link: https://github.com/NeuroSyd/ECG-Attention. Keep in mind that certain terms, conditions, or usage restrictions may apply to the code.

Data availability statement

This paper employs publicly accessible datasets such as CPSC and SNH. The TNMG dataset is not publicly available, but access can be granted upon request via the data custodian’s approval.