Scholarlink

Background <p>Life-threatening arrhythmias (LTAs) are a leading cause of death worldwide. Enhancing LTA detection in wearable monitoring systems is of great importance. One of the main challenges in building robust LTA detection algorithms is the limited availability of labeled LTA data.</p> Methods <p>We introduce an effective deep-learning algorithm for detecting LTAs from single-lead ECGs in out-of-hospital cardiac arrest applications. We address the data-scarcity issue by applying a transfer learning approach. The deep-learning model is pre-trained on a massive dataset (72’952 recordings) for rhythm classification and then fine-tuned on the target dataset with LTA events (102 recordings).</p> Results <p>Our model achieves a sensitivity of 92.68% and a specificity of 99.48%, with a granularity of 1.28 seconds, in detecting LTAs. Additionally, a confidence estimation procedure is introduced to enable emergency service pre-alerts in case of low-confidence detections.</p> Conclusions <p>Our transfer learning based approach has the potential to significantly mitigate the impact of data scarcity, advancing LTA detection in wearable monitoring systems, and supporting rapid, life-saving interventions in out-of-hospital cardiac arrest emergencies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

查看全文
  • Giuliana Monachino,
  • Beatrice Zanchi,
  • Michael Wand,
  • Giulio Conte,
  • Athina Tzovara,
  • Francesca Dalia Faraci

摘要

Background

Life-threatening arrhythmias (LTAs) are a leading cause of death worldwide. Enhancing LTA detection in wearable monitoring systems is of great importance. One of the main challenges in building robust LTA detection algorithms is the limited availability of labeled LTA data.

Methods

We introduce an effective deep-learning algorithm for detecting LTAs from single-lead ECGs in out-of-hospital cardiac arrest applications. We address the data-scarcity issue by applying a transfer learning approach. The deep-learning model is pre-trained on a massive dataset (72’952 recordings) for rhythm classification and then fine-tuned on the target dataset with LTA events (102 recordings).

Results

Our model achieves a sensitivity of 92.68% and a specificity of 99.48%, with a granularity of 1.28 seconds, in detecting LTAs. Additionally, a confidence estimation procedure is introduced to enable emergency service pre-alerts in case of low-confidence detections.

Conclusions

Our transfer learning based approach has the potential to significantly mitigate the impact of data scarcity, advancing LTA detection in wearable monitoring systems, and supporting rapid, life-saving interventions in out-of-hospital cardiac arrest emergencies.