中文标题#
心電圖足夠嗎？ 使用心電圖進行肺栓塞的深度學習分類
英文標題#
Are ECGs enough? Deep learning classification of pulmonary embolism using electrocardiograms
中文摘要#
肺栓塞是院外心臟驟停的主要原因，需要快速診斷。 雖然計算機斷層掃描肺動脈造影是標準的診斷工具，但並非在所有地方都能獲得。 心電圖是診斷多種心臟異常的重要工具，因為它成本低、速度快，並且在許多環境中都可用。 然而，公共心電圖數據集的可用性，特別是針對肺栓塞的，是有限的，在實踐中這些數據集往往較小，這使得優化學習策略變得至關重要。 在本研究中，我們研究了多個神經網絡的性能，以評估各種方法的影響。 此外，我們檢查了當使用遷移學習將從較大的心電圖數據集（如 PTB-XL、CPSC18 和 MedalCare-XL）中學到的信息轉移到一個更小、更具挑戰性的肺栓塞數據集時，這些實踐是否能增強模型的泛化能力。 通過利用遷移學習，我們分析了在有限數據上可以多大程度地提高學習效率和預測性能。 代碼可在 https://github.com/joaodsmarques/Are-ECGs-enough-Deep-Learning-Classifiers 獲取。
英文摘要#
Pulmonary embolism is a leading cause of out of hospital cardiac arrest that requires fast diagnosis. While computed tomography pulmonary angiography is the standard diagnostic tool, it is not always accessible. Electrocardiography is an essential tool for diagnosing multiple cardiac anomalies, as it is affordable, fast and available in many settings. However, the availability of public ECG datasets, specially for PE, is limited and, in practice, these datasets tend to be small, making it essential to optimize learning strategies. In this study, we investigate the performance of multiple neural networks in order to assess the impact of various approaches. Moreover, we check whether these practices enhance model generalization when transfer learning is used to translate information learned in larger ECG datasets, such as PTB-XL, CPSC18 and MedalCare-XL, to a smaller, more challenging dataset for PE. By leveraging transfer learning, we analyze the extent to which we can improve learning efficiency and predictive performance on limited data. Code available at https://github.com/joaodsmarques/Are-ECGs-enough-Deep-Learning-Classifiers .
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