注意力深度學習方法應用於時間序列腦電波圖針對心跳停止後腦神經損傷的預後預測

Abstract

突發性心臟驟停（SCA）患者通常因缺氧時間過長而陷入昏迷，醫師需提供神經系統預後，協助臨床決策。本研究旨在利用早期腦電圖（EEG）數據訓練Transformer模型，預測SCA昏迷患者的神經系統預後。Transformer模型利用自注意力機制從長序列中學習模式。我們利用完整的小時級EEG序列，將其分割為5分鐘的時段，使模型能夠捕捉長距離的時間序列模式。通過將每個EEG序列視為訓練樣本，我們增加了數據樣本量，提高了模型學習特定記錄模式的能力。預測結果按患者進行了整合評估。專注於EEG數據的模型展現出了良好的預測性能，在保留測試集上的AUROC為0.82，AUPRC為0.90，在外部測試集上的AUROC為0.73，AUPRC為0.93。本研究凸顯了注意力機制在識別EEG序列中時間模式方面的潛力，提升了對SCA患者預後的能力。

Surviving sudden cardiac arrest (SCA) patients often remain in a coma due to a prolonged lack of oxygen, requiring physicians to provide prognoses on neurological outcomes to aid in clinical decisions. This study aims to predict neurological outcomes in SCA coma patients using early electroencephalogram (EEG) data to train a Transformer model, which leverages self-attention to learn patterns from lengthy sequences. We utilized full hours of EEG sequences, subdividing them into 5-minute epochs, allowing the model to capture long-distance time series patterns. By treating each individual EEG sequence as a training sample, we increased our data sample size and improved the model's ability to learn recording-specific patterns. Predictions were aggregated for patient-wise evaluation. Focusing exclusively on EEG data, our model demonstrated promising predictive performance, with an AUROC of 0.82 and an AUPRC of 0.90 on the holdout test set, and an AUROC of 0.73 and an AUPRC of 0.93 on an external test set. This study underscores the potential of attention mechanisms to discern temporal patterns in EEG sequences, enhancing SCA patient prognosis.