预测未来的心律不整的发病率使用离散的心跳从正常的鼻腔节律ECG信号通过深度学习方法
Yehyun Kim1, Myeonggyu Lee1, Jaeung Yoon1
1Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea.
Diagnostics (Basel, Switzerland)
|September 9, 2023
概括
来自心电图 (ECG) 信号的离散心跳有效预测未来的心律失常和心房动. 深度学习模型识别这些心跳中的微妙模式,以便在没有额外的患者数据的情况下及时诊断.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学信号处理
背景情况:
- 心律失常和心房动对健康构成重大风险.
- 早期发现这些疾病对于有效管理至关重要.
- 心电图 (ECG) 信号是主要的诊断工具.
研究的目的:
- 为了比较离散心跳的预测能力与未来心律不整和心房动的整个12导电图信号.
- 评估深度学习模型在确定ECG数据中的预测模式方面的有效性.
- 为了确定单独的心跳是否足以准确预测.
主要方法:
- 利用了来自134,447名患者的326,904个心电图信号,分为真正常鼻节律 (T-NSR),心房动-正常鼻节律 (AF-NSR) 和临床重要心律失常-正常鼻节律 (CIA-NSR) 组.
- 使用深度学习模型 (ResNet-18,LSTM,Transformer) 来区分T-NSR和AF-NSR/CIA-NSR组.
- 使用离散心跳的预测准确度与整个10秒12导电图信号作为输入进行了比较.
主要成果:
- 从12导心电图信号中提取的离散心跳显示出在预测未来心律失常和心房动方面具有显著的潜力.
- 深度学习模型成功地在离散的心跳中识别了微妙的预测模式.
- 仅使用离散的心跳,而没有额外的患者信息,可以实现准确的预测.
结论:
- 离散的心跳包含足够的信息,以基于深度学习的预测未来的心律失常和心房动.
- 专注于离散的心跳可以导致更及时,更准确的诊断.
- 这种方法支持使用心电图信号作为生物标志物的自动诊断,可能改善患者的治疗结果.
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An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
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