【摘要】：針對心電信號異常診斷,提出了一種基于無監(jiān)督學習的移動心電信號異常診斷方法。該方法利用層次聚類將心電數(shù)據(jù)進行分類,同時結(jié)合特征量的優(yōu)先級診斷分析法,有效避免了因移動心電信號的數(shù)據(jù)量過大而產(chǎn)生爆炸的時間復雜度和空間復雜度的問題。最后,通過心電信號實例驗證了所提方法具有良好的可靠性和運行效率。
[Abstract]:Aiming at the abnormal diagnosis of ECG signal, an abnormal diagnosis method of mobile ECG signal based on unsupervised learning is proposed. In this method, ECG data are classified by hierarchical clustering, and the problem of explosion time complexity and space complexity caused by excessive amount of data of moving ECG signal is effectively avoided by combining the priority diagnosis analysis of feature quantity. Finally, an example of ECG signal shows that the proposed method has good reliability and operation efficiency.
【作者單位】： 東華大學計算機科學與技術學院;
【基金】：上海市自然基金(16ZR1401100)資助
【分類號】：R540.4;TP311.13
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本文編號：2501182