本文選題：深度信念網 + 心電節(jié)拍分類��； 參考：《計算機工程與設計》2017年05期

【摘要】：提出一種基于深度信念網(deep belief network,DBN)和心電波形采樣的心電自動分類算法。對心電信號進行濾波、R波定位后,以QRS波群的180Hz下采樣表示心拍形態(tài),結合RR間期特征,使用的DBN共6層,隱藏層神經元數(shù)目為30。使用標準數(shù)據(jù)庫對DBN進行訓練和測試,結果為平均Se88.6%,平均P~+62.1%,優(yōu)于現(xiàn)有特征選擇方法的結果,基于深度學習的心拍分類算法無需波形特征提取步驟,解決了目前的波形特征對心拍的人間差異沒有魯棒性的問題。
[Abstract]:An automatic ECG classification algorithm based on deep belief network (DBN) and ECG waveform sampling is proposed. After filtered R wave location of ECG signal, the beat morphology was represented by 180Hz downsampling of QRS wave group. Combined with RR interval characteristics, six layers of DBN were used, and the number of neurons in hidden layer was 30. The standard database is used to train and test the DBN. The results show that the average Se88.6 and P62.1 are better than the existing feature selection methods. The beat classification algorithm based on depth learning does not need waveform feature extraction step. It solves the problem that the current waveform features are not robust to the human differences.
【作者單位】： 中國科學院微電子研究所;中國科學院微電子研究所昆山分所;
【基金】：國家自然科學基金項目(61271423)
【分類號】：R540.4;TN911.7
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本文編號：1962958