本文選題：卷積神經(jīng)網(wǎng)絡(luò) + 多特征融合�。� 參考：《激光雜志》2017年05期

【摘要】：為了解決傳統(tǒng)的房顫檢測(cè)算法中P波形態(tài)多變而不易提取特征的問題,本文提出了一種基于多特征融合與卷積神經(jīng)網(wǎng)絡(luò)結(jié)合的房顫檢測(cè)算法。首先,分別提取單心拍心房活動(dòng)信號(hào)遞歸矩陣的特征值及相鄰兩個(gè)心拍的心房活動(dòng)信號(hào)的相干譜來得到底層特征;然后,分別采用卷積神經(jīng)網(wǎng)絡(luò)對(duì)底層特征進(jìn)行分析;最后,采用決策級(jí)融合來改善算法的性能。經(jīng)MIT-BIH房顫數(shù)據(jù)庫驗(yàn)證,該算法的正確率,靈敏度,特異性分別可達(dá)95.62%,99.88%,91.36%。結(jié)果表明,該方法能有效解決特征提取困難,泛化能力差的問題。
[Abstract]:In order to solve the problem that P-wave shape is changeable and difficult to extract features in traditional atrial fibrillation detection algorithm, a novel detection algorithm based on multi-feature fusion and convolution neural network is proposed in this paper.Firstly, the eigenvalues of the recurrent matrix of atrial activity signals and the coherent spectrum of the two adjacent atrial activity signals are extracted to obtain the underlying features. Then, convolution neural networks are used to analyze the underlying features.Decision level fusion is used to improve the performance of the algorithm.The accuracy, sensitivity and specificity of the algorithm are 95.62% and 99.88%, respectively. The accuracy, sensitivity and specificity of the algorithm are 99.88% and 91.36%, respectively, verified by the MIT-BIH atrial fibrillation database.The results show that this method can effectively solve the problem of difficult feature extraction and poor generalization ability.
【作者單位】： 河北大學(xué);
【基金】：國家自然科學(xué)基金項(xiàng)目(61673158) 河北省自然科學(xué)基金項(xiàng)目(F2015201112) 河北省高等學(xué)校科學(xué)研究重點(diǎn)項(xiàng)目(ZD2015067)
【分類號(hào)】：R541.75;TP183
，

本文編號(hào)：1769995