結論 - 基於深度學習之心律不整輔助診斷系統

5.1 結論

本研究參考Serkan Kiranyaz 所提出的一維卷積神經網路分類方法，建構一個包含三層卷積隱藏層，兩層全連接隱藏層之分類器，能良好的對

本研究再次證實了卷積神經網路分類器良好的分類能力，其不僅在圖像辨識領域有良好效能，應用於心電圖資料亦非常有效。再搭配本研究提出之離群資料處理技術更可達到99.41%的正確率，高於過去文獻所提出之方法，

且降低漏診率至1.23%。成功建構出一穩定、高效之心律不整輔助診斷系統。

5.2 未來展望

本研究目前還是只專注於對心電圖進行分類，尚未建構成完整的心律不整輔助診斷系統，未來希望透過結合居家型心電圖量測儀、心電圖特徵描繪系統以及整體流程的建立，讓系統達到實際應用所需的完整度，並實際投入，

同時希望透過醫療人員實際使用後的回饋進行系統的修正，使系統更加切合實際需求，最終造福心臟病患者及醫療人員。

參考文獻

[1] 中華民國衛生福利部, "104 年死因統計年報," 2015.

[2] S. Kiranyaz, T. Ince, and M. Gabbouj, "Real-time patient-specific ECG classification by 1-D convolutional neural networks," IEEE Transactions on Biomedical Engineering, vol. 63, no. 3, pp. 664-675, 2016.

[3] 張家熏, "基於 K-means 演算法、小波轉換及支持向量機之心電訊號辨識系統," 碩士, 機電科技研究所, 國立臺灣師範大學, 台北市, 2011.

[4] M. Abadi et al., "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," arXiv preprint arXiv:1603.04467, 2016.

[5] P. Davey, 圖解心電圖快速導讀 (ECG at a Glance). 合計圖書出版社, 2011.

[6] MIT-BIH Arrhythmia Database. Available:

https://physionet.org/physiobank/database/mitdb/#additional-references

[7] J. Malmivuo and R. Plonsey, Bioelectromagnetism: principles and

applications of bioelectric and biomagnetic fields. Oxford University Press, USA, 1995.

[8] 賀立婷. (2016). 心律不整的臨床治療-你的心臟沒電了嗎? Available:

http://www.ntuh.gov.tw/cvc/knowledge/%E4%BD%A0%E5%BF%83%E 8%87%9F%E6%B2%92%E9%9B%BB%E4%BA%86%E5%97%8E.aspx

[9] M. Lewell and M. Davis, "12 Lead ECG," 2011.

[10] R. MacLeod and B. Birchler, "ECG measurement and analysis," ed: March, 2005.

[11] 林昆宏, "針對用於遠距醫療的量測裝置之心電圖特徵描繪演算法開發," 碩士, 機電工程學系, 國立臺灣師範大學, 台北市, 2016.

[12] G. B. Moody and R. G. Mark, "The impact of the MIT-BIH arrhythmia database," IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45-50, 2001.

[13] R. Y. Rubinstein, A. Ridder, and R. Vaisman, "Cross‐Entropy Method," Fast Sequential Monte Carlo Methods for Counting and Optimization, pp. 6-36.

[14] D. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.

[15] J. A. Hartigan and M. A. Wong, "Algorithm AS 136: A k-means clustering algorithm," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, no. 1, pp. 100-108, 1979.

[16] D. MacKay, "An example inference task: clustering," Information theory, inference and learning algorithms, vol. 20, pp. 284-292, 2003.

[17] G. Hamerly and C. Elkan, "Alternatives to the k-means algorithm that find better clusterings," in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 600-607: ACM.

[18] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no.

7553, pp. 436-444, 2015.

[19] W. S. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," The bulletin of mathematical biophysics, vol. 5, no. 4, pp.

115-133, 1943.

[20] P. J. Werbos, "Beyond regression: new tools for prediction and analysis in the behavioral science," Ph. D. Thesis, Harvard University, 1974.

[21] K. Fukushima and S. Miyake, "Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition," in Competition and cooperation in neural nets: Springer, 1982, pp. 267-285.

[22] Y. LeCun et al., "Backpropagation applied to handwritten zip code recognition," Neural computation, vol. 1, no. 4, pp. 541-551, 1989.

[23] S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber, "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies," ed: A field guide to dynamical recurrent neural networks. IEEE Press, 2001.

[24] G. E. Hinton, "Learning multiple layers of representation," Trends in cognitive sciences, vol. 11, no. 10, pp. 428-434, 2007.

[25] R. Raina, A. Madhavan, and A. Y. Ng, "Large-scale deep unsupervised learning using graphics processors," in Proceedings of the 26th annual international conference on machine learning, 2009, pp. 873-880: ACM.

[26] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105.

[27] R. J. Martis, U. R. Acharya, and L. C. Min, "ECG beat classification using PCA, LDA, ICA and discrete wavelet transform," Biomedical Signal Processing and Control, vol. 8, no. 5, pp. 437-448, 2013.

在文檔中基於深度學習之心律不整輔助診斷系統 (頁 72-75)