全文总字数：3337字
1. 毕业设计（论文）主要内容：
心电图是诊断心律失常、急性冠状动脉综合征等心脏疾患的重要依据，然而现有的心电图分析方法耗时费力且误诊率高。本课题拟通过患者的心电图数据，利用深度学习方法进行心电信号的自动特征选择（时域、频域、双频谱、非线性特征等），实现对常见的心律失常的检测和分类，有效地平衡算法的敏感性和特异性。通过对该课题的研究，进行一次综合运用所学理论和技能的训练，进一步提高分析问题和解决问题的能力。
具体内容包括：1. 了解心电信号处理方法和深度学习基本原理等；2. 进行心电数据的分析处理和特征提取；3. 对心律失常信号自动分类检测模型进行设计和实现；4. 对相关模型进行性能比较和分析。
2. 毕业设计（论文）主要任务及要求
1.查阅15篇相关文献（不少于3篇外文文献），并每篇书写200—300字文献摘要（装订成册，带封面）；2.认真填写周记，完成至少1500字开题报告（“设计的目的及意义”至少800汉字；“基本内容和技术方案”至少400汉字；进度安排应尽可能详细；教指导教师意见应包含：学生的调研是否充分？基本内容和技术方案是否已明确？是否已经具备开始设计（论文）的条件？能否达到预期的目标？是否同意进入设计（论文）阶段？）；3.完成5000中文字以上的相关英文专业文献翻译，并装订成册（中英文一起，带封面）；4.完成系统的编码与调试；5.完成10000字以上的毕业论文；6.进行论文答辩。
3. 毕业设计（论文）完成任务的计划与安排
(1)2020/1/13—2020/2/28：确定选题，查阅文献，外文翻译和撰写开题报告；(2)2020/3/1—2020/4/30：系统架构、程序设计与开发、系统测试与完善；(3)2020/5/1—2020/5/25：撰写及修改毕业论文；(4)2020/5/26—2020/6/5：准备答辩。
4. 主要参考文献
[1] Awni Y. Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi,etc. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network[J]. Nature Medicine,2019,25(1):65-69.[2] P. Bizopoulos and D. Koutsouris, "Deep Learning in Cardiology," in IEEE Reviews in Biomedical Engineering, vol. 12, pp. 168-193, 2019[3] D. Li, J. Zhang, Q. Zhang, and X. Wei, “Classification of ecg signals based on 1d convolution neural network,” in e-Health Networking,Applications and Services (Healthcom), 2017 IEEE 19th InternationalConference on. IEEE, 2017, pp. 1–6.[4] 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.[5] A. Isin and S. Ozdalili, “Cardiac arrhythmia detection using deep,learning,” Procedia Computer Science, vol. 120, pp. 268–275, 2017.[6] M.-H. Wu, E. J. Chang, and T.-H. Chu, “Personalizing a generic ecg heartbeat classification for arrhythmia detection: A deep learning approach,” in 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2018, pp. 92–99.[7] K. Luo, J. Li, Z. Wang, and A. Cuschieri, “Patient-specific deep architectural model for ecg classification,” Journal of healthcare en[1]gineering, vol. 2017, 2017.[8] C. Jiang, S. Song, and M. Q.-H. Meng, “Heartbeat classification system based on modified stacked denoising autoencoders and neural networks,” in Information and Automation (ICIA), 2017 IEEE Interna[1]tional Conference on. IEEE, 2017, pp. 511–516.[9] J. Yang, Y. Bai, F. Lin, M. Liu, Z. Hou, and X. Liu, “A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression,” International Journal of Machine Learning and Cybernetics, pp. 1–8, 2017.[10] P. Rajpurkar, A. Y. Hannun, M. Haghpanahi, C. Bourn, and A. Y. Ng, “Cardiologist-level arrhythmia detection with convolutional neural networks,” arXiv preprint arXiv:1707.01836, 2017.[11] U. R. Acharya, H. Fujita, O. S. Lih, Y. Hagiwara, J. H. Tan, and M. Adam, “Automated detection of arrhythmias using different inter[1]vals of tachycardia ecg segments with convolutional neural network,” Information sciences, vol. 405, pp. 81–90, 2017.[12] P. Schwab, G. C. Scebba, J. Zhang, M. Delai, and W. Karlen, “Beat by beat: Classifying cardiac arrhythmias with recurrent neural networks,” Computing, vol. 44, p. 1, 2017.
以上是毕业论文任务书，课题毕业论文、开题报告、外文翻译、程序设计、图纸设计等资料可联系客服协助查找。
