时间:2019-06-21 15:42 来源:www.zqdh.com.cn 作者:论文发表 点击:次
1) 本文的目的或要解决的问题(What I want to do?)
2) 解决问题的方法及过程(How I did it?)
3) 主要结果及结论(What results did I get and what conclusions can I draw?)
4) 本文的创新、独到之处(What is new and original in this paper?)
每个部分的内容用一两句话来表达就够了。[论文书写格式]英文论文摘要写法心得以总分总结构来写，从大领域写到你做的领域 再到未来扩展方向，有的没有写扩展方向 那么也可以写一下自身的效果和地位（state-of-the-art）也是可以的。行文要简洁，组织好逻辑顺序 并尽可能详尽的描述重要信息。
Electroencephalography (EEG) is an important tool for studying the human brain activity and epileptic processes in particular. EEG signals provide important information about epileptogenic networks that must be analyzed and understood before the initiation of therapeutic procedures. Very small variations in EEG signals depict a definite type of brain abnormality. The challenge is to design and develop signal processing algorithms which extract this subtle information and use it for diagnosis, monitoring and treatment of patients with epilepsy. This paper presents a review of wavelet techniques for computer-aided seizure detection and epilepsy diagnosis with an emphasis on research reported during the past decade. A multiparadigm approach based on the integration of wavelets, nonlinear dynamics and chaos theory, and neural networks advanced by Adeli and associates is the most effective method for automated EEG-based diagnosis of epilepsy.
In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief net- works to audio data and empirically evaluate them on various audio classification tasks. In the case of speech data, we show that the learned features correspond to phones/phonemes. In addition, our feature representations learned from unlabeled audio data show very good performance for multiple audio classification tasks. We hope that this paper will inspire more research on deep learning approaches applied to a wide range of audio recognition tasks.