婴幼儿脑电信号不仅可以反映大脑当时的神经发育状态，而且能在很大程度上预测大脑以后的神经发育趋势，尤其对于早产儿(孕龄小于30周)和高危足月儿来说，不正常的脑电信号通常会导致预后神经发育不良，所以，为了能够尽早地发现婴幼儿大脑病变情况并加以及时的治疗，必须对婴幼儿脑电信号进行长时间的监测和分析。aEEG(amplitude-integrated electroencephalography, 振幅整合脑电信号)就是一种在时间和幅值上经过压缩的长时间(几小时甚至几天)的脑电信号，它能反映出大脑发育的整体水平。本研究在实现数字化aEEG算法的基础上，也针对有发作式样的脑电信号实现了癫痫发作检测算法和爆发和爆发抑制检测算法，这样，不仅能对婴幼儿发育有一个整体的认识，而且能自动检测出癫痫发作期和其他非癫痫式样发作信号的爆发期和爆发抑制期。癫痫发作检测算法是基于Line Length的标准化癫痫检测算法，而爆发和爆发抑制检测算法是基于NLEO(non-linear energy operator, 非线性能量算子)的多通道自动检测算法。本论文的目的不仅仅是实现上述三种婴幼儿脑电信号分析核心方法，而是设计并实现一个功能全面、操作简单的婴幼儿脑电信号分析系统。这个系统实现了用户基本信息的记录和存储、电极阻抗检测、脑电信号的采集、脑电信号采集现场视频的录制和存储、脑电信号的实时显示、实时分析、离线分析、坏通道替换和数据存储等功能。它是一个功能强大、可操作性强的婴幼儿脑电信号分析系统，该系统目前主要实现了上述三种婴幼儿脑电信号分析方法，分别是新生儿aEEG算法、癫痫发作检测算法和多通道脑电信号爆发和爆发抑制自动检测算法。虽然国际上关于这些方法的研究有很多，但国内并没有一个完整的可操作的脑电信号采集和分析的仪器，尤其是针对婴幼儿的脑电信号分析系统。

Infants electroencephalography (EEG) can not only reflect the status of the brain at the time, but also can predict the trend of the neurodevelopment to a great extent. Abnormal EEG usually results in a relatively poor prognosis of neurodevelopment especially for preterm infants and high risk term infants. So, in order to find the brain lesions as soon as possible and make timely treatment, we need a long-term monitoring and analysis for the infants EEG.Amplitude-integrated electroencephalography (aEEG) is a long-term EEG which is compressed both in time and amplitude, and it reflects the overall level of brain development. This study has implemented the digital aEEG algorithm, and at the same time, has also implemented the seizure detection algorithm and burst and inter-burst automated detection algorithm. The seizure detection algorithm is based on the Line Length feature and burst and inter-burst automated detection algorithm is based on the NLEO (non-linear energy operator) feature.The object of this study is not only to implement these three key infants EEG analysis methods, but to design and implement a full-featured and convenient infants EEG analysis system. This system includes several aspects as follows: the inputting and storage of the users basic information; the impedance detection of electrodes; the recording and storage of the EEG collection site video; the real time showing, real time analysis and offline analysis of EEG data; the replacement of bad channels and the saving of the original EEG data and analysis result data. It is a powerful and operational infants EEG analysis system which implemented thus three infants EEG analysis methods. They are neonatal digital aEEG algorithm, seizure detection algorithm and automated detection of burst and inter-burst of multi-channel EEG. Although these methods have been studied for many years, there isn’t even one comprehensive and operable EEG recording and analysis machine in China.