Temko Andriy, Sarkar Achintya Kr, Boylan Geraldine B, Mathieson Sean, Marnane William P, Lightbody Gordon
Department of Electrical and Electronic Engineering and Irish Centre for Fetal and Neonatal Translational ResearchUniversity College Cork.
Department of Electronic SystemsAalborg University.
IEEE J Transl Eng Health Med. 2017 Sep 11;5:2800414. doi: 10.1109/JTEHM.2017.2737992. eCollection 2017.
The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures.
本文探讨了为新生儿创建个性化癫痫发作检测算法的问题。提出了一种用于通用的与患者无关的新生儿癫痫发作检测器的半监督自适应概率框架。设计了一个基于患者自适应（生成式）和患者无关（判别式）分类器组合的系统，并在一个持续时间超过800小时的未经编辑的连续多通道新生儿脑电图记录的大型数据库上进行了评估。结果表明，通过即时纳入患者特定的脑电图特征，在长时间脑电图记录过程中检测新生儿癫痫发作的能力可得到提高。在临床环境中，所开发系统的应用将使癫痫发作检测率保持在70%，同时将每小时的误报数量减半，从每小时0.4次降至每小时0.2次。这是第一项提出使用无临床标签的在线自适应方法来构建用于检测新生儿癫痫发作的个性化诊断系统的研究。
