基于动态字典特征分解的脑电实时压缩

H. Daou, F. Labeau
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引用次数: 0

摘要

本文提出了一种新的实时脑内脑电图(iEEG)压缩技术。该技术使用特征分解和动态字典更新将EEG通道减少到只有一个与装饰相关的通道或特征通道。实验结果表明,该技术能够在非常低的比特率(BRs)下提供低失真值。此外,该方法的性能结果表明,该方法比JPEG2000更好,更稳定。结果在时间和不同患者之间变化不大,证明了该方法的稳定性。
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Real-Time Compression of Intra-Cerebral EEG Using Eigendecomposition with Dynamic Dictionary
A novel technique for Intra-cerebral Electroencephalogram (iEEG) compression in real-time is proposed in this article. This technique uses eigendecomposition and dynamic dictionary update to reduce the EEG channels to only one decor related channel or eigenchannel. Experimental results show that this technique is able to provide low distortion values at very low bit rates (BRs). In addition, performance results of this method show to be better and more stable than JPEG2000. Results do not vary a lot both in time and between different patients which proves the stability of the method.
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