COMPRESSIVE DATA STORAGE FOR LONG-TERM EEG: VALIDATION BY VISUAL ANALYSIS

IF 2 Q3 NEUROSCIENCES

Clinical Neurophysiology Practice Pub Date : 2025-01-01 DOI:10.1016/j.cnp.2025.07.005

Giridhar P. Kalamangalam , Subeikshanan Venkatesan , Maria-Jose Bruzzone , Yue Wang , Carolina B. Maciel , Sotiris Mitropanopoulos , Jean Cibula , Kajal Patel , Abbas Babajani-Feremi

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引用次数: 0

Abstract

Objectives

Long-term EEG monitoring (LTM) in acute neurology generates massive data volumes. We investigated whether data-analytic techniques could reduce LTM data size yet conserve their visual diagnostic features.

Methods

LTM exemplars from 50 patients underwent singular value decomposition (SVD). High-variance SVD components were transformed using discrete cosine transform (DCT), and significant elements run-length encoded. Two regimes were tested: (I) SVD and DCT compression ratio (CR) of 1.7 and 12, and (II) CR of 3.7 and 5.7; each achieved an overall CR of ≈20. Compressed data were reconstructed alongside uncompressed originals, to create a total of 200 recordings that were scored by two blinded reviewers. Scores of original and reconstructed data were statistically analyzed.

Results

Score differences between original recordings were smaller than comparisons involving reconstructions using the first regime but did not differ significantly from reconstructions using the second regime.

Conclusions

Raw LTM EEG has sufficient redundancy to undergo extreme (20-fold) data compression without compromising visual diagnostic information. A balanced mix of SVD and DCT appears to be a suitable data-analytic pipeline for achieving such compression.

Significance

Dimension reduction is a significant goal in managing big biomedical data. Our results suggest a pathway for archival of meaningful representations of entire LTM datasets. The latent space suggests new lines of data-scientific inquiry of the EEG in acute neurological illness.

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长期脑电图压缩数据存储：视觉分析验证

目的急性神经内科长期脑电图监测（LTM）产生海量数据。我们研究了数据分析技术是否可以减少LTM数据大小，同时保留其视觉诊断特征。方法对50例患者的sltm样本进行奇异值分解（SVD）。采用离散余弦变换（DCT）对高方差SVD分量进行变换，并对重要元素进行游程编码。测试两种方案：(I) SVD和DCT压缩比（CR）分别为1.7和12，（II） CR分别为3.7和5.7；每个细胞的总CR≈20。压缩后的数据与未压缩的原始数据一起重建，共创建200个录音，由两位盲法评论者评分。对原始数据和重建数据的评分进行统计分析。结果原始记录之间的评分差异小于使用第一种制度重建的比较，但与使用第二种制度重建的比较没有显著差异。结论原始LTM脑电图具有足够的冗余，可以在不影响视觉诊断信息的情况下进行极端（20倍）的数据压缩。SVD和DCT的平衡混合似乎是实现这种压缩的合适数据分析管道。降维是生物医学大数据管理的重要目标。我们的研究结果为整个LTM数据集的有意义表示的存档提供了一条途径。这一潜在的空间为脑电图在急性神经系统疾病中的数据科学探究提供了新的思路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Neurophysiology Practice NEUROSCIENCES-

CiteScore

3.90

自引率

0.00%

发文量

审稿时长

71 days

期刊介绍： Clinical Neurophysiology Practice (CNP) is a new Open Access journal that focuses on clinical practice issues in clinical neurophysiology including relevant new research, case reports or clinical series, normal values and didactic reviews. It is an official journal of the International Federation of Clinical Neurophysiology and complements Clinical Neurophysiology which focuses on innovative research in the specialty. It has a role in supporting established clinical practice, and an educational role for trainees, technicians and practitioners.