统计二值模式协同网络在精神分裂症严重程度诊断中的应用

IF 1.1 Q4 BIOPHYSICS AIMS Biophysics Pub Date : 2023-01-01 DOI:10.3934/biophy.2023021
Dr. P. Esther Rani, B.V.V.S.R.K.K. Pavan
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引用次数: 0

摘要

& lt; abstract>脑电图(EEG)是一种广泛使用的医疗程序,有助于识别脑电波模式的异常,并测量大脑的电活动。EEG信号包含不同的特征,这些特征需要基于特定的属性来区分,以显示可识别的度量和功能组件,然后用于评估EEG信号中的模式。通过提取,将特征损失与嵌入的信号信息最小化。此外,资源被最小化以准确地计算大量数据。为了提高信息压缩性能,必须降低信息处理成本和实现复杂度。目前,对脑电信号进行特征提取的方法多种多样。现有的方法受到不同的检测方案,有效地刺激脑信号与医疗康复和诊断的接口。精神分裂症是一种异常影响个体现实的精神疾病。提出了一种基于统计局部二值模式(SLBP)的脑电信号特征提取方法。提出的SLBP模型利用统计特征计算脑电信号的特征。利用局部二值模式和提出的SLBP模型纹理,在标记信号的基础上,通过二值搜索对信号的邻域进行估计。分类是对早期预测的精神分裂症阶段进行的,无论是轻度还是重度。分析考虑了三个类别,即正常、轻度和严重。仿真结果表明,所提出的SLBP模型的分类准确率达到98%,比现有方法提高了约12%。& lt; / abstract>
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Multi-class EEG signal classification with statistical binary pattern synergic network for schizophrenia severity diagnosis

Electroencephalography (EEG) is a widely used medical procedure that helps to identify abnormalities in brain wave patterns and measures the electrical activity of the brain. The EEG signal comprises different features that need to be distinguished based on a specified property to exhibit recognizable measures and functional components that are then used to evaluate the pattern in the EEG signal. Through extraction, feature loss is minimized with the embedded signal information. Additionally, resources are minimized to compute the vast range of data accurately. It is necessary to minimize the information processing cost and implementation complexity to improve the information compression. Currently, different methods are being implemented for feature extraction in the EEG signal. The existing methods are subjected to different detection schemes that effectively stimulate the brain signal with the interface for medical rehabilitation and diagnosis. Schizophrenia is a mental disorder that affects the individual's reality abnormally. This paper proposes a statistical local binary pattern (SLBP) technique for feature extraction in EEG signals. The proposed SLBP model uses statistical features to compute EEG signal characteristics. Using Local Binary Pattern with proposed SLBP model texture based on a labeling signal with an estimation of the neighborhood in signal with binary search operation. The classification is performed for the earlier-prediction shizophrenia stage, either mild or severe. The analysis is performed considering three classes, i.e., normal, mild, and severe. The simulation results show that the proposed SLBP model achieved a classification accuracy of 98%, which is ~12% higher than the state-of-the-art methods.

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来源期刊
AIMS Biophysics
AIMS Biophysics BIOPHYSICS-
CiteScore
2.40
自引率
20.00%
发文量
16
审稿时长
8 weeks
期刊介绍: AIMS Biophysics is an international Open Access journal devoted to publishing peer-reviewed, high quality, original papers in the field of biophysics. We publish the following article types: original research articles, reviews, editorials, letters, and conference reports. AIMS Biophysics welcomes, but not limited to, the papers from the following topics: · Structural biology · Biophysical technology · Bioenergetics · Membrane biophysics · Cellular Biophysics · Electrophysiology · Neuro-Biophysics · Biomechanics · Systems biology
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