Effectual seizure detection using MBBF-GPSO with CNN network.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-06-01 Epub Date: 2023-02-27 DOI:10.1007/s11571-023-09943-1
Dinesh Kumar Atal, Mukhtiar Singh
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引用次数: 0

Abstract

EEG is the most common test for diagnosing a seizure, where it presents information about the electrical activity of the brain. Automatic Seizure detection is one of the challenging tasks due to limitations of conventional methods with regard to inefficient feature selection, increased computational complexity and time and less accuracy. The situation calls for a practical framework to achieve better performance for detecting the seizure effectively. Hence, this study proposes modified Blackman bandpass filter-greedy particle swarm optimization (MBBF-GPSO) with convolutional neural network (CNN) for effective seizure detection. In this case, unwanted signals (noise) is eliminated by MBBF as it possess better ability in stopband attenuation, and, only the optimized features are selected using GPSO. For enhancing the efficacy of obtaining optimal solutions in GPSO, the time and frequency domain is extracted to complement it. Through this process, an optimized features are attained by MBBF-GPSO. Then, the CNN layer is employed for obtaining the productive classification output using the objective function. Here, CNN is employed due to its ability in automatically learning distinct features for individual class. Such advantages of the proposed system have made it explore better performance in seizure detection that is confirmed through performance and comparative analysis.

基于CNN网络的MBBF-GPSO有效检测癫痫发作
脑电图是诊断癫痫发作最常用的检测方法,它能提供大脑电活动的信息。由于传统方法在特征选择效率低、计算复杂度和时间增加以及准确性较低等方面的局限性,癫痫发作自动检测是一项具有挑战性的任务。在这种情况下,需要一个实用的框架来实现更好的性能,从而有效地检测癫痫发作。因此,本研究提出了改进的布莱克曼带通滤波-贪婪粒子群优化(MBBF-GPSO)和卷积神经网络(CNN),用于有效检测癫痫发作。在这种情况下,由于 MBBF 具有更好的阻带衰减能力,因此它能消除不需要的信号(噪声),而 GPSO 只能选择优化的特征。为了提高 GPSO 获得最优解的效率,提取了时域和频域作为补充。通过这一过程,MBBF-GPSO 获得了优化特征。然后,采用 CNN 层,利用目标函数获得富有成效的分类输出。在这里,采用 CNN 是因为它能够自动学习各个类别的不同特征。拟议系统的这些优势使其在癫痫发作检测方面具有更好的性能,这一点已通过性能和对比分析得到证实。
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
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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