Siksha 'O' Anusandhan Deemed to Be University, Bhubaneswar, Odisha, India; International Institute of Information Technology, Bhubaneswar, Odisha, India.
Siksha 'O' Anusandhan Deemed to Be University, Bhubaneswar, Odisha, India.
Comput Biol Med. 2021 May;132:104299. doi: 10.1016/j.compbiomed.2021.104299. Epub 2021 Mar 3.
In this paper, the extracted features using variational mode decomposition (VMD) and approximate entropy (ApEn) privileged information of the input EEG signals are combined with multilayer multikernel random vector functional link network plus (MMRVFLN+) classifier to recognize the epileptic seizure epochs efficaciously. In our experiment Bonn University single-channel intracranial electroencephalogram (iEEG) and Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) multichannel scalp EEG (sEEG) recordings are considered to evaluate the efficacy of the proposed method. The VMD is applied on chaotic, non-stationary, nonlinear, and complex EEG signal to decompose it into three band-limited intrinsic mode functions (BLIMFs). The Hilbert transform (HT) is applied on BLIMFs to extract informative spectral and temporal features. The ApEn is computed from the raw EEG signals as the privileged information and given to the multi-hidden layer structure to obtain the most discriminative compressed form. The scatter plots show the distinct nature of compressed privileged ApEn information among the seizure pattern classes. The linear as well as nonlinear mapping, local and global kernel function, high-learning speed, less computationally complex MMRVFLN+ classifier is proposed to recognize the seizure events accurately by importing the efficacious features with ApEn as the input. The advanced signal processing algorithm i.e., Hilbert Huang transform (HHT) with ApEn and MMRVFLN+ are combined to compare the performance with the proposed VMDHTApEn-MMRVFLN+ method. The proposed method has remarkable recognition ability, superior classification accuracy, and excellent overall performance as compared to other methods. The digital architecture of the multifuse MMRVFLN+ is developed and implemented on a high-speed reconfigurable FPGA hardware platform to validate the effectiveness of the proposed method. The superior classification accuracy, the negligible false positive rate per hour (FPR/h), simplicity, feasibility, robustness, and practicability of the proposed method validate its ability to recognize the epileptic seizure epochs automatically.
本文将变分模态分解（VMD）提取的特征和近似熵（ApEn）作为输入 EEG 信号的特权信息相结合，与多层多核随机向量功能链接网络加（MMRVFLN+）分类器相结合，有效地识别癫痫发作期。在我们的实验中，考虑了波恩大学单通道颅内脑电图（iEEG）和波士顿儿童医院-麻省理工学院（CHB-MIT）多通道头皮脑电图（sEEG）记录，以评估所提出方法的有效性。变分模态分解（VMD）应用于混沌、非平稳、非线性和复杂 EEG 信号，将其分解为三个带限固有模态函数（BLIMFs）。希尔伯特变换（HT）应用于 BLIMFs 以提取信息丰富的光谱和时间特征。ApEn 是从原始 EEG 信号中计算出来的特权信息，并提供给多隐藏层结构，以获得最具判别力的压缩形式。散点图显示了癫痫模式类之间压缩特权 ApEn 信息的明显性质。线性和非线性映射、局部和全局核函数、高速学习、计算复杂度低的多层随机向量功能链接网络加（MMRVFLN+）分类器被提出，通过将具有 ApEn 的有效特征作为输入来准确识别癫痫发作事件。先进的信号处理算法，即希尔伯特黄变换（HHT）与 ApEn 和 MMRVFLN+相结合，与所提出的 VMDHTApEn-MMRVFLN+方法进行比较，以比较性能。与其他方法相比，所提出的方法具有显著的识别能力、较高的分类精度和出色的整体性能。多融合 MMRVFLN+的数字架构被开发并实现在高速可重构 FPGA 硬件平台上，以验证所提出方法的有效性。所提出的方法具有卓越的分类精度、可忽略的每小时假阳性率（FPR/h）、简单性、可行性、鲁棒性和实用性，验证了其自动识别癫痫发作期的能力。
