MFCC-CNN: A patient-independent seizure prediction model.

IF 2.7 4区 医学 Q2 CLINICAL NEUROLOGY Neurological Sciences Pub Date : 2024-12-01 Epub Date: 2024-08-09 DOI:10.1007/s10072-024-07718-y
Fan Zhang, Boyan Zhang, Siyuan Guo, Xinhong Zhang
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Abstract

Background: Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications.

Methods: This paper proposes a patient-independent seizure prediction model, named MFCC-CNN, to improve the generalization ability. MFCC-CNN model introduces Mel-Frequency Cepstrum Coefficients (MFCC) features and Linear Predictive Cepstral Coefficients (LPCC) features concentrated in the low frequency region, which contains more detailed information. Convolutional neural network (CNN) is used to construct a seizure prediction model.

Results: Experimental results showed that the proposed model obtained accuracy of 96 % , sensitivity of 92 % , specificity of 84 % and F1-score of 85 % for 24 cases in CNHB-MIT dataset. The overall performance of MFCC-CNN model is better than the other models.

Conclusion: MFCC-CNN model does not need to be specifically customized for different patients. As a patient-independent seizure prediction model, it has good generalization ability.

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MFCC-CNN:与患者无关的癫痫发作预测模型
背景:自动预测癫痫发作是癫痫领域的一个主要目标。然而,不同患者脑电图(EEG)信号的高变异性限制了预测模型在临床应用中的使用:本文提出了一种与患者无关的癫痫发作预测模型,命名为 MFCC-CNN,以提高泛化能力。MFCC-CNN 模型引入了 Mel-Frequency Cepstrum Coefficients(MFCC)特征和线性预测倒频谱系数(LPCC)特征,这些特征集中在低频区域,包含更详细的信息。卷积神经网络(CNN)用于构建癫痫发作预测模型:实验结果表明,在 CNHB-MIT 数据集中的 24 个病例中,所提出的模型获得了 96 % 的准确率、92 % 的灵敏度、84 % 的特异性和 85 % 的 F1 分数。MFCC-CNN 模型的整体性能优于其他模型:结论:MFCC-CNN 模型无需针对不同患者进行特别定制。结论:MFCC-CNN 模型无需针对不同患者进行专门定制,作为一种与患者无关的癫痫发作预测模型,它具有良好的泛化能力。
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来源期刊
Neurological Sciences
Neurological Sciences 医学-临床神经学
CiteScore
6.10
自引率
3.00%
发文量
743
审稿时长
4 months
期刊介绍: Neurological Sciences is intended to provide a medium for the communication of results and ideas in the field of neuroscience. The journal welcomes contributions in both the basic and clinical aspects of the neurosciences. The official language of the journal is English. Reports are published in the form of original articles, short communications, editorials, reviews and letters to the editor. Original articles present the results of experimental or clinical studies in the neurosciences, while short communications are succinct reports permitting the rapid publication of novel results. Original contributions may be submitted for the special sections History of Neurology, Health Care and Neurological Digressions - a forum for cultural topics related to the neurosciences. The journal also publishes correspondence book reviews, meeting reports and announcements.
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