论文标题
基于小波的多级癫痫发作类型分类系统
Wavelet-Based Multi-Class Seizure Type Classification System
论文作者
论文摘要
癫痫是影响世界人口超过1％的最常见脑部疾病之一。它的特征是复发性癫痫发作，这些癫痫发作具有不同的类型，对治疗方式有所不同。脑电图（EEG）通常用于医疗服务中，以诊断癫痫发作及其类型。癫痫发作的准确鉴定有助于为患者提供最佳的治疗和准确的信息。但是，癫痫发作的手动诊断程序是费力的且高度特殊的。此外，脑电图手动评估是一个已知专家之间的评估者间协议较低的过程。本文提出了一种新型的自动技术，该技术涉及使用Dual-Tree Complece小波变换（DTCWT）从EEG信号中提取特定特征并将其分类。我们评估了TUH EEG癫痫发作语料库（TUSZ）VER.1.5.2数据集的提议技术，并使用总体F1评分进行了与现有最新技术的性能，这是由于类不平衡癫痫发作类型。我们提出的技术分别为癫痫发作和患者分类的加权F1分数为99.1 \％和74.7 \％的最佳结果，从而为该数据集设置了新的基准结果。
Epilepsy is one of the most common brain diseases that affect more than 1\% of the world's population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and highly-specialized. Moreover, EEG manual evaluation is a process known to have a low inter-rater agreement among experts. This paper presents a novel automatic technique that involves extraction of specific features from EEG signals using Dual-tree Complex Wavelet Transform (DTCWT) and classifying them. We evaluated the proposed technique on TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with existing state-of-the-art techniques using overall F1-score due to class imbalance seizure types. Our proposed technique achieved the best results of weighted F1-score of 99.1\% and 74.7\% for seizure-wise and patient-wise classification respectively, thereby setting new benchmark results for this dataset.
