ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the classification of the epileptic electroencephalography (EEG) signals. Therefore, a signal classification algorithm which uses TF images obtained with ConceFT to feed the transfer learning structure has been presented. Epilepsy is a common neurological disorder that millions of people suffer worldwide. Daily lives of the patients are quite difficult because of the unpredictable time of seizures. EEG signals monitoring the electrical activity of the brain can be used to detect approaching seizures and make possible to warn the patient before the attack. GoogLeNet which is a well-known deep learning model has been preferred to classify TF images. Classification performance is directly related to the TF representation accuracy of the ConceFT. The proposed method has been tested for various classification scenarios and obtained accuracies between 95.83% and 99.58% for two and three-class classification scenarios. High results show that ConceFT is a successful and promising TF analysis method for non-stationary biomedical signals.

中文翻译：

---

使用集中时频方法对癫痫脑电图信号进行基于深度学习的分类。

ConceFT（频率和时间的集中）是一种新的时频（TF）分析方法，它结合了多锥技术和同步压缩变换（SST）。这种组合产生高度集中的 TF 表示，具有近乎完美的时间和频率分辨率。本文旨在通过使用 ConceFT 对癫痫脑电图（EEG）信号进行分类来展示 ConceFT 的 TF 表示性能和鲁棒性。因此，提出了一种信号分类算法，该算法使用 ConceFT 获得的 TF 图像来输入迁移学习结构。癫痫是一种常见的神经系统疾病，全世界有数百万人患有这种疾病。由于癫痫发作时间不可预测，患者的日常生活相当困难。监测大脑电活动的脑电图信号可用于检测即将发生的癫痫发作，并可以在发作前警告患者。GoogLeNet是著名的深度学习模型，已被首选用于对TF图像进行分类。分类性能与 ConceFT 的 TF 表示准确性直接相关。该方法已针对各种分类场景进行了测试，对于二类和三类分类场景获得了 95.83% 至 99.58% 的准确率。高结果表明ConceFT是一种成功且有前途的非平稳生物医学信号的TF分析方法。