Bahhah Muawiyah A, Attar Eyad Talal
Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Diagnostics (Basel). 2024 Nov 12;14(22):2525. doi: 10.3390/diagnostics14222525.
Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure detection accuracy in epilepsy patients using an advanced preprocessing technique that could remove such noxious artifacts. In the frame of this paper, the core tool in the area of epilepsy, EEG, will be applied to record and analyze the electrical patterns of the brain. The dataset includes recordings of seven epilepsy patients taken by the Unit of Neurology and Neurophysiology, University of Siena. The preprocessing techniques employed include advanced artifact removal and signal enhancement methods. We introduced Peak-to-Peak Amplitude Fluctuation (PPAF) to assess amplitude variability within Event-Related Potential (ERP) waveforms. This approach was applied to data from patients experiencing 3-5 seizures, categorized into three distinct groups. The results indicated that the frontal and parietal regions, particularly the electrode areas Cz, Pz and Fp2, are the main contributors to epileptic seizures. Additionally, the implementation of the PPAF metric enhanced the effectiveness of seizure detection and classification algorithms, achieving accuracy rates of 99%, 98% and 95% for datasets with three, four and five seizures, respectively. The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. The introduction of PPAF as a metric could have promising potential in improving both the accuracy and reliability of epilepsy seizure detection algorithms. These observations provide important implications for control and treatment both in focal and in generalized epilepsy.
自然地，存在一些挑战，例如肌肉伪迹、眼球运动和电干扰，这些都依赖于精确的诊断和分类，从而妨碍了癫痫发作的准确检测。本研究旨在使用一种先进的预处理技术来提高癫痫患者发作检测的准确性，该技术可以去除此类有害伪迹。在本文的框架内，癫痫领域的核心工具——脑电图（EEG）将被用于记录和分析大脑的电活动模式。数据集包括锡耶纳大学神经学和神经生理学单元采集的七名癫痫患者的记录。所采用的预处理技术包括先进的伪迹去除和信号增强方法。我们引入了峰峰值幅度波动（PPAF）来评估事件相关电位（ERP）波形内的幅度变异性。该方法应用于经历3 - 5次发作的患者的数据，并分为三个不同的组。结果表明，额叶和顶叶区域，特别是电极区域Cz、Pz和Fp2，是癫痫发作的主要贡献区域。此外，PPAF指标的实施提高了癫痫发作检测和分类算法的有效性，对于有三次、四次和五次发作的数据集，准确率分别达到了99%、98%和95%。本研究通过提供癫痫发作期间大脑活动的线索扩展了癫痫诊断，并进一步证明了先进预处理技术的有效性。将PPAF作为一种指标引入可能在提高癫痫发作检测算法的准确性和可靠性方面具有广阔的潜力。这些观察结果对局灶性和全身性癫痫的控制和治疗具有重要意义。
