基于一维CNN与多头注意力机制的混合深度学习模型用于癫痫发作分类
Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism
AI总结：
提出一种结合一维卷积神经网络和多头注意力机制的混合深度学习模型，用于从脑电图信号中分类癫痫发作，以提高分类准确性。
摘要：
癫痫是一种全球常见的神经系统疾病，影响约5000万人。癫痫发作源于大脑突然的异常电活动，这可以表现为脑电图信号的突然显著变化。信号的严重程度和频率各不相同，导致短暂意识丧失和肌肉收缩。癫痫患者常因某些工作环境的安全问题面临显著的就业挑战。涉及高空作业、操作重型机械或其他潜在危险环境的工作可能对癫痫患者受限，这无疑限制了他们的就业机会和经济可能性。
英文摘要：
Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people \cite{WHO_epilepsy_50million}. Epileptic seizures result from sudden abnormal electrical activity in the brain, which can be read as sudden and significant changes in the EEG signal of the brain. The signal can vary in severity and frequency, which results in loss of consciousness and muscle contractions for a short period of time \cite{epilepsyfoundation_myoclonic}. Individuals with epilepsy often face significant employment challenges due to safety concerns in certain work environments. Many jobs that involve working at heights, operating heavy machinery, or in other potentially hazardous settings may be restricted for people with seizure disorders. This certainly limits job options and economic opportunities for those living with epilepsy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
