Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia.
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, 7941169, Chile.
BMC Med Inform Decis Mak. 2024 Mar 1;24(1):60. doi: 10.1186/s12911-024-02460-z.
Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy.
To optimize these processes and make them more efficient, we have resorted to innovative artificial intelligence methods essential in classifying EEG signals. For this, comparing traditional models, such as machine learning or deep learning, with cutting-edge models, in this case, using Capsule-Net architectures and Transformer Encoder, has a crucial role in finding the most accurate model and helping the doctor to have a faster diagnosis.
In this paper, a comparison was made between different models for binary and multiclass classification of the epileptic seizure detection database, achieving a binary accuracy of 99.92% with the Capsule-Net model and a multiclass accuracy with the Transformer Encoder model of 87.30%.
Artificial intelligence is essential in diagnosing pathology. The comparison between models is helpful as it helps to discard those that are not efficient. State-of-the-art models overshadow conventional models, but data processing also plays an essential role in evaluating the higher accuracy of the models.
癫痫是一种以神经元过度放电为特征的疾病，通常在没有任何外部刺激的情况下引发，这种过度放电被称为癫痫发作。全世界每年约有 200 万人被诊断出患有这种疾病。这个过程是由神经科医生使用脑电图（EEG）来进行的，这个过程很漫长。
为了优化这些过程并提高效率，我们求助于创新的人工智能方法，这些方法对于分类 EEG 信号至关重要。为此，我们比较了传统模型，如机器学习或深度学习，以及前沿模型，在这种情况下，使用胶囊网络架构和 Transformer Encoder，在寻找最准确的模型和帮助医生更快做出诊断方面起着关键作用。
在本文中，对癫痫发作检测数据库的二进制和多类分类的不同模型进行了比较，胶囊网络模型的二进制准确率达到 99.92%，Transformer Encoder 模型的多类准确率达到 87.30%。
人工智能在诊断病理学方面至关重要。模型之间的比较很有帮助，因为它有助于排除那些效率不高的模型。最先进的模型超越了传统模型，但数据处理在评估模型的更高准确性方面也起着重要作用。
