Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2000, Sydney, Australia.
Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan.
J Healthc Eng. 2021 Jan 27;2021:6624764. doi: 10.1155/2021/6624764. eCollection 2021.
In healthcare applications, deep learning is a highly valuable tool. It extracts features from raw data to save time and effort for health practitioners. A deep learning model is capable of learning and extracting the features from raw data by itself without any external intervention. On the other hand, shallow learning feature extraction techniques depend on user experience in selecting a powerful feature extraction algorithm. In this article, we proposed a multistage model that is based on the spectrogram of biosignal. The proposed model provides an appropriate representation of the input raw biosignal that boosts the accuracy of training and testing dataset. In the next stage, smaller datasets are augmented as larger data sets to enhance the accuracy of the classification for biosignal datasets. After that, the augmented dataset is represented in the TensorFlow that provides more services and functionalities, which give more flexibility. The proposed model was compared with different approaches. The results show that the proposed approach is better in terms of testing and training accuracy.
在医疗保健应用中，深度学习是一种非常有价值的工具。它从原始数据中提取特征，为医疗从业者节省时间和精力。深度学习模型能够自行学习和提取原始数据中的特征，而无需任何外部干预。另一方面，浅层学习特征提取技术依赖于用户经验来选择强大的特征提取算法。在本文中，我们提出了一种基于生物信号频谱图的多阶段模型。所提出的模型为输入原始生物信号提供了适当的表示，从而提高了训练和测试数据集的准确性。在下一阶段，较小的数据集被扩充为较大的数据集，以提高生物信号数据集的分类准确性。之后，扩充后的数据集在提供更多服务和功能的 TensorFlow 中表示，从而提供更多的灵活性。我们将所提出的模型与不同的方法进行了比较。结果表明，在所提出的方法在测试和训练精度方面表现更好。
