A novel approach to create synthetic biomedical signals using BiRNN
| Autor: | Hector Perez-Meana, Hamido Fujita, Andres Hernandez-Matamoros |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Information Systems and Management
Computer science 02 engineering and technology Machine learning computer.software_genre Synthetic data Theoretical Computer Science Domain (software engineering) Human health Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Medical diagnosis Private information retrieval business.industry 05 social sciences 050301 education Construct (python library) Computer Science Applications Recurrent neural network Control and Systems Engineering 020201 artificial intelligence & image processing Artificial intelligence business 0503 education computer Software |
| Zdroj: | Information Sciences. 541:218-241 |
| ISSN: | 0020-0255 |
| Popis: | Human health is threatened by several diseases for this reason automated medical diagnosis systems has been developed several years ago. These systems need databases, the creation of these databases is tedious, arduous and stops being done so the created database is incomplete or unbalanced. Sometimes the databases are private to protect the private information of the patients, among other problems. For this reason, the researchers have started to use synthetic data. The synthetic data have been applied by different hospitals in the USA. The creation of synthetic data has different problems like the synthetic data are generated using rules defined by the user, the proposed approaches only can create one kind of data, the proposals require input from domain experts, among others. To address these kinds of problems, we propose a novel approach, which consists of the Bidirectional Recurrent Neural Network and the statistical stage to generate synthetic biomedical signals. The approach is able to create 5 kinds of biomedical signals (ECG, EEG, BCG, PPG, and Respiratory Impedance). Our approach is able to create synthetic data for patients or for specific events. The performance of our approach is compared with other generative models (GAN’s) through evaluation metrics. The created synthetic data are used to construct models; these models are able to successfully differentiate between different signals with high accuracies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect