Task-Dependent Compression Performance Assessment in Body Physiology Signals
| Autor: | Didem Gokcay, Fatih Ileri |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry 0206 medical engineering Pattern recognition Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology 020601 biomedical engineering Task (project management) 03 medical and health sciences 0302 clinical medicine Transmission (telecommunications) 030220 oncology & carcinogenesis Compression (functional analysis) Metric (mathematics) Discrete cosine transform Artificial intelligence business |
| Zdroj: | SIU |
| DOI: | 10.1109/siu49456.2020.9302049 |
| Popis: | Compression of biological signals have great importance in storing, offline processing, and transmission of these signals to other entities. There have been many researches based on assessing the performance of newly published compression methods. Main performance metrics used in these researches are the compression ratio (CR) and the error metric, generally the PRD (percent mean square difference). In this research we assess the performance of our compression method in a task-dependent fashion. We applied a neural network based classification on original and compressed EMG signals, collected from subjects participated a defined task. With this approach, we were able to understand what amount of necessary information of EMG signals is suppressed during compression. |
| Databáze: | OpenAIRE |
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