| Abstrakt: |
A recent report from the University of North Florida discusses the challenges of analyzing high-dimensional physiological signals obtained from EEG, EMG, and ECG. Traditional machine learning techniques can lead to the loss of important information, so researchers have been exploring the use of deep convolutional neural networks (CNNs) for automated feature learning. The report reviews recent advances in signal-to-image transformation techniques to convert one-dimensional time series into images, preserving important characteristics. The authors present a systematic analysis of different transformation approaches and CNN-based analysis techniques for various applications, such as brain-computer interfaces, seizure detection, and sleep stage classification. The report aims to encourage further innovations in effective systems for extracting clinically relevant information from physiological data using CNNs. [Extracted from the article] |
