Li and colleagues developed a deep-learning model to analyze EEG recordings and detect event-level EEG spikes. 2. The model achieved high accuracy and a low false-positive rate, with only 32% of human ...
BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...
This is where AI-augmented data quality engineering emerges. It shifts data quality from deterministic, Boolean checks to ...
Abstract: This study presents a novel perspective on multimodal deep learning for biomedical signal classification, systematically analyzing the impact of complementary feature domains on model ...
Therefore, these results confirm that deep learning classifiers, especially CNNs, when combined with COA-based feature optimization, offer a powerful strategy for accurate and robust EEG signal ...
Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.
Abstract: We propose a new cognitive technique for blind adaptive beamforming which uses a pre-trained deep learningbased signal classifier to protect a signal of interest (SOI) from interference. The ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results