Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, emphasizing the need for accurate and early diagnosis. Electrocardiograms (ECG) provide a non-invasive means of diagnosing various cardiac conditions. However, traditional m...
Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges ...
Implementing counter-pulsation (CP) control in pulsatile extracorporeal membrane oxygenator (p-ECMO) systems offers a refined approach to mitigate risks commonly associated with conventional ECMOs. To attain CP between the p-ECMO and heart, accurate ...
This article proposes a novel approach for vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation named MixNet. In industrial environments where equipment reliability directly im...
This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal processing with physiologically-constrained deep learning. Our approach addresses three fundamental li...
Speech enhancement (SE) and automatic speech recognition (ASR) in real-time processing involve improving the quality and intelligibility of speech signals on the fly, ensuring accurate transcription as the speech unfolds. SE eliminates unwanted backg...
Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitioning and cross-validation are crucial for assessing model performance and...
Existing deep learning methods for brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) face several challenges, such as overfitting when training data are insufficient, and the difficulty of effectively capturing ...
Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific applications. However, eye blink detection can instead be transformed into a potential application of b...
Real-time Electrocardiogram (ECG) anomaly detection is critical for accurate diagnosis and timely intervention in cardiac disorders. Existing models, such as CNNs and LSTMs, often struggle with long-range dependencies, generalization across multiple ...
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