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Signal Processing, Computer-Assisted

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A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the ...

Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.

Neural networks : the official journal of the International Neural Network Society
Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits...

An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction.

Medical engineering & physics
Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for det...

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This...

A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning.

Sensors (Basel, Switzerland)
In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in ou...

Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition.

IEEE journal of biomedical and health informatics
Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and ...

Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.

IEEE journal of biomedical and health informatics
Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and appli...

ST-Phys: Unsupervised Spatio-Temporal Contrastive Remote Physiological Measurement.

IEEE journal of biomedical and health informatics
Remote photoplethysmography (rPPG) is a non-contact method that employs facial videos for measuring physiological parameters. Existing rPPG methods have achieved remarkable performance. However, the success mainly profits from supervised learning ove...

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network.

IEEE journal of biomedical and health informatics
This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by int...

MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea in Classification Problem.

IEEE journal of biomedical and health informatics
Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient tool for the classification problem in electrocardiogram (ECG)-based SA diagnoses. Despite these advanc...