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Signal Processing, Computer-Assisted

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Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

Scientific reports
The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental techn...

A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games.

IEEE journal of biomedical and health informatics
Stress is revealed by the inability of individuals to cope with their environment, which is frequently evidenced by a failure to achieve their full potential in tasks or goals. This study aims to assess the feasibility of estimating the level of stre...

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement.

IEEE journal of biomedical and health informatics
Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address t...

MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition.

IEEE journal of biomedical and health informatics
Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder regulation to human-computer interaction. In this paper, we address two fundamental aspects of EEG ...

A Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity.

IEEE journal of biomedical and health informatics
Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact a...

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

IEEE journal of biomedical and health informatics
Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, ...

A Deep Learning Approach for Fear Recognition on the Edge Based on Two-Dimensional Feature Maps.

IEEE journal of biomedical and health informatics
Applying affective computing techniques to recognize fear and combining them with portable signal monitors makes it possible to create real-time detection systems that could act as bodyguards when users are in danger. With this aim, this paper presen...

Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks.

IEEE journal of biomedical and health informatics
OBJECTIVE: wearable sensor technology has progressed significantly in the last decade, but its clinical usability for the assessment of obstructive sleep apnea (OSA) is limited by the lack of large and representative datasets simultaneously acquired ...

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

IEEE journal of biomedical and health informatics
The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-based patients. Epilepsy, a chronic brain syndrome characterized by recurrent, unpredictable attacks, affects individuals of all ages. IoT-based seizur...

Machine learning-based atrial fibrillation detection and onset prediction using QT-dynamicity.

Physiological measurement
. This study examines the value of ventricular repolarization using QT dynamicity for two different types of atrial fibrillation (AF) prediction.. We studied the importance of QT-dynamicity (1) in the detection and (2) the onset prediction (i.e. fore...