AIMC Topic: Signal Processing, Computer-Assisted

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A fine-tuning deep residual convolutional neural network for emotion recognition based on frequency-channel matrices representation of one-dimensional electroencephalography.

Computer methods in biomechanics and biomedical engineering
Emotion recognition (ER) plays a crucial role in enabling machines to perceive human emotional and psychological states, thus enhancing human-machine interaction. Recently, there has been a growing interest in ER based on electroencephalogram (EEG) s...

A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG).

Journal of cardiovascular translational research
A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and ...

Deep learning with fetal ECG recognition.

Physiological measurement
Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG reco...

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.

IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification.

Computer methods in biomechanics and biomedical engineering
As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for...

A deep learning-based approach for distinguishing different stress levels of human brain using EEG and pulse rate.

Computer methods in biomechanics and biomedical engineering
In today's world, people suffer from many fatal maladies, and stress is one of them. Excessive stress can have deleterious effects on the health, brain, mind, and nervous system of humans. The goal of this paper is to design a deep learningbased huma...

Comparative Analysis of Audio Processing Techniques on Doppler Radar Signature of Human Walking Motion Using CNN Models.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipm...

Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals.

BMC medical informatics and decision making
BACKGROUND: Cardiac arrhythmia is a cardiovascular disorder characterized by disturbances in the heartbeat caused by electrical conduction anomalies in cardiac muscle. Clinically, ECG machines are utilized to diagnose and monitor cardiac arrhythmia n...

Detection and classification of adult epilepsy using hybrid deep learning approach.

Scientific reports
The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinc...

Unraveling the complexities of pathological voice through saliency analysis.

Computers in biology and medicine
The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice ...