AIMC Topic: Signal Processing, Computer-Assisted

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Research progress and future prospects in intelligent lung sound diagnosis: models, lightweight design, and hardware platform implementation.

Biomedizinische Technik. Biomedical engineering
Lung sounds, as an important physiological signal of human body, play a crucial role in the diagnosis and monitoring of respiratory diseases. In recent years, deep learning-based lung sound intelligent recognition technology has made significant prog...

Enhanced epileptic seizure detection using CNNs with convolutional block attention and short-term memory networks.

Behavioural brain research
Analyzing the electroencephalography (EEG) signals of epilepsy patients can monitor the condition, detect and intervene in epileptic seizures in time. To enhance the lives of these patients, it is necessary to develop accurate methods to detect epile...

A cough detection method based on the conformer-BiLSTM model.

Biomedical physics & engineering express
Cough is a common symptom of respiratory disease, and its detection is a basic step in cough sound analysis. Manual cough sound segmentation is tedious, subjective, and inefficient. Cough sounds from real-world scenarios can be collected in various e...

Machine learning-based CAD detection using integrated ECG and PCG parameter features.

Biomedical physics & engineering express
The combined analysis of electrocardiogram (ECG) and phonocardiogram signals(PCG) has demonstrated significant potential in the non-invasive detection of coronary artery disease (CAD). The efficacy of combining cardiac pathological parameters such as...

Improved state refinement for LSTM determined 3D CAISR-LSTM model for automatic myocardial infarction detection.

Physiological measurement
Electrocardiograms (ECGs) contain valuable information in the clinical diagnosis of myocardial infarction (MI). However, its interpretation process is dependent on cardiologists with extensive clinical experience and expertise. The issue not only cau...

Segmentation-enhanced approach for emotion detection from EEG signals using the fuzzy C-mean and SVM.

Scientific reports
The analysis of EEG signals for determining emotion is one of the most important topics in the field of artificial intelligence. It can be applied in a wide variety of areas, such as emotional health care and the man/machine interface. The purpose of...

A model for epileptic EEG detection and recognition based on Multi-Attention mechanism and Spatiotemporal.

Scientific reports
In the field of neuroscience, epilepsy is a chronic non-communicable brain disease that affects approximately 50 million people worldwide. Electroencephalography (EEG) has become a key tool in detecting and characterizing human neurological diseases ...

Integrating physiological signals for enhanced sleep apnea diagnosis with SleepNet.

Scientific reports
Sleep apnea, a prevalent respiratory disorder, poses significant health risks, including cardiovascular complications and behavioral issues, if left untreated. Traditional diagnostic methods like polysomnography, although effective, are often expensi...

Assessment of pulse wave velocity through weighted visibility graph metrics from photoplethysmographic signals.

Scientific reports
Pulse Wave Velocity (PWV) is a widely recognized non-invasive biomarker of arterial stiffness and an independent predictor of cardiovascular risk, including atherosclerosis, hypertension, and vascular aging. Accurate, accessible estimation of PWV is,...

Principal component conditional generative adversarial networks for imbalanced ECG classification enhancement.

PloS one
With over a century of development, electrocardiogram (ECG) diagnostics has become the preferred tool for healthcare professionals in cardiovascular disease diagnosis and monitoring. As wearable devices and mobile monitoring technologies become wides...