AIMC Topic: Signal Processing, Computer-Assisted

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GenEEG: Improving epileptic EEG detection through patient-adaptive latent diffusion and continual learning.

Computers in biology and medicine
Automated seizure detection systems face significant challenges due to the limited availability of clinical EEG data, a substantial class imbalance between seizure and non-seizure recordings, considerable variability among patients, and the issue of ...

Epileptic spasm recognition: EEG classification using time-frequency features and machine learning.

Biomedical engineering online
Epileptic spasm (ES), characterized by sudden muscle contractions and loss of consciousness, poses significant challenges in early diagnosis and treatment, especially in infants and young children. Despite advances in EEG-based seizure detection, the...

Early detection of paroxysmal atrial fibrillation from non-episodic ECG data using cardiac dynamics features and different classification models.

Biomedical physics & engineering express
Intelligent computer-aided diagnosis techniques enable inspection of invisible electrocardiogram (ECG) pathological changes for early detection of latent heart diseases. This study concentrates on latent pathological changes within non-episodic ECG d...

An electrical pulse artifact signal for estimating arterial blood pressure: a proof-of-concept study.

Physiological measurement
Hypertension is a leading cause of mortality worldwide, for which myriad treatment options are available. It is widely considered that continuous measurement of arterial blood pressure (BP) could improve the treatment of hypertension; however, chroni...

Dual-channel TRCA-net based on cross-subject positive transfer for SSVEP-BCI.

Biomedical physics & engineering express
. To enhance the decoding accuracy and information transfer rate of steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems and to reduce inter-subject variability for broader SSVEP-BCI applications, a dual-channel TRC...

NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer's Disease detection.

Scientific reports
Alzheimer's Disease (AD) is a very common neurodegenerative disorders and early detection using electroencephalography (EEG) can enable timely intervention, however, existing computational models often lack robustness, interpretability, and clinical ...

Hybrid machine learning models for enhanced arrhythmia detection from ECG signals using autoencoder and convolution features.

PloS one
Automated arrhythmia detection from electrocardiogram (ECG) signals is crucial and important for the early treatment of cardiac disease (CD). In this investigation, eight machine-learning models have been developed to identify improved ECG arrhythmia...

Dynamic reward-augmented ensemble learning for EEG signal classification in major depressive disorder.

Biomedical physics & engineering express
Major Depressive Disorder (MDD) diagnosis through Electroencephalography (EEG) is hindered by the non-stationary characteristics of neural oscillations and the limited adaptability of conventional classification frameworks. Static ensemble models, wh...

Assessing photoplethysmography signal quality for wearable devices during unrestricted daily activities.

Biomedical physics & engineering express
Photoplethysmography (PPG) is widely used in wearable health monitors for tracking fundamental physiological parameters (e.g., heart rate and blood oxygen saturation) and advancing applications requiring high-quality signals-such as blood pressure as...

Stress detection using time-frequency analysis and machine learning framework.

Biomedical physics & engineering express
Stress is a prevalent and inherent phenomenon in people. It triggers the production of hormones that assist in managing the scenarios; nevertheless, chronic stress adversely impacts physical and mental health, which may result in detrimental effects ...