AI Medical Compendium Journal:
Physiological measurement

Showing 61 to 70 of 122 articles

A deep neural network for estimating the bladder boundary using electrical impedance tomography.

Physiological measurement
OBJECTIVE: Accurate bladder size estimation is an important clinical parameter that assists physicians, enabling them to provide better treatment for patients who are suffering from urinary incontinence. Electrical impedance tomography (EIT) is a non...

Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing.

Physiological measurement
OBJECTIVE: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing.

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis.

Physiological measurement
OBJECTIVE: Multi-frequency symmetry difference electrical impedance tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfull...

Neural network-based supervised descent method for 2D electrical impedance tomography.

Physiological measurement
OBJECTIVE: In this work, we study the application of the neural network-based supervised descent method (NN-SDM) for 2D electrical impedance tomography.

Photoplethysmographic-based automated sleep-wake classification using a support vector machine.

Physiological measurement
OBJECTIVE: Sleep quality has a significant impact on human mental and physical health. The detection of sleep-wake states is thus of paramount importance in the study of sleep. The gold standard method for sleep-wake classification is multi-sensor-ba...

Classification of cognitive reserve in healthy older adults based on brain activity using support vector machine.

Physiological measurement
OBJECTIVE: Cognitive reserve (CR) refers to the capacity of the brain to actively cope with damage via the implementation of remedial cognitive processes. Traditional CR measurements focus on static proxies, which may not be able to appropriately est...

Localization of origins of premature ventricular contraction in the whole ventricle based on machine learning and automatic beat recognition from 12-lead ECG.

Physiological measurement
OBJECTIVE: The localization of origins of premature ventricular contraction (PVC) is the key factor for the success of ablation of ventricular arrhythmias. Existing methods rely heavily on manual extraction of PVC beats, which limits their applicatio...

Detection of strict left bundle branch block by neural network and a method to test detection consistency.

Physiological measurement
OBJECTIVE: To develop an automatic algorithm to detect strict left bundle branch block (LBBB) on electrocardiograms (ECG) and propose a procedure to test the consistency of neural network detections.

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising.

Physiological measurement
OBJECTIVE: Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. However, the low signal-to-noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of t...

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

Physiological measurement
OBJECTIVE: Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as ...