AI Medical Compendium Journal:
Biosensors

Showing 71 to 80 of 94 articles

Robust PVC Identification by Fusing Expert System and Deep Learning.

Biosensors
Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early wa...

Intelligent Bio-Impedance System for Personalized Continuous Blood Pressure Measurement.

Biosensors
Continuous blood pressure (BP) measurement is crucial for long-term cardiovascular monitoring, especially for prompt hypertension detection. However, most of the continuous BP measurements rely on the pulse transit time (PTT) from multiple-channel ph...

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry.

Biosensors
The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as autom...

Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals.

Biosensors
This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein-Wiener model, the first part of this study outlines the ...

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.

Biosensors
Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography ...

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.

Biosensors
Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address thes...

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

Biosensors
Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of...

A Fluorescent Biosensor for Sensitive Detection of Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network.

Biosensors
In this study, a fluorescent biosensor was developed for the sensitive detection of typhimurium using a low-gradient magnetic field and deep learning via faster region-based convolutional neural networks (R-CNN) to recognize the fluorescent spots on...

Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network.

Biosensors
Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the simi...

Building a Cardiovascular Disease Prediction Model for Smartwatch Users Using Machine Learning: Based on the Korea National Health and Nutrition Examination Survey.

Biosensors
Smartwatches have the potential to support health care in everyday life by supporting self-monitoring of health conditions and personal activities. This paper aims to develop a model that predicts the prevalence of cardiovascular disease using health...