AI Medical Compendium Topic:
Signal Processing, Computer-Assisted

Clear Filters Showing 951 to 960 of 1879 articles

An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition.

Sensors (Basel, Switzerland)
Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher c...

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.

Journal of healthcare engineering
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a chal...

Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest.

Journal of healthcare engineering
Premature ventricular contraction (PVC) is one of the most common arrhythmias in the clinic. Due to its variability and susceptibility, patients may be at risk at any time. The rapid and accurate classification of PVC is of great significance for the...

Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

Pediatric research
BACKGROUND: To characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries soun...

Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network.

International journal of neural systems
Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work, a novel approach, combining Stockwell transform (S-transform) with deep Convolutional Neural Networ...

A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection.

Physiological measurement
UNLABELLED: Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patients, the detection of epileptic seizures is of great significance.

Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, ...

Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects.

IEEE journal of biomedical and health informatics
Motor fluctuations are a frequent complication in patients with Parkinson's disease (PD) where the response to medication fluctuates between ON states (medication working) and OFF states (medication has worn off). This paper describes a new data anal...

EEG-based single-channel authentication systems with optimum electrode placement for different mental activities.

Biomedical journal
BACKGROUND: Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authentication and security is a very important issue in our life and brainwave-based authentication is a...