AIMC Topic: Signal Processing, Computer-Assisted
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An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.
Journal of healthcare engineering
Oct 13, 2019
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
Oct 7, 2019
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
Oct 4, 2019
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
Sep 30, 2019
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
Sep 30, 2019
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
Sep 27, 2019
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, ...
Sleep Wake Disorders
Sleep Stages
Machine Learning
Algorithms
Humans
Neural Networks, Computer
Electrocardiography
Electroencephalography
Signal Processing, Computer-Assisted
Sensitivity and Specificity
Calibration
Databases, Factual
Sleep Apnea, Obstructive
Male
Polysomnography
Reproducibility of Results
Sleep
Time Factors
Adult
Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects.
IEEE journal of biomedical and health informatics
Sep 26, 2019
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
Sep 24, 2019
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...
Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks.
IEEE journal of biomedical and health informatics
Sep 23, 2019
This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach especially designed for ECG is introduced, which can jointly represent the morphology and rhythm of t...
Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory.
International journal of neural systems
Sep 18, 2019
Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using de...
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