AIMC Topic: Signal Processing, Computer-Assisted

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A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals.

Computational and mathematical methods in medicine Apr 7, 2020
The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and he...
Nonlinear Dynamics Computational Biology Databases, Factual Humans Models, Neurological Diagnosis, Computer-Assisted Neural Networks, Computer Electroencephalography Models, Statistical Algorithms Seizures Deep Learning Signal Processing, Computer-Assisted
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An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Apr 2, 2020
OBJECTIVE: To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy.
Humans Artificial Intelligence Algorithms Electroencephalography Epilepsy Signal Processing, Computer-Assisted Brain Sensitivity and Specificity Deep Learning
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Doppler-Spectrum Feature-Based Human-Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor.

Sensors (Basel, Switzerland) Apr 2, 2020
In this paper, we propose a Doppler-spectrum feature-based human-vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering poi...
Support Vector Machine Signal Processing, Computer-Assisted Automobile Driving Pedestrians Ultrasonography, Doppler Humans Radar Machine Learning Algorithms
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Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques.

Physical and engineering sciences in medicine Apr 1, 2020
An approach is proposed for the detection of chronic heart disorders from the electrocardiogram (ECG) signals. It utilizes an intelligent event-driven ECG signal acquisition system to achieve a real-time compression and effective signal processing an...
Humans Machine Learning Area Under Curve Signal Processing, Computer-Assisted Cloud Computing ROC Curve Analog-Digital Conversion Electrocardiography Support Vector Machine
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EEG based Classification of Long-term Stress Using Psychological Labeling.

Sensors (Basel, Switzerland) Mar 29, 2020
Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavaila...
Brain-Computer Interfaces Algorithms Female Young Adult Humans Machine Learning Support Vector Machine Electroencephalography Signal Processing, Computer-Assisted Adult Male Stress, Psychological
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Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks.

Nature communications Mar 25, 2020
Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challeng...
Magnetic Phenomena Motion Human Activities Wireless Technology Wearable Electronic Devices Monitoring, Physiologic Signal Processing, Computer-Assisted Humans Deep Learning
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Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals.

IEEE transactions on biomedical circuits and systems Mar 23, 2020
The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural netw...
Heart Rate Electrocardiography Electroencephalography Signal Processing, Computer-Assisted Humans Neural Networks, Computer Brain
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Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.

Journal of the American Heart Association Mar 21, 2020
Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The...
Humans Retrospective Studies Signal Processing, Computer-Assisted Early Diagnosis Middle Aged Aged Severity of Illness Index Aged, 80 and over Republic of Korea Aortic Valve Electrocardiography Deep Learning Male Female Predictive Value of Tests Aortic Valve Stenosis Reproducibility of Results
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Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning.

IEEE transactions on biomedical circuits and systems Mar 18, 2020
The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model t...
Respiratory Sounds Neural Networks, Computer Signal Processing, Computer-Assisted Patient-Specific Modeling Humans
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Automatic Seizure Detection using Fully Convolutional Nested LSTM.

International journal of neural systems Mar 16, 2020
The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are base...
Seizures Signal Processing, Computer-Assisted Cerebral Cortex Humans Sensitivity and Specificity Deep Learning Models, Theoretical Electroencephalography
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