AIMC Topic: Signal Processing, Computer-Assisted
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Optimal length of R-R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning.
Biomedical engineering online
Jun 16, 2020
BACKGROUND: Heartbeat interval Lorenz plot (LP) imaging is a promising method for detecting atrial fibrillation (AF) in long-term monitoring, but the optimal segment window length for the LP images is unknown. We examined the performance of AF detect...
Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.
Circulation. Arrhythmia and electrophysiology
Jun 14, 2020
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch b...
Bundle-Branch Block
Unsupervised Machine Learning
Recovery of Function
Humans
Predictive Value of Tests
Retrospective Studies
Signal Processing, Computer-Assisted
Aged
Heart Failure
Diagnosis, Computer-Assisted
Stroke Volume
Male
Cardiac Resynchronization Therapy
Time Factors
Middle Aged
Ventricular Function, Left
Heart-Assist Devices
Electrocardiography
Risk Assessment
Female
Heart Transplantation
Treatment Outcome
Disease Progression
Risk Factors
Convolutional neural network for detection and classification of seizures in clinical data.
Medical & biological engineering & computing
Jun 12, 2020
Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usual...
EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks.
Sensors (Basel, Switzerland)
Jun 12, 2020
Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an...
Binary CorNET: Accelerator for HR Estimation From Wrist-PPG.
IEEE transactions on biomedical circuits and systems
Jun 11, 2020
Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly su...
Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off.
PloS one
Jun 11, 2020
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the...
Machine-learning-based diagnostics of EEG pathology.
NeuroImage
Jun 10, 2020
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding ...
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
Jun 10, 2020
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...
Single-Trial EEG Responses Classified Using Latency Features.
International journal of neural systems
Jun 2, 2020
Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are increasingly adopted to classify single-trial EEG responses thereby primarily relying on amplitude-base...
A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.
IEEE transactions on biomedical circuits and systems
May 28, 2020
In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset...
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