Transactions on Neural Systems and Rehabilitation Engineering

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Improved High-density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural NetworkT
The objective of this work is to develop a robust method for myoelectric control towards alleviating the in-terference of electrode shift. Methods: In the proposed method, a preprocessing approach was first performed to convert high-den-sity surface electromyogram (HD-sEMG) signals into a series of images, and the electrode shift appeared as pixel shift in these im-ages... Read more
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Deep Learning Architecture to Assist with Steering a Powered Wheelchair
This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for steering a powered wheelchair... Read more
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A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data
Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection have not been effectively harnessed due to sub-optimal classifier design... Read more
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Mammography Image Quality Assurance Using Deep Learning
Image quality assurance is crucial in mammography to ensure reliable breast cancer diagnostics. Analyzing images of a technical phantom allows to routinely and reliably assess image quality. Current state-of-the-art analysis determines local image quality features by applying pre-processing and regression procedures for a set of repeatedly recorded images. This proof of concept paper demonstrates that mammography image quality assessment can benefit from deep learning. A neural network is trained on a large database of phantom images, and it is shown that the trained net retrieves the local image quality features already from single images without cumbersome pre-processing. This allows to maintain quality standards at significantly less labor... Read more
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Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients using Primary Care Electronic Health Records
A growing elderly population suffering from incurable, chronic conditions such as dementia present a continual strain on medical services due to mental impairment paired with high comorbidity resulting in increased hospitalization risk. The identification of at risk individuals allows for... Read more
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COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings
Abstract: Goal: We hypothesized that COVID-19 subjects, especially including asymptomatics, could be accurately discriminated only from a forced-cough cell phone recording using Artificial Intelligence. To train our MIT Open Voice model we built a data collection pipeline of COVID-19 cough recordings... Read more
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Lung Nodule Malignancy Prediction from Longitudinal CT Scans with Siamese Convolutional Attention Networks
Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. Methods: Our proposed framework is evaluated for single- and multi-time-point classification to explore... Read more
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AI in Medical Imaging Informatics: Current Challenges and Future Directions
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity... Read more
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Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach
Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack... Read more
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Localizing B-lines in Lung Ultrasonography by Weakly-Supervised Deep Learning, in-vivo results
Lung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. Of particular interest are several imaging-artifacts, e.g., A- and B- line artifacts. While A-lines are a visual pattern which essentially represent a healthy lung surface,... Read more