296 research outputs found
Overview of processing techniques for surface electromyography signals
Surface electromyography (sEMG) is a technology to assess muscle activation,
which is an important component in applications related to diagnosis,
treatment, progression assessment, and rehabilitation of specific individuals'
conditions. Recently, sEMG potential has been shown, since it can be used in a
non-invasive manner; nevertheless, it requires careful signal analysis to
support health professionals reliably. This paper briefly described the basic
concepts involved in the sEMG, such as the physiology of the muscles, the data
acquisition, the signal processing techniques, and classification methods that
may be used to identify disorders or signs of abnormalities according to
muscular patterns. Specifically, classification methods encompass digital
signal processing techniques and machine learning with high potential in the
field. We hope that this work serves as an introduction to researchers
interested in this field.Comment: 11 pages, 7 figure
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
The research in myoelectric control systems primarily focuses on extracting
discriminative representations from the electromyographic (EMG) signal by
designing handcrafted features. Recently, deep learning techniques have been
applied to the challenging task of EMG-based gesture recognition. The adoption
of these techniques slowly shifts the focus from feature engineering to feature
learning. However, the black-box nature of deep learning makes it hard to
understand the type of information learned by the network and how it relates to
handcrafted features. Additionally, due to the high variability in EMG
recordings between participants, deep features tend to generalize poorly across
subjects using standard training methods. Consequently, this work introduces a
new multi-domain learning algorithm, named ADANN, which significantly enhances
(p=0.00004) inter-subject classification accuracy by an average of 19.40%
compared to standard training. Using ADANN-generated features, the main
contribution of this work is to provide the first topological data analysis of
EMG-based gesture recognition for the characterisation of the information
encoded within a deep network, using handcrafted features as landmarks. This
analysis reveals that handcrafted features and the learned features (in the
earlier layers) both try to discriminate between all gestures, but do not
encode the same information to do so. Furthermore, using convolutional network
visualization techniques reveal that learned features tend to ignore the most
activated channel during gesture contraction, which is in stark contrast with
the prevalence of handcrafted features designed to capture amplitude
information. Overall, this work paves the way for hybrid feature sets by
providing a clear guideline of complementary information encoded within learned
and handcrafted features.Comment: The first two authors shared first authorship. The last three authors
shared senior authorship. 32 page
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention
Heart disease is one of the most common diseases causing morbidity and
mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart
diseases for its simplicity and non-invasive property. Automatic ECG analyzing
technologies are expected to reduce human working load and increase diagnostic
efficacy. However, there are still some challenges to be addressed for
achieving this goal. In this study, we develop an algorithm to identify
multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline,
several preprocessing methods are firstly applied on the ECG data for
denoising, augmentation and balancing recording numbers of variant classes. In
consideration of efficiency and consistency of data length, the recordings are
padded or truncated into a medium length, where the padding/truncating time
windows are selected randomly to sup-press overfitting. Then, the ECGs are used
to train deep neural network (DNN) models with a novel structure that combines
a deep residual network with an attention mechanism. Finally, an ensemble model
is built based on these trained models to make predictions on the test data
set. Our method is evaluated based on the test set of the First China ECG
Intelligent Competition dataset by using the F1 metric that is regarded as the
harmonic mean between the precision and recall. The resultant overall F1 score
of the algorithm is 0.875, showing a promising performance and potential for
practical use.Comment: 8 pages, 2 figures, conferenc
Variational Autoencoder and Sensor Fusion for Robust Myoelectric Controls
Myoelectric control schemes aim to utilize the surface electromyography (EMG) signals which are the electric potentials directly measured from skeletal muscles to control wearable robots such as exoskeletons and prostheses. The main challenge of myoelectric controls is to increase and preserve the signal quality by minimizing the effect of confounding factors such as muscle fatigue or electrode shift. Current research in myoelectric control schemes are developed to work in ideal laboratory conditions, but there is a persistent need to have these control schemes be more robust and work in real-world environments. Following the manifold hypothesis, complexity in the world can be broken down from a high-dimensional space to a lower-dimensional form or representation that can explain how the higher-dimensional real world operates. From this premise, the biological actions and their relevant multimodal signals can be compressed and optimally pertinent when performed in both laboratory and non-laboratory settings once the learned representation or manifold is discovered. This thesis outlines a method that incorporates the use of a contrastive variational autoencoder with an integrated classifier on multimodal sensor data to create a compressed latent space representation that can be used in future myoelectric control schemes
Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and
compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work
is to design a unified compression and classification framework for delivery of EEG
data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is
practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, NaĂŻve Bayes, k-Nearest
Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data.
Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed
framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems
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