Auto-encoder based deep learning for surface electromyography signal processing

© 2018 Advances in Science, Technology and Engineering Systems. All Rights Reserved. Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages) to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU) will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF) for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and Analysis of Variance will be estimated for different classifiers. Classifier fusion layer will be implemented to glean the classifier which will progress the best accuracies' values for both testing and training to develop the results. Classifier fusion layer brought an encouraging value for both accuracies either training or testing ones

ICA based feature learning and feature selection

© 2016 IEEE. Feature extraction is playing a major role in bio signal processing. Feature identification and selection has two approaches. The common approach is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The idea behind feature learning is to avoid dealing with any feature extraction or reduction algorithms and to train the suggested model on learning with avoiding the exposure to feature extraction which is mainly based on researcher experience. In this paper, Independent component analysis (ICA) will be implemented as a feature learning technique to learn the model extract the features from the input data. Deep learning approach will be proposed by implementing ICA to learn features. In the proposed model, the raw data will be read then represented by using different signal representation as Spectrogram, Wavelet and Wavelet Packet. Then, the new represented data will be fed to Independent component analysis layer to generate features and finally, the performance of the suggested scheme will be evaluated by applying different classifiers such as Support Vector Machine, Extreme Learning Machine and Discriminate Analysis. And As an improving step for the results, classifier fusion layer will be implemented to select the most accurate result for both training and testing set. Classifier fusion layer resulted in a promising training and testing accuracies. On the other side, Feature Selection is the process of selecting subset of features from a pool of features

Head and neck cancer surgery during the COVID-19 pandemic: An international, multicenter, observational cohort study

Background: The aims of this study were to provide data on the safety of head and neck cancer surgery currently being undertaken during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This international, observational cohort study comprised 1137 consecutive patients with head and neck cancer undergoing primary surgery with curative intent in 26 countries. Factors associated with severe pulmonary complications in COVID-19–positive patients and infections in the surgical team were determined by univariate analysis. Results: Among the 1137 patients, the commonest sites were the oral cavity (38%) and the thyroid (21%). For oropharynx and larynx tumors, nonsurgical therapy was favored in most cases. There was evidence of surgical de-escalation of neck management and reconstruction. Overall 30-day mortality was 1.2%. Twenty-nine patients (3%) tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within 30 days of surgery; 13 of these patients (44.8%) developed severe respiratory complications, and 3.51 (10.3%) died. There were significant correlations with an advanced tumor stage and admission to critical care. Members of the surgical team tested positive within 30 days of surgery in 40 cases (3%). There were significant associations with operations in which the patients also tested positive for SARS-CoV-2 within 30 days, with a high community incidence of SARS-CoV-2, with screened patients, with oral tumor sites, and with tracheostomy. Conclusions: Head and neck cancer surgery in the COVID-19 era appears safe even when surgery is prolonged and complex. The overlap in COVID-19 between patients and members of the surgical team raises the suspicion of failures in cross-infection measures or the use of personal protective equipment. Lay Summary: Head and neck surgery is safe for patients during the coronavirus disease 2019 pandemic even when it is lengthy and complex. This is significant because concerns over patient safety raised in many guidelines appear not to be reflected by outcomes, even for those who have other serious illnesses or require complex reconstructions. Patients subjected to suboptimal or nonstandard treatments should be carefully followed up to optimize their cancer outcomes. The overlap between patients and surgeons testing positive for severe acute respiratory syndrome coronavirus 2 is notable and emphasizes the need for fastidious cross-infection controls and effective personal protective equipment

Outcomes after perioperative SARS-CoV-2 infection in patients with proximal femoral fractures: an international cohort study

Objectives Studies have demonstrated high rates of mortality in people with proximal femoral fracture and SARS-CoV-2, but there is limited published data on the factors that influence mortality for clinicians to make informed treatment decisions. This study aims to report the 30-day mortality associated with perioperative infection of patients undergoing surgery for proximal femoral fractures and to examine the factors that influence mortality in a multivariate analysis. Setting Prospective, international, multicentre, observational cohort study. Participants Patients undergoing any operation for a proximal femoral fracture from 1 February to 30 April 2020 and with perioperative SARS-CoV-2 infection (either 7 days prior or 30-day postoperative). Primary outcome 30-day mortality. Multivariate modelling was performed to identify factors associated with 30-day mortality. Results This study reports included 1063 patients from 174 hospitals in 19 countries. Overall 30-day mortality was 29.4% (313/1063). In an adjusted model, 30-day mortality was associated with male gender (OR 2.29, 95% CI 1.68 to 3.13, p80 years (OR 1.60, 95% CI 1.1 to 2.31, p=0.013), preoperative diagnosis of dementia (OR 1.57, 95% CI 1.15 to 2.16, p=0.005), kidney disease (OR 1.73, 95% CI 1.18 to 2.55, p=0.005) and congestive heart failure (OR 1.62, 95% CI 1.06 to 2.48, p=0.025). Mortality at 30 days was lower in patients with a preoperative diagnosis of SARS-CoV-2 (OR 0.6, 95% CI 0.6 (0.42 to 0.85), p=0.004). There was no difference in mortality in patients with an increase to delay in surgery (p=0.220) or type of anaesthetic given (p=0.787). Conclusions Patients undergoing surgery for a proximal femoral fracture with a perioperative infection of SARS-CoV-2 have a high rate of mortality. This study would support the need for providing these patients with individualised medical and anaesthetic care, including medical optimisation before theatre. Careful preoperative counselling is needed for those with a proximal femoral fracture and SARS-CoV-2, especially those in the highest risk groups. Trial registration number NCT0432364

Delaying surgery for patients with a previous SARS-CoV-2 infection

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