Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. autoencoders (AEs) can be used as an automatic feature extraction tool, ta...
详细信息
Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them. In this work we show that morphological features extracted using a Sparse AE are sufficient to distinguish AFib from Normal Sinus Rhythm (NSR) beats. In addition to the morphological features, rhythm information was included in the model using a proposed short-term feature called Local Change of Successive Differences (LCSD). Using single-lead ECG recordings from two referenced public databases, and with features from the AE, the model was able to achieve an F1-score of 88.8%. These results show that morphological features appear to be a distinct and sufficient factor for detecting AFib in ECG recordings, especially when designed for patient-specific applications. This is an advantage over state-of-the-art algorithms that need longer acquisition times to extract engineered rhythm features, which also requires careful preprocessing steps. To the best of our knowledge, this is the first work that presents a near real-time morphological approach for AFib detection under naturalistic ECG acquisition with a mobile device.
Targeted stimulation of the brain has the potential to treat mental illnesses. The objective of this work is to develop methodology that enables scientists to design stimulation methods based on the electrophysiologic...
详细信息
Targeted stimulation of the brain has the potential to treat mental illnesses. The objective of this work is to develop methodology that enables scientists to design stimulation methods based on the electrophysiological dynamics. We first develop several factor models that characterize aspects of the dynamics relevant to these illnesses. Using a novel approach, we can then find a single predictive factor of the trait of interest. To improve the quality of the associated loadings, we develop a method for removing concomitant variables that can dominate the observed dynamics. We also develop a novel inference technique that increases the relevance of the predictive loadings. Finally, we demonstrate the efficacy of our methodology by finding a single factor responsible for social behavior. This factor is stimulated in new subjects and modifies behavior in the new individuals. These results indicate that our methodology has high potential in developing future cures of mental illness.
Predicting if a person is an adult or a minor has several applications such as inspecting underage driving, preventing purchase of alcohol and tobacco by minors, and granting restricted access. The challenging nature ...
详细信息
Predicting if a person is an adult or a minor has several applications such as inspecting underage driving, preventing purchase of alcohol and tobacco by minors, and granting restricted access. The challenging nature of this problem arises due to the complex and unique physiological changes that are observed with age progression. This paper presents a novel deep learning based formulation, termed as Class Specific Mean autoencoder, to learn the intra-class similarity and extract class-specific features. We propose that the feature of a particular class if brought similar/closer to the mean feature of that class can help in learning class-specific representations. The proposed formulation is applied for the task of adulthood classification which predicts whether the given face image is of an adult or not. Experiments are performed on two large databases and the results show that the proposed algorithm yields higher classification accuracy compared to existing algorithms and a Commercial-Off-The-Shelf system. (C) 2018 Elsevier B.V. All rights reserved.
Background: Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool ...
详细信息
Background: Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool to support AD diagnosis. In this article, we explore the application of supervised Switching autoencoders (SSAs) to perform AD classification using only one structural Magnetic Resonance Imaging (sMRI) slice. SSAs are revised supervised autoencoder architectures, combining unsupervised representation and supervised classification as one unified model. In this work, we study the capabilities of SSAs to capture complex visual neurodegeneration patterns, and fuse disease semantics simultaneously. We also examine how regions associated to disease state can be discovered by SSAs following a local patch-based approach. Methods: Patch-based SSAs models are trained on individual patches extracted from a single 2D slice, independently for Axial, Coronal, and Sagittal anatomical planes of the brain at selected informative locations, exploring different patch sizes and network parameterizations. Then, models perform binary class prediction - healthy (CDR = 0) or AD-demented (CDR > 0) - on test data at patch level. The final subject classification is performed employing a majority rule from the ensemble of patch predictions. In addition, relevant regions are identified, by computing accuracy densities from patch-level predictions, and analyzed, supported by Atlas-based regional definitions. Results: Our experiments employing a single 2D T1-w sMRI slice per subject show that SSAs perform similarly to previous proposals that rely on full volumetric information and feature-engineered representations. SSAs classification accuracy on slices extracted along the Axial, Coronal, and Sagittal anatomical planes from a balanced cohort of 40 independent test subjects was 87.5%, 90.0%, and 90.0%, respectively. A top sensitivity of 95.0% on both Coronal and Sagittal planes was also
Age has always been an important attribute of identity. It also has been an important factor in social interaction. The posture, vocabulary, facial wrinkles and the intonation are all elements that facilitate the pred...
详细信息
Age has always been an important attribute of identity. It also has been an important factor in social interaction. The posture, vocabulary, facial wrinkles and the intonation are all elements that facilitate the prediction of the user's age. Age estimation from the face by numerical analysis finds many potential applications such as the development of intelligent human-machine interfaces and improvement of safety and protection in various sectors such as transport, security and medicine. In many works, researchers are particularly interested in the face's features to regress the age. Recent advances in Artificial Intelligence (AI) and particulary Deep Learning (DL) techniques increase motivations to use this methods to estimate age. In this work, we present a novel method for age estimation from a facial images based on autoencoders. autoencoder is an artificial neural network used for unsupervised learning of efficient coding. Its aim is to learn a representation for a set of data. The purpose of this work is to exploit the performance of autoencoders to learn features in a supervised manner to estimate user's age. We use MORPH, FG-NET datasets to test the performance of our proposed method. Experimental results show the robustness and effectiveness of the proposed method through the MAE (Men Average Error) rate showing a value of 3.34% for MORPH dataset and 3.75% for FG-NET.
Unsupervised feature extraction is gaining a lot of research attention following its success to represent any kind of noisy data. Owing to the presence of a lot of training parameters, these feature learning models ar...
详细信息
Unsupervised feature extraction is gaining a lot of research attention following its success to represent any kind of noisy data. Owing to the presence of a lot of training parameters, these feature learning models are prone to overfitting. Different regularization methods have been explored in the literature to avoid overfitting in deep learning models. In this research, we consider autoencoder as the feature learning architecture and propose l(2.1)-norm based regularization to improve its learning capacity, called as Group Sparse autoencoder (GSAE). l(2.1) -norm is based on the postulate that the features from the same class will have a common sparsity pattern in the feature space. We present the learning algorithm for group sparse encoding using majorization-minimization approach. The performance of the proposed algorithm is also studied on three baseline image datasets: MNIST, CIFAR-10, and SVHN. Further, using GSAE, we propose a novel deep learning based image representation for minutia detection from latent fingerprints. Latent fingerprints contain only a partial finger region, very noisy ridge patterns, and depending on the surface it is deposited, contain significant background noise. We formulate the problem of minutia extraction as a two-class classification problem and learn the descriptor using the novel formulation of GSAE. Experimental results on two publicly available latent fingerprint datasets show that the proposed algorithm yields state-of-the-art results for automated minutia extraction. (C) 2017 Elsevier B.V. All rights reserved.
暂无评论