Inequality Indexes as Sparsity Measures Applied to Ventricular Ectopic Beats Detection and its Efficient Hardware Implementation

Meeting application requirements under a tight power budget is of a primary importance to enable connected health internet of things applications. This paper considers using sparse representation and well-defined inequality indexes drawn from the theory of inequality to distinguish ventricular ectopic beats (VEBs) from non-VEBs. Our approach involves designing a separate dictionary for each arrhythmia class using a set of labeled training QRS complexes. Sparse representation, based on the designed dictionaries of each new test QRS complex is then calculated. Following this, its class is predicted using the winner-takes-all principle by selecting the class with the highest inequality index. The experiments showed promising results ranging between 80% and 100% for the detection of VEBs considering the patient-specific approach, 80% using cross validation and 70% on unseen data using independent sets for training and testing, respectively. An efficient hardware implementation of the alternating direction method of multipliers algorithm is also presented. The results show that the proposed hardware implementation can classify a QRS complex in 69.3 ms that use only 0.934 W energy

le rôle des technologies de l'information qui soutiennent la gestion des connaissances dans la création d'un avantage concurrentiel étude de cas Banques exploitation (wilaya d’Annaba)

Cette étude vise à mettre en évidence le rôle de la technologie d'information aide pour la gestion des connaissances et de son rôle dans la création d'un avantage concurrentiel, et pour atteindre cet objectif, nous avons une étude de terrain sur un échantillon de banques commerciales opérant (wilaya d’Annaba), à travers la conception d'un questionnaire et distribué comme le nombre de répondants (92) employés, et cela pour connaître leur point de vue à propos de la contribution de ce type de technologie dans le succès du processus de gestion des connaissances(création, le stockage, la distribution et l'application), et le rôle important qu'ils jouent dans la création un des établissements distingués, l'étude a révélé plusieurs résultats, notamment la présence d'un impact positif de la technologie d'information aide pour la gestion des connaissances dans la création d'un avantage concurrentiel pour la banque, à la lumière de ces résultats, il est recommandé la nécessité de travailler pour fournir le nécessaire matériel et des logiciels pour faciliter les opérations, et d'accroître la sensibilisation parmi le personnel de l'importance du travail d'équipe (Group work) à travers le réseau interne (intranet) et reposent sur des systèmes d'intelligence artificielle pour appuyer la prise de décisions et obtenir un avantage concurrentiel

Efficient Pre-Designed Convolutional Front-End for Deep Learning

2019 IEEE. This paper introduces a hierarchical learning paradigm based on a predesigned directional filter bank front-end analogous to the energy model for complex cells. The filter bank front-end is designed to extract common primitive features such as orientations and edges. Each energy response is subjected to a shunting inhibition operator to enhance contrast and reduce the effects of illumination variations. This is followed by a divisive-normalization, which bounds the responses of the feature maps. The normalized responses are then propagated through a two-layer convolutional neural network (CNN) back-end for classification. The efficiency of the proposed approach is demonstrated using the CIFAR-10 dataset, and its performance is compared against that of the DTCWT ScaterNet front-end

An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG) sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP) algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process

Design of acoustic absorbing metasurfaces using a data-driven approach

Abstract The design of acoustic metasurfaces with desirable properties is challenging due to their artificial nature and the large space of physical and geometrical parameters. Until recently, design strategies were primarily based on numerical simulations based on finite-element or finite-difference time-domain methods, which are limited in terms of computational speed or complexity. Here, we present an efficient two-stage data-driven approach for analyzing and designing membrane-type metasurface absorbers with desirable characteristics. In the first stage, a forward model consisting of a neural network is trained to map an input, comprising the membrane parameters, to the observed sound absorption spectrum. In the second stage, the learned forward model is inverted to infer the input parameters that produce the desired absorption response. The metasurface membrane parameters, which serve as input to the neural network, are estimated by minimizing a loss function between the desired absorption profile and the output of the learned forward model. Two devices are then fabricated using the estimated membrane parameters. The measured acoustic absorption responses of the fabricated devices show a very close agreement with the desired responses

A transform-based feature extraction approach for motor imagery tasks classification

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain-computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling's $T^{2}$ statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%

Identification of Potential Risk Factors of Diabetes for the Qatari Population

© 2020 IEEE. Large-scale cohorts are established in different regions of the world to identify the complex interaction of genetic, environmental, and lifestyle-related factors that may contribute to chronic diseases including diabetes. Qatar Biobank (QBB) is the largest repository for cohort study specific to the Qatari population. There are few studies based on the QBB cohort, which highlighted multiple risk factors responsible for diabetes in the Qatari population. However, no comprehensive research has been done using machine learning techniques to identify key factors that may contribute to diabetes specific to the Qatari population. We developed several machine-learning models using QBB data to classify diabetic patients from the non-diabetic participants forming the control group for this study. From the roster of several hundred measurements, we identified 25 potential risk factors that might be influential in distinguishing diabetic patients from nondiabetic participants. From the identified risk factors, we ranked HbAlc, Glucose, and LDL-Cholesterol as the most influential risk factors. Using these risk factors, we also developed several machine-learning models to classify diabetic subjects from healthy subjects. Overall, the classifiers achieved 0.85 F1-score in classifying diabetic subjects from non-diabetic subjects. Further investigation will pave the way for the inclusion of the identified risk factors into the standard diabetes screening process of the Qatari population

Classification of Sleep Arousal using Compact CNN

© 2020 IEEE. Sleep arousal is a common health problem that negatively affects the quality of sleep. This study investigates the use of a compact convolutional neural network (CNN) to classify apnea and non-apnea sleep arousal categories. The experiments are conducted on a randomly selected subset of the physiological signals provided by the PhysioNet 2018 challenge dataset. In particular, three electroencephalography (EEG) channels, two electromyography (EMG) channels, electrooculography (EOG), and airflow data are used to build the classification model. Physiological signals are down-sampled by a factor of 2 and then split into two-second long non-overlapping window segments. A data augmentation technique is then applied to overcome the large class imbalance ratio between two sleep arousal categories. The network is trained on 80% of the segments extracted from the data of 100 subjects. With only 594 trainable parameters, our approach achieves an area under the precision-recall curve (AUPRC) of 0.677 for the intra-subject test (20% of the data from the 100 subjects), and 0.183 on the inter-subject test on the data of another 12 unseen test subjects. This result falls within the range of the official scores of the challenge winners, indicating a promising application in using this lightweight CNN model for automated classification of sleep arousal