Antepartum Fetal Monitoring through a Wearable System and a Mobile Application

Prenatal monitoring of Fetal Heart Rate (FHR) is crucial for the prevention of fetal pathologies and unfavorable deliveries. However, the most commonly used Cardiotocographic exam can be performed only in hospital-like structures and requires the supervision of expert personnel. For this reason, a wearable system able to continuously monitor FHR would be a noticeable step towards a personalized and remote pregnancy care. Thanks to textile electrodes, miniaturized electronics, and smart devices like smartphones and tablets, we developed a wearable integrated system for everyday fetal monitoring during the last weeks of pregnancy. Pregnant women at home can use it without the need for any external support by clinicians. The transmission of FHR to a specialized medical center allows its remote analysis, exploiting advanced algorithms running on high-performance hardware able to obtain the best classification of the fetal condition. The system has been tested on a limited set of pregnant women whose fetal electrocardiogram recordings were acquired and classified, yielding an overall score for both accuracy and sensitivity over 90%. This novel approach can open a new perspective on the continuous monitoring of fetus development by enhancing the performance of regular examinations, making treatments really personalized, and reducing hospitalization or ambulatory visits. Keywords: tele-monitoring; wearable devices; fetal heart rate; telemedicin

Enhanced immunological recovery with early start of antiretroviral therapy during acute or early HIV infection–results of Italian Network of ACuTe HIV InfectiON (INACTION) retrospective study

ABSTRACT Background: Viral load peak and immune activation occur shortly after exposure during acute or early HIV infection (AEHI). We aimed to define the benefit of early start of antiretroviral treatment (ART) during AEHI in terms of immunological recovery, virological suppression, and treatment discontinuation. Setting: Patients diagnosed with AEHI (Fiebig stages I-V) during 2008-2014 from an analysis of 20 Italian centers. Methods: This was an observational, retrospective, and multicenter study. We investigated the ef- fect of early ART (defined as initiation within 3 months from AEHI diagnosis) on time to virolog- ical suppression, optimal immunological recovery (defined as CD4 count ≥ 500/μL, CD4 ≥ 30%, and CD4/CD8 ≥ 1), and first-line ART regimen discontinuation by Cox regression analysis. Results: There were 321 patients with AEHI included in the study (82.9% in Fiebig stage III-V). At diagnosis, the median viral load was 5.67 log10 copies/mL and the median CD4 count was 456 cells/μL. Overall, 70.6% of patients started early ART (median time from HIV diagnosis to ART initiation 12 days, IQR 6-27). Higher baseline viral load and AEHI diagnosis during 2012-2014 were independently associated with early ART. HBV co-infection, baseline CD4/CD8 ≥ 1, lower baseline HIV-RNA, and AEHI diagnosis in recent years (2012-2014) were independently associ- ated with a shorter time to virological suppression. Early ART emerged as an independent predic- tor of optimal immunological recovery after adjustment for baseline CD4 (absolute and percent- age count) and CD4/CD8 ratio. The only independent predictor of first-line ART discontinuation was an initial ART regimen including > 3 drugs. Conclusions: In a large cohort of well-characterized patients with AEHI, we confirmed the ben- eficial role of early ART on CD4+ T-cell recovery and on rates of CD4/CD8 ratio normalization. Moreover, we recognized baseline CD4/CD8 ratio as an independent factor influencing time to virological response in the setting of AEHI, thus giving new insights into research of immunolog- ical markers associated with virological control

Asymptomatic neurocognitive disorders in patients infected by HIV: fact or fiction?

Neurocognitive disorders are emerging as a possible complication in patients infected with HIV. Even if asymptomatic, neurocognitive abnormalities are frequently detected using a battery of tests. This supported the creation of asymptomatic neurocognitive impairment (ANI) as a new entity. In a recent article published in BMC Infectious Diseases, Magnus Gisslén and colleagues applied a statistical approach, concluding that there is an overestimation of the actual problem. In fact, about 20% of patients are classified as neurocognitively impaired without a clear impact on daily activities. In the present commentary, we discuss the clinical implications of their findings. Although a cautious approach would indicate a stricter follow-up of patients affected by this disorder, it is premature to consider it as a proper disease. Based on a review of the data in the current literature we conclude that it is urgent to conduct more studies to estimate the overall risk of progression of the asymptomatic neurocognitive impairment. Moreover, it is important to understand whether new biomarkers or neuroimaging tools can help to identify better the most at risk population

Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~ 150 × with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system

Parallel real-time virtual dimensionality estimation for hyperspectral images

One of the most important tasks in hyperspectral imaging is the estimation of the number of endmembers in a scene, where the endmembers are the most spectrally pure components. The high dimensionality of hyperspectral data makes this calculation computationally expensive. In this paper, we present several new real-time implementations of the well-known Harsanyi–Farrand–Chang method for virtual dimensionality estimation. The proposed solutions exploit multi-core processors and graphic processing units for achieving real-time performance of this algorithm, together with better performance than other works in the literature. Our experimental results are obtained using both synthetic and real images. The obtained processing times show that the proposed implementations outperform other hardware-based solutions

OpenMP and CUDA Simulations of Sella Zerbino Dam Break on Unstructured Grids

This paper presents two 2D dam break parallelized models based on shallow water equations (SWE) written in conservative form. The models were implemented exploiting multicore PC systems and graphics processor unit (GPU) architectures under the OpenMP and the NVIDIA™’s compute unified device architecture (CUDA) frameworks. The mathematical model is solved using a finite-volume technique on an unstructured grid, with Roe’s approximate Riemann solver, a first-order upwind scheme. The upwind treatment of the source terms is implemented. A technique to cope with a wetting-drying advance front is adopted, together with the inclusion of the influence of source terms in the stability constraint in order to prevent negative water depths at the dry fronts. The proposed model is first applied to a laboratory test and then to a real dam break that occurred in Italy in 1935. Results on different grid sizes are compared to show the computing efficiency between the original sequential model and the parallelized models

FPGA High Level Synthesis for the classification of skin tumors with hyperspectral images

Cancer is the main cause of premature death in the world, with 18 million diagnoses in 2018, 3.9 million of which in Europe. In particular, according to studies conducted by the American Academy of Dermatology, skin cancer is the most prevalent type in the US. Diagnostic tools are generally invasive, hence research focuses on emerging technologies, like hyperspectral images, since they are non-invasive, contactless and non-ionizing. A hyperspectral image acquisition system has been used to produce a database of 49 images from 36 patients, used to validate an innovative machine learning algorithm. Starting from our original serial implementation, a novel version has been developed with modern technologies of High Level Synthesis (HLS) using FPGA, to verify the feasibility of a portable instrument. Differences in implementation, HLS optimizations and latency times have been compared and evaluated. The algorithm has been tested on different FPGAs, to identify the optimal device for the purpose. Finally, the proposed hardware architecture processes hyperspectral images dissipating less energy than state-of-the-art GPU solutions

A low power and real-time hardware recurrent neural network for time series analysis on wearable devices

The research presented in this paper addresses the exploitation of Deep Learning methods on wearable devices. We propose a hardware architecture capable of analyzing time series signals through a Recurrent Neural Network implemented on FPGA technology. This architecture has been validated using a real dataset, which includes three-axial accelerometer data acquired by a wearable device used for fall detection. The experiments have been conducted considering different devices and demonstrates that the proposed hardware architecture outperforms the state of the art solutions both in terms of processing time and power consumption. Indeed, the proposed architecture is real-time compliant in the elaboration of the fall detection dataset adopted for the validation. The power consumption is in the order of dozens μW. Finally, futher functionalities could be added in the same chip since the resource usage is low

A Hybrid CPU–GPU Real-Time Hyperspectral Unmixing Chain

Hyperspectral images are used in different applications in Earth and space science, and many of these applications exhibit real- or near real-time constraints. A problem when analyzing hyperspectral images is that their spatial resolution is generally not enough to separate different spectrally pure constituents (endmembers); as a result, several of them can be found in the same pixel. Spectral unmixing is an important technique for hyperspectral data exploitation, aimed at finding the spectral signatures of the endmembers and their associated abundance fractions. The development of techniques able to provide unmixing results in real-time is a long desired goal in the hyperspectral imaging community. In this paper, we describe a real-time hyperspectral unmixing chain based on three main steps: 1) estimation of the number of endmembers using the hyperspectral subspace identification with minimum error (HySime); 2) estimation of the spectral signatures of the endmembers using the vertex component analysis (VCA); and 3) unconstrained abundance estimation. We have developed new parallel implementations of the aforementioned algorithms and assembled them in a fully operative real-time unmixing chain using graphics processing units (GPUs), exploiting NVIDIA's compute unified device architecture (CUDA) and its basic linear algebra subroutines (CuBLAS) library, as well as OpenMP and BLAS for multicore parallelization. As a result, our real-time chain exploits both CPU (multicore) and GPU paradigms in the optimization. Our experiments reveal that this hybrid GPU-CPU parallel implementation fully meets real-time constraints in hyperspectral imaging applications