scholarly journals Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6311
Author(s):  
Eoin Brophy ◽  
Maarten De Vos ◽  
Geraldine Boylan ◽  
Tomás Ward
Keyword(s):  
Blood Pressure ◽  
Time Series ◽  
Large Scale ◽  
Low Cost ◽  
Public Healthcare ◽  
Generative Adversarial Network ◽  
The Public ◽  
Distributed Models ◽  
Adversarial Network ◽  
Arterial Blood

Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.

scholarly journals CrossGR

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Xinyi Li ◽  
Liqiong Chang ◽  
Fangfang Song ◽  
Ju Wang ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
User Involvement ◽  
Recognition System ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Target User ◽  
Order Of Magnitude ◽  
Training Examples

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.

Abstract 104: Dynamic Autoregulation of Glomerular Capillary Pressure

Hypertension ◽  
2016 ◽  
Vol 68 (suppl_1) ◽  
Author(s):  
Scott C Thomson
Keyword(s):  
Blood Pressure ◽  
Time Series ◽  
Blood Flow ◽  
Capillary Pressure ◽  
Tubuloglomerular Feedback ◽  
Step Response ◽  
Glomerular Capillary ◽  
Arterial Blood ◽  
Glomerular Capillary Pressure ◽  
Step Responses

It is generally accepted that renal blood flow (RBF) autoregulation is mediated by myogenic and tubuloglomerular feedback responses acting on the pre-glomerular resistance. If this is so, then autoregulation of RBF and glomerular capillary pressure (PGC) should change in the same direction throughout an autoregulatory step response. We computed autoregulatory step responses from time series recordings of arterial blood pressure (BP) and RBF (Transonics) blood flow or tubular stop-flow pressure (micropuncture), which is a surrogate for PGC in Wistar-Froemter rats fed for one week on low or high salt diets (n=6-10 ). Autoregulatory step responses were generated from time series by an algorithm that treats BP as a leading indicator of RBF or PGC and uses the projection theorem to solve for the impulse response which is integrated to obtain the step response. Step responses shown in the figure represent the uncompensated changes in RBF and PGC (mean + SEM) following a 1 mmHg BP step. The data clearly reveal that the time courses of RBF and PGC differ such that changes in RBF cannot predict changes in PGC. This implies that the renal hemodynamic response to a blood pressure disturbance is not confined to the pre-glomerular resistance. Furthermore, the participation of post-glomerular resistance in the autoregulatory response is sensitive to dietary salt such that PGC is more sensitive to BP on low salt diet.

Basic life support training in schools: Reflecting on students' and teachers' perspectives in Portugal

2020 ◽  
Vol 1 (3) ◽  
pp. 124-128
Author(s):  
Nuno Pinto ◽  
Alexandra Carvalho ◽  
Rita Silva ◽  
Eleonora Marianucci ◽  
Beatriz Novo
Keyword(s):  
Large Scale ◽  
Basic Life Support ◽  
Life Support ◽  
Low Cost ◽  
Training Programme ◽  
The Public ◽  
Confidence Levels ◽  
Effective Training ◽  
Training Courses ◽  
Teachers Perspectives

Cardiovascular events are the third cause of death in the world. It is generally accepted by all the main health organisations dedicated to this topic that increasing the number of potential members of the public who could intervene if necessary will lead to an increase in the survival rate in the case of cardiac arrest. To achieve this goal, offering effective training courses to as many individuals as possible, on a large scale and at a low cost, is recommended. Schools are by nature one of the ideal places for implementing this type of large-scale training programme. With this study the authors aim to measure how open students and teachers are to basic life-support training in their school and how this can improve their confidence levels in performing basic life-support if needed.

scholarly journals Adherence to antihypertensive medications in patients attending public hospitals in Kano State, Nigeria

2020 ◽  
Vol 12 (1) ◽  
pp. 395-400
Author(s):  
Umar Idris Ibrahim ◽  
Shafiu Mohammed ◽  
Abdulkadir Umar Zezi ◽  
Basira Kankia Lawal
Keyword(s):  
Blood Pressure ◽  
Medication Adherence ◽  
Antihypertensive Medication ◽  
Public Hospitals ◽  
Poor Adherence ◽  
Chronic Medical Condition ◽  
Public Healthcare ◽  
Good Adherence ◽  
Adherence To Medication ◽  
Arterial Blood

Hypertension is a chronic medical condition characterized by an elevated arterial blood pressure with increasing prevalence in developing countries including Nigeria. One of the integral elements in management of hypertension is adherence to medication and life-style modification. This study aimed to assess adherence level for anti-hypertensive medications among adult hypertensive patients attending public hospitals in Kano State, Nigeria. The study was a cross sectional prospective survey involving 600 patients from six public healthcare facilities selected by multistage sampling technique. Adherence status was assessed using Morisky medication adherence scale. Sociodemographic data and other factors that may influence adherence to hypertension medications were evaluated. Out of the 598 patients that participated in the study, only 178 (29.8%) have their BP controlled based on JNC8. Three hundred and thirty two (55.5%) out of 598 patients have good adherence, while 266 (45.5%) have poor adherence. Of the 178 patients who had good BP control, 120 (67.5%) have good adherence while 58 (32.5%) have poor adherence. BP control was significantly higher in those that adhered to antihypertensive medication compared with non-adhering patients (χ2 = 14.526; df = 1; p-value = < 0.001). Additionally, Chi-square test showed significant association between number of antihypertensives and blood pressure control. (χ2=37.556, df=3, p<0.001). The study established that 55.5% of the respondents have good adherence to their antihypertensive medication while 29.8% had their BP controlled. Adherence and number of antihypertensive medication a patient is taking were found to have significant relationship with BP control. Keywords: Medication, adherence, hypertension, antihypertensive

scholarly journals GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement

2018 ◽  
Author(s):  
Gongbo Liang ◽  
Sajjad Fouladvand ◽  
Jie Zhang ◽  
Michael A. Brooks ◽  
Nathan Jacobs ◽  
...  
Keyword(s):  
Large Scale ◽  
Model Performance ◽  
Image Synthesis ◽  
Ct Images ◽  
Image Features ◽  
Training Data ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Training Strategy ◽  
Adversarial Network

AbstractComputed tomography (CT) is a widely-used diag-reproducibility regarding radiomic features, such as intensity, nostic image modality routinely used for assessing anatomical tissue characteristics. However, non-standardized imaging pro-tocols are commonplace, which poses a fundamental challenge in large-scale cross-center CT image analysis. One approach to address the problem is to standardize CT images using generative adversarial network models (GAN). GAN learns the data distribution of training images and generate synthesized images under the same distribution. However, existing GAN models are not directly applicable to this task mainly due to the lack of constraints on the mode of data to generate. Furthermore, they treat every image equally, but in real applications, some images are more difficult to standardize than the others. All these may lead to the lack-of-detail problem in CT image synthesis. We present a new GAN model called GANai to mitigate the differences in radiomic features across CT images captured using non-standard imaging protocols. Given source images, GANai composes new images by specifying a high-level goal that the image features of the synthesized images should be similar to those of the standard images. GANai introduces an alternative improvement training strategy to alternatively and steadily improve model performance. The new training strategy enables a series of technical improvements, including phase-specific loss functions, phase-specific training data, and the adoption of ensemble learning, leading to better model performance. The experimental results show that GANai is significantly better than the existing state-of-the-art image synthesis algorithms on CT image standardization. Also, it significantly improves the efficiency and stability of GAN model training.

scholarly journals Multi-omics Data Integration by Generative Adversarial Network

2021 ◽  
Author(s):  
Khandakar Tanvir Ahmed ◽  
Jiao Sun ◽  
Jeongsik Yong ◽  
Wei Zhang
Keyword(s):  
Large Scale ◽  
Synthetic Data ◽  
Interaction Network ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Omics Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cancer Outcome

Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network (GAN) model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. Large-scale experiments on The Cancer Genome Atlas (TCGA) breast cancer and ovarian cancer datasets validate that (1) the model can effectively integrate two omics data (i.e., mRNA and microRNA expression data) and their interaction network (i.e., microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (2) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals.

scholarly journals Unified Generative Adversarial Networks for Multidomain Fingerprint Presentation Attack Detection

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1089
Author(s):  
Soha B. Sandouka ◽  
Yakoub Bazi ◽  
Haikel Alhichri ◽  
Naif Alajlan
Keyword(s):  
Attack Detection ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Source Domain ◽  
The Public ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Biometric Systems ◽  
Security And Reliability ◽  
Presentation Attack Detection

With the rapid growth of fingerprint-based biometric systems, it is essential to ensure the security and reliability of the deployed algorithms. Indeed, the security vulnerability of these systems has been widely recognized. Thus, it is critical to enhance the generalization ability of fingerprint presentation attack detection (PAD) cross-sensor and cross-material settings. In this work, we propose a novel solution for addressing the case of a single source domain (sensor) with large labeled real/fake fingerprint images and multiple target domains (sensors) with only few real images obtained from different sensors. Our aim is to build a model that leverages the limited sample issues in all target domains by transferring knowledge from the source domain. To this end, we train a unified generative adversarial network (UGAN) for multidomain conversion to learn several mappings between all domains. This allows us to generate additional synthetic images for the target domains from the source domain to reduce the distribution shift between fingerprint representations. Then, we train a scale compound network (EfficientNetV2) coupled with multiple head classifiers (one classifier for each domain) using the source domain and the translated images. The outputs of these classifiers are then aggregated using an additional fusion layer with learnable weights. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset. The experimental results show that the proposed method improves the average classification accuracy over twelve classification scenarios from 67.80 to 80.44% after adaptation.

scholarly journals CAPTCHA recognition based on a generative-adversarial network

2020 ◽  
pp. 40-48
Author(s):  
Anatoliy Parfenov ◽  
Peter Sychov
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Research Topic ◽  
Generative Adversarial Network ◽  
The Past ◽  
Adversarial Network ◽  
Competitive Network ◽  
New Research

CAPTCHA recognition is certainly not a new research topic. Over the past decade, researchers have demonstrated various ways to automatically recognize text-based CAPTCHAs. However, in such methods, the recognition setup requires a large participation of experts and carries a laborious process of collecting and marking data. This article presents a general, low-cost, but effective approach to automatically solving text-based CAPTCHAs based on deep learning. This approach is based on the architecture of a generative-competitive network, which will significantly reduce the number of real required CAPTCHAs.

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