Seasonal variation in 24 h blood pressure profile in healthy adults- A prospective observational study

Goyal, Abhishek; Narang, Kanika; Ahluwalia, Gautam; Sohal, P.M.; Singh, Bhupinder; Chhabra, Shibba Takkar; Aslam, Naved; Mohan, Bishav; Wander, Gurpreet Singh

doi:10.1038/s41371-019-0173-3

Cited by 20 publications

(16 citation statements)

References 24 publications

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“…These emerging technologies provide storage, computation, and communication to edge devices, which facilitate and enhance mobility, privacy, security, low latency, and network bandwidth so that fog computing can perfectly match latency-sensitive or real-time applications [2,6,10,12,27,40,45,46,47,48]. Now, cloud computing frameworks also extend support to emerging application paradigms such as IoT, Fog computing, Edge, and Big Data through service and infrastructure [3,4]. Fog computing uses routers, compute nodes and gateways to provide services with minimum possible energy consumption, network latency and response time.…”

Section: Introductionmentioning

confidence: 99%

“…Prior works have reported that there are two major types of healthcare data collection schemes for heart patients using different devices (IoT sensors and file input data). The first is Little data which is processed at fog nodes and the second is Big data processed at Cloud Data Centers (CDC) [1,3]. The healthcare patient data is received by the network at high speeds (250 MB per minute or more) [1].…”

Section: Introductionmentioning

confidence: 99%

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HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

471

248

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Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

471

248

View full text Add to dashboard Cite

show abstract

“…[2]- [7] Currently, cloud computing structure similarly helps to generate the applications standards, for example, through IoT devices, fog computing, edge computing, and big data support and infrastructure. [8], [9] Routers, switches, computing nodes, and gateways are used by fog computing to offer the least conceivable type of assistance with energy usage, network deactivation and the response time of the network.…”

Section: Introductionmentioning

confidence: 99%

“…The first short length of data is handled at the center of the fog node and the second is the high volume data prepared in the cloud data center (CDC). [1], [8] Medical services receive quiet data by the organization at high speeds (configured as 250 MB for each moment or upgraded).…”

Section: Introductionmentioning

confidence: 99%

FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis

et al. 2022

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These days cloud-based infrastructure is facing many challenges, out of which the major issue is their syncing data before cutover and data migration. Due to the limited scalability in terms of security concerns of cloud computing, the need for a centralized IoTs based environment has been constrained to a limited extent. The sensitivity of device latency emerged during healthy systems such as health monitoring, etc. is the main reason, because healthy systems require computing operations on highvolume data. Fog computing provides an innovative solution to improve the performance of cloud computing, providing the ability to take the necessary resources and those that are closer to the end-users. Existing fog computing models retain several limitations, such as either considering result accuracy or overestimating response time, but managing both together impairs system compatibility. FETCH is a proposed framework that integrates with edge computing devices to work on deep learning technology and automated monitoring and offers a highly useful framework for real-life health care systems such as heart disease and more. The proposed Fog-enabled cloud computing framework uses FogBus, which demonstrates utility in the form of consumption of power, network bandwidth, jitter, latency, process execution time, and their accuracy as well.

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“…Sharma et al [25] from the public health foundation of India discuss a new strategy of providing education to primary care physicians in the rural setting and proving that this improves patient care. Some of the other topics covered in this spotlight include seasonal variation in blood pressure in India [26], and the role of repeated blood pressure measurements in the indian setting [27].…”

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confidence: 99%

Spotlight on hypertension in the Indian subcontinent

Nadar

2019

J Hum Hypertens

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The Indian subcontinent (a loose term comprising of India, Nepal, Bangladesh, Pakistan, Nepal, Srilanka, Bhutan and the Maldives) is home to more than 1.5 billion people (with more than 1.3 billion in India alone) as per the latest World Bank estimates [1]. This accounts for nearly a fifth of the worlds population. The population here is mainly "young" with more than 65% of the population below the age of 35. The median age is expected to be 29 years by the year 2020 [1]. It is a diverse area with wide variations in race, ethinicity, socioeconomic status, religion, language and cultural practices across the region [2]. It is also an area of rapidly changing demographics. Life expectancy in the region is also gradually increasing from around 36 years in 1947 and is currently around 67 years [1]. There is an increae in urbanisation across the region, with only 11% of the population living in cities at the turn of the 20th century, but increasing to more than 30% a century later [2].With this increasing urbanisation, improving economic status, changing lifestyles and increasing life expectancy, comes increasing incidence of chronic illnessness such as diabetes, hypertension and cardiovascular diseases [3]. Studies have shown that the prevalence of diabetes and hypertension is increasing in the region [4,5]. Recent data suggest that the prevalence of diabetes in the adult population in India is around 8.8% as a whole [6], though in some urban areas it is as high as 17% [7]. The prevalence of hypertension is much higher and currently is around 33% for the whole of south asia [8], and specifically around 33% in urban india and 25% in rural india [9]. The reasons for this increase are multifactorial. Changing lifestyles leading to a more sedentary behaviour could be a major factor. It is also likely that improved access to health care leads to higher detection and hence higher reporting of these hitherto undiagnosed and unreported conditions. Indeed studies have shown that even in the rural areas the incidence of hypertension is increasing and this could be a combination of all the above factors and not least due to improved access to health care [10].Bearing this is mind, the Journal of Human Hypertension has decided to bring out this special spotlight issue on hypertension in the India. In this issue we look at the various aspcects of hypertension in the subcontinent. Gupta et al. [11] and Ghimire et al. [12] describe the current epidemiology of hypertension in India and Nepal respectively. Gupta et al. [11] especially point out the increasing incidence of hypertension even with in the rural populations, where previously, the incidence of hypertension and other non communicable diseases were traditionally lower than the urban populations. Similarly in Nepal, Ghimire et al. [12] found that hypertension and other cardiovascular risk factors appear to be increasing in the elderly population. They have studied the various aspects of risk factors in this elderly population.The hypertension society of India, along with th...

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Seasonal variation in 24 h blood pressure profile in healthy adults- A prospective observational study

Cited by 20 publications

References 24 publications

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis

Spotlight on hypertension in the Indian subcontinent

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