Cloud Computing-Based Non-Invasive Glucose Monitoring for Diabetic Care

Pai, Praful P.; Sanki, Pradyut Kumar; Sahoo, S.; De, Arijit; Bhattacharya, Sourangshu; Banerjee, Swapna

doi:10.1109/tcsi.2017.2724012

Cited by 41 publications

(19 citation statements)

References 64 publications

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“…Julian et al [ 22 ] also proposed a near infrared sensor to measure glucose concentration. Pai et al [ 23 ] implemented FPGA-based glucose monitoring with photoacoustic signal amplitude. Anas et al [ 24 ] measured the blood electrical impedance in which the electrode was made using four tin-lead solder electrodes.…”

Section: Literature Reviewmentioning

confidence: 99%

Hyperglycemia Identification Using ECG in Deep Learning Era

Cordeiro

Karimian

Park

2021

Sensors

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A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

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Section: Literature Reviewmentioning

confidence: 99%

Hyperglycemia Identification Using ECG in Deep Learning Era

Cordeiro

Karimian

Park

2021

Sensors

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“…The cloud has some shortcomings in handling big data such as response delays and network loads [132]. Due to the exploding volume of data collected from IoT devices, it Hosseini et al [102] Spanakis et al [41] Jara et al [121] Rivera et al [114] García et al [97] Pai et al [39] Stradolini et al [56] Lizarduy et al [100] Choi et al [26] Figure 9: Security methods used in studies [26,39,41,56,68,79,80,94,97,100,102,108,112,114,116,121].…”

Section: Medical Iot Modelmentioning

confidence: 99%

Technological Features of Internet of Things in Medicine: A Systematic Mapping Study

Behmanesh

Sayfouri

Sadoughi

2020

Wireless Communications and Mobile Computing

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Nowadays, applications for the Internet of Things (IoT) have been introduced in different fields of medicine to provide more efficient medical services to the patients. A systematic mapping study was conducted to answer ten research questions with the purposes of identifying and classifying the present medical IoT technological features as well as recognizing the opportunities for future developments. We reviewed how cloud, wearable technologies, wireless communication technologies, messaging protocols, security methods, development boards, microcontrollers, mobile/IoT operating systems, and programming languages have been engaged in medical IoT. Based on specific inclusion/exclusion criteria, 89 papers, published between 2000 and 2018, were screened and selected. It was found that IoT studies, with a publication rise between 2015 and 2018, predominantly dealt with the following IoT features: (a) wearable sensor types of chiefly accelerometer and ECG placed on 16 different body parts, especially the wrist (33%) and the chest (21%) or implanted on the bone; (b) wireless communication technologies of Bluetooth, cellular networks, and Wi-Fi; (c) messaging protocols of mostly MQTT; (d) utilizing cloud for both storing and analyzing data; (e) the security methods of encryption, authentication, watermark, and error control; (f) the microcontrollers belonging to Atmel ATmega and ARM Cortex-M3 families; (g) Android as the commonly used mobile operating system and TinyOS and ContikiOS as the commonly used IoT operating systems; (h) Arduino and Raspberry Pi development boards; and finally (i) MATLAB as the most frequently employed programming language in validation research. The identified gaps/opportunities for future exploration are, namely, employment of fog/edge computing in storage and processing big data, the overlooked efficient features of CoAP messaging protocol, the unnoticed advantages of AVR Xmega and Cortex-M microcontroller families, employment of the programming languages of Python for its significant capabilities in evaluation and validation research, development of the applications being supported by the mobile/IoT operating systems in order to provide connection possibility among all IoT devices in medicine, exploiting wireless communication technologies such as BLE, ZigBee, 6LoWPAN, NFC, and 5G to reduce power consumption and costs, and finally uncovering the security methods, usually used in IoT applications, in order to make other applications more trustworthy.

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“…Conventional measurement techniques require a blood sample of patients which is collected using finger-prick or venepuncture [9]. These methods are painful, inconvenient and costly for the patient due to frequent blood collection, and do not allow real-time monitoring [10], [11]. However, invasive techniques are more precise and reliable, but it This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique for Non-Invasive Measurement of Human Blood Component Levels From Fingertip Video Using DNN Based Models

et al. 2021

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Blood components such as hemoglobin, glucose, creatinine measuring are essential for monitoring one's health condition. The current blood component measurement approaches still depend on invasive techniques that are painful, and uncomfortable for the patients. To facilitate measurement at home, we proposed a novel non-invasive technique to measure blood hemoglobin, glucose, and creatinine level based on PPG signal using Deep Neural Networks (DNN). Fingertip videos from 93 subjects have been collected using a smartphone. The PPG signal is generated from each video, and 46 characteristic features are then extracted from the PPG signal, its derivatives (1 st and 2 nd ) and from Fourier analysis. Additionally, age and gender are also included to feature because of the significant effects on hemoglobin, glucose, and creatinine. A correlation-based feature selection (CFS) using genetic algorithms (GA) has been used to select the optimal features to avoid redundancy and over-fitting. Finally, DNN based models have been developed to estimate the blood Hemoglobin (Hb), Glucose (Gl), and Creatinine (Cr) levels from the selected features. The approach provides the best-estimated accuracy of R 2 = 0.922 for Hb, R 2 = 0.902 for Gl, and R 2 = 0.969 for Cr. Experimental aftermaths show that the proposed method is a suitable technique to be used clinically to measure human blood component levels without taking blood samples. This paper also reveals that smartphone-based PPG signal has a great potential to measure the different blood components.

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Cloud Computing-Based Non-Invasive Glucose Monitoring for Diabetic Care

Cited by 41 publications

References 64 publications

Hyperglycemia Identification Using ECG in Deep Learning Era

Hyperglycemia Identification Using ECG in Deep Learning Era

Technological Features of Internet of Things in Medicine: A Systematic Mapping Study

A Novel Technique for Non-Invasive Measurement of Human Blood Component Levels From Fingertip Video Using DNN Based Models

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