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Xiong C, Ren Z, Liu T. Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks. BIOMEDICAL OPTICS EXPRESS 2024; 15:2719-2740. [PMID: 38855672 PMCID: PMC11161381 DOI: 10.1364/boe.521059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 06/11/2024]
Abstract
In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.
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Affiliation(s)
- Chengxin Xiong
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Zhong Ren
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- Key Laboratory of Optic-electronic Detection and Information Processing of Nanchang City, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Tao Liu
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
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2
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Chorney W, Wang H. Towards federated transfer learning in electrocardiogram signal analysis. Comput Biol Med 2024; 170:107984. [PMID: 38244469 DOI: 10.1016/j.compbiomed.2024.107984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/17/2023] [Accepted: 01/13/2024] [Indexed: 01/22/2024]
Abstract
Modern methods in artificial intelligence perform very well on many healthcare datasets, at times outperforming trained doctors. However, many assumptions made in model training are not justifiable in clinical settings. In this work, we propose a method to train classifiers for electrocardiograms, able to deal with data of disparate input dimensions, distributed across different institutions, and able to protect patient privacy. In addition, we propose a simple method for creating federated datasets from any centralized dataset. We use autoencoders in conjunction with federated learning to model a highly heterogeneous modeling problem using the Massachusetts Institute of Technology Beth Israel Hospital Arrhythmia dataset, the Computing in Cardiology 2017 challenge dataset, and the PTB-XL dataset. For an encoding dimension of 1000, our federated classifier achieves an accuracy, precision, recall, and F1 score of 73.0%, 66.6%, 73.0%, and 69.7%, respectively. Our results suggest that dropping commonly made assumptions significantly complicate training and that as a result, estimates of performance of many machine learning models may overestimate performance when adopted for clinical settings.
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Affiliation(s)
- Wesley Chorney
- Computational Engineering, Mississippi State University, Mississippi State, 39762, USA.
| | - Haifeng Wang
- Industrial and Systems Engineering, Mississippi State University, Mississippi State, 39762, USA.
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3
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Dave D, Vyas K, Branan K, McKay S, DeSalvo DJ, Gutierrez-Osuna R, Cote GL, Erraguntla M. Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry. J Diabetes Sci Technol 2024; 18:351-362. [PMID: 35927975 PMCID: PMC10973850 DOI: 10.1177/19322968221116393] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. METHODS In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model. RESULTS The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. CONCLUSIONS Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.
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Affiliation(s)
- Darpit Dave
- Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
| | - Kathan Vyas
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Kimberly Branan
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Siripoom McKay
- Baylor College of Medicine, Houston, TX, USA
- Texas Children’s Hospital Clinical Care Center, Houston, TX, USA
| | - Daniel J. DeSalvo
- Baylor College of Medicine, Houston, TX, USA
- Texas Children’s Hospital Clinical Care Center, Houston, TX, USA
| | - Ricardo Gutierrez-Osuna
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Gerard L. Cote
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Madhav Erraguntla
- Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
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4
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Fan B, Li W, Dong L, Li J, Nie Z. Automatic Chinese Food recognition based on a stacking fusion model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083522 DOI: 10.1109/embc40787.2023.10340620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
With commercialization of deep learning models, daily precision dietary record based on images from smartphones becomes possible. This study took advantage of Deep-learning techniques on visual recognition tasks and proposed a big-data-driven Deep-learning model regressing from food images. We established the largest data set of Chinese dishes to date, named CNFOOD-241. It contained more than 190,000 images with 241 categories, covering Staple food, meat, vegetarian diet, mixed meat and vegetables, soups, dessert category. This study also compares the prediction results of three popular deep learning models on this dataset, ResNeXt101_32x32d achieving up to 82.05% for top-1 accuracy and 97.13% for top-5 accuracy. Besides, this paper uses a multi-model fusion method based on stacking in the field of food recognition for the first time. We built a meta-learner after the base model to integrate the three base models of different architectures to improve robustness. The accuracy achieves 82.88% for top-1 accuracy.Clinical Relevance-This study proves that the application of artificial intelligence technology in the identification of Chinese dishes is feasible, which can play a positive role in people who need to control their diet, such as diabetes and obesity.
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5
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Cisuelo O, Stokes K, Oronti IB, Haleem MS, Barber TM, Weickert MO, Pecchia L, Hattersley J. Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions. BMJ Open 2023; 13:e067899. [PMID: 37072364 PMCID: PMC10124264 DOI: 10.1136/bmjopen-2022-067899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/20/2023] Open
Abstract
INTRODUCTION Hypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, including, but not limited to, trembling, palpitations, sweating, dry mouth, confusion, seizures, coma, brain damage or even death if untreated. A pilot study with healthy (euglycaemic) participants previously demonstrated that hypoglycaemia can be detected non-invasively with artificial intelligence (AI) using physiological signals obtained from wearable sensors. This protocol provides a methodological description of an observational study for obtaining physiological data from people with T1DM. The aim of this work is to further improve the previously developed AI model and validate its performance for glycaemic event detection in people with T1DM. Such a model could be suitable for integrating into a continuous, non-invasive, glucose monitoring system, contributing towards improving surveillance and management of blood glucose for people with diabetes. METHODS AND ANALYSIS This observational study aims to recruit 30 patients with T1DM from a diabetes outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to 36 hours in a calorimetry room under controlled conditions, followed by a phase of free-living, for up to 3 days, in which participants will go about their normal daily activities unrestricted. Throughout the study, the participants will wear wearable sensors to measure and record physiological signals (eg, ECG and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep learning methods. ETHICS AND DISSEMINATION This study has received ethical approval from National Research Ethics Service (ref: 17/NW/0277). The findings will be disseminated via peer-reviewed journals and presented at scientific conferences. TRIAL REGISTRATION NUMBER NCT05461144.
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Affiliation(s)
- Owain Cisuelo
- School of Engineering, University of Warwick, Coventry, UK
| | - Katy Stokes
- School of Engineering, University of Warwick, Coventry, UK
| | | | | | - Thomas M Barber
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Human Metabolism Research Unit, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Martin O Weickert
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, UK
- Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - John Hattersley
- School of Engineering, University of Warwick, Coventry, UK
- Human Metabolism Research Unit, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
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Abasi S, Aggas JR, Garayar-Leyva GG, Walther BK, Guiseppi-Elie A. Bioelectrical Impedance Spectroscopy for Monitoring Mammalian Cells and Tissues under Different Frequency Domains: A Review. ACS MEASUREMENT SCIENCE AU 2022; 2:495-516. [PMID: 36785772 PMCID: PMC9886004 DOI: 10.1021/acsmeasuresciau.2c00033] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 05/13/2023]
Abstract
Bioelectrical impedance analysis and bioelectrical impedance spectroscopy (BIA/BIS) of tissues reveal important information on molecular composition and physical structure that is useful in diagnostics and prognostics. The heterogeneity in structural elements of cells, tissues, organs, and the whole human body, the variability in molecular composition arising from the dynamics of biochemical reactions, and the contributions of inherently electroresponsive components, such as ions, proteins, and polarized membranes, have rendered bioimpedance challenging to interpret but also a powerful evaluation and monitoring technique in biomedicine. BIA/BIS has thus become the basis for a wide range of diagnostic and monitoring systems such as plethysmography and tomography. The use of BIA/BIS arises from (i) being a noninvasive and safe measurement modality, (ii) its ease of miniaturization, and (iii) multiple technological formats for its biomedical implementation. Considering the dependency of the absolute and relative values of impedance on frequency, and the uniqueness of the origins of the α-, β-, δ-, and γ-dispersions, this targeted review discusses biological events and underlying principles that are employed to analyze the impedance data based on the frequency range. The emergence of BIA/BIS in wearable devices and its relevance to the Internet of Medical Things (IoMT) are introduced and discussed.
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Affiliation(s)
- Sara Abasi
- Center
for Bioelectronics, Biosensors and Biochips (C3B®), Department
of Biomedical Engineering, Texas A&M
University, 400 Bizzell Street, College Station, Texas 77843, United States
- Cell
Culture Media Services, Cytiva, 100 Results Way, Marlborough, Massachusetts 01752, United States
| | - John R. Aggas
- Center
for Bioelectronics, Biosensors and Biochips (C3B®), Department
of Biomedical Engineering, Texas A&M
University, 400 Bizzell Street, College Station, Texas 77843, United States
- Test
Development, Roche Diagnostics, 9115 Hague Road, Indianapolis, Indiana 46256, United
States
| | - Guillermo G. Garayar-Leyva
- Center
for Bioelectronics, Biosensors and Biochips (C3B®), Department
of Biomedical Engineering, Texas A&M
University, 400 Bizzell Street, College Station, Texas 77843, United States
- Department
of Electrical and Computer Engineering, Texas A&M University, 400 Bizzell Street, College Station, Texas 77843, United States
| | - Brandon K. Walther
- Center
for Bioelectronics, Biosensors and Biochips (C3B®), Department
of Biomedical Engineering, Texas A&M
University, 400 Bizzell Street, College Station, Texas 77843, United States
- Department
of Cardiovascular Sciences, Houston Methodist
Institute for Academic Medicine and Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, Texas 77030, United States
| | - Anthony Guiseppi-Elie
- Center
for Bioelectronics, Biosensors and Biochips (C3B®), Department
of Biomedical Engineering, Texas A&M
University, 400 Bizzell Street, College Station, Texas 77843, United States
- Department
of Electrical and Computer Engineering, Texas A&M University, 400 Bizzell Street, College Station, Texas 77843, United States
- Department
of Cardiovascular Sciences, Houston Methodist
Institute for Academic Medicine and Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, Texas 77030, United States
- ABTECH Scientific,
Inc., Biotechnology Research Park, 800 East Leigh Street, Richmond, Virginia 23219, United
States
- . Tel.: +1(804)347.9363.
Fax: +1(804)347.9363
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7
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Feng Y, Geng S, Chu J, Fu Z, Hong S. Building and training a deep spiking neural network for ECG classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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8
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Alhaddad AY, Aly H, Gad H, Al-Ali A, Sadasivuni KK, Cabibihan JJ, Malik RA. Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection. Front Bioeng Biotechnol 2022; 10:876672. [PMID: 35646863 PMCID: PMC9135106 DOI: 10.3389/fbioe.2022.876672] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.
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Affiliation(s)
- Ahmad Yaser Alhaddad
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | - Hussein Aly
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | - Hoda Gad
- Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Abdulaziz Al-Ali
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | | | - John-John Cabibihan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | - Rayaz A. Malik
- Weill Cornell Medicine - Qatar, Doha, Qatar
- *Correspondence: Rayaz A. Malik,
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9
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Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis. J Pers Med 2022; 12:jpm12050756. [PMID: 35629177 PMCID: PMC9146635 DOI: 10.3390/jpm12050756] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/29/2022] [Accepted: 05/03/2022] [Indexed: 02/06/2023] Open
Abstract
Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques—naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)—were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine.
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Suresh R, Helenprabha K. IoMT aware data collective quadratic ensembled cat boost module classification algorithm for non-invasive blood glucose monitoring in VLSI design. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Internet of Medical Things (IoMT) is the combination of medical devices and utilization by networking technologies. But, the response time and cost were not reduced. In order to address these issues, IoMT Aware Data Collective Quadratic Ensembled Cat Boost Module Classification (IoMT-DCQECBMC) Method is introduced. Initially, IoMT Aware Data Collection is used for gathering data from medical devices. After the data collection process, Quadratic Ensembled Cat Boost Module Classification (QECBM) is carried out in IoMT-DCQECBMC Method to design an efficient VLSI architecture with minimal cost and area. The quadratic classifier is considered the weak learner that categorizes the module for efficient VLSI design. Finally, the weak learners are joined to form the strong classifier to perform non-invasive blood glucose monitoring efficiently. Experimental evaluation is carried out on the factors such as computation cost, area, and accuracy with respect to a number of modules in VLSI circuits. The accuracy of the IoMT-DCQECBMC method is increased by 4% than conventional methods. In addition, the area consumption and computation cost of the proposed IoMT-DCQECBMC method are reduced by 13% to 30% other than existing methods.
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Affiliation(s)
- R. Suresh
- Muthayammal Engineering College, Namakkal, TamilNadu, India
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11
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COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network. Healthcare (Basel) 2022; 10:healthcare10030422. [PMID: 35326900 PMCID: PMC8949056 DOI: 10.3390/healthcare10030422] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/16/2022] [Accepted: 02/20/2022] [Indexed: 12/23/2022] Open
Abstract
Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases.
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12
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Hyperglycemia Identification Using ECG in Deep Learning Era. SENSORS 2021; 21:s21186263. [PMID: 34577473 PMCID: PMC8472987 DOI: 10.3390/s21186263] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/02/2021] [Accepted: 09/13/2021] [Indexed: 11/17/2022]
Abstract
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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