Displaying publications 61 - 80 of 209 in total

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  1. Fulltext A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal
    Ullah A, Rehman SU, Tu S, Mehmood RM, Fawad, Ehatisham-Ul-Haq M
    Sensors (Basel), 2021 Feb 01;21(3).
    PMID: 33535397 DOI: 10.3390/s21030951
    Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.
    Matched MeSH terms: Electrocardiography*
  2. Fulltext Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review
    Hasnul MA, Aziz NAA, Alelyani S, Mohana M, Aziz AA
    Sensors (Basel), 2021 Jul 23;21(15).
    PMID: 34372252 DOI: 10.3390/s21155015
    Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.
    Matched MeSH terms: Electrocardiography*
  3. Fulltext Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor
    Nizam Y, Mohd MNH, Jamil MMA
    Sensors (Basel), 2018 Jul 13;18(7).
    PMID: 30011823 DOI: 10.3390/s18072260
    Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.
    Matched MeSH terms: Electrocardiography
  4. Fulltext Multi-Agent Systems in Fog-Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring
    Mutlag AA, Ghani MKA, Mohammed MA, Lakhan A, Mohd O, Abdulkareem KH, et al.
    Sensors (Basel), 2021 Oct 19;21(20).
    PMID: 34696135 DOI: 10.3390/s21206923
    In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.
    Matched MeSH terms: Electrocardiography*
  5. Fulltext A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals
    Mahmud S, Ibtehaz N, Khandakar A, Tahir AM, Rahman T, Islam KR, et al.
    Sensors (Basel), 2022 Jan 25;22(3).
    PMID: 35161664 DOI: 10.3390/s22030919
    Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.
    Matched MeSH terms: Electrocardiography
  6. Fulltext A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability
    Awais M, Badruddin N, Drieberg M
    Sensors (Basel), 2017 Aug 31;17(9).
    PMID: 28858220 DOI: 10.3390/s17091991
    Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.
    Matched MeSH terms: Electrocardiography
  7. Fulltext Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform
    Lim PK, Ng SC, Jassim WA, Redmond SJ, Zilany M, Avolio A, et al.
    Sensors (Basel), 2015 Jun 16;15(6):14142-61.
    PMID: 26087370 DOI: 10.3390/s150614142
    We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = -0.3 ± 5.8 mmHg; SVR and -0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = -1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity.
    Matched MeSH terms: Electrocardiography
  8. Fulltext Remote Arrhythmia Detection for Eldercare in Malaysia
    Chew KT, Raman V, Then PHH
    Sensors (Basel), 2021 Dec 08;21(24).
    PMID: 34960291 DOI: 10.3390/s21248197
    Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings.
    Matched MeSH terms: Electrocardiography*
  9. Methyl Gallate Attenuates Doxorubicin-Induced Cardiotoxicity in Rats by Suppressing Oxidative Stress
    Ahmed AZ, Satyam SM, Shetty P, D'Souza MR
    Scientifica (Cairo), 2021;2021:6694340.
    PMID: 33510932 DOI: 10.1155/2021/6694340
    Doxorubicin-induced cardiotoxicity is the leading cause of morbidity and mortality among cancer survivors. The present study was aimed to investigate the cardioprotective potential of methyl gallate; an active polyphenolic nutraceutical, against doxorubicin-induced cardiotoxicity in Wistar rats. Twenty-four female Wistar rats (150-200 g) were divided into four groups (n = 6) which consist of normal control (group I), doxorubicin control (group II), test-A (group III), and test-B (group IV). Group III and group IV animals were prophylactically treated with methyl gallate 150 mg/kg/day and 300 mg/kg/day orally, respectively, for seven days. Doxorubicin (25 mg/kg; single dose) was administered through an intraperitoneal route to group II, III, and IV animals on the seventh day to induce acute cardiotoxicity. On the 8th day, besides ECG analysis, serum CK, CK-MB, LDH, AST, MDA, and GSH were assayed. Following gross examination of isolated hearts, histopathological evaluation was performed by light microscopy. A significant (p 
    Matched MeSH terms: Electrocardiography
  10. Fulltext A comparison study of radiation effective dose in ECG-Gated Coronary CT Angiography and calcium scoring examinations performed with a dual-source CT scanner
    Sabarudin A, Siong TW, Chin AW, Hoong NK, Karim MKA
    Sci Rep, 2019 03 13;9(1):4374.
    PMID: 30867480 DOI: 10.1038/s41598-019-40758-5
    In this report we have evaluated radiation effective dose received by patients during ECG-gated CCTA examinations based on gender, heart rate, tube voltage protocol and body mass index (BMI). A total of 1,824 patients were retrospectively recruited (1,139 men and 685 women) and they were divided into Group 1 (CCTA with calcium scoring), Group 2 (CCTA without calcium scoring) and Group 3 (only calcium scoring), where the association between gender, heart rate, tube voltage protocol and body mass index (BMI) were analysed. Examinations were performed using a retrospective ECG-gated CCTA protocol and the effective doses were calculated from the dose length product with a conversion coefficient of 0.026 mSv.mGy-1cm-1. No significant differences were observed in the mean effective dose between gender in all groups. The mean estimated dose was significantly higher when the heart rate was lower in Group 1 (p 
    Matched MeSH terms: Electrocardiography*
  11. Fulltext The effects of ageing and adrenergic challenge on electrocardiographic phenotypes in a murine model of long QT syndrome type 3
    Chadda KR, Ahmad S, Valli H, den Uijl I, Al-Hadithi AB, Salvage SC, et al.
    Sci Rep, 2017 09 11;7(1):11070.
    PMID: 28894151 DOI: 10.1038/s41598-017-11210-3
    Long QT Syndrome 3 (LQTS3) arises from gain-of-function Nav1.5 mutations, prolonging action potential repolarisation and electrocardiographic (ECG) QT interval, associated with increased age-dependent risk for major arrhythmic events, and paradoxical responses to β-adrenergic agents. We investigated for independent and interacting effects of age and Scn5a+/ΔKPQ genotype in anaesthetised mice modelling LQTS3 on ECG phenotypes before and following β-agonist challenge, and upon fibrotic change. Prolonged ventricular recovery was independently associated with Scn5a+/ΔKPQ and age. Ventricular activation was prolonged in old Scn5a+/ΔKPQ mice (p = 0.03). We associated Scn5a+/ΔKPQ with increased atrial and ventricular fibrosis (both: p 
    Matched MeSH terms: Electrocardiography
  12. The use of BLRP model for disaggregating daily rainfall affected by monsoon in Peninsular Malaysia
    Zulkifli Yusop, Harisaweni, Fadhilah Yusof
    Sains Malaysiana, 2016;45:87-97.
    Rainfall intensity is the main input variable in various hydrological analysis and modeling. Unfortunately, the quality of rainfall data is often poor and reliable data records are available at coarse intervals such as yearly, monthly and daily. Short interval rainfall records are scarce because of high cost and low reliability of the measurement and the monitoring systems. One way to solve this problem is by disaggregating the coarse intervals to generate the short one using the stochastic method. This paper describes the use of the Bartlett Lewis Rectangular Pulse (BLRP) model. The method was used to disaggregate 10 years of daily data for generating hourly data from 5 rainfall stations in Kelantan as representative area affected by monsoon period and 5 rainfall stations in Damansara affected by inter-monsoon period. The models were evaluated on their ability to reproduce standard and extreme rainfall model statistics derived from the historical record over disaggregation simulation results. The disaggregation of daily to hourly rainfall produced monthly and daily means and variances that closely match the historical records. However, for the disaggregation of daily to hourly rainfall, the standard deviation values are lower than the historical ones. Despite the marked differences in the standard deviation, both data series exhibit similar patterns and the model adequately preserve the trends of all the properties used in evaluating its performances.
    Matched MeSH terms: Electrocardiography
  13. Highly-sensitive graphene-based flexible pressure sensor platform
    Muhammad Aniq Shazni, Lee MW, Lee HW
    Sains Malaysiana, 2017;46:1155-1161.
    In this work, graphene has been utilized as the sensing material for the development of a highly-sensitive flexible pressure sensor platform. It has been demonstrated that a graphene-based pressure sensor platform that is able to measure pressure change of up to 3 psi with a sensitivity of 0.042 psi-1 and a non-linearity of less than 1% has been accomplished. The developed device, which resides on a flexible platform, will be applicable for integration in continuous wearables health-care monitoring system for the measurement of blood pressure.
    Matched MeSH terms: Electrocardiography
  14. Personalized administration of contrast medium with high delivery rate in low tube voltage coronary computed tomography angiography
    Tan SK, Ng KH, Yeong CH, Raja Aman RRA, Mohamed Sani F, Abdul Aziz YF, et al.
    Quant Imaging Med Surg, 2019 Apr;9(4):552-564.
    PMID: 31143647 DOI: 10.21037/qims.2019.03.13
    Background: High delivery rate is an important factor in optimizing contrast medium administration in coronary computed tomography angiography (CCTA). A personalized contrast volume calculation algorithm incorporating high iodine delivery rate (IDR) can reduce total iodine dose (TID) and produce optimal vessel contrast enhancement (VCE) in low tube voltage CCTA. In this study, we developed and validated an algorithm for calculating the volume of contrast medium delivered at a high rate for patients undergoing retrospectively ECG-gated CCTA with low tube voltage protocol.

    Methods: The algorithm for an IDR of 2.22 gI·s-1 was developed based on the relationship between VCE and contrast volume in 141 patients; test bolus parameters and characteristics in 75 patients; and, tube voltage in a phantom study. The algorithm was retrospectively tested in 45 patients who underwent retrospectively ECG-gated CCTA with a 100 kVp protocol. Image quality, TID and radiation dose exposure were compared with those produced using the 120 kVp and routine contrast protocols.

    Results: Age, sex, body surface area (BSA) and peak contrast enhancement (PCE) were significant predictors for VCE (P<0.05). A strong linear correlation was observed between VCE and contrast volume (r=0.97, P<0.05). The 100-to-120 kVp contrast enhancement conversion factor (Ec) was calculated at 0.81. Optimal VCE (250 to 450 HU) and diagnostic image quality were obtained with significant reductions in TID (32.1%) and radiation dose (38.5%) when using 100 kVp and personalized contrast volume calculation algorithm compared with 120 kVp and routine contrast protocols (P<0.05).

    Conclusions: The proposed algorithm could significantly reduce TID and radiation exposure while maintaining optimal VCE and image quality in CCTA with 100 kVp protocol.

    Matched MeSH terms: Electrocardiography
  15. Are examination findings important in screening for angina in the Malaysian patient?
    Bulgiba AM
    Prev Med, 2005 Jun;40(6):696-701.
    PMID: 15850867
    The objective of this study is to look at how well patient history and examination findings can be used in screening for angina.
    Matched MeSH terms: Electrocardiography*
  16. Isolated right atrium tuberculoma causing complete heart block
    Ngow HA, Wan Khairina WM
    Postgrad Med J, 2010 Oct;86(1020):624-6.
    PMID: 20971714 DOI: 10.1136/pgmj.2010.102236
    Matched MeSH terms: Electrocardiography
  17. Fulltext An Asian validation of the TIMI risk score for ST-segment elevation myocardial infarction
    Selvarajah S, Fong AY, Selvaraj G, Haniff J, Uiterwaal CS, Bots ML
    PLoS One, 2012;7(7):e40249.
    PMID: 22815733 DOI: 10.1371/journal.pone.0040249
    Risk stratification in ST-elevation myocardial infarction (STEMI) is important, such that the most resource intensive strategy is used to achieve the greatest clinical benefit. This is essential in developing countries with wide variation in health care facilities, scarce resources and increasing burden of cardiovascular diseases. This study sought to validate the Thrombolysis In Myocardial Infarction (TIMI) risk score for STEMI in a multi-ethnic developing country.
    Matched MeSH terms: Electrocardiography*
  18. Fulltext Congestive Heart Failure Leads to Prolongation of the PR Interval and Atrioventricular Junction Enlargement and Ion Channel Remodelling in the Rabbit
    Nikolaidou T, Cai XJ, Stephenson RS, Yanni J, Lowe T, Atkinson AJ, et al.
    PLoS One, 2015;10(10):e0141452.
    PMID: 26509807 DOI: 10.1371/journal.pone.0141452
    Heart failure is a major killer worldwide. Atrioventricular conduction block is common in heart failure; it is associated with worse outcomes and can lead to syncope and bradycardic death. We examine the effect of heart failure on anatomical and ion channel remodelling in the rabbit atrioventricular junction (AVJ). Heart failure was induced in New Zealand rabbits by disruption of the aortic valve and banding of the abdominal aorta resulting in volume and pressure overload. Laser micro-dissection and real-time polymerase chain reaction (RT-PCR) were employed to investigate the effects of heart failure on ion channel remodelling in four regions of the rabbit AVJ and in septal tissues. Investigation of the AVJ anatomy was performed using micro-computed tomography (micro-CT). Heart failure animals developed first degree heart block. Heart failure caused ventricular myocardial volume increase with a 35% elongation of the AVJ. There was downregulation of HCN1 and Cx43 mRNA transcripts across all regions and downregulation of Cav1.3 in the transitional tissue. Cx40 mRNA was significantly downregulated in the atrial septum and AVJ tissues but not in the ventricular septum. mRNA abundance for ANP, CLCN2 and Navβ1 was increased with heart failure; Nav1.1 was increased in the inferior nodal extension/compact node area. Heart failure in the rabbit leads to prolongation of the PR interval and this is accompanied by downregulation of HCN1, Cav1.3, Cx40 and Cx43 mRNAs and anatomical enlargement of the entire heart and AVJ.
    Matched MeSH terms: Electrocardiography
  19. Fulltext Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter
    Ghaleb FA, Kamat MB, Salleh M, Rohani MF, Abd Razak S
    PLoS One, 2018;13(11):e0207176.
    PMID: 30457996 DOI: 10.1371/journal.pone.0207176
    The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects.
    Matched MeSH terms: Electrocardiography
  20. Fulltext An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment
    Shukla S, Hassan MF, Khan MK, Jung LT, Awang A
    PLoS One, 2019;14(11):e0224934.
    PMID: 31721807 DOI: 10.1371/journal.pone.0224934
    Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
    Matched MeSH terms: Electrocardiography
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