Real-Time Intelligent Healthcare Monitoring and Diagnosis System Through Deep Learning and Segmented Analysis

Panganiban, Edward B.; Chung, Wen-Yaw; Tai, Wei-Chieh; Paglinawan, Arnold C.; Lai, Jheng-Siang; Cheng, Ren-Wei; Chang, M.; Chang, Peter S.

doi:10.1007/978-3-030-30636-6_3

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…In these studies, as discussed in Table 2 , different processing units, connector modules, and power sources were used to build the wearable device. Alternatively, in [ 82 , 84 , 95 , 97 ], the authors combined different sensor materials with the AD8232 ECG sensor in their wearable device. Specifically, the authors in [ 82 , 95 ] used the MAX30100 blood oxygen sensor in addition to the ECG sensor, whereas the authors in [ 84 ] used the ADXL345 triaxial accelerometer, and the authors in [ 97 ] used the MAX30102 pulse oximeter sensor.…”

Section: Resultsmentioning

confidence: 99%

“…Alternatively, in [ 82 , 84 , 95 , 97 ], the authors combined different sensor materials with the AD8232 ECG sensor in their wearable device. Specifically, the authors in [ 82 , 95 ] used the MAX30100 blood oxygen sensor in addition to the ECG sensor, whereas the authors in [ 84 ] used the ADXL345 triaxial accelerometer, and the authors in [ 97 ] used the MAX30102 pulse oximeter sensor. Other studies also used different ECG sensors, with the authors in [ 87 ] building their portable devices using the “Ternary Second-Order Delta Modulator Circuits” to acquire ECG data.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Within the same context, Panganiban et al [24] recommended an innovative unfathomable dynamic self-stepped learning approach to diminish gloss exertion and create consumption of the largest illustrations per the arrangement of inactive learning and the self-stepped policies. To estimate the routine of the deep active self-paced learning strategies, binary distinctive difficulties in image analysis, nodule segmentation in 3-dimensional computed tomography scan imageries, and diabetic retinopathy recognitions in arithmetical retinal-fundus phantasmagorias are verified.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

Shafik

Matinkhah

Ahmad

2020

Wireless Communications and Mobile Computing

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The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

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“…Within the same context, Wang et al [17] propose a novel deep active self-paced learning (DASL) strategy to reduce annotation effort and also make use of unannotated samples, based on a combination of active learning (AL) and self-paced learning (SPL) strategies. To evaluate the performance of the DASL strategy, two typical problems in biomedical image analysis, pulmonary nodule segmentation in 3D CT images, and diabetic retinopathy (DR) identification in digital retinal fundus images are tested.…”

Section: Motivationmentioning

confidence: 99%

Theoretical Understanding of Deep Learning in UAV Biomedical Engineering Technologies Analysis

2020

View full text Add to dashboard Cite

The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, search, and rescue operations among others. The current technologies are using fixed base stations to operate on-site and off-site in the fixed position with its associated problems like poor connectivity. This opens gates for the UAVs technology to be used as a mobile alternative to increase accessibility with a fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general, and provides detailed steps that are involved in the multi-armed bandit approach in solving UAV biomedical engineering technologies devices and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal medical engineered devices' performance and efficiency. The simulated results depicted that a multi-armed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technologies systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

show abstract

Real-Time Intelligent Healthcare Monitoring and Diagnosis System Through Deep Learning and Segmented Analysis

Cited by 9 publications

References 7 publications

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

Theoretical Understanding of Deep Learning in UAV Biomedical Engineering Technologies Analysis

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