Development of an ECG Smart Jersey Based on Next Generation Computing for Automated Detection of Heart Defects Among Athletes

Adetiba, Emmanuel; Onosenema, Ekpoki N.; Akande, Victor; Adetiba, Joy N.; Kala, Jules R.; Olaloye, Folarin

doi:10.1007/978-3-030-17935-9_47

Cited by 3 publications

(11 citation statements)

References 12 publications

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“…"Reviews" was the next single largest group with eight (29% studies [15,17,18,23,29,30,35,38]. "Wearables" was the smallest single group with fou (14%) studies [20,25,28,37]. The final six (21%) unassigned studies [4,19,24,33,36,39] wer Of the 49 studies that remained, 1 study was excluded from the review due to issues with accessing the full manuscript, leaving 48 studies to be included for full-text readings and to form the dataset for this review.…”

Section: Study Subgroupsmentioning

confidence: 99%

“…"Reviews" was the next single largest group with eight (29%) studies [15,17,18,23,29,30,35,38]. "Wearables" was the smallest single group with four (14%) studies [20,25,28,37]. The final six (21%) unassigned studies [4,19,24,33,36,39] were placed in the "Others" group as they did not meet the inclusion criteria for the previous groups.…”

Section: Study Subgroupsmentioning

confidence: 99%

“…However, they differ in their individual implementations of the wearable technology, and in how the data are collected, stored, and analysed. Adetiba et al [25] developed a smart jersey to be worn by athletes to automatically record an ECG signal. These data are then automatically passed through an ANN that has been pre-trained to identify heart defects and returns whether the result is normal or not to a smartphone application.…”

Section: Wearablesmentioning

confidence: 99%

See 2 more Smart Citations

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

Bellfield

Ortega‐Martorell

Lip

et al. 2022

JCDD

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Background: Intense training exercise regimes cause physiological changes within the heart to help cope with the increased stress, known as the “athlete’s heart”. These changes can mask pathological changes, making them harder to diagnose and increasing the risk of an adverse cardiac outcome. Aim: This paper reviews which machine learning techniques (ML) are being used within athlete’s heart research and how they are being implemented, as well as assesses the uptake of these techniques within this area of research. Methods: Searches were carried out on the Scopus and PubMed online datasets and a scoping review was conducted on the studies which were identified. Results: Twenty-eight studies were included within the review, with ML being directly referenced within 16 (57%). A total of 12 different techniques were used, with the most popular being artificial neural networks and the most common implementation being to perform classification tasks. The review also highlighted the subgroups of interest: predictive modelling, reviews, and wearables, with most of the studies being attributed to the predictive modelling subgroup. The most common type of data used was the electrocardiogram (ECG), with echocardiograms being used the second most often. Conclusion: The results show that over the last 11 years, there has been a growing desire of leveraging ML techniques to help further the understanding of the athlete’s heart, whether it be by expanding the knowledge of the physiological changes or by improving the accuracies of models to help improve the treatments and disease management.

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Section: Study Subgroupsmentioning

confidence: 99%

Section: Study Subgroupsmentioning

confidence: 99%

Section: Wearablesmentioning

confidence: 99%

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The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

Bellfield

Ortega‐Martorell

Lip

et al. 2022

JCDD

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show abstract

“…Reliability [11], [12], [14], [16], [17], [18], [19], [20], [21], [22], [23], [24], [26], [28], [29], [30], [31], [32], [33], [34], [36], [41].…”

Section: Table 4: Summary Of References Performances Criteria Referencesunclassified

“…Applicability [11], [16], [17], [18], [19], [20], [21], [22], [23], [26], , [28], [29], [30], [31], [32], [33], [34], [35], [41]. Cost [12], [17], [19], [23], [36], [37].…”

Section: Table 4: Summary Of References Performances Criteria Referencesunclassified

IoT based on Health Caring Systems Survey

Mahmood¹,

Rafaa²

2021

IJCDS

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Nowadays, using E-Health technologies has enabled health services to reach patients worldwide efficiently anytime anywhere. Internet of things (IoT) revolution has brought booming in the healthcare sector. E-Health caring has created a dazzling and cheap checking organism for obtaining greater comfortable living to the people tormented by a variety of diseases through utilizing diverse techniques like wireless exchanges, wearable, and convenient remote health monitoring tools. This paper provides a recent review of the e-health care platform tabulated according to the IoT layers along with the advantages and disadvantages to guide researchers on the most important future work in each layer.

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Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete’s heart?

Palermi,

Vecchiato,

Saglietto

et al. 2024

European Journal of Preventive Cardiology

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The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. AI offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex data sets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.

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Development of an ECG Smart Jersey Based on Next Generation Computing for Automated Detection of Heart Defects Among Athletes

Cited by 3 publications

References 12 publications

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research

IoT based on Health Caring Systems Survey

Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete’s heart?

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