Designing a Remote Closed-Loop Automatic Oxygen Control in Preterm Infants

Moradi, Mohammad; Kalhori, Sharareh R. Niakan; Saeedi, Marian Ghazi; Zarkesh, Mohammad Reza; Habibelahi, Abbas; Panahi, Amir

doi:10.5812/ijp.101715

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…MATLAB's intuitive syntax facilitates rapid prototyping and implementation, allowing researchers, engineers, and scientists to focus on solving complex problems rather than wrestling with intricate programming details. Additionally, MATLAB's extensive community support and documentation contribute to its importance, fostering collaboration and knowledge exchange across diverse domains 71 . Overall, MATLAB's versatility and user‐friendly features make it an indispensable tool for professionals and researchers engaged in a wide array of scientific and engineering endeavors.…”

Section: Results and Comparisonmentioning

confidence: 99%

“…Additionally, MATLAB's extensive community support and documentation contribute to its importance, fostering collaboration and knowledge exchange across diverse domains. 71 Overall, MATLAB's versatility and user-friendly features make it an indispensable tool for professionals and researchers engaged in a wide array of scientific and engineering endeavors. The prevalence of MATLAB as the most used programming language in nature-inspired algorithms within IoT-based healthcare services underscores its significant importance in this domain.…”

Section: F I G U R Ementioning

confidence: 99%

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The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

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Nature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

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Section: Results and Comparisonmentioning

confidence: 99%

Section: F I G U R Ementioning

confidence: 99%

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Meanwhile, TGs provide an intricate understanding of dynamic patient interactions over time, aiding in disease progression modeling and personalized treatment plans [23]. The convergence of these technologies optimizes medical data processing, fostering precision medicine, remote patient monitoring, and efficient healthcare delivery, thereby revolutionizing patient care and augmenting medical research endeavors within the IoT-driven healthcare domain [24].…”

Section: B Literature Studymentioning

confidence: 99%

Efficient Processing of Large-Scale Medical Data in IoT: A Hybrid Hadoop-Spark Approach for Health Status Prediction

Lina,

Wenlong

2024

IJACSA

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In the realm of Internet of Things (IoT)-driven healthcare, diverse technologies, including wearable medical devices, mobile applications, and cloud-based health systems, generate substantial data streams, posing challenges in real-time operations, especially during emergencies. This study recommends a hybrid architecture utilizing Hadoop for real-time processing of extensive medical data within the IoT framework. By employing distributed machine learning models, the system analyzes health-related data streams ingested into Spark streams via Kafka threads, aiming to transform conventional machine learning methodologies within Spark's real-time processing, crafting scalable and efficient distributed approaches for predicting health statuses related to diabetes and heart disease while navigating the landscape of big data. Furthermore, the system provides real-time health status forecasts based on a multitude of input features, disseminates alert messages to caregivers, and stores this valuable information within a distributed database, which is instrumental in health data analysis and the production of flow reports. We compute a range of evaluation parameters to evaluate the proposed methods' efficacy. This assessment phase encompasses measuring the performance of the Spark-based machine learning algorithm in a distributed parallel computing environment.

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“…Advances in technologies such as IoT, machine learning, and Wireless Sensor Networks (WSN) have significantly enhanced the ability to interpret these signals, particularly in fields like biomedicine, where mechanisms change over time as their underlying characteristics continually evolve [2,3]. These alterations can be abrupt, where the internal properties of the system change gradually over time, or gradual, where the internal properties change slowly over time [4]. The signals from these systems are also time-varying in nature, and their time-varying aspects can unveil the dynamics of these systems [5].…”

Section: Introductionmentioning

confidence: 99%

Exploring Cutting-Edge Developments in Deep Learning for Biomedical Signal Processing

Zhu,

Zhang,

Liu

et al. 2024

ijacsa

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Biomedical condition monitoring devices are progressing quickly by incorporating cost-effective and noninvasive sensors to track vital signs, record medical circumstances, and deliver meaningful responses. These sophisticated innovations rely on breakthrough technology to provide intelligent platforms for health monitoring, quick illness recognition, and precise treatment. Biomedical signal processing determines patterns of signals and serves as the backbone for reliable applications, medical diagnostics, and research. Deep Learning (DL) methods have brought significant innovation in biomedical signal processing, leading to the transformation of the health sector and medical diagnostics. This article covers an entire range of technical innovations evolved for DL-based biomedical signal processing where different modalities have been considered, including Electrocardiography (ECG), Electromyography (EMG), and Electroencephalography (EEG). A vast amount of biomedical data in various forms is available, and DL concepts are required to extract and model this data in order to identify hidden complex patterns that can be utilized to improve the diagnosis, prognosis, and personalized treatment of diseases in an individual. The nature of this developing topic certainly gives rise to a number of challenges. First, the application of sensitive and noisy time series data requires truly robust models. Second, many inferences made at the bedside must have interpretability by design. Third, the field will require that processing be performed in real-time if used for therapeutic interventions. We systematically evaluate these challenges and highlight areas where continued research is needed. The general expansion of DL technologies into the biomedical domain gives rise to novel concerns about accountability and transparency of algorithmic decision-making, a subject which we briefly touch upon as well.

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Designing a Remote Closed-Loop Automatic Oxygen Control in Preterm Infants

Cited by 9 publications

References 13 publications

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Efficient Processing of Large-Scale Medical Data in IoT: A Hybrid Hadoop-Spark Approach for Health Status Prediction

Exploring Cutting-Edge Developments in Deep Learning for Biomedical Signal Processing

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