Model predictive control optimisation using the metaheuristic optimisation for blood pressure control

Ahmadpour, Mohammad Reza; Ghadiri, Hamid; Hajian, Saeed Reza

doi:10.1049/syb2.12012

Cited by 13 publications

(3 citation statements)

References 27 publications

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“…Considering limited hardware, not all hospital equipment can acquire high‐quality images. When applying their methods to low‐quality images, the performance is not very good [ 31 , 32 , 33 , 34 ]. On the other hand, most of these methods have poor classification effects on sex chromosomes.…”

Section: Related Workmentioning

confidence: 99%

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Liu

Lin

et al. 2022

IET Systems Biology

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Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super‐resolution network, Self‐Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS‐net. The method first inputs the low‐resolution chromosome images into the super‐resolution network to generate high‐resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state‐of‐the‐art methods.

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Section: Related Workmentioning

confidence: 99%

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Liu

Lin

et al. 2022

IET Systems Biology

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“…Ahmadpour et al [54] developed a genetic-algorithm-based solution to track subjects' blood pressure, significantly improving their overall quality of life and allowing for the early detection of preventable diseases. Khan et al [55] explore an IoT environment that enables all-day monitoring of patients' conditions and greatly improves their cardiovascular health.…”

Section: Metaheuristicsmentioning

confidence: 99%

“…Ahmadpour et al. [ 54 ] developed a genetic-algorithm-based solution to track subjects’ blood pressure, significantly improving their overall quality of life and allowing for the early detection of preventable diseases. Khan et al.…”

Section: Introductionmentioning

confidence: 99%

Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data

Minic,

Jovanovic,

Bacanin

et al. 2023

Sensors

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Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.

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An adaptive fractional order optimizer based optimal tilted controller design for artificial ventilator

Acharya,

Das

2024

Optim Control Appl Methods

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Artificial ventilators are vital respiratory support systems in the field of medical care, especially for patients in critical condition. It is crucial to make sure the ventilator keeps the intended airway pressure because variations might be harmful to the brain and lungs. Thus, achieving accurate pressure tracking is a primary objective in designing optimal controllers for pressure‐controlled ventilators (PCVs). To address this need, a novel approach is proposed: a mixed integer tilted fractional order integral and integer order derivation controller tailored for PCV systems. The gains of different parameters of the proposed controller are optimized using an adaptive chaotic search fractional order class topper optimization algorithm, augmented with a Gaussian‐based mutation operator. Moreover, the controller is designed to minimize oscillations in its output signal, thereby mitigating physical risks and reducing the size of actuators required. The efficacy of the optimized controller is further examined across various scenarios, including different lung resistances and compliances across different age groups of patients. Additionally, the impact of endotracheal tube resistance on air pressure is assessed as a potential disturbance in the PCV system. Through comprehensive testing, the proposed controller demonstrates superior performance in accurately tracking airway pressure to the desired levels. Across all evaluated cases, the proposed controller structure and accompanying algorithm outperform existing solutions. Notably, improvements are observed in system response time, overshoot, and settling time. This underscores the significance of employing advanced control strategies to enhancing the functionality and safety of PCV systems in medical settings.

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Model predictive control optimisation using the metaheuristic optimisation for blood pressure control

Cited by 13 publications

References 27 publications

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data

An adaptive fractional order optimizer based optimal tilted controller design for artificial ventilator

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