A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Xu, Yanting; Yang, Zhengyuan; Li, Gang; Tian, Jinghong; Jiang, Yonghua

doi:10.3390/healthcare9111453

Cited by 10 publications

(5 citation statements)

References 34 publications

(65 reference statements)

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“…Left ventricular assist devices (LVAD) and mild traumatic brain injury are terms in Quadrant II. These terms refer to key areas of research where fiber optic sensing and machine learning have been used to solve critical healthcare problems [31,96,97]. For example, fiber-sensing technology has been used to improve LVAD monitoring and control.…”

Section: Analysis Of Emerging Wordsmentioning

confidence: 99%

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

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Section: Analysis Of Emerging Wordsmentioning

confidence: 99%

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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“…They performed a study on patients after vascular intervention surgery and healthy subjects to prove the ability of BCG in long-duration monitoring of high-risk patients. Moreover, Liu et al [ 167 ] and Xu et al [ 168 ] revealed that the HRV parameters obtained from BCG could be used as the identifying indicators to detect hypertension and brain fatigue, respectively.…”

Section: Studies That Combine Cardiac and Pulmonary Informationmentioning

confidence: 99%

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography—A Narrative Review

Balali

Rabineau

Hossein

et al. 2022

Sensors

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Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body’s center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

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“…Advanced features such as wireless transmission, miniaturized amplifiers ( Wascher et al, 2023 ), and dry electrodes ( Ramírez-Moreno et al, 2021 ) have enhanced the application of highly portable EEG head-mounted devices, proving crucial for research in real mobile environments. Technologies like the ballistocardiogram (BCG), using a fiber sensor cushion ( Xu et al, 2021 ) or photoplethysmogram (PPG) integrated into a helmet ( Wilson et al, 2020 ) have enabled the simultaneous collection of biological signals alongside the primary task, offering the capability to capture changes in the operators’ functional status before an alteration in task performance occurs. With superior temporal resolution compared to subjective methods, these approaches serve as pivotal tools for real-time mental workload assessment during tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have demonstrated a correlation between ECG signals and psychomotor vigilance task (PVT) performance, as well as the effectiveness of HRV indices in assessing cognitive task-related errors. Leveraging sensitive features extracted from ECG, algorithms like learning vector quantization and random forest tree classifiers achieve impressive accuracy in identifying fatigue states, underscoring HRV as a potential indicator for evaluating worker fatigue ( Chua et al, 2012 ; Zhao et al, 2012 ; Pan et al, 2021 ; Xu et al, 2021 ; Takada et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks

Cheng,

Li,

Hui

et al. 2024

Front. Physiol.

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Backgrounds: The validity of heart rate variability (HRV) has been substantiated in mental workload assessments. However, cognitive tasks often coincide with physical exertion in practical mental work, but their synergic effects on HRV remains insufficiently established. The study aims were to investigate the combined effects of cognitive and physical load on autonomic nerve functions.Methods: Thirty-five healthy male subjects (aged 23.5 ± 3.3 years) were eligible and enrolled in the study. The subjects engaged in n-back cognitive tasks (1-back, 2-back, and 3-back) under three distinct physical conditions, involving isotonic contraction of the left upper limb with loads of 0 kg, 3 kg, and 5 kg. Electrocardiogram signals and cognitive task performance were recorded throughout the tasks, and post-task assessment of subjective experiences were conducted using the NASA-TLX scale.Results: The execution of n-back tasks resulted in enhanced perceptions of task-load feelings and increased reaction times among subjects, accompanied by a decline in the accuracy rate (p < 0.05). These effects were synchronously intensified by the imposition of physical load. Comparative analysis with a no-physical-load scenario revealed significant alterations in the HRV of the subjects during the cognitive task under moderate and high physical conditions. The main features were a decreased power of the high frequency component (p < 0.05) and an increased low frequency component (p < 0.05), signifying an elevation in sympathetic activity. This physiological response manifested similarly at both moderate and high physical levels. In addition, a discernible linear correlation was observed between HRV and task-load feelings, as well as task performance under the influence of physical load (p < 0.05).Conclusion: HRV can serve as a viable indicator for assessing mental workload in the context of physical activities, making it suitable for real-world mental work scenarios.

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A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Cited by 10 publications

References 34 publications

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography—A Narrative Review

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks

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