Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

Cimr, Dalibor; Studnička, Filip; Fujita, Hamido; Šlégr, Jan

doi:10.1016/j.cmpb.2021.106149

Cited by 13 publications

(9 citation statements)

References 29 publications

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“…The derivative of arc length (DAL) was calculated as a difference between two adjacent samples in each sampling time. Arc length approach has already been proven to be a strong tool in biosignal processing [ 3 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

Possibilities and limits of using gyroscopic sensors in the diagnosis of progression of osteoarthritis and femoroacetabular impingement syndrome

et al. 2022

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Osteoarthritis is the most common type of degenerative joint disease and affects millions of people. In this paper, we propose a non-obtrusive and straightforward method to assess the progression of osteoarthritis. In standard medicine praxis, osteoarthritis is observed with X-rays. In this study, we use widely available wearable sensors with gyroscopes to make the observation. Two novel methods are proposed for gyroscope data processing. A small-scale study has shown that these methods can be used to monitor osteoarthritis’s progression, and to differentiate between healthy subjects and subjects with femoroacetabular impingement syndrome.

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Section: Methodsmentioning

confidence: 99%

Possibilities and limits of using gyroscopic sensors in the diagnosis of progression of osteoarthritis and femoroacetabular impingement syndrome

et al. 2022

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“…To evaluate sleep-related disorders (see Section 5.1.1 ) more comfortably than with traditional polysomnography, BCG has been shown to have a great potential, as it does not disrupt a subject’s normal sleep patterns to gather data [ 8 ]. Bed-based BCG can benefit from different kinds of sensors, from static charge sensitive beds [ 78 , 79 , 80 ] to pressure pads or pressure sensors [ 70 , 81 , 82 ], force sensors [ 69 , 83 , 84 ], loadcells in bed legs [ 85 , 86 , 87 , 88 , 89 ], fiber optic sensors [ 71 , 72 , 90 ], or electromagnetic film sensors [ 68 , 91 ]. These systems can offer comfort to the user, as well as the opportunity to easily install the systems at home [ 30 ].…”

Section: Sensors and Preprocessingmentioning

confidence: 99%

“…Respiration can also potentially contaminate the signal, primarily by generating a baseline wander caused by chest movement during respiration. It can also have an impact on the amplitude and time intervals [ 84 , 111 ], as already discussed. Nevertheless, these amplitude and interval fluctuations can sometimes be a source of information, for instance, for measuring the respiratory rate, phase, and amplitude.…”

Section: Sensors and Preprocessingmentioning

confidence: 99%

“…Subsequently, Liu et al [ 137 ] presented a new framework for enhancing the performance of apneic event detection based on breaking each possible event segment into distinct phases. Very recently, the potential of BCG in sleep screening and sleep apnea detection was further studied [ 91 , 132 ], including with new devices based on microbend fiber optic sensors [ 138 ] and with new detection methods, such as convolutional neural networks [ 84 ] and wavelet decomposition and the iterative cumulative sums of squares [ 139 ].…”

Section: Studies That Combine Cardiac and Pulmonary Informationmentioning

confidence: 99%

“…Cimr et al [ 84 ] designed a convolutional neural network classifier. They used the Cartan curvatures (a geometrical invariant that can be used to evaluate small changes in multivariate time series) and Euclidean arc length of the measured signal from different sensors embedded in the bed for detecting respiratory disorders.…”

Section: Studies That Combine Cardiac and Pulmonary Informationmentioning

confidence: 99%

See 2 more Smart Citations

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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Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network

et al. 2021

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Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

Cited by 13 publications

References 29 publications

Possibilities and limits of using gyroscopic sensors in the diagnosis of progression of osteoarthritis and femoroacetabular impingement syndrome

Possibilities and limits of using gyroscopic sensors in the diagnosis of progression of osteoarthritis and femoroacetabular impingement syndrome

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography—A Narrative Review

Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network

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