Controlled Breathing Effect on Respiration Quality Assessment Using Machine Learning Approaches

Rozo, Andrea; Buil, Jeroen; Moeyersons, Jonathan; Morales, John; Westen, Roberto Garcia van der; Lijnen, Lien; Smeets, C. J. P. P.; Jantzen, Sjors; Monpellier, Valerie M.; Ruttens, David; Hoof, Chris Van; Huffel, Sabine Van; Groenendaal, Willemijn; Varon, Carolina

doi:10.23919/cinc53138.2021.9662854

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…The effect of these two approaches is more noticeable when compared to the results presented by Rozo et al (2021) , where TL was applied to the models pre-trained with the COPD data without DA. In that case, SVM presented a higher improvement than CNN.…”

Section: Discussionmentioning

confidence: 92%

“…Previously, a quality index for respiratory signals was developed by Charlton et al (2021) , which then was compared to a machine learning framework for quality assessment by Moeyersons et al (2021) . Rozo et al (2021) presented the results of applying transfer learning to the previous framework. In this context, this study extends the latter work including a data augmentation approach to improve the performance of the machine learning framework when applied to new data.…”

Section: Introductionmentioning

confidence: 99%

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Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring

Rozo

Moeyersons

Morales

et al. 2022

Front. Bioeng. Biotechnol.

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Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine–SVM, and convolutional neural network–CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring

Rozo

Moeyersons

Morales

et al. 2022

Front. Bioeng. Biotechnol.

Self Cite

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“…Shifting focus to respiratory monitoring, Rozo et al developed machine learning models to assess thoracic bio-impedance (BioZ) measurements. Using SVM and CNN classifiers, transfer learning, and feature-based classification, they evaluated the impact of different breathing patterns on model performance [32].…”

Section: Bio-impedance and Electro-chemical Wearablesmentioning

confidence: 99%

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Olyanasab,

Annabestani

2024

JPM

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This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer’s physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual’s physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.

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Controlled Breathing Effect on Respiration Quality Assessment Using Machine Learning Approaches

Cited by 2 publications

References 6 publications

Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring

Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

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