Key result
Machine learning classification using Support Vector Machine and Linear Discriminant Analysis accurately detected periodic breathing in elderly patients, achieving accuracies of 88.2% and 82.4%.
Why the study?
Do machine learning classifiers using respiratory and clinical features accurately detect periodic breathing in elderly patients?
Observational (n=44)
Do machine learning classifiers using respiratory and clinical features accurately detect periodic breathing in elderly patients?
Machine learning classifiers, particularly SVM, can accurately detect periodic breathing using respiratory flow signals and clinical variables in elderly patients.
Answers are grounded in this paper and cite their sources.
Machine learning may aid periodic breathing detection in elderly patients; hypothesis-generating and requires prospective validation before clinical use.
Aging population is a major concern that is reflected in the increase of chronic diseases. Heart Failure (HF) is one of the most common chronic diseases of elderly people that is punctuated with acute episodes, which result in hospitalization. The periodic modulation of the amplitude of the breathing pattern is proved to be one of the multiple symptoms of an acute episode, and thus, the features extracted from its characterization contribute in the improvement of the first diagnosis of the clinical practice. The main objective of this study is to evaluate if the features extracted from the breathing pattern along with common clinical variables are reliable enough to detect Periodic Breathing (PB). A dataset of 44 elderly patients containing clinical information and a short record of electrocardiogram and respiratory flow signal was used to train two machine learning classification methods: Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). All the available clinical parameters within the dataset along with the parameters characterizing the respiratory pattern were used to classify the observations into two groups. SVM classification was optimized and performed using a = -8 and C = 10.04 giving an accuracy of 88.2 % sensitivity of 90 % and specificity of 85.7 % Similar results were achieved with LDA classifying with an accuracy of 82.4 %, a sensitivity of 81.8% and specificity of 83.3 % PB has been accurately detected using both classifiers.
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Argerich et al. (2016) conducted an observational in Heart Failure / Periodic Breathing (n=44). Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) was evaluated on Detection of Periodic Breathing (PB). Machine learning classification using Support Vector Machine and Linear Discriminant Analysis accurately detected periodic breathing in elderly patients, achieving accuracies of 88.2% and 82.4%.
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