Classification of Cheyne Stokes Breathing and Obstructive Sleep Apnea Using ECG

Suhas, S.R.; Vijendra, S.; Burk, John R.; Lucas, E.A.; Behbehani, Khosrow

doi:10.1109/iembs.2006.259983

Cited by 11 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classification of some possible sleep disorders such as Cheyne stokes breathing and obstructive sleep apnea was performed by Suhas et al (2006) using the ECG data and polysomnographic data. From the data generated using the ECG, two types of signals were derived namely, R-wave attenuation and heart rate variability, these features were further subjected to frequency domain analysis for the identification of OSA and CSR events.…”

Section: Related Workmentioning

confidence: 99%

A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques

Chakraborty

Aich

Kim

2021

International Journal of System Dynamics Applications

View full text Add to dashboard Cite

Maintaining the suited amount of sleep is considered the prime component for maintaining a proper and adequate health condition. Often it has been observed that people having sleep inconsistency tend to jeopardize the health and appeal to many physiological and psychological disorders. To overcome such difficulties, it is often required to keep a requisite note of the duration and quality of sleep that one is having. This work defines an algorithm that can be utilized in smart wearables or mobile phones to perceive the duration of sleep and also to classify a particular instance as slept or awake on the basis of data fetched from the triaxial accelerometer. A comparative analysis was performed based on the results obtained from some previously developed algorithms, rule-based models, and machine learning models, and it was observed that the algorithm developed in the work outperformed the previously developed algorithms. Moreover, the algorithm developed in the work will very much define the scoring of sleep of an individual for maintaining a proper health balance.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques

Chakraborty

Aich

Kim

2021

International Journal of System Dynamics Applications

View full text Add to dashboard Cite

show abstract

“…where OSA c is the number of correctly detected OSA epochs and NOR c is the number of correctly detected normal epochs [10].…”

Section: Multilayer Perceptron Classifiermentioning

confidence: 99%

Cross correlation and scatter plots of the heart rate variability and R-peak Envelope as features in the detection of obstructive sleep apnea

Al-Abed

Behbehani

Burk

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Self Cite

View full text Add to dashboard Cite

Interbeat heart rate as measured by the RR interval (RR) and R-Peak Envelope (RPE) are two signals that can be extracted from an Electrocardiogram (ECG) with relative ease and high reliability. RR and RPE have been shown to carry markers for detecting sleep disordered breathing (SDB). In this pilot study, we explore the cross correlation of RR and RPE in normal and SDB patients. Nocturnal ECG from 7 normal subjects and 7 SDB patients were used to obtain RR and RPE. The results revealed that the cross correlation of RR and RPE signals is significantly different between normal subjects and SDB patients (p 2x10(-6)). Furthermore, a new scatter plot of RR vs. RPE was developed. Optimum features from the RR vs. RPE scatter plot were extracted and used as input to a multilayer perceptron (MLP) classifier to distinguish between normal and SDB subjects, The detection sensitivity, specificity and accuracy for the training data set were 95.0%, 100.0%, and 97.5%, respectively; and for the test data were 76.6%, 93.2%, and 84.7%, respectively.

show abstract

“…Garde et al [9] proposed two highly effective methods for distinguishing healthy patients from those with abnormal breathing patterns using both linear and non-linear classifiers with an accuracy level of 93% and 100% respectively. Another method to discern obstructive sleep apnea (OSA) from Cheyne-Stokes respiration (CSR) using frequency domain analysis from overnight electrocardiography (ECG) was described by Suhas, Vijendra, Burk, Lucas and Behbehani [10] yielding an 87.5% sensitivity and a specificity of 75%. Neural networks classifiers have also been applied to the detection of irregular breathing patterns [11] .…”

Section: Introductionmentioning

confidence: 99%

Characterization of the respiratory pattern in patients with chronic heart failure applying joint symbolic dynamics

Molina-Ramírez¹

View full text Add to dashboard Cite

This work proposes a method to characterize the respiratory pattern of patients with chronic heart failure (CHF) to determine non-periodic breathing (nPB), periodic breathing (PB) and Cheyne-Stokes respiration (CSR) through non-linear, symbolic analysis of biological signals. A total of 43 patients were examined for their cardiorespiratory profiles, their ECG and respiratory pattern signals were processed, analyzed and studied for parameters that could be of potential use in clinical decision making, specifically in patient classification. Patients in the study were characterized through their cardiorespiratory signals, applying joint symbolic dynamics (JSD) analysis to cardiac beat and respiratory interval durations. The most statistically significant parameters across all groups were identified through a Kruskal-Wallis two tailed test (α = 0.05) and a linear discriminant analysis (LDA) classification method based on such parameters was developed. The best result achieved with this classification method uses 10 features to discriminate patients with a 97.67% Accuracy (Acc). The best features to discriminate among groups are related to cardiorespiratory interaction rather than just respiration patterns alone. Results further support the idea that abnormal breathing patterns derive from physiological abnormalities in chronic heart failure.

show abstract

Classification of Cheyne Stokes Breathing and Obstructive Sleep Apnea Using ECG

Cited by 11 publications

References 9 publications

A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques

A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques

Cross correlation and scatter plots of the heart rate variability and R-peak Envelope as features in the detection of obstructive sleep apnea

Characterization of the respiratory pattern in patients with chronic heart failure applying joint symbolic dynamics

Contact Info

Product

Resources

About