An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram

Chen, Lili; Zhang, Xi; Song, Changyue

doi:10.1109/tase.2014.2345667

Cited by 135 publications

(70 citation statements)

References 33 publications

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“…Varon et al [99] proposed principal components of the QRS complexes as features for detecting OSA. Chen et al [100] used a severity index of OSA with support vector machines for computer-assisted sleep apnoea identification. Single lead ECG signals had been used in this work.…”

Section: Sleep Disorders Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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Section: Sleep Disorders Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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“…The related work is mostly based on features obtained from HRV only [3,[8][9][10]17,18], but in the present study, features from HRV and EDR were employed together, since they provide better classification performance than using HRV features alone [11,14]. In the related work, HRV and EDR parameters were mostly analyzed in the frequency domain [5,[13][14][15][16]. In the present study, the parameters were analyzed in the time domain, similar to [14].…”

Section: Related Workmentioning

confidence: 99%

k-NN-based classification of sleep apnea types using ECG

Timuş¹,

Bolat²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder that yields cardiovascular diseases, excessive daytime sleepiness, and poor quality of life if not treated. Classification of OSAS from electrocardiograms (ECGs) is a noninvasive method and much more affordable than traditional methods. This study proposes a pattern recognition system for automated apnea diagnosis based on heart rate variability (HRV) and ECG-derived respiratory signals. The k-nearest neighbor (k-NN) classifier has been used to develop the models for classifying the sleep apnea types. For comparison purposes, classification models based on multilayer perceptron, support vector machines, and C4.5 decision tree (C4.5 DT) have also been developed. The first database used for training contains 12 and the second used for testing contains 35 whole-night polysomnography recordings from real subjects. Wrapper-based feature selection, optimal parameter calculation, and 10-fold cross-validation were applied to the training dataset. The performance of the classifiers was evaluated by accuracy, sensitivity, and specificity metrics. The k-NN classifier yields higher classification accuracy, sensitivity, and specificity by successfully separating 100% of apnea recordings from normal recordings, and it also achieves a classification rate of 97% accuracy, 89% sensitivity, and 100% specificity of the subjects in the test database. Median, mean, absolute deviation, and interquartile range values of HRV were the most descriptive parameters.These results indicate significant potential for achieving basic estimates for OSAS patients.

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“…[14] A new automatically approach based on analysis of respiratory rate to detect sleep apnea with the help of respiratory and ECG signal recordings. [15] Defined a work on automatic screening for sleep apnea identification through ECG signal evaluate. Author diagnoses sign under a couple of channels of physiological signal and obtain segmented event evaluation to become aware of disorder occurrence over the signal and obtained 92% approx accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Technique to Improve ECG Signal using SVM

Verma¹,

Guatam²

2017

IJARCSSE

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Abstract-A beneficial finding of the sleep apnea disorder (SAS) depends on a proper examination of the patient's polysomnograph, comprising of simultaneously recording electrophysiological and pneumological signals amid a night's rest. As of now, the pervasiveness of this issue has brought on an expansion in the interest for pro clinical help and rest units. In this examination, a robotized grouping calculation is offered which forms little length ages of the electrocardiogram (ECG) information. SVM is utilized as a part of this computerized grouping and has been prepared and experienced on SA recordings from subjects with and without SA.

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An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram

Cited by 135 publications

References 33 publications

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

k-NN-based classification of sleep apnea types using ECG

A Novel Technique to Improve ECG Signal using SVM

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