Obstructive sleep apnea detection using SVM-based classification of ECG signal features

Almazaydeh, Laiali; Elleithy, Khaled; Faezipour, Miad

doi:10.1109/embc.2012.6347100

Cited by 72 publications

(38 citation statements)

References 12 publications

(14 reference statements)

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“…They turn nonlinear boundaries when returning them to the initial input space. This implementation employs a linear kernel function to map the training data on the kernel space …”

Section: Methodsmentioning

confidence: 99%

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Detection and classification of sleep apnea using genetic algorithms and SVM‐based classification of thoracic respiratory effort and oximetric signal features

Abedi

Naghavi

Rezaeitalab

2017

Computational Intelligence

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Sleep apnea is a relatively prevalent breathing disorder characterized by temporary interruptions in airflow during sleep. There are 2 major types of sleep apnea. Obstructive sleep apnea occurs when air cannot flow through the upper airway despite efforts to breathe. Central sleep apnea occurs when the brain fails to signal to the muscles to maintain breathing. The standard diagnostic test is polysomnography, which is expensive and time consuming. The aim of this study was to design an automatic diagnostic and classifying algorithm for sleep apneas employing thoracic respiratory effort and oximetric signals. This algorithm was trained and tested applying a database of 54 subjects who had undergone polysomnography. A feature extraction stage was conducted to compute features. An optimal genetic algorithm was applied to select optimal features of these 2 kinds of signals. The classification technique was based on the support vector machine classifier to classify the selected features in 3 classes as healthy, obstructive, and central sleep apnea events. The results show that our automated classification algorithm can diagnose sleep apnea and its types with an average accuracy level of 90.2% (87.5‐95.8) in the test set and 90.9% in the validation set with high acceptable accuracy.

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“…They turn nonlinear boundaries when returning them to the initial input space. This implementation employs a linear kernel function to map the training data on the kernel space …”

Section: Methodsmentioning

confidence: 99%

“…To discover events of sleep apnea, Almazaydeh et al extracted the periodic changes during heart beat time, recognized as RR intervals. The proposed classification technique was based on the support vector machine (SVM) and was tested and trained on sleep apnea both with and without OSA.…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of sleep apnea using genetic algorithms and SVM‐based classification of thoracic respiratory effort and oximetric signal features

Abedi

Naghavi

Rezaeitalab

2017

Computational Intelligence

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“…In this study we experimented with three kernels, namely linear, radial basis function (RBF) and polynomial, as described in Table 2. SVM has by now been used for several applications like ECG beat detection and classification; ECG arrhythmia classification; Obstructive sleep apnea syndrome from ECG recordings, as found in other studies [30][31][32][33][34][35][36][37][38][39][40][41][42]. Physical activities recognition from ambulatory ECG signals 145…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Kher¹,

Pawar²,

Thakar³

et al. 2015

Journal of Medical Engineering & Technology

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The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)-left arm up down, right arm up down, waist twisting and walking-have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time-frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.

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“…There are some researches have been performed in sleep apnea identification. Almazaydeh, et al, performs obstructive sleep apnea detection using support vector machine (SVM) method [8]. The feature used in the paper are mean epoch, standard deviation epoch, NN50 (variant 1), NN50 (variant 2), pNN50, etc.…”

Section: Introductionmentioning

confidence: 99%

Sleep Apnea Identification using HRV Features of ECG Signals

Sulistyo

Surantha

Isa

2018

IJECE

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Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subject-specific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.

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Obstructive sleep apnea detection using SVM-based classification of ECG signal features

Cited by 72 publications

References 12 publications

Detection and classification of sleep apnea using genetic algorithms and SVM‐based classification of thoracic respiratory effort and oximetric signal features

Detection and classification of sleep apnea using genetic algorithms and SVM‐based classification of thoracic respiratory effort and oximetric signal features

Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Sleep Apnea Identification using HRV Features of ECG Signals

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