Automated Detection and Classification of Sleep Apnea Types Using Electrocardiogram (ECG) and Electroencephalogram (EEG) Features

Koçak, Onur; Bayrak, Tuncay; Erdamar, Aykut; Ozparlak, Levent; Telatar, Ziya; Eroğul, Osman

doi:10.5772/22782

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…The Physionet apnea-ECG database was most often used by related works [5,[10][11][12][13][14][16][17][18] in which QRS complexes were provided; therefore, no R-wave detection procedure was required in those investigations. In this work, TEO was applied for R-wave detection similar to [28]. In addition, to evaluate the performance of TEO, the Pan-Tompkins algorithm used by [5,13] was also applied, and it was determined that the detected R-peak locations were alike.…”

Section: Discussionmentioning

confidence: 99%

“…Many R-wave detection approaches can be found in the literature [14,15,[24][25][26][27]. These techniques can be summarized as Teager energy operator (TEO), wavelet transform, SVM, the Pan-Tompkins algorithm, filtering in the frequency domain, and zero crossing rate [28]. Since TEO is the most commonly used approach utilized for numerous applications such as one-dimensional signals, images, and color image processing [29] and is convenient for studying a single component in both continuous and discrete domains, it was used in this study to obtain the RR intervals.…”

Section: Hrv and Edr Calculationmentioning

confidence: 99%

“…In the continuous and discrete domains, TEO is expressed as in Eqs. (4) and (5), respectively [28,29]:…”

Section: Hrv and Edr Calculationmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

Section: Hrv and Edr Calculationmentioning

confidence: 99%

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

Timuş¹,

Bolat²

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

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“…Performance evaluations of six types of decision tree classifiers was conducted using the kfold cross validation technique. The average prediction accuracy is 74.3% [7]. Another research work was observing a sleep stage classification from EEG.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

Wongsirichot¹,

Elz²,

Kajkamhaeng³

et al. 2019

IJMLC

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Sleep quality is highly significant for the people's overall health. A standard diagnosis for sleep-related syndromes and illnesses is Polysomnography (PSG) or a sleep test in a controlled laboratory. However, PSG requires a sleep specialist to interpret bio-signals collected. It is a time consuming procedure. One of the fundamental step in the PSG is Sleep Stage Classification (SSC). In this study, we propose an investigation of Automatic Sleep Stage Classification (ASSC) using data mining techniques as an alternative to the PSG in order to reduce the time necessary for accurately diagnosing sleep quality. We studied 2,535 subjects' polysomnographic data with 14 channels of biomedical signals from the Sleep Heart Health Study (SHHS) Dataset. Subsequently, four data mining techniques including Decision Trees, Random Forests, Neural Network, and k-Nearest Neighbors were selected to compare the classification performances. The classification results in k-Nearest Neighbors achieved the greatest accuracy at 83.76%.

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“…In addition, accessibility to diagnosis and treatment is limited due to insufficient resources, both human (trained specialists) and technical (specialized sleep units), which have led to large waiting lists [9]. In this context, automated computer-aided diagnosis systems have emerged as very useful tools to deal with complex rules involving several biomedical recordings simultaneously, in order to expedite diagnosis and treatment [10][11][12]. Among all the machine learning-based tools, ANNs have been widely applied in the context of SAHS and merit a thorough analysis.…”

Section: Introductionmentioning

confidence: 99%

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Álvarez¹,

Cerezo-Hernández²,

Lopez-Muniz³

et al. 2017

Sleep Apnea - Recent Updates

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Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients' needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed.

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Automated Detection and Classification of Sleep Apnea Types Using Electrocardiogram (ECG) and Electroencephalogram (EEG) Features

Cited by 13 publications

References 19 publications

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

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

An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

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