Automated detection of obstructive sleep apnoea syndrome from oxygen saturation recordings using linear discriminant analysis

Marcos, J. Víctor; Hornero, Roberto; Álvarez, Daniel; Campo, Félix del

doi:10.1007/s11517-010-0646-6

Cited by 53 publications

(37 citation statements)

References 38 publications

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“…It relies on the assumption that the conditional class density function of each class, p(x k | C l ), follows a multivariate normal distribution with identical covariance matrices, , for all the classes [37]. A discriminant score y l is computed for each class using [38]:…”

Section: ) Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings

Gutiérrez‐Tobal

Álvarez

Crespo

et al. 2019

IEEE J. Biomed. Health Inform.

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Abstract-Complexity, costs, and waiting lists issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients' home. They were used to automatically obtain statistical, spectral, non-linear, and clinical SAHS-related information. Relevant and non-redundant data from these analyses were subsequently used to train and validate four machine-learning methods with ability to classify SpO2 signals into one out of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multilayer perceptron, and AdaBoost), outperformed the diagnostic ability of the conventionally-used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen's  in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine learning can be used along with SpO2 information acquired at patients' home to help in SAHS diagnosis simplification.

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Section: ) Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings

Gutiérrez‐Tobal

Álvarez

Crespo

et al. 2019

IEEE J. Biomed. Health Inform.

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“…Welch's method was applied for this purpose since it is suitable for non-stationary signals [30]. A Hamming window of 2 15 points (50% overlap), along with a discrete Fourier transform of 2 16 points, were used to compute PSD. To avoid the influence of factors not related to the pathophysiology of SAHS, each PSD was normalized (PSDn) dividing the amplitude value at each frequency by their corresponding total power [31].…”

Section: A Feature Extraction 1) Spectral Analysismentioning

confidence: 99%

“…As weak classifiers we propose two wellknown machine learning algorithms based on i) linear discriminant analysis (LDA) and ii) classification and regression trees (CART). Both of them have been already assessed in the context of SAHS [16], [23]. Since classifiers favor the right sorting of classes with more subjects, one major issue in the present work is how to deal with imbalanced classes.…”

Section: Introductionmentioning

confidence: 99%

Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow

Gutiérrez‐Tobal

Álvarez

Campo

et al. 2016

IEEE Trans. Biomed. Eng.

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Abstract-Goal:The purpose of this study is to evaluate the usefulness of the boosting algorithm AdaBoost (AB) in the context of the sleep apnea-hypopnea syndrome (SAHS) diagnosis. Methods: We characterize SAHS in single-channel airflow (AF) signals from 317 subjects by the extraction of spectral and nonlinear features. Relevancy and redundancy analyses are conducted through the fast correlation-based filter (FCBF) to derive the optimum set of features among them. These are used to feed classifiers based on linear discriminant analysis (LDA) and classification and regression trees (CART). LDA and CART models are sequentially obtained through AB, which combines their performances to reach higher diagnostic ability than each of them separately. Results: Our AB-LDA and AB-CART approaches showed high diagnostic performance when determining SAHS and its severity. The assessment of different apnea-hypopnea index cutoffs using an independent test set derived into high accuracy: 86.5% (5 events/h), 86.5% (10 events/h), 81.0% (15 events/h), and 83.3% (30 events/h). These results widely outperformed those from logistic regression and a conventional event-detection algorithm applied to the same database. Conclusion: Our results suggest that AB applied to data from single-channel AF can be useful to determine SAHS and its severity. Significance: SAHS detection might be simplified through the only use of single-channel AF data.

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“…A major goal is to model the statistical characteristics of the problem under study, leading to expert systems able to assist physicians in decision-making processes. Among pattern recognition algorithms, conventional statistical classifiers, such as discriminant analysis [3] or logistic regression (LR) [4], and more recently artificial neural networks (ANNs) [5], have been widely applied. The widely known statistical classifiers assume that the class density function of input data is known a priori.…”

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 of obstructive sleep apnoea syndrome from oxygen saturation recordings using linear discriminant analysis

Cited by 53 publications

References 38 publications

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings

Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow

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

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