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Journal ArticleDOI

Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings

01 Jan 2009-Vol. 13, Iss: 1, pp 37-48
TL;DR: The results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG and demonstrate considerable potential in applying SVM in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.
Abstract: Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS- ) and subjects with OSAS (OSAS+), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.
Citations
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Journal ArticleDOI
TL;DR: An iterative cluster Primal Dual Splitting algorithm for solving the large-scale sSVM problem in a decentralized fashion, which extracts important features discovered by the algorithm that are predictive of future hospitalizations, thus providing a way to interpret the classification results and inform prevention efforts.

577 citations

Journal ArticleDOI
TL;DR: The literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above.

326 citations

Journal ArticleDOI
TL;DR: A novel computationally intelligent-based electrocardiogram (ECG) signal classification methodology using a deep learning (DL) machine that autonomously learns representative and key features of the PAF to be used by a classification module.
Abstract: In this paper, a novel computationally intelligent-based electrocardiogram (ECG) signal classification methodology using a deep learning (DL) machine is developed. The focus is on patient screening and identifying patients with paroxysmal atrial fibrillation (PAF), which represents a life threatening cardiac arrhythmia. The proposed approach operates with a large volume of raw ECG time-series data as inputs to a deep convolutional neural networks (CNN). It autonomously learns representative and key features of the PAF to be used by a classification module. The features are therefore learned directly from the large time domain ECG signals by using a CNN with one fully connected layer. The learned features can effectively replace the traditional ad hoc and time-consuming user’s hand-crafted features. Our experimental results verify and validate the effectiveness and capabilities of the learned features for PAF patient screening. The main advantages of our proposed approach are to simplify the feature extraction process corresponding to different cardiac arrhythmias and to remove the need for using a human expert to define appropriate and critical features working with a large time-series data set. The extensive simulations and case studies conducted indicate that combining the learned features with other classifiers will significantly improve the performance of the patient screening system as compared to an end-to-end CNN classifier. The effectiveness and capabilities of our proposed ECG DL classification machine is demonstrated and quantitative comparisons with several conventional machine learning classifiers are also provided.

312 citations


Additional excerpts

  • ...Among these methods, support vector machines (SVMs) and hidden Markov models (HMMs) are commonly used with the hand-crafted features that have produced acceptable results in [3]–[7]....

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Journal ArticleDOI
01 May 2011
TL;DR: A low-cost, real-time sleep apnea monitoring system that uses patient's single channel nocturnal ECG to extract feature sets, and uses the support vector classifier (SVC) to detect apnea episodes with a high degree of accuracy for both home and clinical care applications.
Abstract: We have developed a low-cost, real-time sleep apnea monitoring system ``Apnea MedAssist” for recognizing obstructive sleep apnea episodes with a high degree of accuracy for both home and clinical care applications. The fully automated system uses patient's single channel nocturnal ECG to extract feature sets, and uses the support vector classifier (SVC) to detect apnea episodes. “Apnea MedAssist” is implemented on Android operating system (OS) based smartphones, uses either the general adult subject-independent SVC model or subject-dependent SVC model, and achieves a classification F-measure of 90% and a sensitivity of 96% for the subject-independent SVC. The real-time capability comes from the use of 1-min segments of ECG epochs for feature extraction and classification. The reduced complexity of “Apnea MedAssist” comes from efficient optimization of the ECG processing, and use of techniques to reduce SVC model complexity by reducing the dimension of feature set from ECG and ECG-derived respiration signals and by reducing the number of support vectors.

268 citations


Cites methods from "Support Vector Machines for Automat..."

  • ...The application of SVM classifiers in apnea screening are discussed in [10] and [11]....

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Journal ArticleDOI
TL;DR: These results indicate that the use of only ECG sensors can achieve good accuracies in the detection of sleep apnea, and the contamination level of each ECG segment can be used to automatically detect artefacts, and to highlight segments that require further visual inspection.
Abstract: Goal: This paper presents a methodology for the automatic detection of sleep apnea from single-lead ECG. Methods: It uses two novel features derived from the ECG, and two well-known features in heart rate variability analysis, namely the standard deviation and the serial correlation coefficients of the RR interval time series. The first novel feature uses the principal components of the QRS complexes, and it describes changes in their morphology caused by an increased sympathetic activity during apnea. The second novel feature extracts the information shared between respiration and heart rate using orthogonal subspace projections. Respiratory information is derived from the ECG by means of three state-of-the-art algorithms, which are implemented and compared here. All features are used as input to a least-squares support vector machines classifier, using an RBF kernel. In total, 80 ECG recordings were included in the study. Results: Accuracies of about 85% are achieved on a minute-by-minute basis, for two independent datasets including both hypopneas and apneas together. Separation between apnea and normal recordings is achieved with 100% accuracy. In addition to apnea classification, the proposed methodology determines the contamination level of each ECG minute. Conclusion: The performances achieved are comparable with those reported in the literature for fully automated algorithms. Significance: These results indicate that the use of only ECG sensors can achieve good accuracies in the detection of sleep apnea. Moreover, the contamination level of each ECG segment can be used to automatically detect artefacts, and to highlight segments that require further visual inspection.

259 citations


Cites background from "Support Vector Machines for Automat..."

  • ...It has been shown by several authors [5]–[7], [16], [17] that respiratory events can be detected using an ECG derived respiration (EDR)....

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References
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Journal ArticleDOI
TL;DR: An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.

43,884 citations

Book
Vladimir Vapnik1
01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

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"Support Vector Machines for Automat..." refers background in this paper

  • ...Changes in “b” shift the optimal decision hyperplane, and thereby, change the sensitivity and specificity....

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  • ...Thus, there is no prerequisite regarding the stability of the frequency content along the signal analyzed....

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TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher: This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.

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TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Abstract: From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

19,056 citations