Discriminative methods based on sparse representations of pulse oximetry signals for sleep apnea–hypopnea detection

Rolón, Román Emanuel; Larrateguy, Luis; Persia, Leandro E. Di; Spies, Rubén D.; Rufiner, Hugo Leonardo

doi:10.1016/j.bspc.2016.12.013

Cited by 24 publications

(34 citation statements)

References 24 publications

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“…Therefore, this study will focus on apneic event detection based on the SpO 2 signal. Previous studies based on SpO 2 used time and frequency domain features [16], [19]- [21], nonlinear parameters [14], [19], PSG parameters such as the oxygen desaturation index (ODI) [16], [18], [22], [25] and more elaborate methods, such as sparse representations [15], to detect SAHS. Most of these studies classify patients as having SAHS or not, based on features computed over the full night, without detecting the individual apneic events.…”

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confidence: 99%

Automatic Screening of Sleep Apnea Patients Based on the SpO₂ Signal

Deviaene

Testelmans

Buyse

et al. 2019

IEEE J. Biomed. Health Inform.

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This paper presents a methodology to automatically screen for sleep apnea based on the detection of apnea and hypopnea events in the blood oxygen saturation (SpO) signal. It starts by detecting all desaturations in the SpO signal. From these desaturations, a total of 143 time-domain features are extracted. After feature selection, the six most discriminative features are used to construct classifiers to predict if desaturations are caused by respiratory events. From these, a random forest classifier yielded the best classification performance. The number of desaturations, classified as caused by respiratory events per hour of recording can then be used as an estimate of the apnea-hypopnea index (AHI), and to predict if a patient suffers from sleep apnea-hypopnea syndrome (SAHS) or not. All classifiers were developed based on a subset of 500 subjects of the Sleep Heart Health Study (SHHS) and tested on three different datasets, containing 8052 subjects in total. An averaged desaturation classification accuracy of 82.8 % was achieved over the different test sets. Subjects having SAHS with an AHI larger than 15 can be detected with an average accuracy of 87.6 %. The achieved SAHS screening outperforms SpO methods from the literature on the SHHS test dataset. Moreover, the robustness of the method was shown when tested on different independent test sets. These results show that an algorithm based on simple features of SpO desaturations can outperform more elaborate methods in the detection of apneic events and the screening of SAHS patients.

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confidence: 99%

Automatic Screening of Sleep Apnea Patients Based on the SpO₂ Signal

Deviaene

Testelmans

Buyse

et al. 2019

IEEE J. Biomed. Health Inform.

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“…This is mainly so because those algorithms do not take into account any a priori or available information concerning class membership. In order to overcome this difficulty, some strategies which incorporate appropriate class information have been proposed [4,16,33]. In [33], for instance, the authors developed a discriminative dictionary learning method by efficiently integrating a single predictive linear classifier into the cost function of the K-SVD algorithm.…”

Section: Discriminative Subdictionary Constructionmentioning

confidence: 99%

“…Pattern recognition is a discipline which is mainly oriented to the generation of algorithms or methods that can decide an action based upon certain recognized similarities (patterns) in the input data. Within signal classification, which is perhaps one of the most important subfields of pattern recognition, several discrepancy measures have been used in problems coming from a wide variety of areas such as machine learning [1], image and speech processing [2], neural networks [3], and biomedical signal processing [4,5]. Among them, the most commonly used is probably the Kullback-Leibler (KL) divergence [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…We shall follow one consisting of using the discriminative information for detecting those atoms having the most frequent activations in order to provide them as input for a classifier. This approach was initially introduced in [4] where two methods using the absolute value of the activation differences of the atoms as a measure of the discriminative information for the detection of OSAH were presented. In this work, a rigorous formalization of such a measure is introduced and compared with several other discrepancy measures for classifying apnea-hypopnea events.…”

Section: Introductionmentioning

confidence: 99%

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Complexity‐Based Discrepancy Measures Applied to Detection of Apnea‐Hypopnea Events

et al. 2018

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In recent years, an increasing interest in the development of discriminative methods based on sparse representations with discrete dictionaries for signal classification has been observed. It is still unclear, however, what is the most appropriate way for introducing discriminative information into the sparse representation problem. It is also unknown which is the best discrepancy measure for classification purposes. In the context of feature selection problems, several complexity-based measures have been proposed. The main objective of this work is to explore a method that uses such measures for constructing discriminative subdictionaries for detecting apnea-hypopnea events using pulse oximetry signals. Besides traditional discrepancy measures, we study a simple one called Difference of Conditional Activation Frequency (DCAF). We additionally explore the combined effect of overcompleteness and redundancy of the dictionary as well as the sparsity level of the representation. Results show that complexity-based measures are capable of adequately pointing out discriminative atoms. Particularly, DCAF yields competitive averaged detection accuracy rates of 72.57% at low computational cost. Additionally, ROC curve analyses show averaged diagnostic sensitivity and specificity of 81.88% and 87.32%, respectively. This shows that discriminative subdictionary construction methods for sparse representations of pulse oximetry signals constitute a valuable tool for apnea-hypopnea screening.

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“…The type of studies used in our system is type 4 sleep studies, which also refers to continuous single bio-parameter or dual-bio parameter recording. The minimum number of signals that can be used in this type 4 studies is one or two channels such as oxygen saturation and airflow [3][4][5][6][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Normally type 4 studies do not have EEG and EMG signals, so scoring sleep is not possible.…”

Section: Introductionmentioning

confidence: 99%

Sleep apnea detection using deep learning

Chaw

Kamolphiwong

Wongsritrang³

2019

Teh. glas. (Online)

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Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network.

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Discriminative methods based on sparse representations of pulse oximetry signals for sleep apnea–hypopnea detection

Cited by 24 publications

References 24 publications

Automatic Screening of Sleep Apnea Patients Based on the SpO₂ Signal

Automatic Screening of Sleep Apnea Patients Based on the SpO₂ Signal

Complexity‐Based Discrepancy Measures Applied to Detection of Apnea‐Hypopnea Events

Sleep apnea detection using deep learning

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Discriminative methods based on sparse representations of pulse oximetry signals for sleep apnea–hypopnea detection

Cited by 24 publications

References 24 publications

Automatic Screening of Sleep Apnea Patients Based on the SpO2 Signal

Automatic Screening of Sleep Apnea Patients Based on the SpO2 Signal

Complexity‐Based Discrepancy Measures Applied to Detection of Apnea‐Hypopnea Events

Sleep apnea detection using deep learning

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Product

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Automatic Screening of Sleep Apnea Patients Based on the SpO₂ Signal

Automatic Screening of Sleep Apnea Patients Based on the SpO₂ Signal