Detection of sleep apnea events via tracking nonlinear dynamic cardio-respiratory coupling from electrocardiogram signals

Karandikar, Kunal; Le, Trung Q.; Sa-ngasoongsong, Akkarapol; Wongdhamma, Woranat; Bukkapatnam, Satish T. S.

doi:10.1109/embc.2013.6611191

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…One of the major reasons is that the physiological processes underlying the measured physiological signals are highly nonlinear and nonstationary [1, 2]. Hence, the quantifiers extracted from these signals, also referred to as features , exhibit complex spatiotemporal patterns [3]. This challenge is exemplified by noting the evolution pattern of two features, namely the normalized band-limited power spectral density (NPSD) and the longest vertical recurrence length (LVM) extracted from the heart rate variability (HRV) signal of an apneic subject during sleep (see Fig 1).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets

Bukkapatnam

2016

PLoS ONE

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Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets

Bukkapatnam

2016

PLoS ONE

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“…LR provides a more general approach that fits better to the characteristics of the problem under study. Nevertheless, additional automated pattern recognition techniques such as decision trees, artificial neural networks or support vector machines, which have demonstrated its usefulness in the context of adult OSAS [31,[53][54][55][56], need to be assessed in the context of paediatric OSAS.…”

Section: Discussionmentioning

confidence: 99%

Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home

Crespo

Álvarez

Gutiérrez‐Tobal

et al. 2017

Entropy

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Untreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO 2 ) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO 2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO 2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.

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“…Some carry out patient sound analysis [8][9][10], while others study airflow [11][12][13], abdominal and thoracic movement signals [14], and even voice analysis [15,16]. However, most include statistical pattern recognition based on characteristics extracted from single-lead electrocardiogram (ECG) signals [17][18][19][20][21][22][23][24][25][26][27][28][29] and blood oxygen saturation (SpO 2 ) , which are measured by a pulse oximeter. In some cases, the single-lead ECG signal is combined with the SpO 2 signal [56][57][58][59][60][61].…”

Section: Motivation and Problem Descriptionmentioning

confidence: 99%

“…We carry out a per-recording classification according to the AHI estimation (automatic AHI). As we highlighted in our introduction, we estimate the AHI for a subject by calculating the average number of apneic minutes per hour, i.e., adding the total number of apneic minutes, dividing this value by the total number of minutes in the corresponding register, and multiplying the result by 60 [25].…”

Section: Per-recording Classificationmentioning

confidence: 99%

Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients

Martin-Gonzalez

Ravelo-García

Navarro-Mesa

et al. 2023

Sensors

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In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.

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Detection of sleep apnea events via tracking nonlinear dynamic cardio-respiratory coupling from electrocardiogram signals

Cited by 7 publications

References 16 publications

Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets

Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets

Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home

Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients

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