On the generalizability of ECG-based obstructive sleep apnea monitoring: merits and limitations of the Apnea-ECG database

Papini, Gabriele; Fonseca, Pedro; Margarito, Jenny; Gilst, Merel M van; Overeem, Sebastiaan; Bergmans, J.W.M.; Vullings, Rik

doi:10.1109/embc.2018.8513660

Cited by 23 publications

(10 citation statements)

References 18 publications

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“…This data set is rather easy to classify, since the control subjects are volunteers instead of patients with sleep related complaints (Penzel et al, 2000). When algorithms trained on the Apnea-ECG data set are tested on clinical data with more complex disease patterns, drops in accuracy of up to 15 % can be seen (Papini et al, 2018). Therefore, only a direct comparison to the algorithm by Varon et al (Varon et al, 2015a) was included, since this clinical data set, OSA Varon, was available for this study.…”

Section: Apnea Detectionmentioning

confidence: 99%

Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea

et al. 2021

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Objective: The performance of a novel unobtrusive system based on capacitivelycoupled electrocardiography (ccECG) combined with different respiratory measurements is evaluated for the detection of sleep apnea. Approach: A sleep apnea detection algorithm is proposed, which can be applied to electrocardiography (ECG) and ccECG, combined with different unobtrusive respiratory measurements, including ECG derived respiration (EDR), respiratory effort measured using the thoracic belt (TB) and capacitively-coupled bioimpedance (ccBioz). Several ECG, respiratory and cardiorespiratory features were defined, of which the most relevant ones were identified using a random forest based backwards wrapper. Using this relevant feature set, a least-squares support vector machine classifier was trained to decide if a one minute segment is apneic or not, based on the annotated polysomnography (PSG) data of 218 patients suspected of having sleep apnea. The obtained classifier was then tested on the PSG and capacitively-coupled data of 28 different patients. Main results: On the PSG data, an AUC of 76.3 % was obtained when the ECG was combined with the EDR. Replacing the EDR with the TB led to an AUC of 80.0 %. Using the ccECG and ccBioz or the ccECG and TB resulted in similar performances as on the PSG data, while using the ccECG and ccECG-based EDR resulted in a drop in AUC to 67.4 %. Significance: This is the first study which tests an apnea detection algorithm on capacitively-coupled ECG and bioimpedance signals and shows promising results on the capacitively-coupled data set. However, it was shown that the EDR could not be accurately estimated from the ccECG signals. Further research into the effect that respiration has on the ccECG is needed to propose alternative EDR estimates.

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Section: Apnea Detectionmentioning

confidence: 99%

Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea

et al. 2021

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“…The characteristics used in previous studies are relatively many and varied. Some features measure signal statistics [14] and features that capture the heart rate variability of ECG signals [12,25]. It is different from the proposed method, where only one feature is measured (sample entropy) but is carried out at various signal scales.…”

Section: Resultsmentioning

confidence: 99%

Classification of Sleep Apnea using Multi Scale Entropy on Electrocardiogram Signal

2021

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One of sleep-disordered breathing (SDB) form is sleep apnea, commonly known as snoring during sleep, based on various complex mechanisms and predisposing factors. Sleep apnea is also related to various medical problems. It impacts morbidity and mortality so that it becomes a burden on public health services. Its detection needs to be done correctly through electrocardiogram signals to detect sleep apnea more quickly and precisely. This study was conducted to detect sleep apnea based on electrocardiogram signals using multi-scale entropy analysis. Multi-scale entropy (MSE) is used in a finite length of time series for measuring the complexity of the signal. MSE can be applied to both physical and physiological data sets and. In this paper we used MSE to detect Sleep Apnea on electrocardiogram (ECG) signals. MSE was applied two classes of ECG data, normal ECG signals, and apnea ECG signals. In this paper, classification and verification were carried out using the Support Vector Machine (SVM) and N-fold cross-validation (N-fold CV). From the experimental results, the highest accuracy was 85.6% using 5-fold CV and MSE scale of 10. The result shows that the system model that can detect sleep using the multi-scale entropy method

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“…Several alternate apnea detection methods with noninvasive sensing either have inconsistent performance on large cohorts [7] or focus only on desaturation-associated events [9], thereby severely underestimating apnea episode count and duration. Further, most works directly estimate AHI, which does not account for apnea duration that is found to be strongly linked to mortality [14].…”

Section: Discussionmentioning

confidence: 99%

“…ECG has been widely studied for sleep apnea screening with publicly available Physionet 2000 dataset [6]. However, the algorithms requiring robust ECG processing are not generalizable to other ECG datasets [7]. SpO2 and photoplethysmogram (PPG) waveform derived pulse rate variability (PRV) are shown to provide good performance for apnea detection [8].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning based Sleep Apnea Detection using SpO2 and Pulse Rate

Sharma¹,

Jalali²,

Majmudar³

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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This work presents automated apnea event detection using blood oxygen saturation (SpO2) and pulse rate (PR), conveniently recorded with a pulse oximeter. A large, diverse cohort of patients (n=8068, age≥40 years) from the sleep heart health study dataset with annotated sleep events have been employed in this study. A deep-learning model is trained to detect apnea in successive 30 s epochs and performances are assessed on two independent sub-cohorts of test data. The proposed algorithm showcases the highest test performance of 90.4% area under the receiver operating characteristic curve and 58.9% area under the precision-recall curve for epochbased apnea detection. Additionally, the model consistently performs well across various apnea subtypes, with the highest sensitivity of 93.4% for obstructive apnea detection followed by 90.5% for central apnea and 89.1% for desaturation associated hypopnea. Overall, the proposed algorithm provides a robust and sensitive approach for sleep apnea event detection using a noninvasive pulse oximeter sensor.Clinical relevance-The study establishes high sensitivity for automated epoch-based apnea detection across a diverse study cohort with various comorbidities using simply a pulse oximeter. This highly cost-effective approach could also enable convenient sleep and health monitoring over long-term.

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On the generalizability of ECG-based obstructive sleep apnea monitoring: merits and limitations of the Apnea-ECG database

Cited by 23 publications

References 18 publications

Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea

Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea

Classification of Sleep Apnea using Multi Scale Entropy on Electrocardiogram Signal

Deep-Learning based Sleep Apnea Detection using SpO2 and Pulse Rate

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