The apnea-ECG database

Penzel, Thomas; Moody, G.B.; Mark, R.G.; Goldberger, Ary L.; Peter, J. H.

doi:10.1109/cic.2000.898505

Cited by 340 publications

(267 citation statements)

References 6 publications

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“…However, comparison with studies using this database is difficult since borderline subjects were deliberately removed from the competition. Additionally, only one woman was included in the apnea group [59].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Gutiérrez‐Tobal

Álvarez

Gómez‐Pilar

et al. 2015

Entropy

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Heart rate variability (HRV) provides useful information about heart dynamics both under healthy and pathological conditions. Entropy measures have shown their utility to characterize these dynamics. In this paper, we assess the ability of spectral entropy (SE) and multiscale entropy (MsE) to characterize the sleep apnoea-hypopnea syndrome (SAHS) in HRV recordings from 188 subjects. Additionally, we evaluate eventual differences in these analyses depending on the gender. We found that the SE computed from the very low frequency band and the low frequency band showed ability to characterize SAHS regardless the gender; and that MsE features may be able to distinguish gender specificities. SE and MsE showed complementarity to detect SAHS, since several features from both analyses were automatically selected by the forward-selection backward-elimination algorithm. Finally, SAHS was modelled through logistic regression (LR) by using optimum sets of selected features. Modelling SAHS by genders reached significant higher performance than doing it in a jointly way. The highest diagnostic ability was reached by modelling SAHS in women. The LR classifier achieved 85.2% accuracy (Acc) and 0.951 area under the ROC curve (AROC). LR for men reached 77.6% Acc and 0.895 AROC, whereas LR for the whole OPEN ACCESSEntropy 2015, 17 124 set reached 72.3% Acc and 0.885 AROC. Our results show the usefulness of the SE and MsE analyses of HRV to detect SAHS, as well as suggest that, when using HRV, SAHS may be more accurately modelled if data are separated by gender.

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Section: Discussionmentioning

confidence: 99%

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Gutiérrez‐Tobal

Álvarez

Gómez‐Pilar

et al. 2015

Entropy

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“…Database has 70 ECG recordings digitized 100 Hz from normal and OSA patients which duration varying between 401 and 578 minutes. Data were gathered in Philips University in Marburg, Germany having sleep apnea annotations was done by sleep experts according to standard criteria simultaneously recorded respiration signals [24].…”

Section: Data Setmentioning

confidence: 99%

Sleep apnea classification based on respiration signals by using ensemble methods

Avci

Akbaş

2015

BME

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Abstract. In this study, an efficient and robust method classifying the minute based occurrence of sleep apnea is aimed. Three respiration signals obtained from abdominal, chest and nasal way extracted from polysomnography recordings. Wavelet transform based on feature extraction methods are applied on the 1 minute length respiration signals. Dimension reduction process is facilitated by using principal component analysis. The features obtained from 8 recordings are used for the classification sleep apnea by using three ensemble classifiers. According to the results, the classification accuracies have been obtained between 92.07-98.43%, 92.75-98.68% and 92.42-98.61% by using three different ensemble classifier based on abdominal, chest and nasal based analysis, respectively for AdaBoost, Random Forest and Random Subspace. However the best result is obtained analyzing nasal based respiratory signal by using Random Forest method. In this case accuracy is 98.68%.

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“…The perepoch classifications are then combined to form an overall classification of the recording in terms of average minutes/hour of obstructive sleep apnoea, which is then mapped to an estimated apnoea-hypopnoea index (AHI) using a linear transformation. The result of applying this algorithm to an independent test set of 35 ECG recordings taken from the Marburg database (Penzel 2000) was that it correctly separated all clinically significant sleep apnoea (AHIO15) cases from control subjects (AHI!5).…”

Section: Introductionmentioning

confidence: 99%

Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals

Chazal

Heneghan

McNicholas

2008

Phil. Trans. R. Soc. A.

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A method for the detection of sleep apnoea, suitable for use in the home environment, is presented. The method automatically analyses night-time electrocardiogram (ECG) and oximetry recordings and identifies periods of normal and sleep-disordered breathing (SDB). The SDB is classified into one of six classes: obstructive, mixed and central apnoeas, and obstructive, mixed and central hypopnoeas. It also provides an estimated apnoea, hypopnoea and apnoea-hypopnoea index. The basis of the method is a pattern recognition system that identifies episodes of apnoea by analysing the heart variability, an ECG-derived respiration signal and blood oximetry values. The method has been tested on 183 subjects with a range of apnoea severities who have undergone a full overnight polysomnogram study. The results show that the method separates control subjects from subjects with clinically significant sleep apnoea with a specificity of 83 per cent and sensitivity of 95 per cent. These results demonstrate that home-based screening for sleep apnoea is a viable alternative to hospital-based tests with the added benefit of low cost and minimal waiting times.

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The apnea-ECG database

Cited by 340 publications

References 6 publications

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Sleep apnea classification based on respiration signals by using ensemble methods

Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals

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