Continuous analysis and monitoring of snores and their relationship to the apnea‐hypopnea index

Fiz, J.A.; Jané, Raimon; Solà-Soler, Jordi; Abad, Jorge; García, M.A.; Morera, J.

doi:10.1002/lary.20815

Cited by 65 publications

(69 citation statements)

References 25 publications

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“…Snoring sounds have been also assessed in the context of SAHS classification. A LR classifier modelled with nine spectral features reached 81.1% Acc and 0.850 AROC (AHI= 5 e/h threshold); and 86.5% Acc and 0.920 AROC (AHI = 5 e/h threshold) [54]. Another LR classifier obtained from 11 time and frequency domain features was evaluated for AHI = 10 e/h [55].…”

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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“…Investigation of 37 snorers with a range of AHI scores, demonstrated that an all-night recording with automated scoring of intensity and frequency could reliably distinguish AHI classifications (40). A large study of snoring volume intensity in 1634 snorers divided into non-OSA, mild, moderate and severe OSA found a 14 strong correlation between intensity and AHI (0.66) (41).…”

Section: Distinction By Soundmentioning

confidence: 99%

Simple snoring: Not quite so simple after all?

Deary

Ellis

Wilson

et al. 2014

Sleep Medicine Reviews

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“…Snoring sound was recorded with a unidirectional electric condenser microphone placed over the trachea at the level of the cricoid cartilage using an elastic band. The sound signal was amplified and filtered using second order Butterworth pass-band filter between 70 and 2000Hz and digitized with a sampling frequency of 5000Hz and a 12-bit analog/digital converter [5].…”

Section: A Signal Acquisitionmentioning

confidence: 99%

“…Several techniques such as sound intensity calculations, power spectrum analysis, feature extraction in time and frequency domains and attempts to model snore sounds are the latest efforts giving evidence that snoring carries information on SAHS [3][4][5]. Even though these latest works added auspicious information there seems to be a lack of emphasis on how the anatomical structure of the upper airways has effect on the time production of snores.…”

Section: Introductionmentioning

confidence: 99%

Regular and non regular snore features as markers of SAHS

Mesquita

Fiz

Solà-Soler

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Abstract-Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH adaptive and TH median ) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (ApneaHypopnea Index)<5h ). Results showed that TH adaptive outperformed TH median on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.

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Continuous analysis and monitoring of snores and their relationship to the apnea‐hypopnea index

Cited by 65 publications

References 25 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

Simple snoring: Not quite so simple after all?

Regular and non regular snore features as markers of SAHS

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