research-article

Real-time detection of apneas on a PDA

Authors:

Alfredo Burgos,

Arantza Illarramendi,

Jesús BermúdezAuthors Info & Claims

IEEE Transactions on Information Technology in Biomedicine, Volume 14, Issue 4

Pages 995 - 1002

https://doi.org/10.1109/TITB.2009.2034975

Published: 01 July 2010 Publication History

Abstract

Patients suspected of suffering sleep apnea and hypopnea syndrome (SAHS) have to undergo sleep studies such as expensive polysomnographies to be diagnosed. Healthcare professionals are constantly looking for ways to improve the ease of diagnosis and comfort for this kind of patients as well as reducing both the number of sleep studies they need to undergo and the waiting times. Relating to this scenario, some research proposals and commercial products are appearing, but all of them record the physiological data of patients to portable devices and, in the morning, these data are loaded into hospital computers where physicians analyze them by making use of specialized software. In this paper, we present an alternative proposal that promotes not only a transmission of physiological data but also a real-time analysis of these data locally at a mobile device. For that, we have built a classifier that provides an accuracy of 93% and a receiver operating characteristic-area under the curve (ROC-AUC) of 98.5% on SpO₂ signals available in the annotated Apnea-ECG Database. This local analysis allows the detection of anomalous situations as soon as they are generated. The classifier has been implemented taking into consideration the restricted resources of mobile devices.

References

[1]

American Academy of Sleep Medicine Task Force, "Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research," Sleep, vol. 22, no. 5, pp. 667-689, 1999.

[2]

J. Ronald, K Delaive, L. Roos, J. Manfreda, A. Bahammam, and M. H. Kryger, "Health care utilization in the 10 years prior to diagnosis in obstructive sleep apnea syndrome patients," Sleep, vol. 22, no. 2, pp. 225- 229, 1999.

[3]

N. Netzer, A. H. Eliasson, C. Netzer, and D. A. Kristo, "Overnight pulse oximetry for sleep-disordered breathing in adults: A review," Chest, vol. 120, no. 2, pp. 625-633, 2001.

[4]

J. U. Magalang, J. Dmochowski, S. Veeramachaneni, A. Draw, M. J. Mador, A. El-Solh, and J. B. Brydon, "Prediction of the apnea-hypopnea index from overnight pulse oximetry," Chest, vol. 124, pp. 1694-1701, 2003.

[5]

J. L. Pepin, P. Levy, B. Lepaulle, C. Brambilla, and C. Guilleminault, "Does oximetry contribute to the detection of apneic events? Mathematical processing of the SaO₂ signal," Chest, vol. 99, pp. 1151-1157, 1991.

[6]

P. Lévy, J. L. Pépin, C. Deschaux-Blanc, S. Paramelle, and C. Brambilla, "Accuracy of oximetry for detection of respiratory disturbances in sleep apnea syndrome," Chest, vol. 109, pp. 395-399, 1996.

[7]

S. Gyulay, L. G. Olson, M. J. Hensley et al., "A comparison of clinical assessment and home oximetry in the diagnosis of obstructive sleep apnea," Amer. Rev. Respir. Dis., vol. 147, pp. 50-53, 1993.

[8]

J. C. Vázquez, W H. Tsai, W. W. Flemons, A. Masuda, R. Brant, E. Hajduk, W. A. Whitelaw, and J. E. Remmers, "Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea," Thorax, vol. 55, pp. 302-307, 2000.

[9]

H. Rauscher, W. Popp, and H. Zwick, "Computerized detection of respiratory events during sleep from rapid increases in oxyhemoglobin saturation," Lung, vol. 169, no. 1, pp. 335-342, 1991.

[10]

F. del Campo, R Hornero, C. Zamarrón, D. Abasolo, and D. Álvarez, "Oxygen saturation regularity analysis in the diagnosis of obstructive sleep apnea," Artif. Intell. Med., vol. 37, no. 2, pp. 111-118, 2006.

Digital Library

[11]

C. Heneghan, C. P. Chua, J. Garvey, P. de Chazal, R Shouldice, P. Boyle, and W. McNicholas, "A portable automated assessment tool for sleep apnea using a combined holter-oximeter," Sleep, vol. 31, no. 10, pp. 1432- 1439, 2008.

[12]

American Academy of Pediatrics. Policy Statement 2003. Committee on Fetus and Newborn. "Apnea, sudden infant death syndrome, and home monitoring." Pediatrics, vol. 111, no. 4, pp. 914-917, 2003.

[13]

J. Rodríguez, A. Goñi, and A. Illarramendi, "Real-time classification of ECGs on a PDA," IEEE Trans. Inf. Technol. Biomed., vol. 9, no. 1, pp. 23-34, Mar. 2005. ISSN: 1089-7771.

Digital Library

[14]

I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Mateo, CA: Morgan Kaufmann, 2005. ISBN: 0-12-088407-0.

Digital Library

[15]

A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K Peng, and H. E. Stanley. (2000, Jun. 13). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23), pp. e215-e220. {Online}. Available: http://circ.ahajournals.org/cgi/content/full/101/23/e215

[16]

CinC Challenge 2000 data sets: Data for development and evaluation of ECG-based apnea detectors. PhysioNet, Cambridge, MA. (2000). {Online}. Available: http://www.physionet.org/physiobank/database/apnea-ecg/

[17]

Y. K. Lee, M. Bister, P. Blanchfield, and Y. M. Salleh, "Automated detection of obstructive apnea and hypopnea events from oxygen saturation signal," in Proc. 26th Annu. Int. Conf. IEEE EMBS, San Francisco, CA, 2004, vol. 1, pp. 321-324.

[18]

L. Breiman, "Bagging predictors," Mach. Learning, vol. 24, no. 2, pp. 123-140, 1996.

[19]

Y. Freund and L. Mason, "The alternating decision tree algorithm," in Proc. 16th Int. Conf. Mach. Learning, 1999, pp. 124-133.

Digital Library

[20]

J. R. Quinlan, "Comparing connectionist and symbolic learning methods," in Proc. Workshop Comput. Learning Theory Natural Learning Syst. (Constraints and Prospects, vol. 1). Cambridge, MA: MIT Press, 1993.

Digital Library

[21]

D. Álvarez, R. Hornero, D. Abásolo, F. del Campo, and C. Zamarrón, "Nonlinear characteristics of blood oxygen saturation from nocturnal oximetry for obstructive sleep apnoea detection," Physiol. Meas., vol. 27, no. 4, pp. 399-412, 2006.

[22]

J. McNames, A Fraser, and A. Rechtsteiner, "Sleep apnea classification based on frequency of heart rate variability," in Proc. Comput. Cardiol. 2000, Cambridge, MA, p. 207.

[23]

E. Gil, J. M. Vergara, and P. Laguna, "Study of the relationship between Pulse Photopletismography amplitude decrease events and sleep apneas in children," in Proc. 28th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC2006), New York, pp. 3887-3890.

[24]

E. Gil, V. Monasterio, J. M. Vergara, and P. Laguna, "Pulse photo-pletismography amplitude decrease detector for sleep apnea evaluation in children," in Proc. 27th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC2005), Shanghai, China, pp. 2743-2746.

[25]

P. Várady, T. Micsik, S. Benedek, and Z. Benyó, "A novel method for the detection of apnea and hypopnea events in respiration signals," IEEE Trans. Biomed. Eng., vol. 49, no. 9, pp. 936-42, Sep. 2002.

[26]

B. Raymond, R. M. Cayton, R. A. Bates, and M. Chappell, "Screening for obstructive sleep apnoea based on the electrocardiogram--The computers in cardiology challenge," in Proc. Comput. Cardiol. 2000, pp. 267-270.

[27]

P. de Chazal, C. Heneghan, R. B. Reilly, E. Sheridan, P. Nolan, and M. O'Malley, "Automatic detection of apnea with the electrocardiogram," in Proc. Comput. Cardiol. 2000, vol. 27, pp. 745-748.

[28]

T. Penzel, J. McNames, P. de Chazal, B. Raymond, A. Murray, and G. Moody, "Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings," Med. Biol. Eng. Comput. , vol. 40, pp. 402-407, 2002.

[29]

C. Maier, H. Dickhaus, and P. Laguna, "Amplitude variability extraction from multi-lead ECGs for improvement of sleep apnea recognition," in Proc. XXXII Annu. Conf. Comput. Cardiol., Lyon, France: IEEE Press, 2005, pp. 355-358.

[30]

J. Corthout, S. V. Huffel, M. O. Mendez, A. M. Bianchi, T. Penzel, and S. Cerruti, "Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis," in Proc. 30th Annu. Int. IEEE EMBS Conf. (EMBS 2008), Vancouver, BC, Canada, pp. 3608-3611.

Cited By

Sharma MKumbhani DYadav AAcharya U(2021)Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signalsApplied Intelligence10.1007/s10489-021-02422-252:2(1325-1337)Online publication date: 20-May-2021
https://dl.acm.org/doi/10.1007/s10489-021-02422-2
Azimi HBouchard MGoubran RKnoefel F(2019)Apnea Event Detection Methodology using Pressure Sensors2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA)10.1109/MeMeA.2019.8802214(1-6)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1109/MeMeA.2019.8802214
Ivanov RAtanasov NWeimer JPajic MSimpao ARehman MPappas GLee IKoutsoukos XMitchell I(2016)Estimation of blood oxygen content using context-aware filteringProceedings of the 7th International Conference on Cyber-Physical Systems10.5555/2984464.2984492(1-10)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.5555/2984464.2984492
Show More Cited By

Recommendations

Real-time temperature monitoring for the early detection of mastitis in dairy cattle: Methods and case researches
Highlights
- We used the ingestible bio-sensor to measure the cow’s core body temperature.
- ...
Abstract
Mastitis commonly occurs in dairy cattle during lactation. This disease has huge effects on the productivity of dairy cattle and the profits of stock farms. Enhancing the efficiency of mastitis treatment begins with the early detection ...
Real-time apnea-hypopnea event detection during sleep by convolutional neural networks
Abstract
Sleep apnea-hypopnea event detection has been widely studied using various biosignals and algorithms. However, most minute-by-minute analysis techniques have difficulty detecting accurate event start/end positions. Furthermore, they ...
Highlights
- We propose an automated apnea-hypopnea (AH) event detection model, based on Convolutional Neural Network (CNN).
hs-CRP is strongly associated with coronary heart disease (CHD)

Associated risk factors of CAD using decision tree.Sensitivity, specificity, accuracy of 96%, 87%, 94% and respectively.Serum hs-CRP levels as most important variable associated with CAD.Model is accurate, specific and sensitive for investigating risk ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Information Technology in Biomedicine

IEEE Transactions on Information Technology in Biomedicine Volume 14, Issue 4

July 2010

241 pages

ISSN:1089-7771

Issue’s Table of Contents

Copyright © 2010.

Publisher

IEEE Press

Publication History

Published: 01 July 2010

Accepted: 10 June 2009

Revised: 30 April 2009

Received: 26 January 2009

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sharma MKumbhani DYadav AAcharya U(2021)Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signalsApplied Intelligence10.1007/s10489-021-02422-252:2(1325-1337)Online publication date: 20-May-2021
https://dl.acm.org/doi/10.1007/s10489-021-02422-2
Azimi HBouchard MGoubran RKnoefel F(2019)Apnea Event Detection Methodology using Pressure Sensors2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA)10.1109/MeMeA.2019.8802214(1-6)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1109/MeMeA.2019.8802214
Ivanov RAtanasov NWeimer JPajic MSimpao ARehman MPappas GLee IKoutsoukos XMitchell I(2016)Estimation of blood oxygen content using context-aware filteringProceedings of the 7th International Conference on Cyber-Physical Systems10.5555/2984464.2984492(1-10)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.5555/2984464.2984492
Ivanov RWeimer JSimpao ARehman MLee IBayen ABranicky M(2015)Early detection of critical pulmonary shunts in infantsProceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems10.1145/2735960.2735962(110-119)Online publication date: 14-Apr-2015
https://dl.acm.org/doi/10.1145/2735960.2735962
Al-Mardini MAloul FSagahyroon AAl-Husseini L(2014)Classifying obstructive sleep apnea using smartphonesJournal of Biomedical Informatics10.1016/j.jbi.2014.07.00452:C(251-259)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.jbi.2014.07.004
LaFleur SMahgoub ISuzuki JWang H(2013)A personal body area network as a pre-screening surrogate to the polysomnographyProceedings of the 8th International Conference on Body Area Networks10.4108/icst.bodynets.2013.253687(233-236)Online publication date: 30-Sep-2013
https://dl.acm.org/doi/10.4108/icst.bodynets.2013.253687
Zhang JZhang QWang YQiu CAbdelzaher TRömer KRajkumar R(2013)A real-time auto-adjustable smart pillow system for sleep apnea detection and treatmentProceedings of the 12th international conference on Information processing in sensor networks10.1145/2461381.2461405(179-190)Online publication date: 8-Apr-2013
https://dl.acm.org/doi/10.1145/2461381.2461405
Bai YXu BMa YSun GZhao YBalasingham I(2012)Will you have a good sleep tonight?Proceedings of the 7th International Conference on Body Area Networks10.5555/2442691.2442720(124-130)Online publication date: 24-Feb-2012
https://dl.acm.org/doi/10.5555/2442691.2442720

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents