Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 20/2020

04.04.2018 | S.I. : Advances in Bio-Inspired Intelligent Systems

Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection

verfasst von: Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, Antonio G. Ravelo-García

Erschienen in: Neural Computing and Applications | Ausgabe 20/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Obstructive sleep apnea is a disorder characterized by pauses in respiration during sleep. Due to this disturbance in breathing, there is a decrease in the oxygen saturation (SpO2) level. Thus, SpO2 can be used as a source of information for the automatic detection of apnea. Several solutions exist in the literature where different features are used. To find a better discriminant capacity, a subset of few features that obtains higher accuracy with the proper classifier is needed. To face this challenge, this work compares two different feature selection methods. The first one is a filter method named minimum redundancy maximum relevance, and the other one is called sequential forward search. These methods are tested with different classifiers. Two public datasets with 8 and 25 subjects are used to test and compare the performances of the different feature selection methods. A set of features for each classifier is obtained, and the results are compared with the previous work. The results found in this work show a good performance with respect to the state of the art and present a good option for apnea screening with low resources.
Vorheriger Artikel Neural networks fusion for temperature forecasting
Nächster Artikel Writer identification approach by holistic graphometric features using off-line handwritten words

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S (1993) The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 328:1230–1235
2.
Zhang J, Zhang Q, Wang Y, Qiu C (2013) A real-time auto-adjustable smart pillow system for sleep apnea detection and treatment. In: Proceedings of the 12th international conference on Information processing in sensor networks (IPSN), pp 179–190
3.
Nassir A, Barnea O (2012) Wireless body-area network for detection of sleep disorders. In: 27th Convention of electrical and electronics engineers in Israel, IEEEI 2012, pp 1–5
4.
Agarwal R, Gotman J (2001) Computer-assisted sleep staging. IEEE Trans Biomed Eng 48(12):1412–1423
5.
Hillman DR, Murphy AS, Pezzullo L (2006) The economic cost of sleep disorders. Sleep 29(3):299–305
6.
Alghanim N, Comondore VR, Fleetham J, Marra CA, Ayas NT (2008) The economic impact of obstructive sleep apnea. Lung 186(1):7–12
7.
Corbishley P, Rodríguez-Villegas E (2008) Breathing detection: towards a miniaturized, wearable, battery-operated monitoring system. IEEE Trans Biomed Eng 55(1):196–204
8.
Jin J, Sanchez-Sinencio E (2015) A home sleep apnea screening device with time-domain signal processing and autonomous scoring capability. IEEE Trans Biomed Circuits Syst 9(1):96–104
9.
Varon C, Caicedo A, Testelmans D, Buyse B, Van Huffel S (2015) A novel algorithm for the automatic detection of sleep apnea from single-lead ECG. IEEE Trans Biomed Eng 62(9):2269–2278
10.
Penzel T, McNames J, Murray A, de Chazal P, Moody G, Raymond B (2002) Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med Biol Eng Comput 40(4):402–407
11.
Boudaoud S, Rix H, Meste O, Heneghan C, O’Brien C (2007) Corrected integral shape averaging applied to obstructive sleep apnea detection from the electrocardiogram. EURASIP J Adv Signal Process 2007:1–12MATH
12.
de Chazal P, Heneghan C, Sheridan E, Reilly R, Nolan P, O’Malley M (2003) Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans Biomed Eng 50(6):686–696
13.
Patil D, Wadhai VM, Gujar S, Surana K, Devkate P, Waghmare S (2012) APNEA detection on smart phone. Int J Comput Appl 59(7):15–19
14.
Ravelo-García A, Kraemer J, Navarro-Mesa J, Hernández-Pérez E, Navarro-Esteva J, Juliá-Serdá G, Penzel T, Wessel N (2015) Oxygen saturation and RR intervals feature selection for sleep apnea detection. Entropy 17(5):2932–2957
15.
Cover TM (1974) The best two independent measurements are not the two best. IEEE Trans Syst Man Cybern SMC-4(1):116–117MATH
16.
Penzel T, Moody G, Mark R, Goldberger A, Peter J (2000) The apnea-ECG database. Comput Cardiol 2000:255–258
17.
18.
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220
19.
20.
Mostafa SS, Mendonça F, Morgado-dias F, Ravelo-garcía A (2017) SpO2 based sleep apnea detection using deep learning. In: 21st International conference on intelligent engineering systems, pp 91–96
21.
Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus CL, Vaughn BV and Tangredi MM (2012) The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.0. American Academy of Sleep Medicine, Darien, Illinois
22.
Xie B, Minn H (2012) Real-time sleep apnea detection by classifier combination. IEEE Trans Inf Technol Biomed 16(3):469–477
23.
Alvarez D, Hornero R, Abásolo D, del Campo F, Zamarrón C (2006) Nonlinear characteristics of blood oxygen saturation from nocturnal oximetry for obstructive sleep apnoea detection. Physiol Meas 27(4):399–412
24.
Lévy P, Pépin JL, Deschaux-Blanc C, Paramelle B, Brambilla C (1996) Accuracy of oximetry for detection of respiratory disturbances in sleep apnea syndrome. Chest 109(2):395–399
25.
Warley AR, Mitchell JH, Stradling JR (1987) Evaluation of the Ohmeda 3700 pulse oximeter. Thorax 42(11):892–896
26.
Olson LG, Ambrogetti A, Gyulay SG (1999) Prediction of sleep-disordered breathing by unattended overnight oximetry. J Sleep Res 8(1):51–55
27.
Gyulay S, Olson LG, Hensley MJ, King MT, Allen KM, Saunders NA (1993) A comparison of clinical assessment and home oximetry in the diagnosis of obstructive sleep apnea. Am Rev Respir Dis 147(1):50–53
28.
Ravelo-Garcia AG, Navarro-Mesa JL, Murillo-Diaz MJ, Julia-Serda JG (2004) Application of RR series and oximetry to a statistical classifier for the detection of sleep apnoea/hypopnoea. Comput Cardiol 2004:305–308
29.
Awal MA, Mostafa SS, Ahmad M (2011) Quality assessment of ECG signal using symlet wavelet transform. In: International conference on advances in electrical engineering (ICAEE), pp 129–134
30.
Daubechies I (1992) Ten lectures on wavelets. In: CBMS-NSF regional conference series in applied mathematics, vol 61
31.
Lin S, Gao S, He Z, Deng Y (2015) A pilot directional protection for HVDC transmission line based on relative entropy of wavelet energy. Entropy 17(8):5257–5273
32.
Ghorbanian P, Ashrafiuo H (2016) A numerical study of information entropy in ECG wavelet analysis. In: Proceedings of the ASME 2016 dynamic systems and control conference DSCC2016, p V001T10A003
33.
Lee MY, Yu SN (2012) Multiscale sample entropy based on discrete wavelet transform for clinical heart rate variability recognition. In: 2012 Annual international conference of the IEEE Engineering in Medicine and Biology Society, pp 4299–4302
34.
Kumar Y, Dewal ML, Anand RS (2013) Wavelet entropy based EEG analysis for seizure detection. In: 2013 IEEE international conference on signal processing, computing and control (ISPCC), pp 1–6
35.
Peng Hanchuan, Long Fuhui, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238
36.
Radovic M, Ghalwash M, Filipovic N, Obradovic Z (2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform 18(9):1–14
37.
Mostafa SS, Awal M, Ahmad M, Rashid M (2016) Voiceless Bangla vowel recognition using sEMG signal. Springerplus 5(1):1522
38.
Wang Z, Zhou X, Zhao W, Liu F, Ni H, Yu Z (2017) Assessing the severity of sleep apnea syndrome based on ballistocardiogram. PLoS ONE 12(4):e0175351
39.
Ravelo-garcía AG, Navarro-mesa JL, Palmas L, Canaria DG, De Neumología S, Universitario H (2013) Cepstrum feature selection for the classification of sleep apnea-hypopnea syndrome based on heart rate variability cepstrum analysis. In: 2013 Computing in cardiology conference, pp 959–962
40.
Kiang MY (2003) A comparative assessment of classification methods. Decis Support Syst 35(4):441–454MathSciNet
41.
Principe JC, Euliano NR, Lefebvre WC (2000) Neural and adaptive systems: fundamentals through simulations. Wiley, London
42.
Baptista D, Abreu S, Travieso-González C, Morgado-Dias F (2017) Hardware implementation of an artificial neural network model to predict the energy production of a photovoltaic system. Microprocess Microsyst 49:77–86
43.
Ciuca I, Ware JA (1997) Layered neural networks as universal approximators. In: Computational intelligence theory and applications. Fuzzy days 1997. Lecture notes in computer science. Springer, Berlin, pp 411–415
44.
Gupta M, Jin L, Homma N (2004) Static and dynamic neural networks: from fundamentals to advanced theory. Wiley, London
45.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH
46.
Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory—COLT’92, pp 144–152
47.
Vapnik V (2000) The nature of statistical learning theory. Springer, New YorkMATH
48.
Jahid Reza K, Khatun S, Jamlos MF, Fakir M, Mostafa SS (2014) Performance evaluation of diversified SVM kernel functions for breast tumor early prognosis. ARPN J Eng Appl Sci 9(3):329–335
49.
Khandoker AH, Lai DTH, Begg RK, Palaniswami M (2007) Wavelet-based feature extraction for support vector machines for screening balance impairments in the elderly. IEEE Trans Neural Syst Rehabil Eng 15(4):587–597
50.
Hastie T, Tibshirani R, Jerome F (2013) The elements of statistical learning data mining, inference, and prediction. Springer, New YorkMATH
51.
Mendez MO, Ruini DD, Villantieri OP, Matteucci M, Penzel T, Cerutti S, Bianchi AM (2007) Detection of sleep apnea from surface ECG based on features extracted by an autoregressive model. In: Annual international conference of the IEEE engineering in medicine and biology—proceedings, pp 6105–6108
52.
Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis, 5th edn. Wiley, LondonMATH
53.
Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer, New YorkMATH
54.
55.
Zhang H (2004) The optimality of naive Bayes. Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp 562–567
56.
Zhang H (2005) Exploring conditions for the optimality of Naive Bayes. Int J Pattern Recognit Artif Intell 19(2):183–198
57.
Kuncheva L (2006) On the optimality of naive Bayes with dependent binary features. Pattern Recognit Lett 27(7):830–837
58.
Almazaydeh L, Faezipour M, Elleithy K (2012) A neural network system for detection of obstructive sleep apnea through SpO2 signal features. Int J Adv Comput Sci Appl 3(5):7–11
59.
Mostafa SS, Carvalho JP, Morgado-Dias F, Ravelo-García A (2017) Optimization of sleep apnea detection using SpO2 and ANN, 2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo, 2017, pp 1–6
60.
de Chazal P, Heneghan C, McNicholas WT (2009) Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals. Philos Trans R Soc Lond A Math Phys Eng Sci 367(1887):369–389MATH
61.
Nunes CM, Britto ADS Jr, Kaestner CAA, Sabourin R (2004) Feature subset selection using an optimized hill climbing algorithm for handwritten character recognition. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR), pp 1018–1025
Metadaten
Titel
Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection
verfasst von
Sheikh Shanawaz Mostafa
Fernando Morgado-Dias
Antonio G. Ravelo-García
Publikationsdatum
04.04.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 20/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3455-8

Weitere Artikel der Ausgabe 20/2020

Neural Computing and Applications 20/2020 Zur Ausgabe

S.I. : Applying Artificial Intelligence to the Internet of Things

Deep neural learning techniques with long short-term memory for gesture recognition

Recent Advances in Deep Learning for Medical Image Processing

An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network features selection

S.I. : Advances in Bio-Inspired Intelligent Systems

On the analysis of speech and disfluencies for automatic detection of Mild Cognitive Impairment

Applying Artificial Intelligence to the Internet of Things

Firefly-inspired stochastic resonance for spectrum sensing in CR-based IoT communications

S.I. : Advances in Bio-Inspired Intelligent Systems

Monitoring ALS from speech articulation kinematics

S.I. : Advances in Bio-Inspired Intelligent Systems

Comparison of offline and real-time human activity recognition results using machine learning techniques

Premium Partner

    Bildnachweise