Skip to main content
SpringerLink
Log in

SVM-based classification method to identify alcohol consumption using ECG and PPG monitoring

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Driving under the influence (DUI) of alcohol (“drunk driving”) is dangerous and may cause serious harm to people and damage to property. To address this problem, this study developed a system for identifying excess alcohol consumption. Electrocardiogram (ECG) and photoplethysmography (PPG) sensors and intoxilyzers were used to acquire signals regarding the ECG, PPG, and alcohol consumption levels of participants before and after drinking. The signals were preprocessed, segmented, and subjected to feature extraction using specific algorithms to produce ECG and PPG training and test data. Based on the ECG, PPG, and alcohol consumption data we developed a fast and accurate identification scheme using the support vector machine (SVM) algorithm for identifying alcohol consumption. Optimized SVM classifiers were trained using the training data, and the test data were applied to verify the identification performance of the trained SVMs. The identification performance of the proposed classifiers achieved 95% on average. In this study, different feature combinations were tested to select the optimum technological configuration. Because the PPG and ECG features produce identical classification performance and the PPG features are more convenient to acquire, the technological configuration based on PPG is definitely preferable for developing smart and wearable devices for the identification of DUI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Borkenstein R, Zylman R, Ziel W, Shumate R, Crowther R (1974) The role of the drinking driver in traffic accidents (the grand rapids study), 2nd edn. Indiana Univ., Center for Studies in Law in Action, Department of Forensic Studies. https://books.google.com.tw/books?id=mSARngEACAAJ

  2. Global status report on road safety (2015). http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/

  3. Sweedler B M, Biecheler M -B, Laurell H, Kroj G, Lerner M, Mathijssen M, Mayhew D, Tunbridge R (2004) Worldwide trends in alcohol and drug impaired driving. Traffic Inj Prev 5(3):175–184

    Article  Google Scholar 

  4. WHO et al (2007) Drinking and driving: a road safety manual for decision-makers and practitioners. Global Road Safety Partnership c/o International Federation of Red Cross and Red Crescent Societies

  5. Malpas S C, Whiteside E A, Maling T J B (1991) Heart rate variability and cardiac autonomic function in men with chronic alcohol dependence. British Heart J 65(2):84–88

    Article  Google Scholar 

  6. Koskinen P, Virolainen J, Kupari M (1994) Acute alcohol intake decreases short-term heart rate variability in healthy subjects. Clin Sci 87(2):225–230

    Article  Google Scholar 

  7. Ryan J, Howes L (2002) Relations between alcohol consumption, heart rate, and heart rate variability in men. Heart 88(6):641–642

    Article  Google Scholar 

  8. Bau P F, Moraes R S, Bau C H, Ferlin E L, Rosito G A, Fuchs F D (2011) Acute ingestion of alcohol and cardiac autonomic modulation in healthy volunteers. Alcohol 45(2):123–129

    Article  Google Scholar 

  9. Carpeggiani C, Emdin M, Macerata A, Raciti M, Zanchi M, Bianchini S, Kraft G, Abbate A (2000) Heart rate variability modified by altitude exposure. In: Computers in cardiology 2000. IEEE, pp 817–820

  10. Carpeggiani C, Erndin M, Macerata A, Raciti M, Zanchi M, Bianchini S, Abbate A (2001) Altitude distress influence on cardiac function. In: Computers in cardiology 2001. IEEE, pp 325–328

  11. Alsafwah S (2001) Electrocardiographic changes in hypothermia. Heart & Lung: J Acute Crit Care 30(2):161–163

    Article  Google Scholar 

  12. Graham C A, McNaughton G W, Wyatt J P (2001) The electrocardiogram in hypothermia. Wilderness Environ Med 12(4):232–235

    Article  Google Scholar 

  13. Simoons M, Hugenholtz P (1975) Gradual changes of ecg waveform during and after exercise in normal subjects. Circulation 52(4):570–577

    Article  Google Scholar 

  14. Cai J, Liu G, Hao M (2009) The research on emotion recognition from ecg signal. In: International conference on information technology and computer science, 2009 (ITCS 2009), vol 1. IEEE, pp 497–500

  15. Xianhai G (2011) Study of emotion recognition based on electrocardiogram and rbf neural network. Procedia Eng 15:2408–2412

    Article  Google Scholar 

  16. Karlen W, Mattiussi C, Floreano D (2009) Sleep and wake classification with ecg and respiratory effort signals. IEEE Trans Biomed Circ Syst 3(2):71–78

    Article  Google Scholar 

  17. Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Measur 28(3):R1. http://stacks.iop.org/0967-3334/28/i=3/a=R01

    Article  Google Scholar 

  18. Wu C K, Tsang KF, Chi H R, Hung F H (2016) A precise drunk driving detection using weighted kernel based on electrocardiogram. Sensors 16(5):659. http://www.mdpi.com/1424-8220/16/5/659

    Article  Google Scholar 

  19. Intoximeters Alco-Sensor IV Intoximeters

  20. ADInstruments, Gp amp owner’s guide (2008). http://cdn.adinstruments.com/adi-web/manuals/GP_Amp_OG.pdf

  21. ADInstruments, Bio amp owner’s guide (2009). http://cdn.adinstruments.com/adi-web/manuals/Bio_Amp_OG.pdf

  22. ADInstruments, Powerlab /30 series owner’s guide (2009). http://cdn.adinstruments.com/adi-web/manuals/PowerLab_30_Series_OG.pdf

  23. Page R (2005) Twelve-lead ECG for acute and critical care providers, EKG Series. Pearson Prentice Hall. https://books.google.com.tw/books?id=TOxLAQAAIAAJ

  24. Friesen G M, Jannett T C, Jadallah M A, Yates S L, Quint S R, Nagle H T (1990) A comparison of the noise sensitivity of nine qrs detection algorithms. IEEE Trans Biomed Eng 37(1):85–98

    Article  Google Scholar 

  25. Electronics Hub (2015) Butterworth filter Available at http://www.electronicshub.org/butterworth-filter/

  26. Lee H, Lee J, Jung W, Lee G -K (2007) The periodic moving average filter for removing motion artifacts from ppg signals. Int J Control Autom Syst 5(6):701–706

    Google Scholar 

  27. Warner A (1998) Drug abuse handbook. Clin Chem 44(7):1586– 1586. http://clinchem.aaccjnls.org/content/44/7/1586.full.pdf

    Google Scholar 

  28. Stöckl D, Dewitte K, Thienpont L M (1998) Validity of linear regression in method comparison studies: is it limited by the statistical model or the quality of the analytical input data? Clin Chem 44 (11):2340–2346. http://clinchem.aaccjnls.org/content/44/11/2340.full.pdf

    Google Scholar 

  29. Jones A W The relationship between blood alcohol concentration (BAC) and breath alcohol concentration (BrAC): a review of the evidence, Road Safety Web Publication 15

  30. Searle J (2015) Alcohol calculations and their uncertainty. Med Sci Law 55(1):58–64. pMID: 24644224. doi:10.1177/0025802414524385

  31. Allen J, Columbus M (1997) Assessing alcohol problems: a guide for clinicians and researchers, NIAAA treatment handbook series 4, Diane Pub. https://books.google.com.tw/books?id=0xReiqa4WzUC

  32. Sereny G (1971) Effects of alcohol on the electrocardiogram. Circulation 44:558–564. doi:10.1161/01.CIR.44.4.558

    Article  Google Scholar 

  33. He X, Goubran R A, Liu X P (2013) Evaluation of the correlation between blood pressure and pulse transit time. In: IEEE International symposium on medical measurements and applications proceedings (MeMeA 2013). IEEE, pp 17–20

  34. Li C, Zheng C, Tai C (1995) Detection of ecg characteristic points using wavelet transforms. IEEE Trans Biomed Eng 45:21–28

    Google Scholar 

  35. Legarreta I R, Addison P S, Reed M J, Grubb N, Clegg G R, Robertson C E, Watson J N (2005) Continuous wavelet transform modulus maxima analysis of the electrocardiogram: beat characterisation and beat-to-beat measurement. Int J Wavelets, Multiresolution Inf Process 03(01):19–42. doi:10.1142/S0219691305000774

    Article  MATH  Google Scholar 

  36. Chavan M S, Agarwala R, Uplane M (2006) Use of kaiser window for ecg processing. In: Proceedings of the 5th WSEAS international conference on signal processing, robotics and automation. World Scientific and Engineering Academy and Society (WSEAS), pp 285–289

  37. Cubbon R M, Ruff N, Groves D, Eleuteri A, Denby C, Kearney L, Ali N, Walker A M N, Jamil H, Gierula J, Gale C P, Batin P D, Nolan J, Shah A M, Fox K A A, Sapsford R J, Witte K K, Kearney M T (2016) Ambulatory heart rate range predicts mode-specific mortality and hospitalisation in chronic heart failure. Heart 102(3): 223–229

    Article  Google Scholar 

  38. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  39. Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27:1–27:27

    Google Scholar 

Download references

Acknowledgements

The authors thank the National Science Council, Taiwan, for supporting this study under the contract NSC 100-2218-E-224-008-MY3, and the participants who assisted in the experiment.

Author information

Authors and Affiliations

  1. Department of CSIE, National Yunlin University of Science & Technology, Douliu, Yunlin, 640, Taiwan

    Wen-Fong Wang & Yan-Fu Wu

  2. Department of CSIE, National Penghu University of Science & Technology, Makung, Penghu, 880, Taiwan

    Ching-Yu Yang

Authors
  1. Wen-Fong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ching-Yu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan-Fu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wen-Fong Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, WF., Yang, CY. & Wu, YF. SVM-based classification method to identify alcohol consumption using ECG and PPG monitoring. Pers Ubiquit Comput 22, 275–287 (2018). https://doi.org/10.1007/s00779-017-1042-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-017-1042-0

Keywords

Search

Navigation