Skip to main content

Advertisement

SpringerLink
Log in

Impact of sampling rate and interpolation on photoplethysmography and electrodermal activity signals’ waveform morphology and feature extraction

  • S.I.: Computational-based Biomarkers for Mental and Emotional Health(CBMEH2021)
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The availability of low-cost biomedical devices has driven a growing interest in the use of physiological signals for mental and emotional health research. Due to their potential for integration in discrete wearable form factors, Photoplethysmography (PPG) and Electrodermal Activity (EDA) are particularly popular, especially in out-of-the-lab experiments. Although high-resolution data acquisition should be a priority, the sampling rate can greatly affect the power consumption and memory storage of the devices in long-term recordings. Moreover, systems with shared computational resources that simultaneously monitor different signals, can also have communication channel bandwidth constraints that limit the sampling rate. This work seeks to evaluate how the sampling rate and interpolation affect the signal quality of PPG and EDA signals, in terms of waveform morphology and feature extraction capabilities. We study the minimum sampling rate requirements for each signal, as well as the impact of interpolation methods on signal waveform reconstruction. Using a previously recorded dataset with signals originally recorded at 1 kHz, we simulate multiple lower sampling rates. Results show that for PPG a 50 Hz sampling rate with quadratic or cubic interpolation can achieve a temporal resolution identical to that of a 1 kHz acquisition, while for EDA the same can be said but with a 10 Hz sampling rate. Other recommendations are also proposed depending on the signal application.

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

Notes

  1. https://who.int/news-room/fact-sheets/detail/depression.

  2. https://pulsesensor.com/.

  3. https://github.com/PIA-Group/BioSPPy.

  4. https://pulsesensor.com.

  5. https://bitalino.com/storage/uploads/media/eda-sensor-datasheet-revb.pdf.

  6. https://plux.info/electrodes/60-non-gelled-reusable-agagcl-electrodes-870122114.html.

References

  1. Ausín JL, Duque-Carrillo JF, Ramos J, Torelli G (2013). In: Mukhopadhyay SC, Postolache OA (eds) From handheld devices to near-invisible sensors: the road to pervasive e-health. Springer, Berlin, Heidelberg, pp 135–156. https://doi.org/10.1007/978-3-642-32538-0_6

  2. Picard RW (2009) Future affective technology for autism and emotion communication. Philosophical transactions of the Royal Society of London. Ser B, Biol Sci 364(1535):3575–3584

    Article  Google Scholar 

  3. Yang C Fatigue effect on task performance in haptic virtual environment for home-based rehabilitation. Publisher: University of Saskatchewan. https://harvest.usask.ca/handle/10388/etd-06242011-120432 Accessed 2022-02-15

  4. Yang C, Lin Y, Cai M, Qian Z, Kivol J, Zhang W (2017) Cognitive fatigue effect on rehabilitation task performance in a haptic virtual environment system. J Rehabil Assist Technol Eng. https://doi.org/10.1177/2055668317738197

    Article  Google Scholar 

  5. Yun H, Fortenbacher A, Pinkwart N, Bisson T, Moukayed F (2018) A pilot study of emotion detection using sensors in a learning context: Towards an affective learning companion. In: CEUR Workshop Proceedings, vol 2092

  6. Shannon CE (1949) Communication in the presence of noise. Proc IRE 37(1):10–21. https://doi.org/10.1109/JRPROC.1949.232969

    Article  MathSciNet  Google Scholar 

  7. da Silva HP, Fred A, Martins R (2014) Biosignals for everyone. IEEE Pervasive Comput 13(4):64–71. https://doi.org/10.1109/mprv.2014.61

    Article  Google Scholar 

  8. Abay TY, Kyriacou PA (2015) Accuracy of reflectance photoplethysmography on detecting cuff-induced vascular occlusions. In: Int’l Conf. in medicine and biology society, pp 861–864 https://doi.org/10.1109/EMBC.2015.7318498

  9. Motin MA, Karmakar CK, Palaniswami M (2019) PPG derived heart rate estimation during intensive physical exercise. IEEE Access 7:56062–56069. https://doi.org/10.1109/ACCESS.2019.2913148

    Article  Google Scholar 

  10. Winslow BD, Chadderdon GL, Dechmerowski SJ, Jones DL, Kalkstein S, Greene JL, Gehrman P (2016) Development and clinical evaluation of an mhealth application for stress management. Front Psychiatry 7:130. https://doi.org/10.3389/fpsyt.2016.00130

    Article  Google Scholar 

  11. Bota PJ, Wang C, Fred ALN, Plácido Da Silva H (2019) A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals. IEEE Access 7:140990–141020. https://doi.org/10.1109/ACCESS.2019.2944001

    Article  Google Scholar 

  12. Udovičić G, Đerek J, Russo M, Sikora M (2017) Wearable emotion recognition system based on GSR and PPG signals. In: Proceedings of the Int’l workshop on multimedia for personal health and health care, pp 53–59. Association for Computing Machinery, New York, NY, USA https://doi.org/10.1145/3132635.3132641

  13. Everson L, Biswas D, Verhoef B-E, Kim CH, Van Hoof C, Konijnenburg M, Van Helleputte N (2019) Biotranslator: inferring R-Peaks from ambulatory wrist-worn PPG signal. In: Int’l Conf. Eng. in medicine and biology society, pp 4241–4245 https://doi.org/10.1109/EMBC.2019.8856450

  14. Zhu Q, Tian X, Wong C-W, Wu M (2019) ECG reconstruction via PPG: a pilot study. In: IEEE EMBS Int’l Conf. on biomedical health informatics, pp 1–4 https://doi.org/10.1109/BHI.2019.8834612

  15. Choi A, Shin H (2017) Photoplethysmography sampling frequency: pilot assessment of how low can we go to analyze pulse rate variability with reliability? Physiol Meas 38(3):586–600. https://doi.org/10.1088/1361-6579/aa5efa

    Article  Google Scholar 

  16. Spierer DK, Rosen Z, Litman LL, Fujii K (2015) Validation of photoplethysmography as a method to detect heart rate during rest and exercise. J Med Eng Technol 39(5):264–271. https://doi.org/10.3109/03091902.2015.1047536

    Article  Google Scholar 

  17. Fujita D, Suzuki A (2019) Evaluation of the possible use of PPG waveform features measured at low sampling rate. IEEE Access 7:58361–58367. https://doi.org/10.1109/ACCESS.2019.2914498

    Article  Google Scholar 

  18. Kreibig SD (2010) Autonomic nervous system activity in emotion: a review. Biol Psychol 84(3):394–421. https://doi.org/10.1016/j.biopsycho.2010.03.010

    Article  Google Scholar 

  19. Bota PJ, Wang C, Fred ALN, Silva H (2020) A wearable system for electrodermal activity data acquisition in collective experience assessment. In: Filipe J, Smialek M, Brodsky A, Hammoudi S (eds.) Proceedings of the Int’l Conf. on enterprise information systems, ICEIS 2020, May 5-7, 2020, Vol. 2, pp 606–613. SCITEPRESS, Prague, Czech Republic https://doi.org/10.5220/0009816906060613

  20. Abreu M, Fred A, Plácido da Silva H, Wang C (2020) From seizure detection to prediction: a review of wearables and related devices applicable to epilepsy via peripheral measurements. Tech Rep Inst Super Técnico https://doi.org/10.13140/RG.2.2.17428.45447

  21. Loukogeorgakis S, Dawson R, Phillips N, Martyn CN, Greenwald SE (2002) Validation of a device to measure arterial pulse wave velocity by a photoplethysmographic method. Physiol Meas 23(3):581–596. https://doi.org/10.1088/0967-3334/23/3/309

    Article  Google Scholar 

  22. Otsuka T, Kawada T, Katsumata M, Ibuki C (2006) Utility of second derivative of the finger photoplethysmogram for the estimation of the risk of coronary heart disease in the general population. Circ J 70(3):304–310. https://doi.org/10.1253/circj.70.304

    Article  Google Scholar 

  23. Bortolotto LA, Blacher J, Kondo T, Takazawa K, Safar ME (2000) Assessment of vascular aging and atherosclerosis in hypertensive subjects: second derivative of photoplethysmogram versus pulse wave velocity. Am J Hypertens 13(2):165–171. https://doi.org/10.1016/s0895-7061(99)00192-2

    Article  Google Scholar 

  24. Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 28(3):1–39. https://doi.org/10.1088/0967-3334/28/3/r01

    Article  Google Scholar 

  25. Millasseau SC, Ritter JM, Takazawa K, Chowienczyk PJ (2006) Contour analysis of the photoplethysmographic pulse measured at the finger. J Hypertens 24(8):1449–1456. https://doi.org/10.1097/01.hjh.0000239277.05068.87

    Article  Google Scholar 

  26. Allen J, Murray A (2003) Age-related changes in the characteristics of the photoplethysmographic pulse shape at various body sites. Physiol Meas 24(2):297–307. https://doi.org/10.1088/0967-3334/24/2/306

    Article  Google Scholar 

  27. Spigulis J, Gailite L, Lihachev A, Erts R (2007) Simultaneous recording of skin blood pulsations at different vascular depths by multiwavelength photoplethysmography. Appl Opt 46(10):1754–1759. https://doi.org/10.1364/ao.46.001754

    Article  Google Scholar 

  28. Moraes JL, Rocha MX, Vasconcelos GG, Vasconcelos Filho JE, de Albuquerque VHC, Alexandria AR (2018) Advances in photopletysmography signal analysis for biomedical applications. Sensors. https://doi.org/10.3390/s18061894

    Article  Google Scholar 

  29. Elgendi M (2012) On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev 8(1):14–25. https://doi.org/10.2174/157340312801215782

    Article  Google Scholar 

  30. Ghamari M A review on wearable photoplethysmography sensors and their potential future applications in health care https://doi.org/10.15406/ijbsbe.2018.04.00125.Accessed 2021-10-13

  31. Newland J DIY PPG: Comparison of Waveform Parameters from Open-Source vs. Commercial Photoplethysmography. https://doi.org/10.13140/RG.2.2.13067.23842.Publisher: Unpublished. Accessed 2021-10-13

  32. Tamura T, Maeda Y, Sekine M, Yoshida M (2014) Wearable photoplethysmographic sensors-past and present. Electronics 3(2):282–302. https://doi.org/10.3390/electronics3020282

    Article  Google Scholar 

  33. Boucsein W (2013) Electrodermal activity, 2nd edn. Springer, Boston, MA, pp 1–618

    Google Scholar 

  34. Boucsein W, Fowles DC, Grimnes S, Ben-Shakhar G, Roth WT, Dawson ME, Filion DL (2012) Publication recommendations for electrodermal measurements. Psychophysiology 49(8):1017–1034

    Article  Google Scholar 

  35. Venables PH, Christie MJ (1980) Electrodermal activity. Techniques in Psychophysiol 54(3)

  36. Maier M, Elsner D, Marouane C, Zehnle M, Fuchs C (2019) Deepflow: detecting optimal user experience from physiological data using deep neural networks. In: Proceedings of The Int’l Conf. on artificial intelligence, pp 1415–1421 https://doi.org/10.24963/ijcai.2019/196

  37. Schmidt P, Reiss A, Dürichen R, Laerhoven KV (2019) Wearable-based affect recognition-a review. Sensors 19(19):4079

    Article  Google Scholar 

  38. Martinez R, Salazar-Ramirez A, Arruti A, Irigoyen E, Martin JI, Muguerza J (2019) A self-paced relaxation response detection system based on galvanic skin response analysis. IEEE Access 7:43730–43741

    Article  Google Scholar 

  39. Ojha VK, Griego D, Kuliga S, Bielik M, Buš P, Schaeben C, Treyer L, Standfest M, Schneider S, König R (2019) Machine learning approaches to understand the influence of urban environments on human’s physiological response. Inf Sci 474:154–169

    Article  MathSciNet  Google Scholar 

  40. Heart rate variability (1996) Circulation 93(5):1043–1065 https://doi.org/10.1161/01.CIR.93.5.1043.Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology

  41. Mahdiani S, Jeyhani V, Peltokangas M, Vehkaoja A (2015) Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis? In: Int’l Conf. in medicine and biology society, pp 5948–5951 https://doi.org/10.1109/EMBC.2015.7319746

  42. Ellis RJ, Zhu B, Koenig J, Thayer JF, Wang Y (2015) A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability. Physiol Meas 36(9):1827–1852. https://doi.org/10.1088/0967-3334/36/9/1827

    Article  Google Scholar 

  43. Ziemssen T, Gasch J, Ruediger H (2008) Influence of ECG sampling frequency on spectral analysis of RR intervals and baroreflex sensitivity using the eurobavar data set. J Clin Monit Comput 22(2):159

    Article  Google Scholar 

  44. Baek HJ, Shin J, Jin G, Cho J (2017) Reliability of the parabola approximation method in heart rate variability analysis using low-sampling-rate photoplethysmography. J Med Syst 41(12):189

    Article  Google Scholar 

  45. Ahn JM, Kim JK (2020) Effect of the PPG sampling frequency of an IIR filter on heart rate variability parameters. Int J Sci Technol Res 9(3):1924–1928

    Google Scholar 

  46. Bent B, Dunn JP (2021) Optimizing sampling rate of wrist-worn optical sensors for physiologic monitoring. J Clin Trans Sci 5(1):34. https://doi.org/10.1017/cts.2020.526

    Article  Google Scholar 

  47. Béres S, Hejjel L (2021) The minimal sampling frequency of the photoplethysmogram for accurate pulse rate variability parameters in healthy volunteers. Biomed Sig Process Control. https://doi.org/10.1016/j.bspc.2021.102589

    Article  Google Scholar 

  48. iMotions - Biometric Research S (2017) Galvanic skin response the complete pocket guide. Technical report, iMotions

  49. Braithwaite J, Watson D, Jones R, Rowe MA (2013) Guide for analysing electrodermal activity & skin conductance responses for psychological experiments. CTIT technical reports series

  50. Shin HS, Lee C, Lee M (2009) Adaptive threshold method for the peak detection of photoplethysmographic waveform. Comput Biol Med 39(12):1145–1152. https://doi.org/10.1016/j.compbiomed.2009.10.006

    Article  Google Scholar 

  51. Valente J, Kozlova V, Pereira T BrainAnswer platform: Biosignals acquisition for monitoring of physical and cardiac conditions of older people. In: Promoting Healthy and Active Aging. Routledge. Num Pages: 20

  52. Batista D, Silva HPd, Fred A, Moreira C, Reis M, Ferreira HA (2019) Benchmarking of the BITalino biomedical toolkit against an established gold standard. Healthc Technol Lett 6(2):32–36. https://doi.org/10.1049/htl.2018.5037

    Article  Google Scholar 

  53. Carreiras C, Alves AP, Lourenço A, Canento F, Silva H, Fred A, et al. BioSPPy: Biosignal Processing in Python (2015). https://github.com/PIA-Group/BioSPPy/

  54. Elgendi M, Norton I, Brearley M, Abbott D, Schuurmans D (2013) Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions. PloS one 8:76585. https://doi.org/10.1371/journal.pone.0076585

    Article  Google Scholar 

  55. Kim KH, Bang SW, Kim SR (2004) Emotion recognition system using short-term monitoring of physiological signals. Med Biol Eng Comput 42(3):419–427

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially funded by Fundação para a Ciência e Tecnologia (FCT) under the grant 2020.06675.BD and under the project PCIF/SSO/0163/2019 “SafeFire”, by the FCT/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) through national funds and when applicable co-funded by EU funds under the project UIDB/50008/2020, by the Instituto de Telecomunicações (IT), by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalization Programme (COMPETE 2020), and by National Funds (OE) through the FCT under the LISBOA-01-0247-FEDER-069918 “CardioLeather” and LISBOA-1-0247-FEDER-113480 “EpilFootSense”.

Author information

Authors and Affiliations

  1. Department of Bioengineering (DBE), Instituto Superior Técnico (IST), Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal

    Rafael Silva, Gonçalo Salvador, Patrícia Bota, Ana Fred & Hugo Plácido da Silva

  2. Instituto de Telecomunicações (IT), Av. Rovisco Pais 1, Torre Norte - Piso 10, 1049-001, Lisboa, Portugal

    Rafael Silva, Gonçalo Salvador, Patrícia Bota, Ana Fred & Hugo Plácido da Silva

Authors
  1. Rafael Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gonçalo Salvador
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrícia Bota
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ana Fred
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hugo Plácido da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Silva.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

A PPG signal analysis

See Tables 5.

Table 5 PPG time errors, amplitude errors and Pearson correlation coefficient values between down-sampled and original 1kHz signal for different interpolation methods and sampling frequencies. The number of peak counts in each essay is presented as well as mean, standard deviation (SD), minimum (Min) and maximum (Max) values. The Pearson correlation coefficient values and t of the estimated distribution are also described
Full size table

B EDA signal analysis

See Table 6.

Table 6 EDA time errors, amplitude errors and Pearson correlation coefficient values between down-sampled and original 1kHz signal for different interpolation methods and sampling frequencies. The number of peak counts in each essay is presented as well as mean, standard deviation (SD), minimum (Min) and maximum (Max) values. The Pearson correlation coefficient values and kurtosis of the estimated distribution are also described
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Silva, R., Salvador, G., Bota, P. et al. Impact of sampling rate and interpolation on photoplethysmography and electrodermal activity signals’ waveform morphology and feature extraction. Neural Comput & Applic 35, 5661–5677 (2023). https://doi.org/10.1007/s00521-022-07212-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07212-6

Keywords

Search

Navigation