Abstract
Wearable sensing systems enable peripheral physiological data to be collected repeatedly in naturalistic settings. However, the ambulatory nature of wearable biosensors predisposes them to common signal artifacts that researchers must address before analysis. Signal quality assessment procedures are time-consuming and non-standardized across research teams, and transparent reporting of custom, closed-source pipelines needs improvement. This paper presents HeartView, an extensible, open-source, web-based signal quality assessment pipeline that visualizes and quantifies missing beats and invalid segments in heart rate variability (HRV) data obtained from ambulatory electrocardiograph (ECG) and photoplethysmograph (PPG) signals. We demonstrate the utility of our pipeline on two datasets: (1) 34 ECGs recorded with the Actiwave Cardio from children with and without autism, and (2) 15 sets of ECGs and PPGs recorded with the RespiBAN and Empatica E4, respectively, from healthy adults in the publicly available WESAD dataset. Our pipeline demonstrates interpretable group differences in physiological signal quality. ECGs of children with autism contain more missing beats and invalid segments than those without autism. Similarly, PPG data contains more missing beats and invalid segments than ECG data. HeartView has a graphical user interface in the form of a web-based dashboard at https://github.com/cbslneu/heartview.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The survey sample comprised 31% researchers and 69% engineers from the Society of Psychophysiological Research (SPR), the IEEE International Machine Learning for Signal Processing (MLSP) workshop, and snowball sampling using personal contacts and social media.
- 2.
As our primary intent is to introduce a tool for researchers to perform initial assessment checks in their SQA, HeartView currently includes only one of several possible algorithms for ECG beat detection [36,37,38,39]. Future users could select and implement additional or alternative state-of-the-art algorithms.
References
Goodwin, M.S.: Passive telemetric monitoring: novel methods for real-world behavioral assessment. In: Handbook of Research Methods for Studying Daily Life. pp. 251–266. Guilford Press, New York (2012)
Mishra, V., et al.: Continuous detection of physiological stress with commodity hardware. ACM Trans. Comput. Healthcare 1, 1–30 (2020)
Weenk, M., et al.: Continuous monitoring of vital signs using wearable devices on the general ward: Pilot study. JMIR Mhealth Uhealth 5, 1–12 (2017)
Pantelopoulos, A., Bourbakis, N.G.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40, 1–12 (2010)
Orphanidou, C.: Signal Quality Assessment in Physiological Monitoring: State of the Art and Practical Considerations. Springer, Cham (2018)
Fine, J., et al.: Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring. Biosensors 11, 1–36 (2021)
Madhavan, G.: Plethysmography. Biomed. Instrum. Technol. 39, 367–371 (2005)
Kusumoto, F.: ECG Interpretation. Springer Nature, Cham (2020)
Elgendi, M.: Optimal signal quality index for photoplethysmogram signals. Bioengineering (Basel) 3, 21 (2016)
Quintana, D.S., Alvares, G.A., Heathers, J.A.: Guidelines for reporting articles on psychiatry and heart rate variability (GRAPH): recommendations to advance research communication. Transl. Psychiatry 6, 1–10 (2016)
Stupple, A., Singerman, D., Celi, L.A.: The reproducibility crisis in the age of digital medicine. NPJ Digital Med. 2, 3 (2019)
Yamane, N., Mishra, V., Goodwin, M.S.: Developing an open-source web-based data quality assessment pipeline for analysis of ambulatory cardiovascular data in individuals with autism. In: Paper presented at the International Society for Autism Research Annual Meeting (INSAR 2023), Stockholm, Sweden, 3 May 2023 (2023)
Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K.: Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In: Paper presented at the 20th ACM International Conference on Multimodal Interaction (ICMI 2018), New York, NY, 16 October 2018 (2018)
Quesnel, P.X., Chan, A.D.C., Yang, H.: Real-time biosignal quality analysis of ambulatory ECG for detection of myocardial ischemia. In: 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–5 (2013)
Bae, T.W., Kwon, K.K.: ECG PQRST complex detector and heart rate variability analysis using temporal characteristics of fiducial points. Biomed. Signal Process. Control 66, 1–21 (2021)
Clifford, G., Behar, J., Li, Q., Rezek, I.: Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiol. Meas. 33, 1419–1433 (2012)
Daluwatte, C., Johannesen, L., Galeotti, L., Vicente, J., Strauss, D.G., Scully, C.G.: Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs. Physiol. Meas. 37, 1370–1382 (2016)
Sharma, L.N., Dandapat, S., Mahanta, A.: Kurtosis based multichannel ECG signal denoising and diagnostic distortion measures. In: TENCON 2009 - 2009 IEEE Region 10 Conference. pp. 1–5. IEEE, New York (2009)
Zaunseder, S., Vehkaoja, A., Fleischhauer, V., Hoog Antink, C.: Signal-to-noise ratio is more important than sampling rate in beat-to-beat interval estimation from optical sensors. Biomed. Signal Process. Control 74, 103538 (2022)
Castiglioni, P., Parati, G., Faini, A.: Cepstral analysis for scoring the quality of electrocardiograms for heart rate variability. Front. Physiol. 13, 1–13 (2022)
He, R., et al.: Reducing false arrhythmia alarms in the ICU using novel signal quality indices assessment method. Paper presented at the 2015 Computing in Cardiology Conference, New York, NY, 6 September 2015 (2015)
Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., Tarassenko, L.: Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring. IEEE J. Biomed. Health Inf. 19, 832–838 (2014)
Behar, J., Oster, J., Li, Q., Clifford, G.D.: A single channel ECG quality metric. In: 2012 Computing in Cardiology, pp. 381–384. IEEE, Kraków (2012)
Kazemi, K., Laitala, J., Azimi, I., Liljeberg, P., Rahmani, A.M.: Robust PPG peak detection using dilated convolutional neural networks. Sensors 22, 1–22 (2022)
Bizzego, A., Battisti, A., Gabrieli, G., Esposito, G., Furlanello, C.: Pyphysio: a physiological signal processing library for data science approaches in physiology. SoftwareX 10, 1–5 (2019)
Carreiras, C., Alves, A.P., Lourenço, A., Canento, F., Silva, H.P. da, Fred, A.: BioSPPy: Biosignal processing in Python (2015). https://biosppy.readthedocs.io/
Kramer, L., Menon, C., Elgendi, M.: ECGAssess: a python-based toolbox to assess ECG lead signal quality. Frontiers in Digital Health. 4, 1–9 (2022)
Makowski, D., et al.: NeuroKit2: a python toolbox for neurophysiological signal processing. Behav. Res. Methods 53, 1689–1696 (2021)
van Gent, P., Farah, H., van Nes, N., van Arem, B.: HeartPy: a novel heart rate algorithm for the analysis of noisy signals. Transport. Res. F: Traffic Psychol. Behav. 66, 368–378 (2019)
Vollmer, M.: HRVTool—an open-source MATLAB toolbox for analyzing heart rate variability. In: 2019 Computing in Cardiology (CinC), pp. 1–4. IEEE, Singapore (2019)
Blechert, J., Peyk, P., Liedlgruber, M., Wilhelm, F.H.: ANSLAB: integrated multichannel peripheral biosignal processing in psychophysiological science. Behav. Res. Methods 48, 1528–1545 (2016)
Plotly: Dash Python user guide. https://dash.plotly.com/
Jenkins, J.L., Valacich, J.S., Williams, P.: Human-computer interaction movement indicators of response biases in online surveys. In: ICIS 2017 Proceedings, pp. 1–16. Association for Information Systems (2017)
Varni, G., et al.: Interactive sonification of synchronisation of motoric behaviour in social active listening to music with mobile devices. J. Multimodal User Interfaces 5, 157–173 (2012)
Manikandan, M.S., Soman, K.P.: A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomed. Signal Process. Control 7, 118–128 (2012)
Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME 32, 230–236 (1985)
Christov, I.I.: Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed. Eng. Online 3, 28 (2004)
Elgendi, M., Eskofier, B., Dokos, S., Abbott, D.: Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLoS ONE 9, 1–18 (2014)
Qin, Q., Li, J., Yue, Y., Liu, C.: An adaptive and time-efficient ECG R-peak detection algorithm. J Healthc, Eng. 2017, 5980541 (2017)
Feldman, M.: Hilbert transforms. In: Braun, S., Ewins, D., Rao, S.S. (eds.) Encyclopedia of Vibration, pp. 642–648. Elsevier Ltd., Amsterdam (2001)
Sunami, N.: Shannon energy R peak detection (2020). https://github.com/nsunami/Shannon-Energy-R-Peak-Detection
Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, E215–E220 (2000)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001)
Chen, L., Reisner, A.T., Reifman, J.: Automated beat onset and peak detection algorithm for field-collected photoplethysmograms. In: Paper presented at the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, 3 Sept 2009 (2009)
Shin, H.S., Lee, C., Lee, M.: Adaptive threshold method for the peak detection of photoplethysmographic waveform. Comput. Biol. Med. 39, 1145–1152 (2009)
Kuntamalla, S., Reddy, L.R.G.: An efficient and automatic systolic peak detection algorithm for photoplethysmographic signals. Int. J. Comput. Appl. 97, 1–6 (2014)
Mishra, V., et al.: Evaluating the reproducibility of physiological stress detection models. Proc. ACM Interact. Mobile Wearable Ubiq. Technol. 4, 1–29 (2020)
Graham, F.K.: Constraints on measuring heart rate and period sequentially through real and cardiac time. Psychophysiology 15, 492–495 (1978)
Tanaka, H., Monahan, K.D., Seals, D.R.: Age-predicted maximal heart rate revisited. J. Am. Coll. Cardiol. 37, 153–156 (2001)
Palumbo, R.V., Marraccini, M.E., Weyandt, L.L., Wilder-Smith, O., Liu, S., Goodwin, M.S.: Interpersonal autonomic physiology: a systematic review of the literature. Pers. Soc. Psychol. Rev. 21, 99–141 (2016)
Kristiansen, J., et al.: Comparison of two systems for long-term heart rate variability monitoring in free-living conditions: a pilot study. Biomed. Eng. 10, 1–14 (2011)
Goldman, S., Wang, C., Salgado, M.W., Greene, P.E., Kim, M., Rapin, I.: Motor stereotypies in children with autism and other developmental disorders. Dev. Med. Child Neurol. 51, 30–38 (2009)
Rice, C.E., et al.: Reported wandering behavior among children with autism spectrum disorder and/or intellectual disability. J. Pediatr. 174, 232-239.e2 (2016)
Sinzig, J., Walter, D., Doepfner, M.: Attention deficit/hyperactivity disorder in children and adolescents with autism spectrum disorder: symptom or syndrome? J. Atten. Disord. 13, 117–126 (2009)
Kylliäinen, A., Jones, E.J.H., Gomot, M., Warreyn, P., Falck-Ytter, T.: Practical guidelines for studying young children with autism spectrum disorder in psychophysiological experiments. Rev. J. Autism Dev. Disord. 1, 373–386 (2014)
McCarthy, C., Pradhan, N., Redpath, C., Adler, A.: Validation of the empatica E4 wristband. In: 2016 IEEE EMBS International Student Conference (ISC), pp. 1–4 (2016)
Charlton, P.H., et al.: Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physiol. Meas. 38, 669 (2017)
Bonnici, T., Orphanidou, C., Vallance, D., Darrell, A., Tarassenko, L.: Testing of werable monitors in a real-world hospital environment: what lessons can be learnt? In: 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks, pp. 79–84 (2012)
Joshi, M., et al.: Perceptions on the use of wearable sensors and continuous monitoring in surgical patients: interview study among surgical staff. JMIR Form. Res. 6, e27866 (2022)
Areia, C., et al.: Experiences of current vital signs monitoring practices and views of wearable monitoring: a qualitative study in patients and nurses. J. Adv. Nurs. 78, 810–822 (2022)
Zhang, Q., Zhou, D., Zeng, X.: Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed. Eng. Online 16, 23 (2017)
Nelson, B.W., Low, C.A., Jacobson, N., Areán, P., Torous, J., Allen, N.B.: Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research. npj Dig. Med. 3, 1–9 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yamane, N., Mishra, V., Goodwin, M.S. (2024). HeartView: An Extensible, Open-Source, Web-Based Signal Quality Assessment Pipeline for Ambulatory Cardiovascular Data. In: Salvi, D., Van Gorp, P., Shah, S.A. (eds) Pervasive Computing Technologies for Healthcare. PH 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-031-59717-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-59717-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-59716-9
Online ISBN: 978-3-031-59717-6
eBook Packages: Computer ScienceComputer Science (R0)