Measuring fine-grained heart-rate using a flexible wearable sensor in the presence of noise

Lida Zhang, Zachary King, Begum Egilmez, Jonathan Reeder, Roozbeh Ghaffari, John Rogers, Kristen Rosen, Michael Bass, Judith Moskowitz, Darius Tandon, Lauren Wakschlag, Nabil Alshurafa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wearables with embedded electrodes and sensors are capable of continuously performing Electrocardiography (ECG), recording electrical activity of the heart, while estimating heart-rate of the wearer. Recent advances in wearable technology have generated comfortable flexible sensors that conform to the contours of the body and can measure heart-rate in the field by capturing QRS complexes of ECG signals. Due to various activities of daily living (ADL), skin deformation by means of lateral, rotational or skin stretching can cause changes in the current pathways of the sensor creating noisy data. The challenge is to disentangle the noise from the usable data in order to accurately detect QRS complexes of ECG signals. In this paper we design a framework to capture noise from a miniaturized flexible sensor, the Biostamp1 (with 4 leads), worn on the chest of 16 participants performing a set of structured ADL in a home setting, and a baseball player (pitcher). We present a machine-learning framework using a consensus fusion classifier comprising a Support Vector Machine and Neural Network learned model to remove noise while preserving neighboring R-peaks. We evaluate the model using Leave One Subject Out (LOSO) and yield an average of 83% F-measure on the 17 participants (including the pitcher). The low false negative rate provides accurate heart-rate detection on a finegrained (every 5 seconds) level, in the presence of intermittent stretching of the skin. Our results increase the reliability of detecting heart-rate in real-world and player settings, increasing the utility of flexible ECG-based sensors in the field.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages160-164
Number of pages5
ISBN (Electronic)9781538611098
DOIs
StatePublished - Apr 2 2018
Event15th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018 - Las Vegas, United States
Duration: Mar 4 2018Mar 7 2018

Publication series

Name2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
Volume2018-January

Other

Other15th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
CountryUnited States
CityLas Vegas
Period3/4/183/7/18

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Keywords

  • ECG
  • Heart-rate
  • Machine learning
  • Noise
  • Signal processing

ASJC Scopus subject areas

  • Health Informatics
  • Instrumentation
  • Computer Networks and Communications
  • Human-Computer Interaction
  • Biomedical Engineering

Cite this

Zhang, L., King, Z., Egilmez, B., Reeder, J., Ghaffari, R., Rogers, J., Rosen, K., Bass, M., Moskowitz, J., Tandon, D., Wakschlag, L., & Alshurafa, N. (2018). Measuring fine-grained heart-rate using a flexible wearable sensor in the presence of noise. In 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018 (pp. 160-164). (2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BSN.2018.8329683