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Related Experiment Video

Updated: Jun 6, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Intelligent artifact classification for ambulatory physiological signals.

Kevin T Sweeney1, Darren J Leamy, Tomas E Ward

  • 1Department of Electronic Engineering, National University of Ireland Maynooth, Co. Kildare, Ireland. ksweeney@eeng.nuim.ie

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

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Instrumentation Amplifier01:25

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...

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This study introduces a novel method for ambulatory physiological monitoring, enhancing data quality by labeling signals with motion artifact information. This improves the reliability of connected health data collected during daily activities.

Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Signal Processing

Background:

  • Connected health relies on remote physiological monitoring, facing challenges in maintaining data quality.
  • Ambulatory monitoring, crucial for daily living data, is prone to motion-induced artifacts corrupting signals.
  • High-quality, reliable data is essential for diagnostic utility in connected health applications.

Purpose of the Study:

  • To propose a model for ambulatory signal recording with integrated data quality labeling.
  • To address challenges in maintaining high-quality data streams from minimally intrusive sensors.
  • To improve the utility of physiological data collected during everyday activities.

Main Methods:

  • Developed a data quality labeling model for ambulatory signal recording.

Related Experiment Videos

Last Updated: Jun 6, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

  • Utilized motion sensing technology (accelerometers) to derive signal quality measures.
  • Employed multiple accelerometers to differentiate sensor disturbance from tissue movement.
  • Demonstrated the concept using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) brain monitoring.

    • Main Results:

    • Successfully implemented a system for labeling ambulatory signal quality based on motion artifact detection.
    • The proposed method can distinguish between sensor displacement and underlying tissue movement.
    • Artifact tagging provides information for subsequent signal processing and artifact reduction.

    Conclusions:

    • The proposed model enhances the reliability of connected health data by providing associated signal quality labels.
    • Motion artifact labeling is a key step towards improving diagnostic accuracy in ambulatory monitoring.
    • This approach supports the development of more robust and trustworthy remote health-care technologies.