Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Seok, Dongyeol; Lee, Sang‐Hyun; Kim, Minjae; Cho, Jaeouk; Kim, Chul

doi:10.3389/felec.2021.685513

Cited by 65 publications

(41 citation statements)

References 134 publications

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“…The application of classical filtering techniques is restricted because they remove some of this relevant physiological information. To overcome this, especially critical in wearable PPG devices [7,8], new sophisticated denoising techniques have been successfully developed [9][10][11][12] but are beyond the scope of this paper. Self-care today, a culture that improves the quality of life and promotes the sustainability of the healthcare system, encourages the widespread use of wearable pulse oximeters for home healthcare [13], particularly in the last year, during the COVID-19 emergency.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Analysis of Biological Signals with the 0–1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal

2021

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The 0–1 test distinguishes between regular and chaotic dynamics for a deterministic system using a time series as a starting point without appealing to any state space reconstruction method. A modification of the 0–1 test allows for the determination of a more comprehensive range of signal dynamic behaviors, particularly in the field of biological signals. We report the results of applying the test and study with more details the PhotoPlethysmoGraphic (PPG) signal behavior from different healthy young subjects, although its use is extensible to other biological signals. While mainly used for heart rate and blood oxygen saturation monitoring, the PPG signal contains extensive physiological dynamics information. We show that the PPG signal, on a healthy young individual, is predominantly quasi-periodic on small timescales (short span of time concerning the dominant frequency). However, on large timescales, PPG signals yield an aperiodic behavior that can be firmly chaotic or a prior transition via an SNA (Strange Nonchaotic Attractor). The results are based on the behavior of well-known time series that are random, chaotic, aperiodic, periodic, and quasi-periodic.

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Section: Introductionmentioning

confidence: 99%

Dynamical Analysis of Biological Signals with the 0–1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal

2021

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“…One of the great examples of a miniaturized EEG recording system is ear-EEG technology. Two types of ear-EEG recording methods have actively been studied: (1) in-ear EEG which records EEG within the ear canal using an earpiece-shaped electrodes [ 75 – 77 ]; and (2) cEEGrid which records EEG in the area behind and around the ear using an ear hook-shaped electrodes [ 78 ]. Particularly, small size in-ear EEG device has several attractive advantages compared to bulky conventional on-scalp EEG device.…”

Section: Miniaturization Techniques For Eeg Recording Hardwarementioning

confidence: 99%

Miniaturization for wearable EEG systems: recording hardware and data processing

2022

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As more people desire at-home diagnosis and treatment for their health improvement, healthcare devices have become more wearable, comfortable, and easy to use. In that sense, the miniaturization of electroencephalography (EEG) systems is a major challenge for developing daily-life healthcare devices. Recently, because of the intertwined relationship between EEG recording and processing, co-research of EEG recording hardware and data processing has been emphasized for whole-in-one miniaturized EEG systems. This paper introduces miniaturization techniques in analog-front-end hardware and processing algorithms for such EEG systems. To miniaturize EEG recording hardware, various types of compact electrodes and mm-sized integrated circuits (IC) techniques including artifact rejection are studied to record accurate EEG signals in a much smaller manner. Active electrode and in-ear EEG technologies are also researched to make small-form-factor EEG measurement structures. Furthermore, miniaturization techniques for EEG processing are discussed including channel selection techniques that reduce the number of required electrode channel and hardware implementation of processing algorithms that simplify the EEG processing stage.

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“…For example, the combination of ultraflexible organic differential amplifiers and post-mismatch compensation of organic thin-film transistor sensors enabled monitoring of weak electrocardiography signals by simultaneously amplifying the target biosignal and reducing the noise, improving the SNR by 200-fold 145 . The subtraction of signals registered by two sensors closely located on skin 119 or the use of analogue or digital improvements (such as impedance bootstrapping or the control of amplifier gain) 146 are some approaches adopted to reduce motion artefacts. Digital signal amplification can take the form of digital filters or more advanced machine learning (ML) techniques to improve sensor data quality.…”

Section: Assembling Wearable Devicesmentioning

confidence: 99%

End-to-end design of wearable sensors

et al. 2022

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Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.

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Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Cited by 65 publications

References 134 publications

Dynamical Analysis of Biological Signals with the 0–1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal

Dynamical Analysis of Biological Signals with the 0–1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal

Miniaturization for wearable EEG systems: recording hardware and data processing

End-to-end design of wearable sensors

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