Comparison of photoplethysmogram measured from wrist and finger and the effect of measurement location on pulse arrival time

Rajala, Satu; Lindholm, Harri; Taipalus, Tapio

doi:10.1088/1361-6579/aac7ac

Cited by 52 publications

(46 citation statements)

References 42 publications

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“…The classification of a CC as ECC or PCC is based on the evaluation of the delay between electrical and pulse cardiac interferences. According to the electrophysiology of the heart and to the blood pressure dynamics, the electrical cardiac interference typically precedes the pulse interference by about 200–300 ms (Benbadis et al, 2012; Kwon et al, 2018; Rajala et al, 2018). Based on this fact, all possible pairs of CCs in a dataset were evaluated.…”

Section: Methodsmentioning

confidence: 99%

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

2019

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EEG recordings are generally affected by interference from physiological and non-physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement. We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal. Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications.

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Section: Methodsmentioning

confidence: 99%

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

2019

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“…[ 29 ] We note that the finger PPG might be used to estimate the pulse arrival time (PAT) from the time delay between the electrocardiogram (ECG) R ‐peak and the maximum of the PPG waveform. [ 30,31 ] Typically, PATs in the order of hundreds of milliseconds are measured with this method, [ 31 ] indicating that the pulse arrival time is much shorter that the average peak‐to‐peak interval in the PPG signal.…”

Section: Reference N Opd Pixels Area Sensor [Cm2] Resolution [Ppi] Jdmentioning

confidence: 99%

High‐Accuracy Photoplethysmography Array Using Near‐Infrared Organic Photodiodes with Ultralow Dark Current

Simone

Tordera

Delvitto

et al. 2020

Advanced Optical Materials

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NIR) spectral region, to estimate the ratio of oxyhemoglobin and deoxyhemoglobin in arterial blood. Transmission pulse oximeters restrict the sensing location to tissues that can be transilluminated, generally fingertips or ear lobes. To overcome this limitation, pulse oximeters should be used in reflective mode, [7] that is, sensing light that slightly penetrates in the tissue and is then reflected. This opens the possibility to monitor pulse rate and blood oxygenation beyond the traditional sensing locations (e.g., fingertip and earlob).Recent developments in the field of organic electronics have led to reflectance oximeters based on organic photodiodes (OPDs). [8][9][10][11] The potential for large-area manufacturing using industrially scalable coating techniques, [12] combined with the wide absorption spectrum and high color selectivity, [13] makes OPDs attractive for this class of optoelectronic sensors. Organic reflectance oximeters on light-weight flexible substrates [14] can easily adapt to complex shapes of the body, thereby potentially providing a versatile alternative to rigid conventional designs. Furthermore, the signal-to-noise ratio (SNR) of flexible oximeters is enhanced by the formation of a conformal sensor-skin interface with lower effective impedance, [15][16][17] which reduces the electronic noise during PPG acquisition. Lochner et al. [8] first combined organic light emitting diodes (OLEDs) with two OPD pixels in an all-organic pulse oximeter. This sensor successfully measured pulse rate and blood oxygenation level within an experimental error of 1% and 2%, respectively. In 2018, Khan et al. [9] presented a reflectance oximeter array composed of four red and four NIR OLEDs, and eight OPDs. Such configuration introduces the functionality of 2D oxygenation mapping capability. The sensor was used to measure oxygen saturation on the forehead with 1.1% error and to create 2D oxygenation maps of adult forearms under under normal and ischemic conditions.Here, we develop an NIR sensitive OPD array of 16 × 16 pixels and demonstrate its potential in reflectance PPG. Each OPD pixel exhibits NIR sensitivity up to ≈ 950 nm together with a low dark current density in the order of 10 −6 mA cm −2 . As the OPD array is operated in reflective mode, a thin semi-transparent top electrode in each pixel is needed not to hinder light that reaches the fingertip and is then reflected. At the same time, the top electrode must form a continuous layer to ensure low series resistance. To fulfill both requirements, we use a 10 nm Ag top electrode. Notably, we use opaque bottom electrodes in combination with the semi-transparent non-patterned top electrode, so that only reflected light is detected by the OPD array.Reflectance oximeters based on organic photodiode (OPD) arrays offer the potential to map blood pulsation and oxygenation via photoplethysmography (PPG) over a large area and beyond the traditional sensing locations. Here, an organic reflectance PPG array based on 16 × 16 OPD pixels is developed. The individual ...

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“…While studies have examined the PTT-BP relationship under challenging interventions, they have been confined to animals 8 , few healthy humans 9 , or critically ill patients 5 – 7 , 10 who are often hypotensive and thus not reflective of the hypertension management population. Larger studies of normotensive and hypertensive humans have been conducted, but they have mainly involved few or simple BP interventions 1 , 3 , 4 . Often times, only exercise has been invoked wherein finger PAT is already known to decline with the parallel increases in systolic and diastolic BP 1 .…”

Section: Introductionmentioning

confidence: 99%

Conventional pulse transit times as markers of blood pressure changes in humans

Block

Yavarimanesh

Natarajan

et al. 2020

Sci Rep

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Pulse transit time (PTT) represents a potential approach for cuff-less blood pressure (BP) monitoring. Conventionally, PTT is determined by (1) measuring (a) ECG and ear, finger, or toe PPG waveforms or (b) two of these PPG waveforms and (2) detecting the time delay between the waveforms. The conventional PTTs (cPTTs) were compared in terms of correlation with BP in humans. Thirty-two volunteers [50% female; 52 (17) (mean (SD)) years; 25% hypertensive] were studied. The four waveforms and manual cuff BP were recorded before and after slow breathing, mental arithmetic, cold pressor, and sublingual nitroglycerin. Six cPTTs were detected as the time delays between the ECG R-wave and ear PPG foot, R-wave and finger PPG foot [finger pulse arrival time (PAT)], R-wave and toe PPG foot (toe PAT), ear and finger PPG feet, ear and toe PPG feet, and finger and toe PPG feet. These time delays were also detected via PPG peaks. The best correlation by a substantial extent was between toe PAT via the PPG foot and systolic BP [− 0.63 ± 0.05 (mean ± SE); p < 0.001 via one-way ANOVA]. Toe PAT is superior to other cPTTs including the popular finger PAT as a marker of changes in BP and systolic BP in particular.

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Comparison of photoplethysmogram measured from wrist and finger and the effect of measurement location on pulse arrival time

Cited by 52 publications

References 42 publications

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

High‐Accuracy Photoplethysmography Array Using Near‐Infrared Organic Photodiodes with Ultralow Dark Current

Conventional pulse transit times as markers of blood pressure changes in humans

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