Einthoven Unipolar Leads: Towards a better understanding of Wilson Central Terminal

Moeinzadeh, Hossein; Assad, J.; Bifulco, Paolo; Cesarelli, Mario; O'Loughlin, Aiden; Shugman, I.

doi:10.1109/iceerp49088.2019.8956990

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…The elevated number of probes allowed to simultaneously perform the measurements on the two body sides (right and left). The system also permitted to synchronously acquire ECG (Lead I, Lead II and Lead III; Figure 2c) [39] at the same sample frequency of 1 kHz. The locations of PPG probes and ECG electrodes is reported in Figure 2c.…”

Section: Photoplethysmographic (Ppg) and Electrocardiographic (Ecg) Rmentioning

confidence: 99%

Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach

et al. 2020

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Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks.

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Section: Photoplethysmographic (Ppg) and Electrocardiographic (Ecg) Rmentioning

confidence: 99%

Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach

et al. 2020

View full text Add to dashboard Cite

show abstract

Einthoven Unipolar Leads: Towards a better understanding of Wilson Central Terminal

Cited by 1 publication

References 22 publications

Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach

Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach

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