Comparative analysis of LDR and OPT 101 detectors in reflectance type PPG sensor

Daimiwal, Nivedita; Sundhararajan, M.; Shriram, Revati

doi:10.1109/iccsp.2014.6950013

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…In addition, we plan parallel measurement of BP and HR parameters of the currently scanned person during execution of the whole MR scan sequence. For this purpose, we will apply photo-plethysmography (PPG) using optical sensors for non-invasive retrieval of vital information about the cardiovascular system from the skin surface [28,29]. Variations in the photo-detector signal are related to changes in the blood volume inside the tissue.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area

Přibil

Přibilová

Frollo

2019

Sensors

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A system of gradient coils of the magnetic resonance imaging (MRI) device produces significant vibration and noise. Energetic relations of these phenomena are analyzed depending on MRI scan parameters (sequence type, repetition time (TR), echo time (TE), slice orientation, body weight). This issue should be investigated because of negative physiological and psychological effects on a person exposed to vibration and acoustic noise. We also measured the sound pressure level in the MRI scanning area and its vicinity in order to minimize these negative impacts, depending on intensity and time duration of exposition. From the recorded vibration and noise signals, the energy parameters were determined and statistically analyzed, and the obtained results were visually and numerically compared. Finally, subjective evaluation by a listening test method was used to analyze the influence of the generated MRI noise on the human psyche.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area

Přibil

Přibilová

Frollo

2019

Sensors

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“…Traditional methods of estimating cardiac output generally artificially extract the feature of the waveform, such as the area under the curve of the waveform [19], and then, calculate the result through a formula. Deep learning is currently being applied in different medical fields, and it has been shown that the method of estimating cardiac output using deep learningoutperforms traditional models when the blood flow is unstable [20].…”

Section: Introductionmentioning

confidence: 99%

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

Xu,

Tang,

Chen

2023

Sensors

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We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was −0.04 L/min with a −1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between −20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

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“…For this purpose, we apply the photoplethysmography (PPG) using an optical sensor for the non-invasive acquisition of vital information about the cardiovascular system from the skin surface [3]. This optical sensor complies with the requirements for working in the magnetic field environment with present radio frequency and electromagnetic disturbance [4]. Variations in the photo-detector signal are related to changes in the blood volume inside the tissue.…”

Section: Introductionmentioning

confidence: 99%

Physiological Impact of Vibration and Noise in an Open-Air Magnetic Resonance Imager: Analysis of a PPG Signal of an Examined Person

Přibil

Přibilová

Frollo

2019

The 6th International Electronic Conference on Sensors and Applications

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The paper represents a preliminary analysis of the physiological and psychological impact of vibration and acoustic noise on a person examined by a low-field magnetic resonance imaging (MRI) tomograph. First, a methodology for the measurement of different signals of a tested person was found. The main investigation consists of a parallel heart rate and blood pressure measurement using a photoplethysmographic (PPG) optical sensor and standard portable blood pressure monitors. The recorded PPG signal is filtered and processed to obtain a clean waveform used to determine an instantaneous heart rate. Different types of portable blood pressure monitors are tested and compared to choose the best one for further experiments.

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Comparative analysis of LDR and OPT 101 detectors in reflectance type PPG sensor

Cited by 8 publications

References 8 publications

Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area

Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

Physiological Impact of Vibration and Noise in an Open-Air Magnetic Resonance Imager: Analysis of a PPG Signal of an Examined Person

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