Photoplethysmographic signal processing using adaptive sum comb filter for pulse delay measurement

Pilt, Kristjan; Meigas, Kalju; Ferenets, Rain; Kaik, J.

doi:10.3176/eng.2010.1.08

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…Now that the channels are separated by the S/H, it is possible to filter the signal to remove unnecessary components, typically noise. Common cutoff frequencies present in literature range from 8 to 30 Hz [63][64][65][66][67][68]. After analysing the PPG signal and testing some different frequencies, 20 Hz was chosen as a good value for cutoff frequency.…”

Section: Signal Conditioningmentioning

confidence: 99%

Photoplethysmographic logger with contact force and hydrostatic pressure monitoring

Santos

Almeida

Cardoso

et al. 2013

2013 IEEE 3rd Portuguese Meeting in Bioengineering (ENBENG)

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Esta cópia da tese é fornecida na condição de que quem a consulta reconhece que os direitos de autor são pertença da Universidade de Coimbra e que nenhuma citação ou informação obtida a partir dela pode ser publicada sem a referência apropriada.This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognize that its copyright rests with University of Coimbra and that no quotation from the thesis and no information derived from it may be published without proper acknowledgement. This work is funded by FEDER, through the Programa Operacional Factores deCompetitividade -COMPETE -and by National funds through FCT -Fundação para a Ciência e Tecnologia -in the frame of -Centro Aos companheiros Gonçalo Costa, Hugo Ramalho e Tiago Leal, que ao meu lado deram incontáveis braçadas (eu mais do que eles!) dentro e fora da piscina, um agradecimento particular. Uma palavra de apreço também para o amigo de infância, Ricardo Pimentel, pelos muitos momentos partilhados ao longo de todo este tempo.Ainda um brinde em honra de quem nestes anos me acompanhou: Carla Guerra, Carolina Francisco, Gabriel Campos, Mariana Sequeira e Susete Neiva. The current work aims to develop a stand-alone pulse oximeter and a photoplethysmographic (PPG) system with measurements of the contact force and height of the finger relatively to the heart. This module, together with other measurements (e.g.electrocardiogram and pulse wave velocity), will be part of a multichannel platform that will be taken to clinical environment.The project comprises the design of hardware for signal conditioning, as well as the development of firmware to implement in a microcontroller unit (the Arduino™). The latter is used to control the system, acquire data and compute , heart rate ( ), contact force and height.Currently, the system is able to retrieve a stable PPG waveform and compute the in real time through the PPG signal peaks and valleys. Signals acquired in a healthy subject lead to a value of . Moreover, the subject arm and forearm orientation is retrieved by the use of accelerometers and used to compute the offset of the finger relatively to the heart. Contact force between the finger and the probe is also measured. Keywords

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Section: Signal Conditioningmentioning

confidence: 99%

Photoplethysmographic logger with contact force and hydrostatic pressure monitoring

Santos

Almeida

Cardoso

et al. 2013

2013 IEEE 3rd Portuguese Meeting in Bioengineering (ENBENG)

View full text Add to dashboard Cite

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“…The classifiers like SVM polynomial and KNN are at the level of moderate computational complexity as O n 3 and O n 2 log n . ELM classifier provides the high computational complexity of O n 4 and O n 3 log n . The ANN classifier is at the highest computational complexity of O n 5 and O n 4 log n .…”

Section: E Computational Complexity Of the Methodsmentioning

confidence: 99%

“…It is also used for the peripheral blood flow assessment and venous filing time. For measuring oxygen saturation, cardiac output, blood pressure, the PPG technique is used in a wide range of commercially available medical devices [4]. For assessing autonomic functions and for detecting peripheral vascular diseases, it is used.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Prabhakar

Rajaguru²,

Lee

2019

IEEE Access

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One of the most useful methodologies which provide a specific waveform showing the pulsating peripheral blood flow in a non-invasive manner is Photoplethysmography (PPG). The design, application and implementation of a PPG system are quite inexpensive and have a very easy maintenance. Without having direct contact with the surface of the skin, PPG can easily take the measurements. Therefore, PPG has a good medical competency and due to its widespread availability, it has a lot of advantages. A PPG signal can sometimes be substituted or complemented by an Electrocardiography (ECG) signal as it can provide Heart Rate Variability (HRV) analysis. In this work, an in-depth analysis of classification of Cardiovascular Disease (CVD) is done with the help of Capnobase dataset. Initially, metaheuristic optimization algorithms are utilized as dimensionality reduction techniques and then the dimensionally reduced values are classified with the help of different classifiers for the classification of CVD. The results show that for the PPG normal cases, a high classification accuracy of 99.48% is obtained when Chi square Probability Density Function (PDF) optimized values are classified with Artificial Neural Networks (ANN) and a second highest classification accuracy of 98.96% is obtained when Chicken swarm optimized values are classified with Naïve Bayesian Classifier (NBC). Similarly when the PPG abnormal cases or PPG with CVD cases are concerned, a high classification accuracy of 99.48% is obtained when Chi square PDF optimized values are classified with Logistic Regression and a second highest classification accuracy of 98.96% is obtained when Chi square CDF optimized values are classified with Gaussian Support Vector Machine (SVM) and when Chicken swarm optimized values are classified with NBC.

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“…By utilizing the infrared light at the peripheral parts of the body, cardiovascular function can be assessed by a famous non-invasive technique known as Photoplethysmography (PPG) [ 1 ]. In PPG sensors, the source is generally a Light-Emitting Diode (LED), and the detector is generally a Light-Dependent Resistor (LDR) that operates in the infrared range (0.8–1 μm) [ 2 ]. The fundamental difference in the infrared light absorbance by the blood and the rest of the skin tissues determines the sensing of the PPG [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

Prabhakar

Rajaguru²,

Kim

2020

Diagnostics

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The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.

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Photoplethysmographic signal processing using adaptive sum comb filter for pulse delay measurement

Cited by 9 publications

References 10 publications

Photoplethysmographic logger with contact force and hydrostatic pressure monitoring

Photoplethysmographic logger with contact force and hydrostatic pressure monitoring

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

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