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1.
Med Eng Phys ; 109: 103904, 2022 11.
Artigo em Inglês | MEDLINE | ID: mdl-36371085

RESUMO

OBJECTIVE: Coronavirus disease 2019 (COVID-19) targets several tissues of the human body; among these, a serious impact has been observed in the microvascular system. The aim of this study was to verify the presence of photoplethysmographic (PPG) signal modifications in patients affected by COVID-19 at different levels of severity. APPROACH: The photoplethysmographic signal was evaluated in 93 patients with COVID-19 of different severity (46: grade 1; 47: grade 2) and in 50 healthy control subjects. A pre-processing step removes the long-term trend and segments of each pulsation in the input signal. Each pulse is approximated with a model generated from a multi-exponential curve, and a Least Squares fitting algorithm determines the optimal model parameters. Using the parameters of the mathematical model, three different classifiers (Bayesian, SVM and KNN) were trained and tested to discriminate among healthy controls and patients with COVID, stratified according to the severity of the disease. Results are validated with the leave-one-subject-out validation method. MAIN RESULTS: Results indicate that the fitting procedure obtains a very high determination coefficient (above 99% in both controls and pathological subjects). The proposed Bayesian classifier obtains promising results, given the size of the dataset, and variable depending on the classification strategy. The optimal classification strategy corresponds to 79% of accuracy, with 90% of specificity and 67% of sensibility. SIGNIFICANCE: The proposed approach opens the possibility of introducing a low cost and non-invasive screening procedure for the fast detection of COVID-19 disease, as well as a promising monitoring tool for hospitalized patients, with the purpose of stratifying the severity of the disease.


Assuntos
COVID-19 , Fotopletismografia , Humanos , Fotopletismografia/métodos , COVID-19/diagnóstico , Processamento de Sinais Assistido por Computador , Teorema de Bayes , Frequência Cardíaca , Algoritmos
2.
Sci Rep ; 12(1): 19190, 2022 Nov 10.
Artigo em Inglês | MEDLINE | ID: mdl-36357443

RESUMO

Hemorrhagic events are the main focus of attention during antithrombosis therapy in patients with coronary artery disease (CAD). This study aims to investigate the potential of using photoplethysmography (PPG) and machine learning techniques to assess hemorrhagic risk in patients with CAD. A total of 1638 patients with CAD were enrolled from January 2018 to October 2019, among which 114 patients were observed to have at least one positive event. Importantly, 102 patients with 9933 records were finally retained for analysis in this study. Participants were required to collect data using the portable PPG acquisition device and the specially designed Android APP. The data was collected and uploaded to a remote server. Based on collected PPG signals, we extracted features in a total of 30 dimensions from time-domain, frequency-domain, and wavelet packet decomposition. Logistic regression, support vector regression, random forest, and XGBoost regression models were established to achieve hemorrhagic risk evaluation, and then, their performances were compared. In total, 10 features extracted from PPG showed statistical significance (p < 0.01) between negative and positive groups. The newly established XGBoost model performed best in the hemorrhagic risk evaluation experiment, wherein the mean area under the curve (AUC) with tenfold cross-validation was 0.762 ± 0.024 and the sensitivity and specificity were 0.679 ± 0.051 and 0.714 ± 0.014, respectively. We established a data acquisition system for PPG signal collection, and demonstrated that a set of features extracted from PPG and the proposed machine learning model are promising in the evaluation of hemorrhagic risk among patients with CAD. In comparison with the traditional HAS-BLED score, the proposed method can obtain the quantitative risk prediction probability from a single PPG record, which has the advantages of dynamics and continuity, and can provide timely feedback for doctors' antithrombotic treatment, which is of great significance for doctors to quickly determine the effectiveness of the treatment and adjust the timely treatment plans accordingly.


Assuntos
Doença da Artéria Coronariana , Fotopletismografia , Humanos , Fotopletismografia/métodos , Aprendizado de Máquina , Modelos Logísticos , Sensibilidade e Especificidade
3.
J Biomed Opt ; 27(11)2022 11.
Artigo em Inglês | MEDLINE | ID: mdl-36358007

RESUMO

SignificanceAs a noncontact method, imaging photoplethysmography (iPPG) may provide a powerful tool to measure pulsatile pressure wave (PPW) in superficial arteries and extract biomarkers for monitoring of artery wall stiffness.AimWe intend to develop a approach for extraction of the very weak cardiac component from iPPG data by identifying locations of strong PPW signals with optimized illumination wavelength and determining pulse wave velocity (PWV).ApproachMonochromatic in vivo iPPG datasets have been acquired from left hands to investigate various algorithms for retrieval of PPW signals, distribution maps and waveforms, and their dependence on arterial location and wavelength.ResultsA robust algorithm of pixelated independent component analysis (pICA) has been developed and combined with spatiotemporal filtering to retrieve PPW signals. Spatial distributions of PPW signals have been mapped in 10 wavelength bands from 445 to 940 nm and waveforms were analyzed at multiple locations near the palmar artery tree. At the wavelength of 850 nm selected for timing analysis, we determined PWV values from 12 healthy volunteers in a range of 0.5 to 5.8 m/s across the hand region from wrist to midpalm and fingertip.ConclusionsThese results demonstrate the potentials of the iPPG method based on pICA algorithm for translation into a monitoring tool to characterize wall stiffness of superficial artery by rapid and noncontact measurement of PWV and other biomarkers within 10 s.


Assuntos
Pica , Análise de Onda de Pulso , Humanos , Análise de Onda de Pulso/métodos , Fluxo Pulsátil , Artérias/diagnóstico por imagem , Fotopletismografia , Velocidade do Fluxo Sanguíneo
4.
Sensors (Basel) ; 22(22)2022 Nov 08.
Artigo em Inglês | MEDLINE | ID: mdl-36433186

RESUMO

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. The arrhythmia and methods developed to cure it have been studied for several decades. However, professionals worldwide are still working to improve treatment quality. One novel technology that can be useful is a wearable device. The two most used recordings from these devices are photoplethysmogram (PPG) and electrocardiogram (ECG) signals. As the price lowers, these devices will become significant technology to increase sensitivity, for monitoring and for treatment quality support. This is important as AF can be challenging to detect in advance, especially during home monitoring. Modern artificial intelligence (AI) has the potential to respond to this challenge. AI has already achieved state of the art results in many applications, including bioengineering. In this perspective, we discuss wearable devices combined with AI for AF detection, an approach that enables a new era of possibilities for the future.


Assuntos
Fibrilação Atrial , Dispositivos Eletrônicos Vestíveis , Humanos , Fibrilação Atrial/diagnóstico , Inteligência Artificial , Fotopletismografia , Processamento de Sinais Assistido por Computador , Tecnologia
5.
Sensors (Basel) ; 22(22)2022 Nov 13.
Artigo em Inglês | MEDLINE | ID: mdl-36433366

RESUMO

Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension.


Assuntos
Hipertensão , Fotopletismografia , Humanos , Fotopletismografia/métodos , Máquina de Vetores de Suporte , Pressão Sanguínea , Emoções , Hipertensão/diagnóstico
6.
Sensors (Basel) ; 22(21)2022 Oct 24.
Artigo em Inglês | MEDLINE | ID: mdl-36365837

RESUMO

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the "Stress-Predict Dataset", created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.


Assuntos
Dispositivos Eletrônicos Vestíveis , Humanos , Projetos Piloto , Frequência Cardíaca/fisiologia , Monitorização Fisiológica , Taxa Respiratória , Fotopletismografia
7.
Sensors (Basel) ; 22(21)2022 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-36366083

RESUMO

Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models.


Assuntos
Taxa Respiratória , Processamento de Sinais Assistido por Computador , Humanos , Idoso , Incerteza , Reprodutibilidade dos Testes , Fotopletismografia/métodos , Algoritmos , Frequência Cardíaca
8.
Sensors (Basel) ; 22(20)2022 Oct 14.
Artigo em Inglês | MEDLINE | ID: mdl-36298147

RESUMO

BACKGROUND: In the diagnosis and primary health care of an individual, estimation of the pulse rate and blood oxygen saturation (SpO2) is critical. The pulse rate and SpO2 are determined by methods including photoplethysmography (iPPG), light spectroscopy, and pulse oximetry. These devices need to be compact, non-contact, and noninvasive for real-time health monitoring. Reflection-based iPPG is becoming popular as it allows non-contact estimation of the heart rate and SpO2. Most iPPG methods capture temporal data and form complex computations, and thus real-time measurements and spatial visualization are difficult. METHOD: In this research work, reflective mode polarized imaging-based iPPG is proposed. For polarization imaging, a custom image sensor with wire grid polarizers on each pixel is designed. Each pixel has a wire grid of varying transmission axes, allowing phase detection of the incoming light. The phase information of the backscattered light from the fingertips of 12 healthy volunteers was recorded in both the resting as well as the excited states. These data were then processed using MATLAB 2021b software. RESULTS: The phase information provides quantitative information on the reflection from the superficial and deep layers of skin. The ratio of deep to superficial layer backscattered phase information is shown to be directly correlated and linearly increasing with an increase in the SpO2 and heart rate. CONCLUSIONS: The phase-based measurements help to monitor the changes in the resting and excited state heart rate and SpO2 in real time. Furthermore, the use of the ratio of phase information helps to make the measurements independent of the individual skin traits and thus increases the accuracy of the measurements. The proposed iPPG works in ambient light, relaxing the instrumentation requirement and helping the system to be compact and portable.


Assuntos
Oximetria , Fotopletismografia , Humanos , Oximetria/métodos , Monitorização Fisiológica , Frequência Cardíaca , Dedos , Oxigênio
9.
Sensors (Basel) ; 22(20)2022 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-36298346

RESUMO

Continuous measurement of heart rate variability (HRV) in the short and ultra-short-term using wearable devices allows monitoring of physiological status and prevention of diseases. This study aims to evaluate the agreement of HRV features between a commercial device (Bora Band, Biosency) measuring photoplethysmography (PPG) and reference electrocardiography (ECG) and to assess the validity of ultra-short-term HRV as a surrogate for short-term HRV features. PPG and ECG recordings were acquired from 5 healthy subjects over 18 nights in total. HRV features include time-domain, frequency-domain, nonlinear, and visibility graph features and are extracted from 5 min 30 s and 1 min 30 s duration PPG recordings. The extracted features are compared with reference features of 5 min 30 s duration ECG recordings using repeated-measures correlation, Bland-Altman plots with 95% limits of agreements, Cliff's delta, and an equivalence test. Results showed agreement between PPG recordings and ECG reference recordings for 37 out of 48 HRV features in short-term durations. Sixteen of the forty-eight HRV features were valid and retained very strong correlations, negligible to small bias, with statistical equivalence in the ultra-short recordings (1 min 30 s). The current study concludes that the Bora Band provides valid and reliable measurement of HRV features in short and ultra-short duration recordings.


Assuntos
Fotopletismografia , Dispositivos Eletrônicos Vestíveis , Humanos , Gravidez , Feminino , Fotopletismografia/métodos , Frequência Cardíaca/fisiologia , Eletrocardiografia/métodos , Voluntários Saudáveis
10.
Adv Exp Med Biol ; 1384: 205-218, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36217086

RESUMO

Pulse oximeters are routinely used in various medical-grade and consumer-grade applications. They can be used to estimate, for example, blood oxygen saturation, autonomic nervous system activity and cardiac function, blood pressure, sleep quality, and recovery through the recording of photoplethysmography signal. Medical-grade devices often record red and infra-red light-based photoplethysmography signals while smartwatches and other consumer-grade devices usually rely on a green light. At its simplest, a pulse oximeter can consist of one or two photodiodes and a photodetector attached, for example, a fingertip or earlobe. These sensors are used to record light absorption in a medium as a function of time. This time-varying absorption information is used to form a photoplethysmography signal. In this chapter, we discuss the working principles of pulse oximeters and the formation of the photoplethysmography signal. We will further discuss the advantages and disadvantages of pulse oximeters, which kind of applications exist in the medical field, and how pulse oximeters are utilized in daily health monitoring.


Assuntos
Oximetria , Fotopletismografia , Pressão Sanguínea , Dedos , Oxigênio
11.
Med Biol Eng Comput ; 60(12): 3419-3433, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36190610

RESUMO

In order to realize high-accuracy heart rate (HR) estimation based on photoplethysmography (PPG) under the scenes of low signal-to-noise ratio (SNR) and co-frequency caused by motion artifacts (MAs), this paper presents a novel framework integrating two-stage variational mode decomposition (VMD) denoising method, noise compensation technology, and hidden Markov model (HMM)-based tracking algorithm. The two-stage VMD denoising method is designed to separate the HR signal from MA under low SNR scene. The noise compensation technology is applied to solve the problem of co-frequency. HMM-based HR tracking method is adopted to obtain the global optimization performance of HR estimation. The effectiveness and superiority of the proposed framework in solving problems of low SNR and co-frequency associated with motion artifacts have been verified by the HR estimation experiments carried out on three public high-SNR PPG databases (ISPC, BAMI I, BAMI II) and a self-built low-SNR database (WeData). Compared with the two classical frameworks namely joint sparse spectrum reconstruction (JOSS) and convolutional neural network-long short-term memory network (CNN-LSTM), the proposed framework obtains the lowest HR estimation errors (0.94 beats per minute (BPM) and 1.81 BPM respectively) on both BAMI 2 with the highest SNR (0.40 dB) and WeData with the lowest SNR (- 9.07 dB). For the low-SNR database Wedata, the average absolute error (AAE) decreases by more than 21 BPM. The research result of this study provides a solution for the realization of high-accuracy PPG-based HR estimation in exercise scenarios.


Assuntos
Fotopletismografia , Processamento de Sinais Assistido por Computador , Fotopletismografia/métodos , Frequência Cardíaca/fisiologia , Razão Sinal-Ruído , Artefatos , Algoritmos
12.
Physiol Meas ; 43(11)2022 Nov 11.
Artigo em Inglês | MEDLINE | ID: mdl-36301705

RESUMO

Objective. Daily blood pressure (BP) monitoring is essential since BP levels can reflect the functions of heart pumping and vasoconstriction. Although various neural network-based BP estimate approaches have been proposed, they have certain practical shortcomings, such as low estimation accuracy and poor model generalization. Based on the strategy of pre-training and partial fine-tuning, this work proposes a non-invasive method for BP estimation using the photoplethysmography (PPG) signal.Approach. To learn the PPG-BP relationship, the deep convolutional bidirectional recurrent neural network (DC-Bi-RNN) was pre-trained with data from the public medical information mark for intensive care (MIMIC III) database. A tiny quantity of data from the target subject was used to fine-tune the specific layers of the pre-trained model to learn more individual-specific information to achieve highly accurate BP estimation.Main results.The mean absolute error and the Pearson correlation coefficient (r) of the proposed algorithm are 3.21 mmHg and 0.919 for systolic BP, and 1.80 mmHg and 0.898 for diastolic BP (DBP). The experimental results show that our method outperforms other methods and meets the requirements of the Association for the Advancement of Medical Instrumentation standard, and received an A grade according to the British Hypertension Society standard.Significance.The proposed method applies the strategy of pre-training and partial fine-tuning to BP estimation and verifies its effectiveness in improving the accuracy of non-invasive BP estimation.


Assuntos
Determinação da Pressão Arterial , Hipertensão , Humanos , Pressão Sanguínea/fisiologia , Determinação da Pressão Arterial/métodos , Fotopletismografia/métodos , Redes Neurais de Computação , Hipertensão/diagnóstico
13.
Med Biol Eng Comput ; 60(12): 3539-3554, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36245021

RESUMO

The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites. However, this PPG signal is susceptible to artifacts that critically impair the signal quality. To fully exploit neck PPG for reliable physiological parameters extraction and apneas monitoring, this paper aims to develop two classification algorithms for artifacts and apnea detection. Features from the time, correlogram, and frequency domains were extracted. Two SVM classifiers with RBF kernels were trained for different window (W) lengths and thresholds (Thd) of corruption. For artifacts classification, the maximum performance was attained for the parameters combination of [W = 6s-Thd= 20%], with an average accuracy= 85.84%(ACC), sensitivity= 85.43%(SE) and specificity= 86.26%(SP). For apnea detection, the model [W = 10s-Thd= 50%] maximized all the performance metrics significantly (ACC= 88.25%, SE= 89.03%, SP= 87.42%). The findings of this proof of concept are significant for denoising novel neck PPG signals, and demonstrate, for the first time, that it is possible to promptly detect apnea events from neck PPG signals in an instantaneous manner. This could make a big impact in crucial real-time applications, like devices to prevent sudden-unexpected-death-in-epilepsy (SUDEP).


Assuntos
Artefatos , Fotopletismografia , Humanos , Apneia , Frequência Cardíaca/fisiologia , Algoritmos , Dor no Peito , Processamento de Sinais Assistido por Computador
14.
Physiol Meas ; 43(11)2022 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-36215976

RESUMO

Objective. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manually designed regions of interest (ROIs) and the skin reflection model.Approach. This paper presents a short-time end to end HR estimation framework based on facial features and temporal relationships of video frames. In the proposed method, a deep 3D multi-scale network with cross-layer residual structure is designed to construct an autoencoder and extract robust rPPG features. Then, a spatial-temporal fusion mechanism is proposed to help the network focus on features related to rPPG signals. Both shallow and fused 3D spatial-temporal features are distilled to suppress redundant information in the complex environment. Finally, a data augmentation strategy is presented to solve the problem of uneven distribution of HR in existing datasets.Main results. The experimental results on four face-rPPG datasets show that our method overperforms the state-of-the-art methods and requires fewer video frames. Compared with the previous best results, the proposed method improves the root mean square error (RMSE) by 5.9%, 3.4% and 21.4% on the OBF dataset (intra-test), COHFACE dataset (intra-test) and UBFC dataset (cross-test), respectively.Significance. Our method achieves good results on diverse datasets (i.e. highly compressed video, low-resolution and illumination variation), demonstrating that our method can extract stable rPPG signals in short time.


Assuntos
Aprendizado Profundo , Fotopletismografia , Fotopletismografia/métodos , Algoritmos , Frequência Cardíaca , Projetos de Pesquisa
15.
Biosensors (Basel) ; 12(10)2022 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-36290948

RESUMO

Automatically recognizing negative emotions, such as anger or stress, and also positive ones, such as euphoria, can contribute to improving well-being. In real-life, emotion recognition is a difficult task since many of the technologies used for this purpose in both laboratory and clinic environments, such as electroencephalography (EEG) and electrocardiography (ECG), cannot realistically be used. Photoplethysmography (PPG) is a non-invasive technology that can be easily integrated into wearable sensors. This paper focuses on the comparison between PPG and ECG concerning their efficacy in detecting the psychophysical and affective states of the subjects. It has been confirmed that the levels of accuracy in the recognition of affective variables obtained by PPG technology are comparable to those achievable with the more traditional ECG technology. Moreover, the affective psychological condition of the participants (anxiety and mood levels) may influence the psychophysiological responses recorded during the experimental tests.


Assuntos
Eletrocardiografia , Fotopletismografia , Humanos , Eletroencefalografia , Emoções , Frequência Cardíaca/fisiologia , Processamento de Sinais Assistido por Computador
16.
Biosensors (Basel) ; 12(10)2022 Oct 12.
Artigo em Inglês | MEDLINE | ID: mdl-36290998

RESUMO

The widespread use of remote technology has moved medical care services into individuals' homes. In this perspective, the ubiquitous computing research proposes self-management and remote monitoring to help patients with healthcare in low-cost everyday home usage systems based on the latest technological advances in sensors, communication, and portability. This work analyzes recent publications on the paradigm of continuous monitoring through wearable and portable systems, focusing on photoplethysmography (PPG) advances and referencing the current systematic study proposed by Fine et al. The study revised the literature highlighting the pros and cons of using the PPG system for fitness, wellbeing, and medical devices. However, future works should focus on the standardization of the practical use and assessment of the quality of the PPGs' output. For clinical parameter extraction methodology in terms of biological sites of application and signal processing methods, PPG is the most convenient and widely used system potentially suitable for the decentralized paradigm of continuous monitoring healthcare concepts.


Assuntos
Serviços de Assistência Domiciliar , Dispositivos Eletrônicos Vestíveis , Humanos , Fotopletismografia , Processamento de Sinais Assistido por Computador , Atenção à Saúde , Frequência Cardíaca , Algoritmos
17.
Biosensors (Basel) ; 12(10)2022 Oct 12.
Artigo em Inglês | MEDLINE | ID: mdl-36290999

RESUMO

Photoplethysmography (PPG) is garnering substantial interest due to low cost, noninvasiveness, and its potential for diagnosing cardiovascular diseases, such as cardiomyopathy, heart failure, and arrhythmia. The signals obtained through PPG can yield information based on simple analyses, such as heart rate. In contrast, when accompanied by the complex analysis of sophisticated signals, valuable information, such as blood pressure, sympathetic nervous system activity, and heart rate variability, can be obtained. For a complex analysis, a better understanding of the sources of noise, which create limitations in the application of PPG, is needed to get reliable information to assess cardiovascular health. Therefore, this Special Issue handles literature about noises and how they affect the waveform of the PPG caused by individual variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external factors (e.g., motion artifact, ambient light, and applied pressure to the skin). It also covers the issues that still need to be considered in each situation.


Assuntos
Artefatos , Fotopletismografia , Frequência Cardíaca/fisiologia , Monitorização Fisiológica , Respiração , Processamento de Sinais Assistido por Computador , Algoritmos
18.
Sensors (Basel) ; 22(19)2022 Sep 21.
Artigo em Inglês | MEDLINE | ID: mdl-36236265

RESUMO

Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a binary classification task: quality assessment for photoplethysmography (PPG). PPG signal is used to detect physiological changes and its quality can have a significant impact on the subsequent tasks, which makes PPG quality assessment a particularly good target for examining the impact of label noise in the field of biomedicine. Random and class-dependent label noise was introduced separately into the training set to emulate the errors associated with fatigue and bias in labeling data samples. We also tested different representations of the PPG, including features defined by domain experts, 1D raw signal and 2D image. Three different classifiers are tested on the noisy training data, including support vector machine (SVM), XGBoost, 1D Resnet and 2D Resnet, which handle three representations, respectively. The results showed that the two deep learning models were more robust than the two traditional machine learning models for both the random and class-dependent label noise. From the representation perspective, the 2D image shows better robustness compared to the 1D raw signal. The logits from three classifiers are also analyzed, the predicted probabilities intend to be more dispersed when more label noise is introduced. From this work, we investigated various factors related to label noise, including representations, label noise type, and data imbalance, which can be a good guidebook for designing more robust methods for label noise in future work.


Assuntos
Fotopletismografia , Máquina de Vetores de Suporte , Algoritmos , Aprendizado de Máquina , Fotopletismografia/métodos
19.
Tex Heart Inst J ; 49(5)2022 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-36301189

RESUMO

Atrial fibrillation is the most commonly experienced type of cardiac arrhythmia and is the most associated with substantial clinical occurrences and expenses. This arrhythmia often occurs in its "silent" asymptomatic form, revealed only after complications such as a stroke or congestive heart failure have transpired. New smart devices confer effective advantages in the detection of this heart arrhythmia, of which photoplethysmography-based smart devices have shown great potential, according to previous research. However, the solution becomes a problem as widespread use and high availability of various applications and smart devices may lead to substantial amounts of false and misleading recordings and information, causing unnecessary anxiety regarding arrhythmic occurrences diagnosed by the devices but not professionally confirmed. Thus, with most of the devices being photoplethysmography based for detection of atrial fibrillation, it is important to research devices studied up to this point to find the best smart device to detect the aforementioned arrhythmias.


Assuntos
Fibrilação Atrial , Fotopletismografia , Humanos , Fibrilação Atrial/diagnóstico , Eletrocardiografia
20.
Sensors (Basel) ; 22(19)2022 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-36236663

RESUMO

Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels' lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical-optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland-Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.


Assuntos
Oximetria , Fotopletismografia , Pressão Sanguínea , Dedos , Frequência Cardíaca , Humanos , Oximetria/métodos , Fotopletismografia/métodos , Análise de Onda de Pulso/métodos
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