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1.
Sensors (Basel) ; 21(17)2021 Sep 02.
Artigo em Inglês | MEDLINE | ID: mdl-34502807

RESUMO

Photoplethysmography (PPG) is an optical measurement technique that detects changes in blood volume in the microvascular layer caused by the pressure generated by the heartbeat. To solve the inconvenience of contact PPG measurement, a remote PPG technology that can measure PPG in a non-contact way using a camera was developed. However, the remote PPG signal has a smaller pulsation component than the contact PPG signal, and its shape is blurred, so only heart rate information can be obtained. In this study, we intend to restore the remote PPG to the level of the contact PPG, to not only measure heart rate, but to also obtain morphological information. Three models were used for training: support vector regression (SVR), a simple three-layer deep learning model, and SVR + deep learning model. Cosine similarity and Pearson correlation coefficients were used to evaluate the similarity of signals before and after restoration. The cosine similarity before restoration was 0.921, and after restoration, the SVR, deep learning model, and SVR + deep learning model were 0.975, 0.975, and 0.977, respectively. The Pearson correlation coefficient was 0.778 before restoration and 0.936, 0.933, and 0.939, respectively, after restoration.


Assuntos
Fotopletismografia , Processamento de Sinais Assistido por Computador , Volume Sanguíneo , Frequência Cardíaca
2.
Biomed Res Int ; 2021: 3453007, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34532501

RESUMO

To the best of our knowledge, there is no annotated database of PPG signals recorded by smartphone publicly available. This article introduces Brno University of Technology Smartphone PPG Database (BUT PPG) which is an original database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating photoplethysmographic (PPG) signal quality and estimation of heart rate (HR). The data comprises 48 10-second recordings of PPGs and associated electrocardiographic (ECG) signals used for determination of reference HR. The data were collected from 12 subjects (6 female, 6 male) aged between 21 and 61. PPG data were collected by smartphone Xiaomi Mi9 with sampling frequency of 30 Hz. Reference ECG signals were recorded using a mobile ECG recorder (Bittium Faros 360) with a sampling frequency of 1,000 Hz. Each PPG signal includes annotation of quality created manually by biomedical experts and reference HR. PPG signal quality is indicated binary: 1 indicates good quality for HR estimation, 0 indicates signals where HR cannot be detected reliably, and thus, these signals are unsuitable for further analysis. As the only available database containing PPG signals recorded by smartphone, BUT PPG is a unique tool for the development of smart, user-friendly, cheap, on-the-spot, self-home-monitoring of heart rate with the potential of widespread using.


Assuntos
Bases de Dados Factuais , Frequência Cardíaca/fisiologia , Fotopletismografia/estatística & dados numéricos , Adulto , Algoritmos , Artefatos , República Tcheca , Eletrocardiografia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Padrões de Referência , Valores de Referência , Processamento de Sinais Assistido por Computador/instrumentação , Smartphone
3.
Sensors (Basel) ; 21(18)2021 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-34577222

RESUMO

Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.


Assuntos
Fotopletismografia , Dispositivos Eletrônicos Vestíveis , Eletrocardiografia , Eletroencefalografia , Frequência Cardíaca , Humanos , Convulsões/diagnóstico , Processamento de Sinais Assistido por Computador , Taquicardia
4.
Sensors (Basel) ; 21(18)2021 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-34577227

RESUMO

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.


Assuntos
Aprendizado Profundo , Fotopletismografia , Pressão Sanguínea , Determinação da Pressão Arterial , Redes Neurais de Computação
5.
Sensors (Basel) ; 21(18)2021 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-34577279

RESUMO

Capillary refill time (CRT) refers to the time taken for body tissue to regain its colour after an applied blanching pressure is released. Usually, pressure is manually applied and not measured. Upon release of pressure, simple mental counting is typically used to estimate how long it takes for the skin to regain its colour. However, this method is subjective and can provide inaccurate readings due to human error. CRT is often used to assess shock and hydration but also has the potential to assess peripheral arterial disease which can result in tissue breakdown, foot ulcers and ultimately amputation, especially in people with diabetes. The aim of this study was to design an optical fibre sensor to simultaneously detect blood volume changes and the contact pressure applied to the foot. The CRT probe combines two sensors: a plastic optical fibre (POF) based on photoplethysmography (PPG) to measure blood volume changes and a fibre Bragg grating to measure skin contact pressure. The results from 10 healthy volunteers demonstrate that the blanching pressure on the subject's first metatarsal head of the foot was 100.8 ± 4.8 kPa (mean and standard deviation), the average CRT was 1.37 ± 0.46 s and the time to achieve a stable blood volume was 4.77 ± 1.57 s. For individual volunteers, the fastest CRT measured was 0.82 ± 0.11 and the slowest 1.94 ± 0.49 s. The combined sensor and curve fitting process has the potential to provide increased reliability and accuracy for CRT measurement of the foot in diabetic foot ulcer clinics and in the community.


Assuntos
Pé Diabético , Fibras Ópticas , , Humanos , Fotopletismografia , Reprodutibilidade dos Testes
6.
Sensors (Basel) ; 21(18)2021 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-34577448

RESUMO

Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual's autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.


Assuntos
Fotopletismografia , Processamento de Sinais Assistido por Computador , Algoritmos , Sistema Nervoso Autônomo , Dedos , Frequência Cardíaca , Pulso Arterial
7.
Sensors (Basel) ; 21(18)2021 Sep 20.
Artigo em Inglês | MEDLINE | ID: mdl-34577503

RESUMO

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.


Assuntos
Aprendizado Profundo , Algoritmos , Frequência Cardíaca , Humanos , Monitorização Fisiológica , Fotopletismografia , Processamento de Sinais Assistido por Computador
8.
Sensors (Basel) ; 21(18)2021 Sep 21.
Artigo em Inglês | MEDLINE | ID: mdl-34577518

RESUMO

Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.


Assuntos
Determinação da Pressão Arterial , Hipertensão , Pressão Sanguínea , Eletrocardiografia , Humanos , Hipertensão/diagnóstico , Fotopletismografia
9.
Sensors (Basel) ; 21(15)2021 Jul 27.
Artigo em Inglês | MEDLINE | ID: mdl-34372308

RESUMO

Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to "measure" sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success.


Assuntos
Sono , Dispositivos Eletrônicos Vestíveis , Fotopletismografia , Polissonografia , Fases do Sono
10.
Sensors (Basel) ; 21(15)2021 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-34372447

RESUMO

Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during physical activity under free-living conditions. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. Because of their generalization capabilities, deep learning methods can be a major direction in the search for a heart rate estimation solution based on signals from wearable devices. A novel multi-headed convolutional neural network model enriched with long short-term memory cells (MH Conv-LSTM DeepPPG) was proposed for the estimation of heart rate based on signals measured by a wrist-worn wearable device, such as PPG and acceleration signals. For the PPG-DaLiA dataset, the proposed solution improves the performance of previously proposed methods. An experimental approach was used to develop the final network architecture. The average mean absolute error (MAE) of the final solution was 6.28 bpm and Pearson's correlation coefficient between the estimated and true heart rate values was 0.85.


Assuntos
Atividades Cotidianas , Processamento de Sinais Assistido por Computador , Algoritmos , Artefatos , Frequência Cardíaca , Humanos , Fotopletismografia
11.
BMJ Open ; 11(8): e047896, 2021 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-34389569

RESUMO

INTRODUCTION: Physiological signals are essential for assessing human health. The absence of a medical device to carry out these measurements remotely is one of the main limitations of telemedicine. Remote photoplethysmography imaging (rPPG) makes it possible to use a camera video to measure some of the most valuable physiological variables: heart rate (HR), respiratory rate (RR) and oxygen saturation (SpO2). Our objective was to evaluate the value of such remote measurements compared with existing contact point measurements techniques in real-life clinical settings. METHODS AND ANALYSIS: Prospective hospital-based study that will recruit 1045 patients who require a pulmonary function test. For each patient, measurements of HR, RR and SpO2, using a standard acquisition system, will be carried out concomitantly with the measurements made by the rPPG system. 30, 60 and 120 s time frames will be used to take measurements. Age, gender and skin phototype will also be collected. The intraclass coefficient correlation will be performed to determine the accuracy and precision of the rPPG algorithm readings. ETHICS AND DISSEMINATION: The study protocol has been approved by the French Agency for the Safety of Health Products (ANSM registration no. ID RCB 2020-A02428-31) and by a French ethics committee (CPP OUEST I-TOURS-2020T1-30 DM at 30 October 2020). Results will be published in peer-reviewed journals, at scientific conferences and through press releases. TRIAL REGISTRATION NUMBER: NCT04660318.


Assuntos
Fotopletismografia , Taxa Respiratória , Frequência Cardíaca , Humanos , Oxigênio , Estudos Prospectivos
12.
Sensors (Basel) ; 21(16)2021 Aug 09.
Artigo em Inglês | MEDLINE | ID: mdl-34450799

RESUMO

Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.


Assuntos
Eletrocardiografia , Fotopletismografia , Artefatos , Frequência Cardíaca , Monitorização Fisiológica , Processamento de Sinais Assistido por Computador
13.
Sensors (Basel) ; 21(16)2021 Aug 18.
Artigo em Inglês | MEDLINE | ID: mdl-34450986

RESUMO

Alterations of heart rate variability (HRV) are associated with various (patho)physiological conditions; therefore, HRV analysis has the potential to become a useful diagnostic module of wearable/telemedical devices to support remote cardiovascular/autonomic monitoring. Continuous pulse recordings obtained by photoplethysmography (PPG) can yield pulse rate variability (PRV) indices similar to HRV parameters; however, it is debated whether PRV/HRV parameters are interchangeable. In this study, we assessed the PRV analysis module of a digital arterial PPG-based telemedical system (SCN4ALL). We used Bland-Altman analysis to validate the SCN4ALL PRV algorithm to Kubios Premium software and to determine the agreements between PRV/HRV results calculated from 2-min long PPG and ECG captures recorded simultaneously in healthy individuals (n = 33) at rest and during the cold pressor test, and in diabetic patients (n = 12) at rest. We found an ideal agreement between SCN4ALL and Kubios outputs (bias < 2%). PRV and HRV parameters showed good agreements for interbeat intervals, SDNN, and RMSSD time-domain variables, for total spectral and low-frequency power (LF) frequency-domain variables, and for non-linear parameters in healthy subjects at rest and during cold pressor challenge. In diabetics, good agreements were observed for SDNN, LF, and SD2; and moderate agreement was observed for total power. In conclusion, the SCN4ALL PRV analysis module is a good alternative for HRV analysis for numerous conventional HRV parameters.


Assuntos
Fotopletismografia , Telemedicina , Sistema Nervoso Autônomo , Eletrocardiografia , Frequência Cardíaca , Humanos
14.
Herzschrittmacherther Elektrophysiol ; 32(3): 346-352, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34241681

RESUMO

AIMS: Atrial fibrillation (AF) screening in risk populations has the potential to prevent strokes. The authors tested the feasibility of a digital program with initial photoplethysmographic (PPG) self-screening and cardiologist-attended electrocardiographic (ECG) confirmation of screen-positive cases. METHODS: Inhabitants of the city of Ulm aged ≥ 65 years were invited to participate. After digital consent, participants were given access to a smartphone application for 14 days of self-screening (two recordings per day recommended). Screen-positive participants without known AF were invited to present to a cardiologist for AF confirmation with a 14-day ECG event recorder. PPG recordings were first analyzed by algorithm using a combination of linear and non-linear methods. The quality of pathological (classified by algorithm) PPG and all ECG recordings were checked by a telecare service. Primary outcomes included adherence to the screening protocol defined as the proportion of participants performing at least 14 PPG recordings (or until documentation of absolute arrhythmia) and the proportion of pathological PPG and all ECG recordings rejected by the telecare center. RESULTS: A total of 215 participants registered. Of these, 204 (95%) performed at least one recording and 169 (79%) reached the performance target of two sufficient measurements per day; 75 PPG recordings were automatically classified as pathological by algorithm; 14 (19%) were rejected by the telecare service due to poor quality. Of the 12 participants with a suspected first diagnosis of AF, five visited a cardiologist as part of the study. Of 1090 ECG recordings obtained, 390 (36%) were qualified as non-diagnostic. AF was confirmed in three cases. CONCLUSIONS: A digital AF screening program with initial self-screening and referral of screen-positive cases to a cardiologist-attended ECG-confirmation service is feasible with meaningful results in an elderly risk population. However, the availability of the target population of persons > 65 years of age for such a digital screening program appears to be limited despite extensive public relations activities.


Assuntos
Fibrilação Atrial , Idoso , Fibrilação Atrial/diagnóstico , Eletrocardiografia , Estudos de Viabilidade , Humanos , Programas de Rastreamento , Fotopletismografia
15.
Herzschrittmacherther Elektrophysiol ; 32(3): 406-411, 2021 Sep.
Artigo em Alemão | MEDLINE | ID: mdl-34304276

RESUMO

By applying photoplethysmography (PPG), the camera of the mobile phone can be used to remotely assess heart rate and rhythm, which was widely used in conjunction with teleconsultations within the TeleCheck-AF project during the coronavirus disease 2019 (COVID-19) pandemic. Herein, we provide an educational, structured, stepwise practical guide on how to interpret PPG signals. A better understanding of PPG recordings is critical for the implementation of this widely available technology into clinical practice.


Assuntos
Fibrilação Atrial , COVID-19 , Frequência Cardíaca , Humanos , Fotopletismografia , SARS-CoV-2
16.
J Electrocardiol ; 67: 148-157, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34256184

RESUMO

INTRODUCTION: Photoplethysmography (PPG) in wearable sensors potentially plays an important role in accessible heart rhythm monitoring. We investigated the accuracy of a state-of-the-art bracelet (Corsano 287) for heartbeat detection in cardiac patients and evaluated the efficacy of a signal qualifier in identifying medically useful signals. METHODS: Patients from an outpatient cardiology clinic underwent a simultaneous resting ECG and PPG recording, which we compared to determine accuracy of the PPG sensor for detecting heartbeats within 100 and 50 ms of the ECG-detected heart beats and correlation and Limits of Agreement for heartrate (HR) and RR-intervals. We defined subgroups for skin type, hair density, age, BMI and gender and applied a previously described signal qualifier. RESULTS: In 180 patients 7914 ECG-, and 7880 (99%) PPG-heartbeats were recorded. The PPG-accuracy within 100 ms was 94.6% (95% CI 94.1-95.1) and 89.2% (95% CI 88.5-89.9) within 50 ms. Correlation was high for HR (R = 0.991 (95% CI 0.988-0.993), n = 180) and RR-intervals (R = 0.891 (95% CI 0.886-0.895), n = 7880). The 95% Limits of Agreement (LoA) were -3.89 to 3.77 (mean bias 0.06) beats per minute for HR and -173 to 171 (mean bias -1) for RR-intervals. Results were comparable across all subgroups. The signal qualifier led to a higher accuracy in a 100 ms range (98.2% (95% CI 97.9-98.5)) (n = 143). CONCLUSION: We showed that the Corsano 287 Bracelet with PPG-technology can determine HR and RR-intervals with high accuracy in cardiovascular at-risk patient population among different subgroups, especially with a signal quality indicator.


Assuntos
Eletrocardiografia , Fotopletismografia , Algoritmos , Frequência Cardíaca , Humanos , Processamento de Sinais Assistido por Computador , Tecnologia
17.
Sensors (Basel) ; 21(14)2021 Jul 19.
Artigo em Inglês | MEDLINE | ID: mdl-34300657

RESUMO

Continuous monitoring of blood-glucose concentrations is essential for both diabetic and nondiabetic patients to plan a healthy lifestyle. Noninvasive in vivo blood-glucose measurements help reduce the pain of piercing human fingertips to collect blood. To facilitate noninvasive measurements, this work proposes a Monte Carlo photon simulation-based model to estimate blood-glucose concentration via photoplethysmography (PPG) on the fingertip. A heterogeneous finger model was exposed to light at 660 nm and 940 nm in the reflectance mode of PPG via Monte Carlo photon propagation. The bio-optical properties of the finger model were also deduced to design the photon simulation model for the finger layers. The intensities of the detected photons after simulation with the model were used to estimate the blood-glucose concentrations using a supervised machine-learning model, XGBoost. The XGBoost model was trained with synthetic data obtained from the Monte Carlo simulations and tested with both synthetic and real data (n = 35). For testing with synthetic data, the Pearson correlation coefficient (Pearson's r) of the model was found to be 0.91, and the coefficient of determination (R2) was found to be 0.83. On the other hand, for tests with real data, the Pearson's r of the model was 0.85, and R2 was 0.68. Error grid analysis and Bland-Altman analysis were also performed to confirm the accuracy. The results presented herein provide the necessary steps for noninvasive in vivo blood-glucose concentration estimation.


Assuntos
Fótons , Fotopletismografia , Simulação por Computador , Glucose , Humanos , Método de Monte Carlo
18.
Sensors (Basel) ; 21(13)2021 Jun 24.
Artigo em Inglês | MEDLINE | ID: mdl-34202597

RESUMO

BACKGROUND: Feature extraction from photoplethysmography (PPG) signals is an essential step to analyze vascular and hemodynamic information. Different morphologies of PPG waveforms from different measurement sites appear. Various phenomena of missing or ambiguous features exist, which limit subsequent signal processing. METHODS: The reasons that cause missing or ambiguous features of finger and wrist PPG pulses are analyzed based on the concept of component waves from pulse decomposition. Then, a systematic approach for missing-feature imputation and ambiguous-feature resolution is proposed. RESULTS: From the experimental results, with the imputation and ambiguity resolution technique, features from 35,036 (98.7%) of 35,502 finger PPG cycles and 36307 (99.1%) of 36,652 wrist PPG cycles can be successfully identified. The extracted features became more stable and the standard deviations of their distributions were reduced. Furthermore, significant correlations up to 0.92 were shown between the finger and wrist PPG waveforms regarding the positions and widths of the third to fifth component waves. CONCLUSION: The proposed missing-feature imputation and ambiguous-feature resolution solve the problems encountered during PPG feature extraction and expand the feature availability for further processing. More intrinsic properties of finger and wrist PPG are revealed. The coherence between the finger and wrist PPG waveforms enhances the applicability of the wrist PPG.


Assuntos
Fotopletismografia , Punho , Dedos , Frequência Cardíaca , Processamento de Sinais Assistido por Computador
19.
Sensors (Basel) ; 21(12)2021 Jun 21.
Artigo em Inglês | MEDLINE | ID: mdl-34205706

RESUMO

Since photoplethysmography (PPG) sensors are usually placed on open skin areas, temperature interference can be an issue. Currently, green light is the most widely used in the reflectance PPG for its relatively low artifact susceptibility. However, it has been known that hemoglobin absorption peaks at the blue part of the spectrum. Despite this fact, blue light has received little attention in the PPG field. Blue wavelengths are commonly used in phototherapy. Combining blue light-based treatments with simultaneous blue PPG acquisition could be potentially used in patients monitoring and studying the biological effects of light. Previous studies examining the PPG in blue light compared to other wavelengths employed photodetectors with inherently lower sensitivity to blue, thereby biasing the results. The present study assessed the accuracy of heartbeat intervals (HBIs) estimation from blue and green PPG signals, acquired under baseline and cold temperature conditions. Our PPG system is based on TCS3472 Color Sensor with equal sensitivity to both parts of the light spectrum to ensure unbiased comparison. The accuracy of the HBIs estimates, calculated with five characteristic points (PPG systolic peak, maximum of the first PPG derivative, maximum of the second PPG derivative, minimum of the second PPG derivative, and intersecting tangents) on both PPG signal types, was evaluated based on the electrocardiographic values. The statistical analyses demonstrated that in all cases, the HBIs estimation accuracy of blue PPG was nearly equivalent to the G PPG irrespective of the characteristic point and measurement condition. Therefore, blue PPG can be used for cardiovascular parameter acquisition. This paper is an extension of work originally presented at the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.


Assuntos
Eletrocardiografia , Fotopletismografia , Artefatos , Frequência Cardíaca , Humanos , Processamento de Sinais Assistido por Computador , Temperatura
20.
Sensors (Basel) ; 21(12)2021 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-34200635

RESUMO

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.


Assuntos
Algoritmos , Fotopletismografia , Frequência Cardíaca , Processamento de Sinais Assistido por Computador , Software
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