693 results on '"photoplethysmography (ppg)"'
Search Results
2. Photoplethysmography signals and physiological data in feature engineering and machine learning algorithms to calculate human-obesity-related indices
- Author
-
Yen, Chih-Ta, Chang, Chia-Hsang, and Wong, Jung-Ren
- Published
- 2025
- Full Text
- View/download PDF
3. Development of a novel light-sensitive PPG model using PPG scalograms and PPG-NET learning for non-invasive hypertension monitoring
- Author
-
Fahoum, Amjed Al, Al Omari, Ahmad, Al Omari, Ghadeer, and Zyout, Ala'a
- Published
- 2024
- Full Text
- View/download PDF
4. Miniaturized Low-Power Head-Mounted PPG Board
- Author
-
Scandelli, Alice, Crupi, Ilaria, Giudici, Andrea, Bartoli, Pietro, De Vecchi, Arianna, Gervasoni, Giacomo, Trojaniello, Diana, Villa, Federica, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Valle, Maurizio, editor, Gastaldo, Paolo, editor, and Limiti, Ernesto, editor
- Published
- 2025
- Full Text
- View/download PDF
5. Drowsiness Detection Using Vital Sign Sensors and Deep Learning on Smartwatches
- Author
-
Pereira, Vitor Augusto da Rosa, Berri, Rafael Alceste, Osório, Fernando Santos, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Julian, Vicente, editor, Camacho, David, editor, Yin, Hujun, editor, Alberola, Juan M., editor, Nogueira, Vitor Beires, editor, Novais, Paulo, editor, and Tallón-Ballesteros, Antonio, editor
- Published
- 2025
- Full Text
- View/download PDF
6. Estimation of vital parameters from photoplethysmography using deep learning architecture.
- Author
-
Sulochana, C. Helen, Dharshini, S. L. Siva, and Blessy, S. A. Praylin Selva
- Abstract
Vital signs such as blood pressure, heart rate, and respiration rate are continuously monitored in intensive care unit patients to assess their condition. Various methods are available for the continuous monitoring of these vital parameters. To extract parameters, current techniques place multiple sensors on the patient’s body. Patients dealing with medical issues may find it challenging and uncomfortable to have multiple electrodes placed on their bodies. To avoid placing multiple sensors on a patient’s body, the proposed method aims to extract three vital parameters—respiration rate (RR), blood pressure, and heart rate—from a single photoplethysmography sensor, using a unified deep learning model to analyze the photoplethysmographic (PPG) signal. The proposed deep learning framework combines a Convolutional Neural Network (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) and an attention mechanism. This model effectively extracts features by integrating spatial and temporal correlations within the signal, focusing on the most relevant features necessary for estimating multiple parameters from a PPG signal. Optimized through hyperparameter tuning, the CNN-Bi-LSTM architecture achieved a prediction accuracy of 95.67%. The performance of the proposed method is evaluated using the publicly available Multiparameter Intelligent Monitoring in Intensive Care Database and compared to existing methods. The model demonstrated an average mean absolute error (MAE) ± standard deviation (SD) of 0.084 ± 0.20 for heart rate, 0.034 ± 0.23 for blood pressure, and 0.009 ± 0.05 for respiration rate. [ABSTRACT FROM AUTHOR]
- Published
- 2025
- Full Text
- View/download PDF
7. FPGA Implementation of PPG-Based Cardiovascular Diseases and Diabetes Classification Algorithm.
- Author
-
Chowdhury, Aditta, Chowdhury, Mehdi Hasan, Das, Diba, Ghosh, Sampad, and Cheung, Ray C. C.
- Subjects
- *
CARDIOVASCULAR diseases , *SUPPORT vector machines , *GATE array circuits , *CEREBRAL infarction , *CLASSIFICATION algorithms - Abstract
Photoplethysmogram is a noninvasive technique used to detect volumetric changes in the blood. Cardiovascular diseases, related to heart and blood supply problems, are one of the largest causes of death in the world. Our study explored the possibility of classifying different cardiovascular diseases using photoplethysmogram signals for quick diagnosis. Using the support vector machine technique, the classification is done at the software level, while Xilinx Zynq 7000 field-programmable gate array (FPGA) chip is utilized for hardware design. The overall accuracy for detecting cerebral infarction and cerebrovascular disease is 93.48% and 96.43%, respectively, using eleven features. In addition, diabetes which is linked to cardiovascular diseases is classified, and an accuracy of 88.46% is achieved. Considering the PPG signal with a higher signal quality index, the overall accuracy for all the diseases can be further increased. The resource and power utilization of the implemented system is analyzed, which shows that 0.693 W power is required. The developed prototype can be further extended as a point-of-care system for cardiovascular disease detection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
8. A Hybrid Photoplethysmography (PPG) Sensor System Design for Heart Rate Monitoring.
- Author
-
Jhuma, Farjana Akter, Harada, Kentaro, Misran, Muhamad Affiq Bin, Mo, Hin-Wai, Fujimoto, Hiroshi, and Hattori, Reiji
- Abstract
A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., the combination of an inorganic light-emitting diode (LED) and a circular-shaped organic photodetector (OPD) surrounding the LED for efficient light harvest followed by the proper driving circuit for accurate PPG signal acquisition. The performance of the hybrid sensor system was confirmed by the heart rate detection process from the PPG using fast Fourier transform analysis. The PPG signal obtained with a 50% LED duty cycle and 250 Hz sampling rate resulted in accurate heart rate monitoring with an acceptable range of error. The effects of the LED duty cycle and the LED luminous intensity were found to be crucial to the heart rate accuracy and to the power consumption, i.e., indispensable factors for the hybrid sensor. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
9. PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal.
- Author
-
Shuzan, Md Nazmul Islam, Chowdhury, Moajjem Hossain, Alam, Saadia Binte, Reaz, Mamun Bin Ibne, Khan, Muhammad Salman, Murugappan, M., and Chowdhury, Muhammad E. H.
- Abstract
Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual parameter tuning and pre-defined features. To address this challenge, we propose the PPG2RespNet deep-learning framework. It draws inspiration from the UNet and UNet + + models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to autonomously and efficiently extract respiratory signals. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet's performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals: 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory signals non-invasively, making it a valuable tool for calculating respiration rates. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
10. Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data
- Author
-
Xiang ZHAO, Wei WANG, Chenyang LI, Jian GUAN, and Gang LI
- Subjects
millimeter-wave radar ,photoplethysmography (ppg) ,multimodal signal fusion ,deep neural network ,sleep apnea hypopnea syndrome (sahs) ,apnea-hypopnea index (ahi) ,Electricity and magnetism ,QC501-766 - Abstract
Sleep Apnea Hypopnea Syndrome (SAHS) is a common chronic sleep-related breathing disorder that affects individuals’ sleep quality and physical health. This article presents a sleep apnea and hypopnea detection framework based on multisource signal fusion. Integrating millimeter-wave radar micro-motion signals and pulse wave signals of PhotoPlethysmoGraphy (PPG) achieves a highly reliable and light-contact diagnosis of SAHS, addressing the drawbacks of traditional medical methods that rely on PolySomnoGraphy (PSG) for sleep monitoring, such as poor comfort and high costs. This study used a radar and pulse wave data preprocessing algorithm to extract time-frequency information and artificial features from the signals, balancing the accuracy and robustness of sleep-breathing abnormality event detection Additionally, a deep neural network was designed to fuse the two types of signals for precise identification of sleep apnea and hypopnea events, and to estimate the Apnea-Hypopnea Index (AHI) for quantitative assessment of sleep-breathing abnormality severity. Experimental results of a clinical trial dataset at Shanghai Jiaotong University School of Medicine Affiliated Sixth People’s Hospital demonstrated that the AHI estimated by the proposed approach correlates with the gold standard PSG with a coefficient of 0.93, indicating good consistency. This approach is a promiseing tool for home sleep-breathing monitoring and preliminary diagnosis of SAHS.
- Published
- 2025
- Full Text
- View/download PDF
11. A novel computational signal processing framework towards multimodal vital signs extraction using neck-worn wearable devices
- Author
-
Rawan S. Abdulsadig and Esther Rodriguez-Villegas
- Subjects
Vital signs ,Wearable devices ,Neck ,Photoplethysmography (PPG) ,Accelerometer (Acc) ,Exponentially weighted moving average (EWMA) ,Medicine ,Science - Abstract
Abstract Pulse rate (PR) and respiratory rate (RR) are two of the most important vital signs. Monitoring them would benefit from easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems. The neck, although highly susceptible to artifacts, presents an attractive location for a diverse pool of physiological biomarkers monitoring purposes such as airflow sensing in a non-obstructive manner. This paper presents a methodology for PR and RR estimation using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck. Neck PPG and Acc signals were recorded from 22 healthy participants for RR estimation, where the resting subjects performed guided breathing following a visual metronome. Neck PPG signals were obtained from 16 healthy participants who breathed through an altitude generator machine in order to acquire a wider range of PR readings while at rest. The proposed methodology was able to provide rate estimates via a combination of recursive FFT-based dominance scoring coupled with an exponentially weighted moving average (EWMA)-driven aggregation scheme. The recursion aimed at bypassing sudden intra-window amplitude deviations caused by momentary artifacts, while the EWMA-based aggregation was utilized for handling inter-window artifact-induced deviations. To further improve estimation stability and confidence, estimates were calculated in the form of rate bands taking into account the relevant clinically acceptable error margins, and results when considering rate values and rate bands are presented and discussed. The framework was able to achieve an overall pulse rate value accuracy of $$93.67\pm 7.64$$ 93.67 ± 7.64 % within the clinically acceptable ± 5 BPM with reference to the gold-standard reference devices while providing an overall respiratory rate value accuracy within the clinically appropriate ± 3 BrPM of $$94.94\pm 3.56$$ 94.94 ± 3.56 % with reference to the guiding visual metronome, and $$88.4\pm 7.63$$ 88.4 ± 7.63 % with respect to the gold-standard reference device. The proposed methodology achieves acceptable PR and RR estimation capabilities, even when signals are acquired from an unusual location such as the neck. This work introduces novel ideas that can lead to the development of medical device outputs for PR and RR monitoring, especially capitalizing on the advantages of the neck as a multi-modal physiological monitoring location.
- Published
- 2024
- Full Text
- View/download PDF
12. Echo State Network-Based Estimation of Photoplethysmography Sensor-To-Skin Contact Force.
- Author
-
SZUMILAS, M. and WIELEMBOREK, M.
- Subjects
- *
STANDARD deviations , *PHOTOPLETHYSMOGRAPHY , *SIGNALS & signaling - Abstract
A photoplethysmographic signal, widely used in cardiovascular monitoring, is susceptible to the sensor's mounting conditions, including the contact force at the sensor-to-skin interface. We aimed to extract this concomitant parameter from a reflective photoplethysmographic signal to enable better observation of varying measurement conditions. Evaluation of a regressor based on an echo state network yields promising results when modeling the relationship between a reference force signal delivered from a force-sensitive resistor and the infrared and red photoplethysmographic signal components with an average normalized root mean square error of 0.101 (range of 0.051-0.150) for the considered test cases. The echo state network regressors using as few as 10 neurons show potential for deployment and online adaptation in resource-constrained hardware, e.g., microcontrollers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
13. Photoplethysmography-based atrial fibrillation detection in patients after crytpogenic stroke
- Author
-
Marthe J. Huntelaar, Jasper L. Selder, Luuk H. G. A. Hopman, Marieke C. Visser, and Cornelis P. Allaart
- Subjects
cryptogenic stroke ,atrial fibrillation ,photoplethysmography (PPG) ,Holter monitoring ,screening for AF ,Medicine - Abstract
IntroductionUndiagnosed atrial fibrillation (AF) is a potential underlying cause of cryptogenic stroke. Prolonged screening for AF using a photoplethysmography (PPG) smartwatch might offer a solution for detecting AF in patients with cryptogenic stroke. In this study, we aim to investigate this strategy by comparing AF detection rates using a PPG-smartwatch and 48 h Holter monitor.MethodsFrom December 2019, patients with cryptogenic stroke were included to undergo 28 days of semi-continuous AF monitoring using a Fitbit smartwatch with a PPG-based FibriCheck algorithm, with simultaneous Holter monitoring during the first 48 h. From April 2021, a detailed screening log was installed to characterize potential study participants.ResultsAfter logged screening of 1,312 patients, enrollment was prematurely halted due to slower-than-expected inclusion rates. 40.8% of the screened patients had cryptogenic stroke, of which 92.5% were non-eligible for inclusion due to logistical, technological, and study-related challenges. Of the 43 patients enrolled, 37 completed PPG monitoring using a smartwatch. 43% of patients had PPG-detected AF in the 28 days after cryptogenic stroke. During the first 48 h, PPG-based screening detected AF in 2 patients, whereas no AF was detected using concurrent Holter monitoring.ConclusionThe PPG-smartwatch detected AF in 43% of the participants after cryptogenic stroke. However, discrepancies with concurrent Holter monitoring raise major concerns about the accuracy of the detected PPG-based AF. Moreover, the feasibility of a PPG-based screening strategy is limited due to logistical and technological challenges, partly inherent to cryptogenic stroke patients.
- Published
- 2024
- Full Text
- View/download PDF
14. A Dataflow Framework for Cardiovascular Assessment Using Smartphones
- Author
-
Radivojević, Dušan, Stanarčević, Jelena, Mirkov, Nikola, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mitrovic, Nenad, editor, Mladenovic, Goran, editor, and Mitrovic, Aleksandra, editor
- Published
- 2024
- Full Text
- View/download PDF
15. From Screening at Clinic to Diagnosis at Home: How AI/ML/DL Algorithms Are Transforming Sleep Apnea Detection
- Author
-
Lee, Pei-Lin, Gu, Wenbo, Huang, Wen-Chi, Chiang, Ambrose A., Pardalos, Panos M., Series Editor, Thai, My T., Series Editor, Du, Ding-Zhu, Honorary Editor, Belavkin, Roman V., Advisory Editor, Birge, John R., Advisory Editor, Butenko, Sergiy, Advisory Editor, Kumar, Vipin, Advisory Editor, Nagurney, Anna, Advisory Editor, Pei, Jun, Advisory Editor, Prokopyev, Oleg, Advisory Editor, Rebennack, Steffen, Advisory Editor, Resende, Mauricio, Advisory Editor, Terlaky, Tamás, Advisory Editor, Vu, Van, Advisory Editor, Vrahatis, Michael N., Advisory Editor, Xue, Guoliang, Advisory Editor, Ye, Yinyu, Advisory Editor, Berry, Richard B., editor, and Xian, Xiaochen, editor
- Published
- 2024
- Full Text
- View/download PDF
16. AI-Based Prediction for Glucose Levels: A Comparative Study of Machine Learning and Deep Learning Approaches
- Author
-
Othmane, Amani, Youkana, Imane, Kahloul, Laid, Bourekkache, Samir, Luo, Xun, Editor-in-Chief, Almohammedi, Akram A., Series Editor, Chen, Chi-Hua, Series Editor, Guan, Steven, Series Editor, Pamucar, Dragan, Series Editor, Kerrache, Chaker Abdelaziz, editor, Tahari, Abdou El Karim, editor, Kassimi, Dounya, editor, and Chakraborty, Chinmay, editor
- Published
- 2024
- Full Text
- View/download PDF
17. Comparison of Different Methods for Estimation of Arterial Blood Pressure Using PPG Signals
- Author
-
Mladenovska, Teodora, Ackovska, Nevena, Kostoska, Magdalena, Koteska, Bojana, Dineva, Katarina Trojachanec, Bogdanova, Ana Madevska, Chlamtac, Imrich, Series Editor, Gül, Ömer Melih, editor, Fiorini, Paolo, editor, and Kadry, Seifedine Nimer, editor
- Published
- 2024
- Full Text
- View/download PDF
18. Bayesian Optimization-Based CNN Model for Blood Glucose Estimation Using Photoplethysmography Signals
- Author
-
Alghlayini, Saifeddin, Al-Betar, Mohammed Azmi, Atef, Mohamed, Al-Naymat, Ghazi, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Daimi, Kevin, editor, and Al Sadoon, Abeer, editor
- Published
- 2024
- Full Text
- View/download PDF
19. SmartSecur: Integrating an Empatica Watch to Enhance Patient Physical Security
- Author
-
Bordeaux, Kyle, Manning, James, Noonan, Aidan, Azab, Mohamed, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Stephanidis, Constantine, editor, Antona, Margherita, editor, Ntoa, Stavroula, editor, and Salvendy, Gavriel, editor
- Published
- 2024
- Full Text
- View/download PDF
20. A non-invasive heart rate prediction method using a convolutional approach
- Author
-
Karapinar, Ercument and Sevinc, Ender
- Published
- 2024
- Full Text
- View/download PDF
21. UNet-BiLSTM: A Deep Learning Method for Reconstructing Electrocardiography from Photoplethysmography.
- Author
-
Guo, Yanke, Tang, Qunfeng, Chen, Zhencheng, and Li, Shiyong
- Subjects
ARTIFICIAL neural networks ,PHOTOPLETHYSMOGRAPHY ,STANDARD deviations ,ELECTROCARDIOGRAPHY ,ROOT-mean-squares ,DEEP learning ,MEDICAL examinations of athletes - Abstract
Electrocardiography (ECG) is generally used in clinical practice for cardiovascular diagnosis and for monitoring cardiovascular status. It is considered to be the gold standard for diagnosing cardiovascular diseases and assessing cardiovascular status. However, it is not always easy to obtain. Unlike ECG devices, photoplethysmography (PPG) devices can be placed on body parts such as the earlobes, fingertips, and wrists, making them more comfortable and easier to obtain. Several methods for reconstructing ECG signals using PPG signals have been proposed, but some of these methods are subject-specific models. These models cannot be applied to multiple subjects and have limitations. This study proposes a neural network model based on UNet and bidirectional long short-term memory (BiLSTM) networks as a group model for reconstructing ECG from PPG. The model was verified using 125 records from the MIMIC III matched subset. The experimental results demonstrated that the proposed model was, on average, able to achieve a Pearson's correlation coefficient, root mean square error, percentage root mean square difference, and Fréchet distance of 0.861, 0.077, 5.302, and 0.278, respectively. This research can use the correlation between PPG and ECG to reconstruct a better ECG signal from PPG, which is crucial for diagnosing cardiovascular diseases. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements.
- Author
-
Volpes, Gabriele, Valenti, Simone, Genova, Giuseppe, Barà, Chiara, Parisi, Antonino, Faes, Luca, Busacca, Alessandro, and Pernice, Riccardo
- Subjects
GALVANIC skin response ,PATIENT monitoring ,PHYSIOLOGICAL stress ,PSYCHOLOGICAL stress ,OXYGEN saturation - Abstract
Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO
2 ), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
23. Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks.
- Author
-
Omer, Osama A., Salah, Mostafa, Hassan, Ammar M., Abdel-Nasser, Mohamed, Sugita, Norihiro, and Saijo, Yoshifumi
- Subjects
- *
BLOOD pressure , *MYOCARDIAL infarction , *STROKE patients , *DETECTORS , *ARTERIAL pressure - Abstract
One of the most significant indicators of heart and cardiovascular health is blood pressure (BP). Blood pressure (BP) has gained great attention in the last decade. Uncontrolled high blood pressure increases the risk of serious health problems, including heart attack and stroke. Recently, machine/deep learning has been leveraged for learning a BP from photoplethysmography (PPG) signals. Hence, continuous BP monitoring can be introduced, based on simple wearable contact sensors or even remotely sensed from a proper camera away from the clinical setup. However, the available training dataset imposes many limitations besides the other difficulties related to the PPG time series as high-dimensional data. This work presents beat-by-beat continuous PPG-based BP monitoring while accounting for the aforementioned limitations. For a better exploration of beats' features, we propose to use wavelet scattering transform as a better descriptive domain to cope with the limitation of the training dataset and to help the deep learning network accurately learn the relationship between the morphological shapes of PPG beats and the BP. A long short-term memory (LSTM) network is utilized to demonstrate the superiority of the wavelet scattering transform over other domains. The learning scenarios are carried out on a beat basis where the input corresponding PPG beat is used for predicting BP in two scenarios; (1) Beat-by-beat arterial blood pressure (ABP) estimation, and (2) Beat-by-beat estimation of the systolic and diastolic blood pressure values. Different transformations are used to extract the features of the PPG beats in different domains including time, discrete cosine transform (DCT), discrete wavelet transform (DWT), and wavelet scattering transform (WST) domains. The simulation results show that using the WST domain outperforms the other domains in the sense of root mean square error (RMSE) and mean absolute error (MAE) for both of the suggested two scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. An Efficient Blood Pressure Estimation and Risk Analysis System of PPG Signals Using IDA and MPPIW-DLNN Algorithms.
- Author
-
Thakkar, Priyanka Bibay and Talwekar, R. H.
- Subjects
- *
DIASTOLIC blood pressure , *SYSTOLIC blood pressure , *RISK assessment , *IMAGE denoising , *ALGORITHMS , *BLOOD pressure , *HUMAN activity recognition - Abstract
The non-invasive Blood Pressure Estimation (BPE) utilizing the technology of photoplethysmography (PPG) gains significant interest because PPG could be extensively employed to wearable sensors. Here, a method for estimating Systolic Blood pressure (SBP), as well as Diastolic Blood pressure (DBP), grounded only on a PPG signal utilizing the Image Denoising Algorithms (IDA) algorithms is proposed. Also, a classification methodology to execute the risk analysis (RA) of the BP patients utilizing Moore–Penrose Pseudo-Inverse Matrix-Deep Learning Neural Network (MPPIW-DLNN) is proposed. The preprocessing is then done on the input PPG signal utilizing the Modified–Chebyshev Filter (CF) to eradicate the unwanted information existent in the signal. Afterward, the BPE is done utilizing IDA, which categorizes those components into (i) SBP and (ii) DBP. The MPPIW-DLNN provides the results of four sorts of risk classes like (i) stroke, (ii) heart failure (HF), (iii) heart attack (HA), and (iv) aneurysm identified from the inputted PPG signal. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. A simplified PPG based approach for automated recognition of five distinct emotional states.
- Author
-
Paul, Avishek, Chakraborty, Abhishek, Sadhukhan, Deboleena, Pal, Saurabh, and Mitra, Madhuchhanda
- Abstract
Emotion is a complicated state of mind, which normally reflects human perceptions and attitudes. Proper recognition of emotional states and its quality plays crucial role for the detection of critical diseases and subsequent treatment procedures. Generally, multi-lead, complicated Electroencephalogram (EEG) based analysis predominate the characterization of emotion detection. Nowadays, user-friendly, rich-cardiac-information and wearable characteristics of the photoplethysmogram (PPG) signal are also being used to identify the emotional states. However, a majority of the reported emotion detection techniques mostly uses PPG signal in multimodality approach. In this paper, a simple methodology is proposed to identify multiple emotional states via the analysis of the PPG signal alone. Normally, emotion induced alteration in the heart rate causes variation in the blood ejection rate and a subsequent deviation in the balance of the systolic and the diastolic phases. Consequently, a specific time-domain characteristic is identified to quantify such imbalance and its variability is then used as a feature to discriminate between the five most prominent emotional states via a threshold-based classification technique. The algorithm presents superior performance while evaluated on the PPG data collected from the standard DEAP dataset with an average detection accuracy of 97.78%. Compared to existing literatures, the superior results establish the effectiveness of the proposed algorithm for the detection of multiple emotional states using PPG signal only. Moreover, the use of a single PPG feature and the application of a simple threshold-based classification technique also justify its promises for implementation in real-life, healthcare applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Respiratory Rate Estimation on Embedded System.
- Author
-
Morales, Isabel, Martinez-Hornak, Leonardo, Solari, Alfredo, and Oreggioni, Julian
- Abstract
We present the design, implementation, and results of an algorithm for respiratory rate (RR) estimation using respiratory-induced frequency, intensity, and amplitude variation calculated from the infrared (IR) channel of the SEN-15219 board for photoplethysmography (PPG) acquisition. First, the algorithm was developed in Python (on a PC) using synthetic signals and publicly available respiration and PPG data. We also include a graphical user interface to process data from sensors and display vital signs. Later, we ported the algorithm to an MSP432P401R microcontroller to complete our wearable prototype. Results are promissory and show that RR estimation can be performed on the selected platform with our proposed Fourier Product (FP) method, which results in a Mean Absolute Error of 4.1 using 16-s windows of IR-PPG signals. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical studyResearch in context
- Author
-
Stefan Karolcik, Vasileos Manginas, Ho Quang Chanh, John Daniels, Nguyen Thi Giang, Vu Ngo Thanh Huyen, Minh Tu Van Hoang, Khanh Phan Nguyen Quoc, Bernard Hernandez, Damien K. Ming, Hao Nguyen Van, Tu Qui Phan, Huynh Trung Trieu, Tai Luong Thi Hue, Alison H. Holmes, Louise Thwaites, Tho Phan Vinh, Sophie Yacoub, and Pantelis Georgiou
- Subjects
Dengue ,Photoplethysmography (PPG) ,Deep learning ,Medicine ,Medicine (General) ,R5-920 - Abstract
Summary: Background: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. Methods: We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14–21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. Findings: We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21–35) and 12 (IQR, 9–13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. Interpretation: We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. Funding: EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).
- Published
- 2024
- Full Text
- View/download PDF
28. GRU-Based Fusion Models for Enhanced Blood Pressure Estimation From PPG Signals
- Author
-
Syamsul Rizal and Yuniarti Ana Rahma
- Subjects
Blood pressure ,deep learning ,gated recurrent units (GRU) ,neural networks ,photoplethysmography (PPG) ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The current study presents a novel, non-invasive method for estimating both systolic and diastolic blood pressure by combining photoplethysmogram (PPG) signals with physiological data, such as sex, age, weight, height, heart rate, and BMI, using two Gated Recurrent Units (GRUs) models. The first model processes dynamic patterns in PPG signals, while the second model incorporates physiological parameters. Both models are connected through a series of dense layers. To prepare the datasets for the GRU framework, rigorous preprocessing was conducted. This resulted in a robust architecture capable of accurately predicting systolic and diastolic blood pressure. The proposed method achieved a Mean Absolute Error (MAE) of 1.458 for systolic and 1.164 for diastolic blood pressure. These findings demonstrate the potential of this approach for continual and non-intrusive blood pressure monitoring in wearable health technology. The study’s results also make a significant contribution to the field of medical monitoring technology. The proposed solution addresses a major limitation in traditional blood pressure measurement practices and paves the way for advancements in personalized health monitoring, particularly for managing hypertension and cardiovascular conditions.
- Published
- 2024
- Full Text
- View/download PDF
29. Autonomous Calibration of Blood Pressure Dependent Data Using Second-Order Blood Pressure Variation for a Future Mobile Diagnostic: Requirements for a Calibration
- Author
-
Martin Deutges and Holger Redtel
- Subjects
Blood pressure ,calibration ,cuff pressure simulation ,oscillometric blood pressure measurement ,photoplethysmography (PPG) ,pulse transit time (PTT) ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Currently, blood pressure assessment is on the verge of shifting from classical cuff-based measurements to continuous estimation using smart devices based on the absorption and reflection characteristics of light in tissue. This type of blood pressure estimation has been known for a long time and depends on calibration with conventional blood pressure measurement systems. Products from well-known manufacturers in this market already perform calibration with automatic cuffs using an oscillometric estimation. The main aim is to show that classical oscillometric blood pressure estimation is not suitable for calibration, which allows a burden-free and continuous estimation by today’s approaches. This study identifies the reason and also the solution for this in the second-order blood pressure variation. The current approach to oscillometric estimation of blood pressure has an uncorrectable systematic error of the order of the second-order blood pressure variation. Therefore the current method has too much error to be used as a basis for calibration to allow continuous estimation of blood pressure based on another vital parameter. It is shown that when using a measurement of the second-order blood pressure variation as basis for such calibration this problem can be solved.
- Published
- 2024
- Full Text
- View/download PDF
30. QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning
- Author
-
Md Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, Muhammad E. H. Chowdhury, Khalid Abualsaud, Elias Yaacoub, Md Ahasan Atick Faisal, Mazun Alshahwani, Noora Al Bordeni, Fatima Al-Kaabi, Sara Al-Mohannadi, Sakib Mahmud, and Nizar Zorba
- Subjects
Continuous glucose monitoring (CGM) ,Internet of Things (IoT) ,machine learning ,photoplethysmography (PPG) ,wearable device ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Patients with hyperglycemia require routine glucose monitoring to effectively treat their condition. We have developed a lightweight wristband device to capture Photoplethysmography (PPG) signals. We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. Blood glucose was estimated, and diabetic severity (normal, warning, and dangerous) was stratified using Mel frequency cepstral coefficients, time, frequency, and statistical features from PPG and their derivative signals along with physiological parameters. Bagged Ensemble Trees outperform other algorithms in estimating blood glucose level with a correlation coefficient of 0.90. The proposed model’s prediction was all in Zone A and B in the Clarke Error Grid analysis. The predictions are thus clinically acceptable. Furthermore, K-nearest neighbor model classified the severity levels with an accuracy of 98.12%. Furthermore, the proposed models were deployed in Amazon Web Server. The wristband is connected to an Android mobile application to collect real-time data and update the estimated glucose and diabetic severity every 10-seconds, which will allow the users to gain better control of their diabetic health.
- Published
- 2024
- Full Text
- View/download PDF
31. Muscle stimulation for peripheral venous oxygen saturation estimation using photoplethysmography: a proof-of-concept
- Author
-
Badiola Idoia, Lyu Chenglin, Ferchland Arne, Comes Fabian, Blazek Vladimir, Leonhardt Steffen, and Lueken Markus
- Subjects
functional electrical stimulation (fes) ,photoplethysmography (ppg) ,venous muscle pump test (vmpt) ,venous oxygen saturation (svo2) ,venous blood variations ,Medicine - Abstract
The body’s ability to balance oxygen supply and demand can be compromised in conditions such as shock, sepsis, and heart failure. Thus, measuring venous oxygen saturation (SvO2) simultaneously with the well-established peripheral arterial oxygen saturation can help in the clinical management of these conditions. Some authors have suggested a non-invasive SvO2 estimation method that acquires venous blood volume variations generated through the calf muscle pump using photoplethysmography (PPG): the Venous Muscle Pump Test (VMPT). However, the technique presents significant variability in the rhythm and speed of the foot dorsal flexions needed for the VMPT and cannot be performed on unconscious subjects and those with reduced mobility. This study proposes using functional electrical stimulation (FES) to stimulate the calf muscle and generate rhythmic and reproducible muscle contractions. A human proof-of-concept study was conducted with three healthy young male participants. The PPG signals achieved through the VMPT with conventionally active and FES-induced movements were compared. We found that FES-induced movement produced reproducible venous blood volume variations comparable to the ones induced by the active movement. However, it also leads to lower venous refilling time and lower muscle power. Although further individualized tuning of the stimulation parameters is needed to achieve more conclusive results, FES-induced movement proves to be a promising alternative to the conventional VMPT technique to measure venous oxygen saturation and assess venous insufficiency in specific clinical situations.
- Published
- 2023
- Full Text
- View/download PDF
32. Enhanced premature ventricular contraction pulse detection and classification using deep convolutional neural network.
- Author
-
Raj, Remya, kumar, Ushus S, and Maik, Vivek
- Abstract
Access to accurate and precise monitoring systems for cardiac arrhythmia could contribute significantly to preventing damage and subsequent heart disorders. The present research concentrates on using photoplethysmography (PPG) and arterial blood pressure (ABP) with deep convolutional neural networks (CNN) for the classification and detection of fetal cardiac arrhythmia or premature ventricular contractions (PMVCs). The framework for the study entails (Icentia 11k) a public dataset of ECG signals consisting of different cardiac abnormalities. Following this, the weights obtained from the Icentia 11k dataset are transferred to the proposed CNN. Finally, fine-tuning was carried out to improve the accuracy of classification. Results obtained showcase the capacity of the proposed method to detect and classify PMVCs into three types: Normal, P1, and P2 with an accuracy of 99.9%, 99.8%, and 99.5%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Analysis of vital signs using remote photoplethysmography (RPPG).
- Author
-
Karthick, R., Dawood, M. Sheik, and Meenalochini, P.
- Abstract
In health care applications, an evolution of electronics has made drastic advancements. There are some problems created due to this advancement. To estimate the coronary heart rate, till date some problems have been confronted. To overcome these issues, remote photoplethysmography (RPPG) technology is used to determine the heart rate (HR) and respiratory rate (RR) by using normal web cameras, without any additional hardware. Here, a high resolution camera detects the face using a face detector by means of image processing techniques. Hardware part is only used to display the heart rate and respiratory rate using sensors. The performance analysis demonstrates the practicality of the patients. Experimental results of heart rate measurement show that the proposed dynamic ROI method for RIPPG can effectively improve the RIPPG signal quality, compared with the state-of-the-art ROI methods for RIPPG. Objective performance tests show strong correlation with the ground truth values for the estimated heart rate and variation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Computer Vision-Based Contactless Cardiac Pulse Estimation
- Author
-
Turuk, Mousami, Sreemathy, R., Shinde, Shantanu, Naik, Sujay, Khandekar, Shardul, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Shukla, Praveen Kumar, editor, Mittal, Himanshu, editor, and Engelbrecht, Andries, editor
- Published
- 2023
- Full Text
- View/download PDF
35. A Headphone-Based Heart Rate and Heart Rate Variability Monitoring Unit
- Author
-
Hailu, Gashaye Lewtie, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Woldegiorgis, Bereket H., editor, Mequanint, Kibret, editor, Bitew, Mekuanint A., editor, Beza, Teketay B., editor, and Yibre, Abdulkerim M., editor
- Published
- 2023
- Full Text
- View/download PDF
36. Effects of Measurement Site on Heart Rate Variability Derived from Photoplethysmography
- Author
-
Sarkar, Somen, Pahuja, S. K., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tuba, Milan, editor, Akashe, Shyam, editor, and Joshi, Amit, editor
- Published
- 2023
- Full Text
- View/download PDF
37. A Review on Multiparameter Sensor Design for Biomedical SoC Applications
- Author
-
Kulkarni, Sahana M., Jamuna, S., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Ranganathan, G., editor, Fernando, Xavier, editor, and Piramuthu, Selwyn, editor
- Published
- 2023
- Full Text
- View/download PDF
38. Technologies for non-invasive physiological sensing: Status, challenges, and future horizons
- Author
-
Yang Yu, Bhavya Jain, Gautam Anand, Mahdi Heidarian, Andrew Lowe, and Anubha Kalra
- Subjects
Non-invasive diagnostic technique ,Photoplethysmography (PPG) ,Electroencephalography (EEG) ,Electromyography (EMG) ,Electrocardiography (ECG) ,Computed tomography (CT) ,Biotechnology ,TP248.13-248.65 - Abstract
Non-invasive techniques have become increasingly vital in modern medicine, providing valuable diagnostic information without invasive procedures. These techniques encompass a diverse range of procedures, including imaging scans, blood tests, urine tests, and genetic testing, enabling the investigation of various conditions without device insertion. In contrast to conventional invasive methods, non-invasive diagnostics have numerous advantages, including reduced complications, shorter recovery times, improved patient comfort, and lower costs. As a result, the exploration of alternative diagnostic approaches has become imperative. This article provides a comprehensive overview of advances, challenges, and opportunities in the realm of non-invasive diagnostic techniques. It delves into a detailed exploration of non-invasive techniques, including photoplethysmography (PPG), electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and electrical impedance tomography (EIT) discussing their origin, underlying principles, instrumentation, and applications in various medical fields. Furthermore, the advantages and limitations of various Surface Measurement and Imaging Modalities techniques are thoroughly compared and analysed. The article also addresses these challenges and highlights emerging technologies and methodologies that offer solutions. More importantly, we propose several promising directions for future research and development of non-invasive diagnostic techniques.
- Published
- 2024
- Full Text
- View/download PDF
39. A mixed attention-gated U-Net for continuous cuffless blood pressure estimation.
- Author
-
Zhong, Yiting, Chen, Yongyi, Zhang, Dan, Xu, Yanghui, and Karimi, Hamid Reza
- Abstract
Blood pressure (BP) is an important vital sign of the human body. The traditional cuff measurement methods are mainly intermittent, which cannot meet the clinical practice well. Continuous measurements of BP are of great significance for monitoring vital signs of patients. In order to achieve BP estimation in a continuous, cuffless and non-invasive way, this paper proposes a BP estimation model based on mixed attention gating U-Net (MAGU), which can effectively improve the accuracy and efficiency of BP estimation. The photoplethysmography signal is fed into the improved U-Net to extract features. A mixed attention gating mechanism is added between up-sampling and down-sampling, as well as the residual blocks are added in down-sampling to prevent the vanishing gradient, so as to improve the feature extraction efficiency in the deep network. The performance of the MAGU is validated against those state-of-the-art results on the MIMIC-II public dataset. The mean absolute error and standard deviation of systolic blood pressure predicted by the proposed method are 3.49 mmHg and 4.13 mmHg respectively, and those of diastolic blood pressure (DBP) are 2.11 mmHg and 2.49 mmHg. The comparison shows that the proposed method outperforms those state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. Real-Time Stress Detection from Raw Noisy PPG Signals Using LSTM Model Leveraging TinyML
- Author
-
Rostami, Amin, Tarvirdizadeh, Bahram, Alipour, Khalil, and Ghamari, Mohammad
- Published
- 2024
- Full Text
- View/download PDF
41. Fatigue Estimation Using Peak Features from PPG Signals.
- Author
-
Chen, Yi-Xiang, Tseng, Chin-Kun, Kuo, Jung-Tsung, Wang, Chien-Jen, Chao, Shu-Hung, Kau, Lih-Jen, Hwang, Yuh-Shyan, and Lin, Chun-Ling
- Subjects
- *
PHOTOPLETHYSMOGRAPHY , *FATIGUE (Physiology) , *HEART beat , *STATISTICAL correlation , *MATERIAL fatigue - Abstract
Fatigue is a prevalent subjective sensation, affecting both office workers and a significant global population. In Taiwan alone, over 2.6 million individuals—around 30% of office workers—experience chronic fatigue. However, fatigue transcends workplaces, impacting people worldwide and potentially leading to health issues and accidents. Gaining insight into one's fatigue status over time empowers effective management and risk reduction associated with other ailments. Utilizing photoplethysmography (PPG) signals brings advantages due to their easy acquisition and physiological insights. This study crafts a specialized preprocessing and peak detection methodology for PPG signals. A novel fatigue index stems from PPG signals, focusing on the dicrotic peak's position. This index replaces subjective data from the brief fatigue index (BFI)-Taiwan questionnaire and heart rate variability (HRV) indices derived from PPG signals for assessing fatigue levels. Correlation analysis, involving sixteen healthy adults, highlights a robust correlation (R > 0.53) between the new fatigue index and specific BFI questions, gauging subjective fatigue over the last 24 h. Drawing from these insights, the study computes an average of the identified questions to formulate the evaluated fatigue score, utilizing the newfound fatigue index. The implementation of linear regression establishes a robust fatigue assessment system. The results reveal an impressive 91% correlation coefficient between projected fatigue levels and subjective fatigue experiences. This underscores the remarkable accuracy of the proposed fatigue prediction in evaluating subjective fatigue. This study further operationalized the proposed PPG processing, peak detection method, and fatigue index using C# in a computer environment alongside a PPG device, thereby offering real-time fatigue indices to users. Timely reminders are employed to prompt users to take notice when their index exceeds a predefined threshold, fostering greater attention to their physical well-being. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
42. Filtering-induced changes of pulse transmit time across different ages: a neglected concern in photoplethysmography-based cuffless blood pressure measurement.
- Author
-
Shangdi Liao, Haipeng Liu, Wan-Hua Lin, Dingchang Zheng, and Fei Chen
- Subjects
BLOOD pressure measurement ,PULSE wave analysis ,RAYLEIGH waves ,IMPULSE response ,AGE groups - Abstract
Background: Pulse transit time (PTT) is a key parameter in cuffless blood pressure measurement based on photoplethysmography (PPG) signals. In wearable PPG sensors, raw PPG signals are filtered, which can change the timing of PPG waveform feature points, leading to inaccurate PTT estimation. There is a lack of comprehensive investigation of filtering-induced PTT changes in subjects with different ages. Objective: This study aimed to quantitatively investigate the effects of aging and PTT definition on the infinite impulse response (IIR) filtering-induced PTT changes. Methods: One hundred healthy subjects in five different ranges of age (i.e., 20–29, 30–39, 40–49, 50–59, and over 60 years old, 20 subjects in each) were recruited. Electrocardiogram (ECG) and PPG signals were recorded simultaneously for 120 s. PTT was calculated from the R wave of ECG and PPG waveform features. Eight PTT definitions were developed from different PPG waveform feature points. The raw PPG signals were preprocessed then further low-pass filtered. The difference between PTTs derived from preprocessed and filtered PPG signals, and the relative difference, were calculated and compared among five age groups and eight PTT definitions using the analysis of variance (ANOVA) or Scheirer–Ray–Hare test with post hoc analysis. Linear regression analysis was used to investigate the relationship between age and filtering-induced PTT changes. Results: Filtering-induced PTT difference and the relative difference were significantly influenced by age and PTT definition (p < 0.001 for both). Aging effect on filtering-induced PTT changes was consecutive with a monotonous trend under all PTT definitions. The age groups with maximum and minimum filtering-induced PTT changes depended on the definition. In all subjects, the PTT defined by maximum peak of PPG had the minimum filtering-induced PTT changes (mean: 16.16 ms and 5.65% for PTT difference and relative difference). The changes of PTT defined by maximum first PPG derivative had the strongest linear relationship with age (R-squared: 0.47 and 0.46 for PTT difference relative difference). Conclusion: The filtering-induced PTT changes are significantly influenced by age and PTT definition. These factors deserve further consideration to improve the accuracy of PPG-based cuffless blood pressure measurement using wearable sensors. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
43. PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM.
- Author
-
Mahardika T, Nurul Qashri, Fuadah, Yunendah Nur, Jeong, Da Un, and Lim, Ki Moo
- Subjects
- *
DIASTOLIC blood pressure , *SYSTOLIC blood pressure , *CONVOLUTIONAL neural networks , *BLOOD pressure , *ELECTRONICS engineers - Abstract
Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients' health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN–LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN–LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN–LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Robust RPPG Method Based on Reference Signal Envelope to Improve Wave Morphology.
- Author
-
Sun, Lu, Wang, Liting, Shen, Wentao, Liu, Changsong, and Bai, Fengshan
- Subjects
PATIENT monitoring ,MORPHOLOGY ,ANALYSIS of variance ,PHOTOPLETHYSMOGRAPHY ,LIGHT intensity - Abstract
Remote physiological monitoring has become increasingly important in improving quality of life, with remote photoplethysmography (RPPG) being a popular choice. This paper introduces an envelope–based method for RPPG channels to improve wave morphology of the collected signal based on the reference signal from finger PPG. Using a model consistent with physiological and optical principles, the authors divided the signal into linear superpositions, comprising pulse, constant, and disturbance components. The correlation coefficients were used to calculate a linear combination of Red–Green–Blue (RGB) channels to approximate the envelope shape of the reference PPG signal. Experiments with different light intensities and stability were designed to compare the envelope approximation ability and robustness of the proposed method with some common methods. Analysis of variance demonstrated the stable performance of the envelopment–based approach in most cases. Additionally, it improved the morphology of the Green (G) channel, including changing trends and directions, adjusting wave sizes, reducing noise, and reinforcing details of the single waveform. The envelope–based linear model approach has the ability to flexibly improve RPPG signals, which helps RPPG play a full role in many fields such as medicine. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
45. Photoplethysmography upon cold stress—impact of measurement site and acquisition mode.
- Author
-
Fleischhauer, Vincent, Bruhn, Jan, Rasche, Stefan, and Zaunseder, Sebastian
- Subjects
PHOTOPLETHYSMOGRAPHY ,PHYSIOLOGY ,BLOOD pressure ,PULSE wave analysis ,HEART beat - Abstract
Photoplethysmography (PPG) allows various statements about the physiological state. It supports multiple recording setups, i.e., application to various body sites and different acquisition modes, rendering the technique a versatile tool for various situations. Owing to anatomical, physiological and metrological factors, PPG signals differ with the actual setup. Research on such differences can deepen the understanding of prevailing physiological mechanisms and path the way towards improved or novel methods for PPG analysis. The presented work systematically investigates the impact of the cold pressor test (CPT), i.e., a painful stimulus, on the morphology of PPG signals considering different recording setups. Our investigation compares contact PPG recorded at the finger, contact PPG recorded at the earlobe and imaging PPG (iPPG), i.e., non-contact PPG, recorded at the face. The study bases on own experimental data from 39 healthy volunteers. We derived for each recording setup four common morphological PPG features from three intervals around CPT. For the same intervals, we derived blood pressure and heart rate as reference. To assess differences between the intervals, we used repeated measures ANOVA together with paired t-tests for each feature and we calculated Hedges’ g to quantify effect sizes. Our analyses show a distinct impact of CPT. As expected, blood pressure shows a highly significant and persistent increase. Independently of the recording setup, all PPG features show significant changes upon CPT as well. However, there are marked differences between recording setups. Effect sizes generally differ with the finger PPG showing the strongest response. Moreover, one feature (pulse width at half amplitude) shows an inverse behavior in finger PPG and head PPG (earlobe PPG and iPPG). In addition, iPPG features behave partially different from contact PPG features as they tend to return to baseline values while contact PPG features remain altered. Our findings underline the importance of recording setup and physiological as well as metrological differences that relate to the setups. The actual setup must be considered in order to properly interpret features and use PPG. The existence of differences between recording setups and a deepened knowledge on such differences might open up novel diagnostic methods in the future. Photoplethysmography (PPG) allows various statements about the physiological state. It supports multiple recording setups, i.e., application to various body sites and different acquisition modes, rendering the technique a versatile tool for various situations. Owing to anatomical, physiological and metrological factors, PPG signals differ with the actual setup. Research on such differences can deepen the understanding of prevailing physiological mechanisms and path the way towards improved or novel methods for PPG analysis. The presented work systematically investigates the impact of the cold pressor test (CPT), i.e., a painful stimulus, on the morphology of PPG signals considering different recording setups. Our investigation compares contact PPG recorded at the finger, contact PPG recorded at the earlobe and imaging PPG (iPPG), i.e., non-contact PPG, recorded at the face. The study bases on own experimental data from 39 healthy volunteers. We derived for each recording setup four common morphological PPG features from three intervals around CPT. For the same intervals, we derived blood pressure and heart rate as reference. To assess differences between the intervals, we used repeated measures ANOVA together with paired t-tests for each feature and we calculated Hedges’ g to quantify effect sizes. Our analyses show a distinct impact of CPT. As expected, blood pressure shows a highly significant and persistent increase. Independently of the recording setup, all PPG features show significant changes upon CPT as well. However, there are marked differences between recording setups. Effect sizes generally differ with the finger PPG showing the strongest response. Moreover, one feature (pulse width at half amplitude) shows an inverse behavior in finger PPG and head PPG (earlobe PPG and iPPG). In addition, iPPG features behave partially different from contact PPG features as they tend to return to baseline values while contact PPG features remain altered. Our findings underline the importance of recording setup and physiological as well as metrological differences that relate to the setups. The actual setup must be considered in order to properly interpret features and use PPG. The existence of differences between recording setups and a deepened knowledge on such differences might open up novel diagnostic methods in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
46. Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements
- Author
-
Gabriele Volpes, Simone Valenti, Giuseppe Genova, Chiara Barà, Antonino Parisi, Luca Faes, Alessandro Busacca, and Riccardo Pernice
- Subjects
wearable health devices (WHDs) ,electrocardiography (ECG) ,photoplethysmography (PPG) ,galvanic skin response (GSR) ,oxygen saturation (SpO2) ,pulse arrival time (PAT) ,Biotechnology ,TP248.13-248.65 - Abstract
Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals’ physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.
- Published
- 2024
- Full Text
- View/download PDF
47. Testing the Efficacy of Various Artificial Neural Network for Total Haemoglobin Estimation
- Author
-
Pinto, Caje F., Parab, Jivan S., Sequeira, Marlon D., Naik, Gourish M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Chen, Joy Iong-Zong, editor, Tavares, João Manuel R. S., editor, Iliyasu, Abdullah M., editor, and Du, Ke-Lin, editor
- Published
- 2022
- Full Text
- View/download PDF
48. Effective IoT Based Analysis of Photoplethysmography Waveforms for Investigating Arterial Stiffness and Pulse Rate Variability
- Author
-
Sankranti, Srinivasa Rao, Basha, S. Mahaboob, Kantha, B. Laxmi, Bhagyalakshmi, L., Gomathi, N., Kumar, Kuchipudi Prasanth, and Suman, Sanjay Kumar
- Published
- 2024
- Full Text
- View/download PDF
49. Photoplethysmography upon cold stress—impact of measurement site and acquisition mode
- Author
-
Vincent Fleischhauer, Jan Bruhn, Stefan Rasche, and Sebastian Zaunseder
- Subjects
imaging photoplethysmography (iPPG) ,cold pressor test (CPT) ,pulse wave analysis (PWA) ,blood pressure ,photoplethysmography (PPG) ,Physiology ,QP1-981 - Abstract
Photoplethysmography (PPG) allows various statements about the physiological state. It supports multiple recording setups, i.e., application to various body sites and different acquisition modes, rendering the technique a versatile tool for various situations. Owing to anatomical, physiological and metrological factors, PPG signals differ with the actual setup. Research on such differences can deepen the understanding of prevailing physiological mechanisms and path the way towards improved or novel methods for PPG analysis. The presented work systematically investigates the impact of the cold pressor test (CPT), i.e., a painful stimulus, on the morphology of PPG signals considering different recording setups. Our investigation compares contact PPG recorded at the finger, contact PPG recorded at the earlobe and imaging PPG (iPPG), i.e., non-contact PPG, recorded at the face. The study bases on own experimental data from 39 healthy volunteers. We derived for each recording setup four common morphological PPG features from three intervals around CPT. For the same intervals, we derived blood pressure and heart rate as reference. To assess differences between the intervals, we used repeated measures ANOVA together with paired t-tests for each feature and we calculated Hedges’ g to quantify effect sizes. Our analyses show a distinct impact of CPT. As expected, blood pressure shows a highly significant and persistent increase. Independently of the recording setup, all PPG features show significant changes upon CPT as well. However, there are marked differences between recording setups. Effect sizes generally differ with the finger PPG showing the strongest response. Moreover, one feature (pulse width at half amplitude) shows an inverse behavior in finger PPG and head PPG (earlobe PPG and iPPG). In addition, iPPG features behave partially different from contact PPG features as they tend to return to baseline values while contact PPG features remain altered. Our findings underline the importance of recording setup and physiological as well as metrological differences that relate to the setups. The actual setup must be considered in order to properly interpret features and use PPG. The existence of differences between recording setups and a deepened knowledge on such differences might open up novel diagnostic methods in the future.
- Published
- 2023
- Full Text
- View/download PDF
50. Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities.
- Author
-
Pineda-Alpizar, Felipe, Arriola-Valverde, Sergio, Vado-Chacón, Mitzy, Sossa-Rojas, Diego, Liu, Haipeng, and Zheng, Dingchang
- Subjects
- *
PHOTOPLETHYSMOGRAPHY , *COMMUNITIES , *HEART beat , *SITTING position , *RESPIRATION , *POSTURE - Abstract
Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer–Ray–Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.