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An Efficient Fingertip Photoplethysmographic Signal Artifact Detection Method: A Machine Learning Approach

Tasbiraha Athaya, Sunwoong Choi, Antonio Martinez-Olmos
2021 Journal of Sensors  
Photoplethysmogram (PPG) signals are sensitive to artifacts that negatively impact the accuracy of many important measurements.  ...  The proposed real-time algorithm can be an easy and convenient way for real-time PPG signal artifact detection using smartphones and wearable devices.  ...  performance for PPG signal artifact detection using various machine learning models.  ... 
doi:10.1155/2021/9925033 fatcat:h5gpcvmufbbmphsmqqrnf6chya

Cuff-less Calibration-free Blood Pressure Estimation under Ambulatory Environment using Pulse Wave Velocity and Photoplethysmogram Signals

Haruyuki Sanuki, Rui Fukui, Tsukasa Inajima, Shin'ichi Warisawa
2017 Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies  
equation, and the inductive model based on machine learning.  ...  To accomplish our goal, we extracted properties of blood vessels from photoplethysmogram (PPG) signals, and compared several regression models, such as the deductive model based on blood vessel physics  ...  ACKNOWLEDGMENT This research was supported by Pacific Medico Co. for providing ECG and PPG measurement devices.  ... 
doi:10.5220/0006112500420048 dblp:conf/biostec/SanukiFIW17 fatcat:yqwqmc6b7zgwtlthbiourordqu

An Efficient Motion and Noise Artifacts Removal Method using GAIT and Machine Learning Model

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
For overcoming issues and problems, this manuscript presented a new approach for detection of artifacts.  ...  First, present an adaptive filter and adaptive threshold model to detect artifact and obtain derivative of correlation coefficient (CC) for labelling artifacts, respectively.  ...  Figure I. 1 1 Figure I.1 The architecture for artifact detection on PPG signal using machine learning model. a) SpO2 measurement, adaptive filter, and peak tracking model: depicts the amplitude, phase  ... 
doi:10.35940/ijitee.b6176.129219 fatcat:smbjdi4mwrgkdg6v7a36fpy4sy

Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment

Donggeun Roh, Hangsik Shin
2021 Sensors  
The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance  ...  Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21062188 pmid:33804794 pmcid:PMC8004064 fatcat:7whkkzd3grcjtbxu7bdn772ilq

VitaMon

Sinh Huynh, Rajesh Krishna Balan, JeongGil Ko, Youngki Lee
2019 Proceedings of the 17th Conference on Embedded Networked Sensor Systems - SenSys '19  
We evaluated VitaMon with a dataset collected from 30 participants under various conditions involving different light intensity levels and motion artifacts.  ...  VitaMon leverages deep neural network models to extract both spatial and temporal information of the video to reconstruct a pulse waveform signal that is optimized for estimating IBI.  ...  ACKNOWLEDGMENTS We thank the anonymous shepherd and reviewers for their insightful comments.  ... 
doi:10.1145/3356250.3360036 dblp:conf/sensys/HuynhBKL19 fatcat:44j7rckrcrf7zo65do2gggzkii

HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population [article]

Ariane Morassi Sasso, Suparno Datta, Michael Jeitler, Christian S. Kessler, Bert Arnrich, Erwin Boettinger
2020 medRxiv   pre-print
We then trained and compared machine learning (ML) models to predict BP.  ...  Moreover, in order to evaluate the models in a different scenario, we used an openly available set from a stress test with healthy subjects (EVAL).  ...  Wohlbrandt for giving many valuable insights.  ... 
doi:10.1101/2020.05.27.20107243 fatcat:dj7i5dh4kbf4lhd7guvax6h46e

Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Mert Sevil, Mudassir Rashid, Mohammad Reza Askari, Zacharie Maloney, Iman Hajizadeh, Ali Cinar
2020 Signals  
Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur  ...  individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE).  ...  The proposed work consists of signals processing, feature extraction, data preparation, machine learning algorithm development, and evaluation of results ( Figure 2 ).  ... 
doi:10.3390/signals1020011 fatcat:qa3czlng3jcq5olatniwlptjva

Lightweight Photoplethysmography Quality Assessment for Real-time IoT-based Health Monitoring using Unsupervised Anomaly Detection

Aysan Mahmoudzadeh, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg
2021 Procedia Computer Science  
The supervised machine learning-based methods -including deep learning-are also infeasible for real-time monitoring applications since they are slow and are dependent on a massive pool of annotated data  ...  The supervised machine learning-based methods -including deep learning-are also infeasible for real-time monitoring applications since they are slow and are dependent on a massive pool of annotated data  ...  In future work, we implement the proposed method in wearables and evaluate the delay in comparison to other rule-based and machine learning-based methods.  ... 
doi:10.1016/j.procs.2021.03.025 fatcat:al6vogkgo5fohf6dmh247uulm4

Robust Assessment of Photoplethysmogram Signal Quality in the Presence of Atrial Fibrillation

Tania Pereira, Kais Gadhoumi, Mitchell Ma, Rene Colorado, Kevin J Keenan, Karl Meisel, Xiao Hu
2018 2018 Computing in Cardiology Conference (CinC)  
A great deal of algorithms currently available to assess the quality of photoplethysmogram (PPG) signals is based on the similarity between pulses to derive signal quality indices.  ...  This arrhythmicity is reflected on PPG pulses by the presence of non-uniform pulses and poses challenges in the evaluation of the signal quality.  ...  Machine Learning Approach Features Extraction The signals were parametrized by 40 features in the following subsets.  ... 
doi:10.22489/cinc.2018.254 dblp:conf/cinc/PereiraGMCKMH18 fatcat:bg45vcacfbcdrgeb7orgukbz4q

Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks [article]

Sajad Mousavi, Atiyeh Fotoohinasab, Fatemeh Afghah
2019 arXiv   pre-print
The proposed method achieves a sensitivity of 93.88% and a specificity of 92.05% for the alarm classification, considering three different signals.  ...  Most of the current work in the literature are either rule-based methods, requiring prior knowledge of arrhythmia analysis to build rules, or classical machine learning approaches, depending on hand-engineered  ...  Then, several machine learning algorithms are evaluated using the extracted features to detect false alarms.  ... 
arXiv:1909.11791v1 fatcat:ijq424zi5vbg3iohp32o7wmb5a

Active learning for electrodermal activity classification

Victoria Xia, Natasha Jaques, Sara Taylor, Szymon Fedor, Rosalind Picard
2015 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)  
To filter noise or detect features within physiological signals, it is often effective to encode expert knowledge into a model such as a machine learning classifier.  ...  However, training such a model can require much effort on the part of the researcher; this often takes the form of manually labeling portions of signal needed to represent the concept being trained.  ...  ACKNOWLEDGMENT This work was supported by the MIT Media Lab Consortium, the Robert Wood Johnson Foundation, Canada's NSERC program, and the People Programme of the European Union's Seventh Framework Programme  ... 
doi:10.1109/spmb.2015.7405467 fatcat:acl4mwkxlzditcta6bypxieyty

False Alarm Reduction in BSN-Based Cardiac Monitoring Using Signal Quality and Activity Type Information

Tanatorn Tanantong, Ekawit Nantajeewarawat, Surapa Thiemjarus
2015 Sensors  
In the first phase, classification models constructed using machine learning algorithms are used for labeling input signals.  ...  For the BSN dataset, acceleration and ECG signals were collected from 10 young and 10 elderly subjects while they were performing ADLs.  ...  of this research project and paper editing; Surapa Thiemjarus: Methodology, framework design and evaluation, data analysis, and paper editing.  ... 
doi:10.3390/s150203952 pmid:25671512 pmcid:PMC4367394 fatcat:s25bqg2c2rcxnlmlmgvnzll7hq

Multiparameter Respiratory Rate Estimation From the Photoplethysmogram

Walter Karlen, S. Raman, J. M. Ansermino, G. A. Dumont
2013 IEEE Transactions on Biomedical Engineering  
It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations.  ...  We present a novel method for estimating respiratory rate in real-time from the photoplethysmogram (PPG) obtained from pulse oximetry.  ...  ACKNOWLEDGMENTS The authors would like to thank Joanne Lim for kindly helping to revise this manuscript and Erin Cooke for preparing the data sets.  ... 
doi:10.1109/tbme.2013.2246160 pmid:23399950 fatcat:n75ux5jq4zbchlmkfchy3zwonq

Energy-efficient Blood Pressure Monitoring based on Single-site Photoplethysmogram on Wearable Devices [article]

Wenrui Lin, Berken Utku Demirel, Mohammad Abdullah Al Faruque, G.P. Li
2021 arXiv   pre-print
Continuous PPG signal preprocessed and used as input of the Artificial Neural Network (ANN), and outputs systolic BP (SBP), diastolic BP (DBP), and mean arterial BP (MAP) values for each heartbeat.  ...  The improvement of the BPM accuracy is obtained by removing outliers in the preprocessing step and the whole-based inputs compared to parameter-based inputs extracted from the PPG signal.  ...  Whole-based BP estimation is proposed in [6] with machine learning algorithms to predict the BP values.  ... 
arXiv:2108.00672v1 fatcat:54p5ezbjuzdx3b5wgjx2x3gcni

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Malak Abdullah Almarshad, Md Saiful Islam, Saad Al-Ahmadi, Ahmed S. BaHammam
2022 Healthcare  
We also highlight the potential impact of using PPG signals on an individual's quality of life and public health.  ...  Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications  ...  Acknowledgments: The authors would like to thank the Deanship of Scientific Research at King Saud University for funding and supporting this research through the initiative of DSR Graduate Students Research  ... 
doi:10.3390/healthcare10030547 pmid:35327025 pmcid:PMC8950880 fatcat:o4l7bdpkanhyvpxcwej6zuxjue
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