[en] This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenarios and limited data availability scenarios, where clean PPG samples are significantly outnumbered by artifact-contaminated samples. We investigated a dataset comprising PPG recordings from 1571 patients, wherein approximately 82% of the samples were identified as clean, while the remaining 18% were contaminated by artifacts. Our research compares the performance of supervised classifiers, such as conventional classifiers and neural networks (Multi-Layer Perceptron (MLP), Transformers, Fully Convolutional Network (FCN)), with the semi-supervised Label Propagation (LP) algorithm for artifact classification in PPG signals. The results indicate that the LP algorithm achieves a precision of 91%, a recall of 90%, and an F1 score of 90% for the 'artifacts' class, showcasing its effectiveness in annotating a medical dataset, even in cases where clean samples are rare. Although the K-Nearest Neighbors (KNN) supervised model demonstrated good results with a precision of 89%, a recall of 95%, and an F1 score of 92%, the semi-supervised algorithm excels in artifact detection. In the case of imbalanced and limited pediatric intensive care environment data, the semi-supervised LP algorithm is promising for artifact detection in PPG signals. The results of this study are important for improving the accuracy of PPG-based health monitoring, particularly in situations in which motion artifacts pose challenges to data interpretation.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Macabiau, Clara ✱; Biomedical Information Processing Laboratory, École de Technologie Supérieure, Montréal, Canada
LE, Thanh-Dung ✱; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Biomedical Information Processing Laboratory, École de Technologie Supérieure, Montréal, Canada
Albert, Kevin ✱; Université de Montréal, CHU Sainte-Justine Research Center, CHU Sainte-Justine Hospital, Montréal, Canada
Shahriari, Mana ✱; Université de Montréal, CHU Sainte-Justine Research Center, CHU Sainte-Justine Hospital, Montréal, Canada
Jouvet, Philippe ✱; Université de Montréal, CHU Sainte-Justine Research Center, CHU Sainte-Justine Hospital, Montréal, Canada
Noumeir, Rita ✱; Biomedical Information Processing Laboratory, École de Technologie Supérieure, Montréal, Canada
✱ Ces auteurs ont contribué de façon équivalente à la publication.
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Label Propagation Techniques for Artifact Detection in Imbalanced Classes Using Photoplethysmogram Signals
Date de publication/diffusion :
2024
Titre du périodique :
IEEE Access
ISSN :
2169-3536
Maison d'édition :
Institute of Electrical and Electronics Engineers Inc.
J. M. Helm, A. M. Swiergosz, H. S. Haeberle, J. M. Karnuta, J. L. Schaffer, V. E. Krebs, A. I. Spitzer, and P. N. Ramkumar, "Machine learning and artificial intelligence: Definitions, applications, and future directions, " Current Rev. Musculoskeletal Med., vol. 13, no. 1, pp. 69-76, Feb. 2020.
S. Dash, S. K. Shakyawar, M. Sharma, and S. Kaushik, "Big data in healthcare: Management, analysis and future prospects, " J. Big Data, vol. 6, no. 1, pp. 1-12, Dec. 2019.
M. Greco, P. F. Caruso, and M. Cecconi, "Artificial intelligence in the intensive care unit, " Seminars Respiratory Crit. Care Med., vol. 42, no. 1, pp. 2-9, Feb. 2021.
L. N. Sanchez-Pinto and R. G. Khemani, "Development of a prediction model of early acute kidney injury in critically ill children using electronic health record data, " Pediatric Crit. Care Med., vol. 17, no. 6, pp. 508-515, 2016.
E. Choi, A. Schuetz, W. F. Stewart, and J. Sun, "Using recurrent neural network models for early detection of heart failure onset, " J. Amer. Med. Inform. Assoc., vol. 24, no. 2, pp. 361-370, Mar. 2017.
L. Huang, A. L. Shea, H. Qian, A. Masurkar, H. Deng, and D. Liu, "Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records, " J. Biomed. Informat., vol. 99, Nov. 2019, Art. no. 103291.
S. Dara, S. Dhamercherla, S. S. Jadav, C. Babu, and M. J. Ahsan, "Machine learning in drug discovery: A review, " Artif. Intell. Rev., vol. 55, no. 3, pp. 1947-1999, 2022.
L. V. Ho, D. Ledbetter, M. Aczon, and R. Wetzel, "The dependence of machine learning on electronic medical record quality, " in Proc. AMIA Symp., 2018, pp. 883-891.
K. Y. Ngiam and I. W. Khor, "Big data and machine learning algorithms for health-care delivery, " Lancet Oncol., vol. 20, no. 5, pp. e262-e273, May 2019.
A. E. W. Johnson, M. M. Ghassemi, S. Nemati, K. E. Niehaus, D. A. Clifton, and G. D. Clifford, "Machine learning and decision support in critical care, " Proc. IEEE, vol. 104, no. 2, pp. 444-466, Feb. 2016.
H. Habehh and S. Gohel, "Machine learning in healthcare, " Current Genomics, vol. 22, no. 4, pp. 291-300, 2016.
D. Pollreisz and N. TaheriNejad, "Detection and removal of motion artifacts in PPG signals, " Mobile Netw. Appl., vol. 27, no. 2, pp. 728-738, Apr. 2022.
M. T. Petterson, V. L. Begnoche, and J. M. Graybeal, "The effect of motion on pulse oximetry and its clinical significance, " Anesthesia Analgesia, vol. 105, no. 6, pp. S78-S84, 2007.
S.-H. Kim, M. Lilot, K. S. Sidhu, J. Rinehart, Z. Yu, C. Canales, and M. Cannesson, "Accuracy and precision of continuous noninvasive arterial pressure monitoring compared with invasive arterial pressure, " Anesthesiology, vol. 120, no. 5, pp. 1080-1097, May 2014.
B. L. Hill, N. Rakocz, A. Rudas, J. N. Chiang, S. Wang, I. Hofer, M. Cannesson, and E. Halperin, "Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning, " Sci. Rep., vol. 11, no. 1, p. 15755, Aug. 2021.
S. Hanyu and C. Xiaohui, "Motion artifact detection and reduction in PPG signals based on statistics analysis, " in Proc. 29th Chin. Control Decis. Conf. (CCDC), May 2017, pp. 3114-3119.
C.-C. Wu, I.-W. Chen, and W.-C. Fang, "An implementation of motion artifacts elimination for PPG signal processing based on recursive least squares adaptive filter, " in Proc. IEEE Biomed. Circuits Syst. Conf. (BioCAS), Oct. 2017, pp. 1-4.
G. Joseph, A. Joseph, G. Titus, R. M. Thomas, and D. Jose, "Photoplethysmogram (PPG) signal analysis and wavelet de-noising, " in Proc. Annu. Int. Conf. Emerg. Res. Areas, Magn., Mach. Drives, Jul. 2014, pp. 1-5.
Q. Wang, P. Yang, and Y. Zhang, "Artifact reduction based on empirical mode decomposition (EMD) in photoplethysmography for pulse rate detection, " in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol., Aug. 2010, pp. 959-962.
Z. Xu and K. Funaya, "Time series analysis with graph-based semisupervised learning, " in Proc. IEEE Int. Conf. Data Sci. Adv. Analytics (DSAA), Oct. 2015, pp. 1-6.
Y. Shin, S. Yoon, S. Kim, H. Song, J. G. Lee, and B. S. Lee, "Coherence-based label propagation over time series for accelerated active learning, " in Proc. Int. Conf. Learn. Represent., Oct. 2021, pp. 1-20.
D. Bünger, M. Gondos, L. Peroche, and M. Stoll, "An empirical study of graph-based approaches for semi-supervised time series classification, " Frontiers Appl. Math. Statist., vol. 7, pp. 1-18, Jan. 2022.
M. A. Mazurowski, P. A. Habas, J. M. Zurada, J. Y. Lo, J. A. Baker, and G. D. Tourassi, "Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance, " Neural Netw., vol. 21, nos. 2-3, pp. 427-436, Mar. 2008.
D. E. Robbins, V. P. Gurupur, and J. Tanik, "Information architecture of a clinical decision support system, " in Proc. IEEE Southeastcon, Mar. 2011, pp. 374-378.
A. J. Aljaaf, D. Al-Jumeily, A. J. Hussain, P. Fergus, M. Al-Jumaily, and N. Radi, "Applied machine learning classifiers for medical applications: Clarifying the behavioural patterns using a variety of datasets, " in Proc. Int. Conf. Syst., Signals Image Process. (IWSSIP), Sep. 2015, pp. 228-232.
D. Brossier, R. E. Taani, M. Sauthier, N. Roumeliotis, G. Emeriaud, and P. Jouvet, "Creating a high-frequency electronic database in the PICU: The perpetual patient, " Pediatric Crit. Care Med., vol. 19, no. 4, pp. 189-198, 2018.
N. Roumeliotis, G. Parisien, S. Charette, E. Arpin, F. Brunet, and P. Jouvet, "Reorganizing care with the implementation of electronic medical records: Atime-motion study in the PICU, " Pediatric Crit. care Med., vol. 19, no. 4, pp. 172-179, 2018.
A. Mathieu, M. Sauthier, P. Jouvet, G. Emeriaud, and D. Brossier, "Validation process of a high-resolution database in a paediatric intensive care unit-Describing the perpetual patient's validation, " J. Eval. Clin. Pract., vol. 27, no. 2, pp. 316-324, 2021.
P. K. Lim, S.-C. Ng, N. H. Lovell, Y. P. Yu, M. P. Tan, D. McCombie, E. Lim, and S. J. Redmond, "Adaptive template matching of photoplethysmogram pulses to detect motion artefact, " Physiological Meas., vol. 39, no. 10, Oct. 2018, Art. no. 105005.
Q. Li and G. D. Clifford, "Dynamic time warping and machine learning for signal quality assessment of pulsatile signals, " Physiological Meas., vol. 33, no. 9, pp. 1491-1501, Sep. 2012.
R. Krishnan, B. Natarajan, and S. Warren, "Analysis and detection of motion artifact in photoplethysmographic data using higher order statistics, " in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Mar. 2008, pp. 613-616.
K. Fujiwara, Y. Huang, K. Hori, K. Nishioji, M.Kobayashi, M. Kamaguchi, and M. Kano, "Over-and under-sampling approach for extremely imbalanced and small minority data problem in health record analysis, " Frontiers Public Health, vol. 8, p. 178, May 2020.
X. Zhu and Z. Ghahramani, "Learning from labeled and unlabeled data with label propagation, " School Comput. Sci., Tech. Rep. CMU-CALD-02-107, 2002.
Z. Song, X. Yang, Z. Xu, and I. King, "Graph-based semi-supervised learning: A comprehensive review, " IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 11, pp. 1-21, Nov. 2022.
Z. Bodó and L. Csató, "A note on label propagation for semi-supervised learning, " Acta Universitatis Sapientiae, Inf., vol. 7, no. 1, pp. 18-30, Jun. 2015.
R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, "Deep learning for healthcare: Review, opportunities and challenges, " Briefings Bioinf., vol. 19, no. 6, pp. 1236-1246, Nov. 2018.
V. Yogarajan, J. Montiel, T. Smith, and B. Pfahringer, "Transformers for multi-label classification of medical text: An empirical comparison, " in Proc. Int. Conf. Artif. Intell. Med., 2021, pp. 114-123.
Z. Wang, W. Yan, and T. Oates, "Time series classification from scratch with deep neural networks: A strong baseline, " in Proc. Int. Joint Conf. Neural Netw. (IJCNN), May 2017, pp. 1578-1585.
G. Luo, "A review of automatic selection methods for machine learning algorithms and hyper-parameter values, " Netw. Model. Anal. Health Informat. Bioinf., vol. 5, no. 1, pp. 1-16, Dec. 2016.
T. Yu and H. Zhu, "Hyper-parameter optimization: A review of algorithms and applications, " 2020, arXiv:2003.05689.
K. Kazemi, J. Laitala, I. Azimi, P. Liljeberg, and A. M. Rahmani, "Robust PPG peak detection using dilated convolutional neural networks, " Sensors, vol. 22, no. 16, p. 6054, Aug. 2022.
A. A. Khan, O. Chaudhari, and R. Chandra, "A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation, " Expert Syst. Appl., vol. 244, Jun. 2024, Art. no. 122778.
M. Shen, P. Wen, B. Song, and Y. Li, "An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods, " Biomed. Signal Process. Control, vol. 77, Aug. 2022, Art. no. 103820.
A. Zandbagleh, S. Mirzakuchaki, M. R. Daliri, A. Sumich, J. D. Anderson, and S. Sanei, "Graph-based analysis of EEG for schizotypy classification applying flicker ganzfeld stimulation, " Schizophrenia, vol. 9, no. 1, p. 64, Sep. 2023.
