Desarrollo de un Algoritmo de Coordenadas de Intercambio Frecuente para Detección del Intercambio de Electrodos Precordiales ECG basado en Parámetros de Error y Correlación
DOI:
https://doi.org/10.17488/RMIB.45.3.7Palabras clave:
ECG-12 derivaciones, correlación, estimadores de error, intercambio de electrodosResumen
Con objetivo de desarrollar un método de detección de intercambio de electrodos precordiales, se implementó un algoritmo de coordenadas de intercambio frecuentes (FEC) que identifica lo mínimos puntos de correlación necesarios para detectar un intercambio de electrodos específico y a su vez se probó su desempeño con estimadores de error (error cuadrático medio y diferencia cuadrática media porcentual). La validación del algoritmo se hizo mediante la técnica k-fold cross-validation en las bases de datos PTB, Chapman University and Shaoxing Hospital, PTB XL y Georgia 12-lead ECG Challenge. Los resultados indican promedios de Se= 99,16 % y Sp= 99,38 % (MSE), Se= 95,38 % y Sp= 99,47 % (PRD), Se= 98,44 % y Sp= 99,49 % (Pearson), Se= 98,45 % y Sp= 99,48 % (Pearson modificado), Se= 95,39 % y Sp= 99,81 % (Bray Curtis), Se= 80,00 % y Sp= 97,84 % (correlación signo). MSE presenta una mejora significativa en el tiempo de ejecución (61,49µs N=1000), representando en promedio el 44.99 % del tiempo de ejecución para correlación de Pearson. Se valida entonces el algoritmo de coordenadas de intercambio frecuente con análisis de señales con error cuadrático medio (MSE), representando una buena alternativa para detectar el intercambio de electrodos en tiempo real, de fácil implementación y bajo costo computacional.
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D. M. F. Palhares, M. S. Marcolino, T. M. M. Santos, J. L. P. da Silva, et al., “Normal limits of the electrocardiogram derived from a large database of Brazilian primary care patients,” BMC Cardiovasc. Disord., vol. 17, 2017, art. no. 152, doi: https://doi.org/10.1186/s12872-017-0572-8
A. Hadjiantoni, K. Oak, S. Mengi, J. Konya, T. Ungvari, “Is the Correct Anatomical Placement of the Electrocardiogram Electrodes Essential to Diagnosis in the Clinical Setting: a Systematic Review,” Cardiol. Cardiovasc. Med., vol. 5, no. 2, pp. 182-200, 2021, doi: https://www.doi.org/10.26502/fccm.92920192
R. R. Bond, D. D. Finlay, C. D. Nugent, C. Breen, D. Guldenring, and M. J. Daly, “The effects of electrode misplacement on clinicians’ interpretation of the standard 12-lead electrocardiogram,” Eur. J. Intern. Med., vol. 23, no. 7, pp. 610-615, 2012, doi: https://doi.org/10.1016/j.ejim.2012.03.011
C. Han, R. E. Gregg, and S. Babaeizadeh, “Automatic detection of ECG lead wire interchange for conventional and Mason-Likar lead systems,” in Computing in Cardiology 2014, Cambridge, MA, USA, 2014, pp. 145-148.
H. Xia, G. A. Garcia, and X. Zhao, “Automatic detection of ECG electrode misplacement: a tale of two algorithms,” Physiol. Meas., vol. 33, 2012, art. no. 1549, doi: https://doi.org/10.1088/0967-3334/33/9/1549
J. A. Kors and G. van Herpen, “Accurate automatic detection of electrode interchange in the electrocardiogram,” The Am. J. Cardiol., vol. 88, no. 4, pp. 396-399, 2001, doi: https://doi.org/10.1016/S0002-9149(01)01686-1
K. Rjoob, R. Bond, D. Finlay, V. McGilligan, et al., “Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: a systematic review and meta analysis,” J. Electrocardiol., vol. 62, pp. 116-123, 2020, doi: https://doi.org/10.1016/j.jelectrocard.2020.08.013
J. Kors and G. van Herpen, “A novel method to detect electrocardiographic electrode interchanges,” Journal of Electrocardiology, vol. 33, pp. 209-210, 2000, doi: https://doi.org/10.1054/jelc.2000.20352
K. Rjoob, R. Bond, D. Finlay, V. McGilligan, et al., “Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12‑lead electrocardiogram,” J. Electrocardiol., vol. 57, pp. 39-43, 2019, doi: https://doi.org/10.1016/j.jelectrocard.2019.08.017
R. E. Gregg, E. W. Hancock, and S. Babaeizadeh, “Detecting ECG limb lead wire interchanges involving the right leg lead-wire,” in Computing in Cardiology 2017, Rennes, France, 2017, doi: https://doi.org/10.22489/CinC.2017.014-061
B. Hede´n, M. Ohlsson, L. Edenbrandt, R. Rittner, O. Pahlm, and C. Peterson, “Artificial neural networks for recognition of electrocardiographic lead reversal,” Am. J. Cardiol., vol. 75, no. 14, pp. 929-933, 1995, doi: https://doi.org/10.1016/S0002-9149(99)80689-4
B. Hede´n, M. Ohlsson, H. Holst, M. Mjöman, et al., “Detection of frequently overlooked electrocardiographic lead reversals using artificial neural networks,” Am. J. Cardiol., vol. 78, no. 5, pp. 600-604, 1996, doi: https://doi.org/10.1016/S0002-9149(96)00377-3
K. Rjoob, R. Bond, D. Finlay, V. McGilligan, S. J. Leslie, A. Iftikhar, and A. Peace, “Machine learning improves the detection of misplaced v1 and v2 electrodes during 12-lead electrocardiogram acquisition,” in 2019 Computing in Cardiology (CinC), Singapore, Singapore, 2019, pp. 1-4, doi: https://doi.org/10.22489/CinC.2019.035
J. de Bie, D. W. Mortara, and T. F. Clark, “The development and validation of an early warning system to prevent the acquisition of 12-lead resting ECGs with interchanged electrode positions,” J. Electrocardiol., vol. 47, no. 6, pp. 794-797, 2014, doi: https://doi.org/10.1016/j.jelectrocard.2014.08.015
G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Toward an intelligent edge: Wireless communication meets machine learning,” IEEE Commun. Mag., vol. 58, no. 1, pp. 19-25, 2020, doi: https://doi.org/10.1109/MCOM.001.1900103
Khanzode, K. C. A., Sarode, R. D, “Advantages and Disadvantages of Artificial Intelligence and Machine Learning: A Literature Review,” Int. J. Libr. Inf. Sci., vol. 9, no. 1, 30-36, 2020, doi: https://doi.org/10.17605/OSF.IO/GV5T4
I. Jekova, V. Krasteva, R. Leber, R. Schmid, et al., “Interlead correlation analysis for automated detection of cable reversals in 12/16-lead ECG,” Comput. Methods Programs Biomed., vol. 134, pp. 31-41, 2016, doi: https://doi.org/10.1016/j.cmpb.2016.06.003
A. Medina, N. Lopez, J. Galdos, E. Supo, J. Rendulich, and E. Sulla, “Continuous Blood Pressure Estimation in Wearable Devices Using Photoplethysmography: A Review,” Int. J. Emerging Technol. Adv. Eng., vol. 12, no. 10, 104-113, 2022, doi: https://doi.org/10.46338/ijetae1022_12
J. R. Huamani Talavera, E. A. S. Mendoza, N. M. Dávila, and E. Supo, “Implementation of a real-time 60 Hz interference cancellation algorithm for ECG signals based on ARM cortex M4 and ADS1298,” in 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Perú, 2017, pp. 1-4, doi: https://doi.org/10.1109/INTERCON.2017.8079725
T. R. Sulla, S. J. Talavera, C. E. Supo, and A. A. Montoya, “Non invasive glucose monitor based on electric bioimpedance using AFE4300,” in 2019 IEEE XXVI International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Lima, Perú, 2019, pp. 1-3, doi: https://doi.org/10.1109/INTERCON.2019.8853561
J. R. Beingolea, M. A. Zea Vargas, R. Huallpa, X. Vilca, R. Bolivar, and J. Rendulich, “Assistive Devices: Technology Development for the Visually Impaired,” Designs, vol. 5, no. 4, 2021, art. no. 75, doi: https://doi.org/10.3390/designs5040075
J. R. Beingolea, H. A. Rodrigues, M. Zegarra, E. Sulla Espinoza, R. Torres Silva, and J. Rendulich, “Designing a Multiaxial Extensometric Force Platform: A Manufacturing Experience,” Electronics, vol. 10, no. 16, 2021, art. no. 1907, doi: https://doi.org/10.3390/electronics10161907
M. Huisa C., T. E. Figueroa, E. Supo, J. Rendulich, and E. Sulla Espinoza, “PCG heart sounds quality classification using neural networks and Smote Tomek Links for the Think Health project,” in 1st International Conference on Computational Intelligence and Innovative Technologies (ICCIIT), Pune, India, 2022, pp. 803-811, doi: https://doi.org/10.1007/978-981-19-7615-5_65
T. Eslami and F. Saeed, “Fast GPU PCC: A GPU-based technique to compute pairwise Pearson’s correlation coefficients for time series data FMRI study,” High-Throughput, vol. 7, no. 2, 2018, art. no. 11, doi: https://doi.org/10.3390/ht7020011
J. Lian, G. Garner, D. Muessig, and V. Lang, “A simple method to quantify the morphological similarity between signals,” Signal Process., vol. 90, no. 2, pp. 684-688, 2010, doi: https://doi.org/10.1016/j.sigpro.2009.07.010
M. J. Mc Loughlin, “New Electrocardiographic Methods Based on the Standard 12-Leads Ecg: Bipolar Precordial Leads,” 2022, ssrn.4250757, doi: http://dx.doi.org/10.2139/ssrn.4250757
T. N. Nguyen, T. H. Nguyen, and V. T. Ngo, “Artifact elimination in ECG signal using wavelet transform,” Telkomnika, vol. 18, no. 2, pp. 936-944, 2020, doi: https://doi.org/10.12928/TELKOMNIKA.v18i2.14403
V. Gupta, M. Mittal, and V. Mittal, “Performance evaluation of various pre-processing techniques for R peak detection in ECG signal,” IETE J. Res., vol. 68, no. 5, pp. 3267-3282, 2020, doi: https://doi.org/10.1080/03772063.2020.1756473
S. Banerjee and G. S. Kumar, “Quality guaranteed ECG signal compression using tunable-q wavelet transform and möbius transform based AFD,” IEEE Trans. Instrum. Meas., vol. 70, pp. 1-11, 2021, doi: https://doi.org/10.1109/TIM.2021.3122119
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215-e220, 2000, doi: https://doi.org/10.1161/01.cir.101.23.e215
R. Bousseljot, D. Kreiseler, and A. Schnabel, “Nutzung der EKG Signaldatenbank CARDIODAT der PTB über das Internet,” J. Biomed. Eng., vol. 40, suppl. 1, 317, 1995, doi: https://doi.org/10.1515/bmte.1995.40.s1.317
P. Wagner, N. Strodthoff, R. Bousseljot, W. Samek, and T. Schaeffter, PTB-XL, a large publicly available electrocardiography dataset (version 1.0.1), PhysioNet. Accessed: 2024. [Online]. Available: https://doi.org/10.13026/x4td-x982
M. A. Reyna, E. A. Perez Alday, A. Gu, C. Liu, et al., “Classification of 12-lead ECGs: the physionet/computing in cardiology challenge 2020,” in 2020 Computing in Cardiology, Rimini, Italy, 2020, pp. 1-4. doi: https://doi.org/10.22489/CinC.2020.236
J. Zheng, J. Zhang, S. A. Danioko, H. Yao, H. Guo, C. Rakovski, “A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients,” Sci. Data, vol. 7, 2020, art. no. 48, doi: https://doi.org/10.1038/s41597-020-0386-x
N.-T. Bui, G.-s. Byun, “The comparison features of ECG signal with different sampling frequencies and filter methods for real-time measurement,” Symmetry, vol. 13, no. 8, 2021, art. no. 1461, doi: https://doi.org/10.3390/sym13081461
D. Gembris, M. Neeb, M. Gipp, A. Kugel, and R. Männer, “Correlation analysis on GPU systems using NVIDIA’s CUDA,” J. Real Time Image Proc., vol. 6, no. 4, pp. 275-280, 2011, doi: https://doi.org/10.1007/s11554-010-0162-9
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