Técnicas de Adquisición y Procesamiento de Señales Electrocardiográficas en la Detección de Arritmias Cardíacas
Techniques of Acquisition and Processing of Electrocardiographic Signals in the Detection of Cardiac Arrhythmias
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Barbosa Casanova, G. ., Cardozo Sarmiento, D. O. ., Illera Bustos, M. J. ., Orozco Duque, A. ., & Andrade Caicedo, H. . (2019). Técnicas de Adquisición y Procesamiento de Señales Electrocardiográficas en la Detección de Arritmias Cardíacas. Respuestas, 24(2), 91–100. https://doi.org/10.22463/0122820X.1835
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El desarrollo de sistemas de monitoreo ambulatorio y sus técnicas de procesamiento de la señal electrocardiográfica (ECG) se han convertido en un importante campo de investigación, debido a su relevancia en la detección temprana de enfermedades cardiovasculares, tales como arritmias. La tendencia actual de esta tecnología está orientada al uso de equipos portátiles y dispositivos móviles como los Smartphones, que han sido ampliamente aceptados debido a sus características técnicas y a su integración, cada vez más común, en la vida diaria. Una característica fundamental de estos sistemas es su capacidad de reducir los tipos más comunes de ruido mediante técnicas de procesamiento de señales digitales. Entre las técnicas más utilizadas se encuentran los filtros adaptativos y la Transformada Discreta Wavelet (DWT, por sus siglas en inglés), los cuales han sido implementados exitosamente en diversos estudios. Así mismo, se reportan sistemas que integran etapas de clasificación basadas en inteligencia artificial, con lo cual se aumenta el rendimiento en el proceso de detección de arritmias. En este sentido, estas técnicas no solo son evaluadas por su funcionalidad, sino por su costo computacional, debido a que deben ser utilizadas en aplicaciones en tiempo real, e implementadas en sistemas embebidos. Este documento presenta una revisión del estado del arte de cada una de las etapas en la construcción de un sistema de monitoreo ambulatorio estándar, para la contextualización del lector en este tipo de tecnologías.
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World Health Organization, “Cardiovascular disease,” World Health Organization, 2019. [Online]. Available: https://www.who.int/cardiovascular_diseases/en/. [Accessed: 10-Apr-2019].
E. Contreras Zúñiga, S. X. Zuluaga Martínez, and X. Cardozo, “Estratificación del riesgo de muerte súbita en pacientes con corazones estructuralmente sanos,” Rev. Mex. Cardiol., vol. 20, no. 3, pp. 149–159, 2009.
E. Asensio et al., “Conceptos actuales sobre la muerte súbita,” Gac. Med. Mex., vol. 141, no. 2, pp. 89–98, 2005.
W. Liang, S. Hu, Z. Shao, and J. Tan, “A real-time cardiac arrhythmia classification system with wearable electrocardiogram,” in 2011 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, 2011, vol. 12, no. 12, pp. 102–106.
J. J. Oresko et al., “A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 3, pp. 734–740, May 2010.
S. Raj, G. S. S. Praveen Chand, and K. C. Ray, “ARM-based arrhythmia beat monitoring system,” Microprocess. Microsyst., vol. 39, no. 7, pp. 504–511, Oct. 2015.
Q. Li, C. Rajagopalan, and G. D. Clifford, “Ventricular fibrillation and tachycardia classification using a machine learning approach,” IEEE Trans. Biomed. Eng., vol. 61, no. 6, pp. 1607–1613, 2014.
E. J. Benjamin et al., “Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association,” Circulation, vol. 139, no. 10, Mar. 2019.
M. Nichols, N. Townsend, P. Scarborough, and M. Rayner, “Cardiovascular disease in Europe: epidemiological update,” Eur. Heart J., vol. 34, no. 39, pp. 3028–3034, Oct. 2013.
L. Sörnmo and P. Laguna, “The Electrocardiogram-- A Brief Background,” in Bioelectrical Signal Processing in Cardiac and Neurological Applications, 1st ed., California: Elsevier Academic Press, 2005, pp. 411–452.
R. Issac and M. . Ajaynath, “CUEDETA:A real time heart monitoring system using android smartphone,” in 2012 Annual IEEE India Conference (INDICON), 2012, pp. 047–052.
H. Leutheuser et al., “Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp. 2690–2693.
J. A. Gutiérrez Gnecchi, F. Ortega Vargas, V. H. Olivares Peregrino, and D. Lorias Espinoza, “Design and construction of a continuous ambulatory electrocardiogram recorder, auxiliary in the detection of cardiac arrhythmias,” Proc. - 2010 IEEE Electron. Robot. Automot. Mech. Conf. CERMA 2010, pp. 602–606, 2010.
K. Hermawan, A. A. Iskandar, and R. N. Hartono, “Development of ECG signal interpretation software on Android 2.2,” in 2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, 2011, no. November, pp. 259–264.
J. Bustamante, H. Andrade, S. Marín, J. Saenz, and A. Amaya, “Implementación de un Sistema de Telemonitoreo y Geo-localización de pacientes con Arritmias Cardiacas,” Bioingeniería Y Física Médica Cuba., vol. 12, no. 1, pp. 4–14, 2011.
P. K. Gakare, A. M. Patel, J. R. Vaghela, and R. N. Awale, “Real time feature extraction of ECG signal on android platform,” in 2012 International Conference on Communication, Information & Computing Technology (ICCICT), 2012, pp. 1–5.
E. Melgarejo Rojas, “Monitoría electrocardiográfica ambulatoria de 24 horas (Holter) Historia, indicaciones y elaboración de un informe,” in Manual de métodos diagnósticos en electrofisiología cardiovascular, 1st ed., M. F. Cabrales Neira and D. I. Vanegas Cadavid, Eds. Bogotá: Sociedad Colombiana de Cardiología y Cirugía Cardiovascular, 2006, pp. 1–8.
S. Xue, X. Chen, Z. Fang, and S. Xia, “An ECG arrhythmia classification and heart rate variability analysis system based on android platform,” in 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech), 2015, pp. 1–5.
M. Thomas, M. K. Das, and S. Ari, “Automatic ECG arrhythmia classification using dual tree complex wavelet based features,” AEU - Int. J. Electron. Commun., vol. 69, no. 4, pp. 715–721, 2015.
P. K. Jain and A. K. Tiwari, “Heart monitoring systems—A review,” Comput. Biol. Med., vol. 54, pp. 1–13, 2014.
M. M. Baig, H. Gholamhosseini, and M. J. Connolly, “A comprehensive survey of wearable and wireless ECG monitoring systems for older adults,” Med. Biol. Eng. Comput., vol. 51, no. 5, pp. 485–495, 2013.
S. Z. Rosero, V. Kutyifa, B. Olshansky, and W. Zareba, “Ambulatory ECG monitoring in atrial fibrillation management,” Prog. Cardiovasc. Dis., vol. 56, no. 2, pp. 143–152, 2013.
H. M. Rai, A. Trivedi, and S. Shukla, “ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier,” Measurement, vol. 46, no. 9, pp. 3238–3246, 2013.
M. Korürek and B. Doǧan, “ECG beat classification using particle swarm optimization and radial basis function neural network,” Expert Syst. Appl., vol. 37, no. 12, pp. 7563–7569, 2010.
T. Tanaka et al., “Wearable Health Monitoring System and Its Applications,” 2011 Fourth Int. Conf. Emerg. Trends Eng. Technol., pp. 143–146, 2011.
F. a. F. Marques, D. M. D. Ribeiro, M. F. M. Colunas, and J. P. S. Cunha, “A real time, wearable ECG and blood pressure monitoring system,” 6th Iber. Conf. Inf. Syst. Technol. (CISTI 2011), pp. 1–4, 2011.
T. Klingeberg and M. Schilling, “Mobile wearable device for long term monitoring of vital signs,” Comput. Methods Programs Biomed., vol. 106, no. 2, pp. 89–96, 2012.
H. Yang and J. Chai, “A portable wireless ECG monitor based on MSP430FG439,” Proc. - 2011 Int. Conf. Intell. Comput. Bio-Medical Instrumentation, ICBMI 2011, pp. 148–151, 2011.
G. Hayes and P. D. Teal, “Real Time Detection of Atrial Fibrillation using a Low-power ECG Monitor,” Comput. Cardiol., vol. 40, pp. 743–746, 2013.
Y. Jang et al., “Development of a patch type embedded cardiac function monitoring system using dual microprocessor for arrhythmia detection in heart disease patient,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 2162–2165, 2012.
H. M. K. G. S. Jayasumana, T. M. U. a S. Thennakoon, C. M. R. B. Chandrasekara, M. T. Sandaruwan, a. a. Pasqual, and N. D. Nanayakkara, “A stand-alone ECG abnormality detector,” Proc. 2010 5th Int. Conf. Inf. Autom. Sustain. ICIAfS 2010, pp. 489–492, 2010.
G.-Y. J. G.-Y. Jeong, M.-J. Y. M.-J. Yoon, K.-H. Y. K.-H. Yu, and T.-K. K. T.-K. Kwon, “Development of portable ECG measurement device and PC software for automatic ST analysis,” Control Autom. Syst. (ICCAS), 2010 Int. Conf., pp. 1171–1174, 2010.
Y. Noh, G. Hwang, and D. Jeong, “Implementation of Real-Time Abnormal ECG Detection Algorithm for Wearable Healthcare,” 2011 6th Int. Conf. Comput. Sci. Converg. Inf. Technol. (ICCIT), pp. 111–114, 2011.
Z. Sankari and H. Adeli, “HeartSaver: A mobile cardiac monitoring system for auto-detection of atrial fibrillation, myocardial infarction, and atrio-ventricular block,” Comput. Biol. Med., vol. 41, no. 4, pp. 211–220, 2011.
P. Wackel, L. Beerman, L. West, and G. Arora, “Tachycardia Detection Using Smartphone Applications in Pediatric Patients,” J. Pediatr., vol. 164, no. 5, pp. 1133–1135, May 2014.
N. Filipovic, R. Stojanovic, and A. Caplanova, “Real-time processing and analysis of cardiac signals using Android smartphones,” in 2014 3rd Mediterranean Conference on Embedded Computing (MECO), 2014, pp. 307–310.
S. Gradl, P. Kugler, C. Lohmuller, and B. Eskofier, “Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 2452–2455.
J. Park, K. Lee, and K. Kang, “Intelligent Electrocardiogram Monitoring System for Early Arrhythmia Detection,” in 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, 2014, pp. 1105–1110.
P. Klasnja and W. Pratt, “Healthcare in the pocket: Mapping the space of mobile-phone health interventions,” J. Biomed. Inform., vol. 45, no. 1, pp. 184–198, 2012.
D. Lou et al., “A Wireless Health Monitoring System based on Android Operating System,” IERI Procedia, vol. 4, pp. 208–215, 2013.
J. P. Tello, O. Manjarres, M. Quijano, A. Blanco, F. Varona, and M. Manrique, “Remote Monitoring System of ECG and Human Body Temperature Signals,” IEEE Lat. Am. Trans., vol. 11, no. 1, pp. 314–318, 2013.
I. H. de Oliveira and A. Balbinot, “Portable electrocardiograph based on the integrated circuit ADS1294 using an android application as interface,” Health Technol. (Berl)., vol. 5, no. 2, pp. 147–154, Jul. 2015.
C. Worringham, A. Rojek, and I. Stewart, “Development and Feasibility of a Smartphone, ECG and GPS Based System for Remotely Monitoring Exercise in Cardiac Rehabilitation,” PLoS One, vol. 6, no. 2, p. e14669, 2011.
B. Yu, L. Xu, and Y. Li, “Bluetooth Low Energy (BLE) based mobile electrocardiogram monitoring system,” in 2012 IEEE International Conference on Information and Automation, 2012, no. December 2009, pp. 763–767.
Y.-G. Lee, W. S. Jeong, and G. Yoon, “Smartphone-Based Mobile Health Monitoring,” Telemed. e-Health, vol. 18, no. 8, pp. 585–590, 2012.
T. Berset, D. Geng, and I. Romero, “An optimized DSP implementation of adaptive filtering and ICA for motion artifact reduction in ambulatory ECG monitoring,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 6496–6499.
R. Zhang, X. Fang, Y. Liu, J. Liao, B. Li, and M. Q.-H. Meng, “Design of a real-time ECG filter for resource constraint computer,” in 2012 IEEE International Conference on Information and Automation, 2012, no. June, pp. 846–849.
P. Mithun, P. C. Pandey, T. Sebastian, P. Mishra, and V. K. Pandey, “A wavelet based technique for suppression of EMG noise and motion artifact in ambulatory ECG,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 7087–7090.
H. Kim et al., “Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system,” in 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2012, pp. 160–163.
A. Orozco-Duque, F. J. Martinez-Tabares, J. Gallego, I. D. R. C.A. Mora, G. Castellanos-Dominguez, and J. Bustamante, “Classification of premature ventricular contraction based on Discrete Wavelet Transform for real time applications,” in 2013 Pan American Health Care Exchanges (PAHCE), 2013, pp. 1–5.
S. Saxena, R. Jais, and M. K. Hota, “Removal of powerline interference from ECG signal using FIR, IIR, DWT and NLMS adaptive filter,” Proc. 2019 IEEE Int. Conf. Commun. Signal Process. ICCSP 2019, pp. 12–16, 2019.
P. Shetty and S. Bhat, “Analysis of Various Filter Configurations on Noise Reduction in ECG Waveform,” Int’l J. Comput. Commun. Instrum. Engg., vol. 1, no. 1, pp. 1–4, 2014.
S. S. Bhogeshwar, M. K. Soni, and D. Bansal, “Design of Simulink Model to denoise ECG signal using various IIR & FIR filters,” ICROIT 2014 - Proc. 2014 Int. Conf. Reliab. Optim. Inf. Technol., pp. 477–483, 2014.
A. D. Lopez and L. A. Joseph, “Classification of arrhythmias using statistical features in the wavelet transform domain,” in 2013 International Conference on Advanced Computing and Communication Systems, 2013, pp. 1–6.
B. S. Raghavendra, D. Bera, A. S. Bopardikar, and R. Narayanan, “Cardiac arrhythmia detection using dynamic time warping of ECG beats in e-healthcare systems,” in 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2011, pp. 1–6.
V. P. Nambiar, M. Khalil-Hani, and M. N. Marsono, “Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals,” in 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, 2012, no. December, pp. 866–871.
F. J. Chin, Q. Fang, and I. Cosic, “A computationally light-weight real-time classification method to identify different ECG signals,” in International Symposium on Bioelectronics and Bioinformations 2011, 2011, pp. 287–290.
E. Contreras Zúñiga, S. X. Zuluaga Martínez, and X. Cardozo, “Estratificación del riesgo de muerte súbita en pacientes con corazones estructuralmente sanos,” Rev. Mex. Cardiol., vol. 20, no. 3, pp. 149–159, 2009.
E. Asensio et al., “Conceptos actuales sobre la muerte súbita,” Gac. Med. Mex., vol. 141, no. 2, pp. 89–98, 2005.
W. Liang, S. Hu, Z. Shao, and J. Tan, “A real-time cardiac arrhythmia classification system with wearable electrocardiogram,” in 2011 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, 2011, vol. 12, no. 12, pp. 102–106.
J. J. Oresko et al., “A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 3, pp. 734–740, May 2010.
S. Raj, G. S. S. Praveen Chand, and K. C. Ray, “ARM-based arrhythmia beat monitoring system,” Microprocess. Microsyst., vol. 39, no. 7, pp. 504–511, Oct. 2015.
Q. Li, C. Rajagopalan, and G. D. Clifford, “Ventricular fibrillation and tachycardia classification using a machine learning approach,” IEEE Trans. Biomed. Eng., vol. 61, no. 6, pp. 1607–1613, 2014.
E. J. Benjamin et al., “Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association,” Circulation, vol. 139, no. 10, Mar. 2019.
M. Nichols, N. Townsend, P. Scarborough, and M. Rayner, “Cardiovascular disease in Europe: epidemiological update,” Eur. Heart J., vol. 34, no. 39, pp. 3028–3034, Oct. 2013.
L. Sörnmo and P. Laguna, “The Electrocardiogram-- A Brief Background,” in Bioelectrical Signal Processing in Cardiac and Neurological Applications, 1st ed., California: Elsevier Academic Press, 2005, pp. 411–452.
R. Issac and M. . Ajaynath, “CUEDETA:A real time heart monitoring system using android smartphone,” in 2012 Annual IEEE India Conference (INDICON), 2012, pp. 047–052.
H. Leutheuser et al., “Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp. 2690–2693.
J. A. Gutiérrez Gnecchi, F. Ortega Vargas, V. H. Olivares Peregrino, and D. Lorias Espinoza, “Design and construction of a continuous ambulatory electrocardiogram recorder, auxiliary in the detection of cardiac arrhythmias,” Proc. - 2010 IEEE Electron. Robot. Automot. Mech. Conf. CERMA 2010, pp. 602–606, 2010.
K. Hermawan, A. A. Iskandar, and R. N. Hartono, “Development of ECG signal interpretation software on Android 2.2,” in 2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, 2011, no. November, pp. 259–264.
J. Bustamante, H. Andrade, S. Marín, J. Saenz, and A. Amaya, “Implementación de un Sistema de Telemonitoreo y Geo-localización de pacientes con Arritmias Cardiacas,” Bioingeniería Y Física Médica Cuba., vol. 12, no. 1, pp. 4–14, 2011.
P. K. Gakare, A. M. Patel, J. R. Vaghela, and R. N. Awale, “Real time feature extraction of ECG signal on android platform,” in 2012 International Conference on Communication, Information & Computing Technology (ICCICT), 2012, pp. 1–5.
E. Melgarejo Rojas, “Monitoría electrocardiográfica ambulatoria de 24 horas (Holter) Historia, indicaciones y elaboración de un informe,” in Manual de métodos diagnósticos en electrofisiología cardiovascular, 1st ed., M. F. Cabrales Neira and D. I. Vanegas Cadavid, Eds. Bogotá: Sociedad Colombiana de Cardiología y Cirugía Cardiovascular, 2006, pp. 1–8.
S. Xue, X. Chen, Z. Fang, and S. Xia, “An ECG arrhythmia classification and heart rate variability analysis system based on android platform,” in 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech), 2015, pp. 1–5.
M. Thomas, M. K. Das, and S. Ari, “Automatic ECG arrhythmia classification using dual tree complex wavelet based features,” AEU - Int. J. Electron. Commun., vol. 69, no. 4, pp. 715–721, 2015.
P. K. Jain and A. K. Tiwari, “Heart monitoring systems—A review,” Comput. Biol. Med., vol. 54, pp. 1–13, 2014.
M. M. Baig, H. Gholamhosseini, and M. J. Connolly, “A comprehensive survey of wearable and wireless ECG monitoring systems for older adults,” Med. Biol. Eng. Comput., vol. 51, no. 5, pp. 485–495, 2013.
S. Z. Rosero, V. Kutyifa, B. Olshansky, and W. Zareba, “Ambulatory ECG monitoring in atrial fibrillation management,” Prog. Cardiovasc. Dis., vol. 56, no. 2, pp. 143–152, 2013.
H. M. Rai, A. Trivedi, and S. Shukla, “ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier,” Measurement, vol. 46, no. 9, pp. 3238–3246, 2013.
M. Korürek and B. Doǧan, “ECG beat classification using particle swarm optimization and radial basis function neural network,” Expert Syst. Appl., vol. 37, no. 12, pp. 7563–7569, 2010.
T. Tanaka et al., “Wearable Health Monitoring System and Its Applications,” 2011 Fourth Int. Conf. Emerg. Trends Eng. Technol., pp. 143–146, 2011.
F. a. F. Marques, D. M. D. Ribeiro, M. F. M. Colunas, and J. P. S. Cunha, “A real time, wearable ECG and blood pressure monitoring system,” 6th Iber. Conf. Inf. Syst. Technol. (CISTI 2011), pp. 1–4, 2011.
T. Klingeberg and M. Schilling, “Mobile wearable device for long term monitoring of vital signs,” Comput. Methods Programs Biomed., vol. 106, no. 2, pp. 89–96, 2012.
H. Yang and J. Chai, “A portable wireless ECG monitor based on MSP430FG439,” Proc. - 2011 Int. Conf. Intell. Comput. Bio-Medical Instrumentation, ICBMI 2011, pp. 148–151, 2011.
G. Hayes and P. D. Teal, “Real Time Detection of Atrial Fibrillation using a Low-power ECG Monitor,” Comput. Cardiol., vol. 40, pp. 743–746, 2013.
Y. Jang et al., “Development of a patch type embedded cardiac function monitoring system using dual microprocessor for arrhythmia detection in heart disease patient,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 2162–2165, 2012.
H. M. K. G. S. Jayasumana, T. M. U. a S. Thennakoon, C. M. R. B. Chandrasekara, M. T. Sandaruwan, a. a. Pasqual, and N. D. Nanayakkara, “A stand-alone ECG abnormality detector,” Proc. 2010 5th Int. Conf. Inf. Autom. Sustain. ICIAfS 2010, pp. 489–492, 2010.
G.-Y. J. G.-Y. Jeong, M.-J. Y. M.-J. Yoon, K.-H. Y. K.-H. Yu, and T.-K. K. T.-K. Kwon, “Development of portable ECG measurement device and PC software for automatic ST analysis,” Control Autom. Syst. (ICCAS), 2010 Int. Conf., pp. 1171–1174, 2010.
Y. Noh, G. Hwang, and D. Jeong, “Implementation of Real-Time Abnormal ECG Detection Algorithm for Wearable Healthcare,” 2011 6th Int. Conf. Comput. Sci. Converg. Inf. Technol. (ICCIT), pp. 111–114, 2011.
Z. Sankari and H. Adeli, “HeartSaver: A mobile cardiac monitoring system for auto-detection of atrial fibrillation, myocardial infarction, and atrio-ventricular block,” Comput. Biol. Med., vol. 41, no. 4, pp. 211–220, 2011.
P. Wackel, L. Beerman, L. West, and G. Arora, “Tachycardia Detection Using Smartphone Applications in Pediatric Patients,” J. Pediatr., vol. 164, no. 5, pp. 1133–1135, May 2014.
N. Filipovic, R. Stojanovic, and A. Caplanova, “Real-time processing and analysis of cardiac signals using Android smartphones,” in 2014 3rd Mediterranean Conference on Embedded Computing (MECO), 2014, pp. 307–310.
S. Gradl, P. Kugler, C. Lohmuller, and B. Eskofier, “Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 2452–2455.
J. Park, K. Lee, and K. Kang, “Intelligent Electrocardiogram Monitoring System for Early Arrhythmia Detection,” in 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, 2014, pp. 1105–1110.
P. Klasnja and W. Pratt, “Healthcare in the pocket: Mapping the space of mobile-phone health interventions,” J. Biomed. Inform., vol. 45, no. 1, pp. 184–198, 2012.
D. Lou et al., “A Wireless Health Monitoring System based on Android Operating System,” IERI Procedia, vol. 4, pp. 208–215, 2013.
J. P. Tello, O. Manjarres, M. Quijano, A. Blanco, F. Varona, and M. Manrique, “Remote Monitoring System of ECG and Human Body Temperature Signals,” IEEE Lat. Am. Trans., vol. 11, no. 1, pp. 314–318, 2013.
I. H. de Oliveira and A. Balbinot, “Portable electrocardiograph based on the integrated circuit ADS1294 using an android application as interface,” Health Technol. (Berl)., vol. 5, no. 2, pp. 147–154, Jul. 2015.
C. Worringham, A. Rojek, and I. Stewart, “Development and Feasibility of a Smartphone, ECG and GPS Based System for Remotely Monitoring Exercise in Cardiac Rehabilitation,” PLoS One, vol. 6, no. 2, p. e14669, 2011.
B. Yu, L. Xu, and Y. Li, “Bluetooth Low Energy (BLE) based mobile electrocardiogram monitoring system,” in 2012 IEEE International Conference on Information and Automation, 2012, no. December 2009, pp. 763–767.
Y.-G. Lee, W. S. Jeong, and G. Yoon, “Smartphone-Based Mobile Health Monitoring,” Telemed. e-Health, vol. 18, no. 8, pp. 585–590, 2012.
T. Berset, D. Geng, and I. Romero, “An optimized DSP implementation of adaptive filtering and ICA for motion artifact reduction in ambulatory ECG monitoring,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 6496–6499.
R. Zhang, X. Fang, Y. Liu, J. Liao, B. Li, and M. Q.-H. Meng, “Design of a real-time ECG filter for resource constraint computer,” in 2012 IEEE International Conference on Information and Automation, 2012, no. June, pp. 846–849.
P. Mithun, P. C. Pandey, T. Sebastian, P. Mishra, and V. K. Pandey, “A wavelet based technique for suppression of EMG noise and motion artifact in ambulatory ECG,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 7087–7090.
H. Kim et al., “Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system,” in 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2012, pp. 160–163.
A. Orozco-Duque, F. J. Martinez-Tabares, J. Gallego, I. D. R. C.A. Mora, G. Castellanos-Dominguez, and J. Bustamante, “Classification of premature ventricular contraction based on Discrete Wavelet Transform for real time applications,” in 2013 Pan American Health Care Exchanges (PAHCE), 2013, pp. 1–5.
S. Saxena, R. Jais, and M. K. Hota, “Removal of powerline interference from ECG signal using FIR, IIR, DWT and NLMS adaptive filter,” Proc. 2019 IEEE Int. Conf. Commun. Signal Process. ICCSP 2019, pp. 12–16, 2019.
P. Shetty and S. Bhat, “Analysis of Various Filter Configurations on Noise Reduction in ECG Waveform,” Int’l J. Comput. Commun. Instrum. Engg., vol. 1, no. 1, pp. 1–4, 2014.
S. S. Bhogeshwar, M. K. Soni, and D. Bansal, “Design of Simulink Model to denoise ECG signal using various IIR & FIR filters,” ICROIT 2014 - Proc. 2014 Int. Conf. Reliab. Optim. Inf. Technol., pp. 477–483, 2014.
A. D. Lopez and L. A. Joseph, “Classification of arrhythmias using statistical features in the wavelet transform domain,” in 2013 International Conference on Advanced Computing and Communication Systems, 2013, pp. 1–6.
B. S. Raghavendra, D. Bera, A. S. Bopardikar, and R. Narayanan, “Cardiac arrhythmia detection using dynamic time warping of ECG beats in e-healthcare systems,” in 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2011, pp. 1–6.
V. P. Nambiar, M. Khalil-Hani, and M. N. Marsono, “Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals,” in 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, 2012, no. December, pp. 866–871.
F. J. Chin, Q. Fang, and I. Cosic, “A computationally light-weight real-time classification method to identify different ECG signals,” in International Symposium on Bioelectronics and Bioinformations 2011, 2011, pp. 287–290.
