La lucha contra la muerte cardiaca súbita. Dilemás éticos y avances científicos

El objetivo de esta lección es acercarles a uno de los fenómenos más terribles que puede acontecer en una familia o en un grupo social, la aparición de un fallecimiento súbito, no esperado y fulminante. Es otra pandemia crónica de nuestra sociedad, con extensión casi comparable a la COVID-19, y que he escogido, además de por ser parte fundamental de mi quehacer cotidiano, por encerrar en su historia importantes lecciones éticas y de superación. En primer lugar, trataremos de explicar las causas y mecanismos por las que ocurren las muertes súbitas y luego revisaremos las opciones de tratamiento para luchar contra ellas.Medicin

Parallel Study on Surface and Invasive Recordings Across Catheter Ablation Steps of Paroxysmal Atrial Fibrillation

[EN] Catheter ablation (CA) is the star treatment of atrial fibrillation (AF). However, important issues regarding its procedure have only been superficially explored. While universal CA effect is assessed, the role of right (RPVI) and left pulmonary vein isolation (LPVI) is ignored. Although coronary sinus (CS) is the prevailing CA reference, how CS itself is modified by CA is unknown. This work evaluates the effect of each ablation step on the atrial substrate and CS funtion. Five-minute lead II and bipolar CS recordings of 29 patients undergoing paroxysmal AF CA were acquired before CA, after LPVI and after RPVI (end of CA). Separate lead II and CS analysis was performed. Duration, amplitude, area and slope rate were calculated for each surface and invasive activation, then signal-averaged. Dispersion, morphology variability (MV) and time-domain heart-rate variability (HRV) features were also calculated. Non-parametric tests were recruited to compare each feature among all and in pairs of different ablation steps with Bonferroni correction. Variation of each feature was calculated in percentages. In surface recordings, duration was significantly shortened after LPVI (¿= -13%, p=0.001) and HRV showed a trend for attenuation (¿+73%, p<0.04S), tending to decrease after RPVI (¿< -33%, p<0.064). Higher dispersion in variations was observed in CS than surface recordings. LPVI causes major alterations in atrial substrate, more prominently observed from lead II analysis. Notwithstanding, HRV variations are better illustrated in CS recordings. A combined analysis of both is recommended.Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2021/286 from GVA.Vraka, A.; Hornero, F.; Quesada, A.; Ravelli, F.; Alcaraz, R.; Rieta, JJ. (2021). Parallel Study on Surface and Invasive Recordings Across Catheter Ablation Steps of Paroxysmal Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.1109/EHB52898.2021.96576951

Novel Photoplethysmographic and Electrocardiographic Features for Enhanced Detection of Hypertensive Individuals

[EN] Hypertension is a major risk factor for many cardiovascular diseases, which are the leading cause of death worldwide. Regular monitoring is essential to provide early diagnosis since most patients with elevated blood pressure (BP) have asymptomatic hypertension. This work presents a method for BP classification using simultaneous electrocardiographic (ECG), photoplethysmographic (PPG) and BP signals. 86 recordings were used, being 35 normotensive, 26 prehypertensive and 25 hypertensive. It has been proposed 23 novel features to improve the discrimination between healthy and hypertensive individuals based on pulse arrival times (PAT) and morphological features from PPG, VPG and APG signal. Moreover, alternative classification models as Support Vector Machines (SVM), Naive Bayes or Coarse Trees were trained with the defined features to compare the classification performance. The classifier that provided the highest results comparing normotensive with prehypertensive and hypertensive subjects were Coarse Tree, providing an F1 score of 85.44% (Se of 86.27% and Sp of 77.14%). The use of new PPG and ECG features has successfully improved the discrimination between healthy and hypertensive individuals, around 7% of F1 score, so this machine learning methodology would be of high interest to detect HT introducing these techniques in wearable devices.Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2021/286 from GVA.Cano, J.; Quesada, A.; Ravelli, F.; Zangróniz, R.; Alcaraz, R.; Rieta, JJ. (2021). Novel Photoplethysmographic and Electrocardiographic Features for Enhanced Detection of Hypertensive Individuals. IEEE. 1-4. https://doi.org/10.1109/EHB52898.2021.96575461

Reliability of Local Activation Waves Features to Characterize Paroxysmal Atrial Fibrillation Substrate During Sinus Rhythm

[EN] Analysis of coronary sinus (CS) electrograms (EGMs) is vastly used for the assessment of the atrial fibrillation (AF) substrate. As a catheter consists of five dipoles (distal, mid-distal, medial, mid-proximal, proximal), results may vary upon the employed channel: myocardial contraction and bad contact are unavoidable factors affecting the recording. This work aims to specify the most reliable channels in catching AF dynamics, using 44 multichannel bipolar CS recordings in sinus rhythm (SR) of paroxysmal AF with 1-5 minutes duration. Local activation waves (LAWs) were detected and main features obtained: duration, amplitude, area and correlation between dominant morphologies of each channel. Analysis was performed with Kruskal-Wallis test for multichannel comparison and Mann-Whitney U-test for pairs of channels and comparison between one and the remaining channels, using Bonferroni correction. Median values were calculated. Distal channel presented the highest alteration in LAWs features, being the least correlated channel (82.84 - 88.31%) with the lowest amplitude and area (p(max) < 0.01). Contrastly, medial and mid-proximal channels showed the most robust LAW characteristics, with very high correlation (94.53%) and high area and amplitude values (p(max) < 0.02 and p(max) < 0.07, respectively) and their analysis is recommended for AF substrate characterization during SRResearch supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2019/036 from GVA.Vraka, A.; Hornero, F.; Quesada, A.; Faes, L.; Alcaraz, R.; Rieta, JJ. (2020). Reliability of Local Activation Waves Features to Characterize Paroxysmal Atrial Fibrillation Substrate During Sinus Rhythm. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.166S1

Splitting the P-Wave: Improved Evaluation of Left Atrial Substrate Modification after Pulmonary Vein Isolation of Paroxysmal Atrial Fibrillation

[EN] Atrial substrate modification after pulmonary vein isolation (PVI) of paroxysmal atrial fibrillation (pAF) can be assessed non-invasively by analyzing P-wave duration in the electrocardiogram (ECG). However, whether right (RA) and left atrium (LA) contribute equally to this phenomenon remains unknown. The present study splits fundamental P-wave features to investigate the different RA and LA contributions to P-wave duration. Recordings of 29 pAF patients undergoing first-ever PVI were acquired before and after PVI. P-wave features were calculated: P-wave duration (PWD), duration of the first (PWDon-peak) and second (PWDpeak-off) P-wave halves, estimating RA and LA conduction, respectively. P-wave onset (PWon-R) or offset (PWoff-R) to R-peak interval, measuring combined atrial/atrioventricular and single atrioventricular conduction, respectively. Heart-rate fluctuation was corrected by scaling. Pre- and post-PVI results were compared with Mann-Whitney U-test. PWD was correlated with the remaining features. Only PWD (non-scaling: & UDelta;=-9.84%, p=0.0085, scaling: & UDelta;=-17.96%, p=0.0442) and PWDpeak-off (non-scaling: & UDelta;=-22.03%, p=0.0250, scaling: & UDelta;=-27.77%, p=0.0268) were decreased. Correlation of all features with PWD was significant before/after PVI (p < 0.0001), showing the highest value between PWD and PWon-R (rho max=0.855). PWD correlated more with PWDon-peak (rho= 0.540-0.805) than PWDpeak-off (rho= 0.419-0.710). PWD shortening after PVI of pAF stems mainly from the second half of the P-wave. Therefore, noninvasive estimation of LA conduction time is critical for the study of atrial substrate modification after PVI and should be addressed by splitting the P-wave in order to achieve improved estimations.This research received financial support from public grants DPI2017-83952-C3, PID2021-00X128525-IV0 and PID2021-123804OB-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU), SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2021/286 from Generalitat Valenciana.Vraka, A.; Bertomeu-González, V.; Hornero, F.; Quesada, A.; Alcaraz, R.; Rieta, JJ. (2022). Splitting the P-Wave: Improved Evaluation of Left Atrial Substrate Modification after Pulmonary Vein Isolation of Paroxysmal Atrial Fibrillation. Sensors. 22(1):1-13. https://doi.org/10.3390/s2201029011322

Improved Discrimination Between Healthy and Hypertensive Individuals Combining Photoplethysmography and Electrocardiography

[EN] Cardiovascular disease is one of the leading causes of death, with hypertension (HT) being its main risk factor. Its complications can be avoided with early treatment, but since these patients do not present any symptoms, HT is often detected at very advanced stages. This work presents a model for estimating blood pressure (BP) from electrocardiographic (ECG) and photoplethysmographic (PPG) signals, which can be easily obtained by means of wearable continuous monitoring devices. ECG, PPG and BP recordings from 86 patients were analyzed.A total of 34 standard and new features based on previous works were defined, such as pulse arrival times (PAT), and morphological characteristics of PPG signal. 37 classification models, ranging from Logistic Regression, Support Vector Machines (SVM), Nearest Neighbors, Naive Bayes or Coarse Trees were trained to compare discrimination results. The classifier that provided the highest performance when comparing normotensive patients with prehypertensive and hypertensive patients were Coarse Tree, providing an F1 score of 85.44% (Se of 86.27% and Sp of 77.14%). The use of PPG and ECG features has successfully discriminated between healthy and hypertensive individuals and, thus, could be used to detect HT by embedding these techniques in wearable devices.Research supported by grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2021/286 from GVA.Cano, J.; Hornero, F.; Quesada, A.; Martínez-Rodrigo, A.; Alcaraz, R.; Rieta, JJ. (2021). Improved Discrimination Between Healthy and Hypertensive Individuals Combining Photoplethysmography and Electrocardiography. 1-4. https://doi.org/10.22489/CinC.2021.0301

Estudio químico y biológico de especies del género Azorella

143 p.El presente estudio tuvo por finalidad aislar e identificar los metabolitos mayoritarios a partir de algunas plantas del género Azorella, (A.spinosa, A. madreporica) cuyas especies forman parte de la familia Apiaceae, además de obtener derivados semisintéticos en la medida que fue posible y probar la actividad biológica de estos compuestos. Investigaciones realizadas en otros géneros de Azorella han permitido la identificación de varios compuestos diterpenicos del tipo azorellano y mulinano, algunos de estos diterpenos han sido responsables de distintas actividades biológicas, tales como antibacteriana, antiinflamatoria, inhibición de la acetilcolinesterasa, entre otras. El estudio químico de las partes aéreas de la especie A. spinosa permitió aislar diterpenos, un triterpeno, cumarina e isoflavonas. A partir del extracto etéreo fueron aislados cuatro diterpenos dos de ellos reportados previamente en literatura, en otras especies 13α-hidroxiazorellano, ácido mulinólico y dos compuestos nuevos 2-acetoxi-13-hidroxi-mulin-11-eno, 2-acetoxi-mulin-11,13-dieno, a partir de 2-acetoxi-13-hidroxi-mulin-11-eno se obtuvo el compuesto hidrolizado 2,13-dihidroxi-mulin-11-eno. Por otra parte del extracto metanólico se aisló un triterpeno, que resulto ser la lactona del ácido ursólico siendo este compuesto nuevo en esta especie pero ya reportado en otras plantas, de este mismo extracto se aisló quercetina y 7-hidroxicumarina.A partir del estudio químico de la A. madreporica fueron aisladas tres isoflavonas las cuales corresponden a alpinumisoflavona, licoisoflavona A y angustona C, compuestos reportados por primera vez en esta especie. A partir de la alpinumisoflavona se obtuvo el derivado diacetilado 4´,4´´-diacetoxi-alpinumisoflavona y de la acetilación de licoisoflavona se obtuvo el derivado monoacetilado 7-acetoxi-licoisoflavona A. Todos los compuestos aislados que resultaron ser nuevos o informados por primera vez en estas especies, como también los derivados semisintéticos preparados, fueron evaluados mediante el ensayo de microdilución en microplacas como agentes antibacterianos frente a Escherichia coli, Acinetobacter baumanni, Pseudomonas aeuroginosa y Staphilococcus aureus. Solo las isoflavonas alpinumisoflavona y licoisoflavona A fueron activas y selectivos sobre E. coli. Los diterpenos 2-acetoxi-13-hidroxi-mulin-11-eno, 2-acetoxi-mulin-11,13-dieno y el derivado 2,13-dihidroxi-mulin-11-eno, fueron evaluados en el ensayo de inhibición de la enzima acetilcolinesterasa (AChE) y la butirilcolinesterasa (BuChE) y sobre la actividad antioxidante empleando el ensayo DPPH. Por otro lado, el extracto metanólico de A. spinosa recolectada en la zona de Constitución (Región del Maule) y tres extractos metanólicos de A. monantha recolectadas en tres localidades distintas Paso Vergara y Enladrillado (Región del Maule), y Torres del Paine (Región de Magallanes) se evaluaron sobre todas las actividades biológicas anteriormente descritas, incluyendo la actividad antiplaquetaria, siendo los extractos de A. spinosa y A. monantha (Paso Vergara) las que mostraron mejores resultados./ABSTRACT: The aim of this study was to isolate and to identify the main metabolites from some plants of the Azorella (A.spinosa, A. madreporica) genre whose species are part of the Apiaceae family. Also, this study helped to obtain semisynthetic derivatives as possible as far and to test the biological activity of these compounds. Research in other Azorella genres have allowed the identification of several diterpene compounds of the mulinane and azorellane type. Some of these diterpenes have been responsible of the various biological activities such as antibacterial, anti-inflammatory, inhibition of acetylcholinesterase, and others. The chemical study of the aerial parts of the species A. spinosa allowed to isolate diterpenes, triterpenes, isoflavones and coumarin. From the ethereal extract, four diterpenes were isolated, two of them were previously reported in literature, in other species like the 13α-hydroxyazorellane and mulinolic acid, and the other two new compounds, 2-acetoxy-13-hydroxy- mulin-11-en, 2-acetoxy-mulin 11,13-diene, starting from the 2-acetoxy-13-hydroxy- mulin-11-en compound was obtained the 2,13-dihydroxy-mulin-11-en. By the other side, a triterpene was isolated from the methanol extract, which has proved to be the lactone of the ursolic acid being this a new compound in this specie but already reported in other plants. From the same extract was isolated quercetin and 7-hydroxycoumarin. From the chemical study of the A. madreprica, three isoflavones were isolated which correspond to alpinumisoflavone, angustone C and licoisoflavone A, compounds reported by the first time in this species. Starting from the alpinumisoflavone, the diacetyl derivative was obtained 4',4''-diacetoxy-alpinumisoflavoe and from the licoisoflavone acetylation, 7-acetoxy-licoisoflavone A monoacetylated derivative was obtained. All these compounds were elucidated using conventional spectroscopic techniques and by mean of the comparison with literature data. All those isolated compounds were found to be new or reported by first time in these species. Also, semisynthetic derivatives were evaluated by the microdilution in assay microplates as antibacterial against Escherichia coli, Acinetobacter baumanni, Pseudomonas aeruginosa and Staphylococcus aureus. Only the alpinumisoflavone and licoisoflavone A isoflavones were active and selective over the E. coli. The diterpenes 2-acetoxy-13-hydroxy-mulin-11-en, 2-acetoxy-mulin-11,13-diene and 2,13-dihydroxy mulin-11-en derivative were evaluated in the inhibition assay of the acetylcholinesterase (AChE) enzyme and the butyrylcholinesterase (BuChE). Also, the antioxidant activity were evaluated using the DPPH assay.By the other hand, the methanolic extract of A. spinosa collected from Constitución zone (Region of Maule) and the three methanol extracts of A. monantha collected from three different locations like Paso Vergara and Enladrillado (Region of Maule), and Torres del Paine (Region of Magallanes) were evaluated on over all the biological activities described above, including antiplatelet activity, being the extracts of A. spinosa and A. monantha (Paso Vergara) that showed the best result

Limb Versus Precordial ECG Leads as Improved Predictors of Electrical Cardioversion Outcome in Persistent Atrial Fibrillation

[EN] Electrical cardioversion (ECV) is an effective and low-cost rhythm control strategy for persistent atrial fibrillation (AF). Because of its limited mid- and long-term success rates, prediction of early failure could avoid patients with reduced chance to maintain sinus rhythm (SR). To this end and due to its proximity to the right atrium, several indices characterizing atrial activity have been proposed based on lead V1. However, information from other leads has been discarded to date. Hence, this work studies how effective some common indices computed over the whole set of 12 standard ECG leads are in predicting ECV outcome. Precisely, amplitude, dominant frequency, and sample entropy were computed from the fibrillatory (f-) waves extracted for each one of 12 standard leads acquired before ECV from 58 patients in persistent AF. The classification between the patients who relapsed to AF and maintained sinus rhythm after a follow-up of 4 weeks achieved by these parameters was better from limb lead II than from V1, thus reporting improvements about 6 and 12%. As a consequence, characterization of f-waves from the more accessible limb lead II has proven to be the best choice to improve AF ECV outcome prediction from the ECG.This research was funded by the projects DPI2017-83952C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from "Junta de Castilla La Mancha" and AICO/2019/036 from "Generalitat Valenciana".Cirugeda, EM.; Calero, S.; Quesada, A.; Hidalgo, VM.; Rieta, JJ.; Alcaraz, R. (2020). Limb Versus Precordial ECG Leads as Improved Predictors of Electrical Cardioversion Outcome in Persistent Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.373S1

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

[EN] Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient's electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.Huerta Herraiz, Á.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Quesada, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. Entropy. 22(7):1-17. https://doi.org/10.3390/e22070733S117227Lippi, G., Sanchis-Gomar, F., & Cervellin, G. (2020). Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. International Journal of Stroke, 16(2), 217-221. doi:10.1177/1747493019897870Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T., & Liu, X. (2013). Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population. The American Journal of Cardiology, 112(8), 1142-1147. doi:10.1016/j.amjcard.2013.05.063Khoo, C. W., Krishnamoorthy, S., Lim, H. S., & Lip, G. Y. H. (2012). Atrial fibrillation, arrhythmia burden and thrombogenesis. International Journal of Cardiology, 157(3), 318-323. doi:10.1016/j.ijcard.2011.06.088Warmus, P., Niedziela, N., Huć, M., Wierzbicki, K., & Adamczyk-Sowa, M. (2020). Assessment of the manifestations of atrial fibrillation in patients with acute cerebral stroke – a single-center study based on 998 patients. Neurological Research, 42(6), 471-476. doi:10.1080/01616412.2020.1746508Sposato, L. A., Cipriano, L. E., Saposnik, G., Vargas, E. R., Riccio, P. M., & Hachinski, V. (2015). Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. The Lancet Neurology, 14(4), 377-387. doi:10.1016/s1474-4422(15)70027-xSchotten, U., Dobrev, D., Platonov, P. G., Kottkamp, H., & Hindricks, G. (2016). Current controversies in determining the main mechanisms of atrial fibrillation. Journal of Internal Medicine, 279(5), 428-438. doi:10.1111/joim.12492Ferrari, R., Bertini, M., Blomstrom-Lundqvist, C., Dobrev, D., Kirchhof, P., Pappone, C., … Vicedomini, G. G. (2016). An update on atrial fibrillation in 2014: From pathophysiology to treatment. International Journal of Cardiology, 203, 22-29. doi:10.1016/j.ijcard.2015.10.089Meyre, P., Blum, S., Berger, S., Aeschbacher, S., Schoepfer, H., Briel, M., … Conen, D. (2019). Risk of Hospital Admissions in Patients With Atrial Fibrillation: A Systematic Review and Meta-analysis. Canadian Journal of Cardiology, 35(10), 1332-1343. doi:10.1016/j.cjca.2019.05.024Van Wagoner, D. R., Piccini, J. P., Albert, C. M., Anderson, M. E., Benjamin, E. J., Brundel, B., … Wehrens, X. H. T. (2015). Progress toward the prevention and treatment of atrial fibrillation: A summary of the Heart Rhythm Society Research Forum on the Treatment and Prevention of Atrial Fibrillation, Washington, DC, December 9–10, 2013. Heart Rhythm, 12(1), e5-e29. doi:10.1016/j.hrthm.2014.11.011De Vos, C. B., Pisters, R., Nieuwlaat, R., Prins, M. H., Tieleman, R. G., Coelen, R.-J. S., … Crijns, H. J. G. M. (2010). Progression From Paroxysmal to Persistent Atrial Fibrillation. Journal of the American College of Cardiology, 55(8), 725-731. doi:10.1016/j.jacc.2009.11.040SCHUCHERT, A., BEHRENS, G., & MEINERTZ, T. (1999). Impact of Long-Term ECG Recording on the Detection of Paroxysmal Atrial Fibrillation in Patients After an Acute Ischemic Stroke. Pacing and Clinical Electrophysiology, 22(7), 1082-1084. doi:10.1111/j.1540-8159.1999.tb00574.xPagola, J., Juega, J., Francisco-Pascual, J., Moya, A., Sanchis, M., Bustamante, A., … Arenillas, J. F. (2018). Yield of atrial fibrillation detection with Textile Wearable Holter from the acute phase of stroke: Pilot study of Crypto-AF registry. International Journal of Cardiology, 251, 45-50. doi:10.1016/j.ijcard.2017.10.063Luong, D. T., Ha, N. T., & Thuan, N. D. (2019). Android Smart Phones Application in Tele-monitoring Electrocardiogram (ECG). American Journal of Biomedical Sciences, 15-21. doi:10.5099/aj190100015Haverkamp, H. T., Fosse, S. O., & Schuster, P. (2019). Accuracy and usability of single-lead ECG from smartphones - A clinical study. Indian Pacing and Electrophysiology Journal, 19(4), 145-149. doi:10.1016/j.ipej.2019.02.006Nagai, S., Anzai, D., & Wang, J. (2017). Motion artefact removals for wearable ECG using stationary wavelet transform. Healthcare Technology Letters, 4(4), 138-141. doi:10.1049/htl.2016.0100Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment. IEEE Reviews in Biomedical Engineering, 11, 36-52. doi:10.1109/rbme.2018.2810957Aboukhalil, A., Nielsen, L., Saeed, M., Mark, R. G., & Clifford, G. D. (2008). Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. Journal of Biomedical Informatics, 41(3), 442-451. doi:10.1016/j.jbi.2008.03.003Bashar, S. K., Ding, E., Walkey, A. J., McManus, D. D., & Chon, K. H. (2019). Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients. IEEE Access, 7, 88357-88368. doi:10.1109/access.2019.2926199Yoon, D., Lim, H. S., Jung, K., Kim, T. Y., & Lee, S. (2019). Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model. Healthcare Informatics Research, 25(3), 201. doi:10.4258/hir.2019.25.3.201Oster, J., Behar, J., Sayadi, O., Nemati, S., Johnson, A. E. W., & Clifford, G. D. (2015). Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters. IEEE Transactions on Biomedical Engineering, 62(9), 2125-2134. doi:10.1109/tbme.2015.2402236Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: a review of the subtraction procedure. BioMedical Engineering OnLine, 4(1). doi:10.1186/1475-925x-4-50Luo, S., & Johnston, P. (2010). A review of electrocardiogram filtering. Journal of Electrocardiology, 43(6), 486-496. doi:10.1016/j.jelectrocard.2010.07.007Martínez, A., Alcaraz, R., & Rieta, J. J. (2010). Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiological Measurement, 31(11), 1467-1485. doi:10.1088/0967-3334/31/11/005Manikandan, M. S., & Ramkumar, B. (2014). Straightforward and robust QRS detection algorithm for wearable cardiac monitor. Healthcare Technology Letters, 1(1), 40-44. doi:10.1049/htl.2013.0019Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). An automated ECG signal quality assessment method for unsupervised diagnostic systems. Biocybernetics and Biomedical Engineering, 38(1), 54-70. doi:10.1016/j.bbe.2017.10.002Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring. IEEE Journal of Biomedical and Health Informatics, 22(3), 722-732. doi:10.1109/jbhi.2017.2686436Zhang, Q., Fu, L., & Gu, L. (2019). A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG. Computational and Mathematical Methods in Medicine, 2019, 1-12. doi:10.1155/2019/7095137Xu, X., Wei, S., Ma, C., Luo, K., Zhang, L., & Liu, C. (2018). Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. Journal of Healthcare Engineering, 2018, 1-8. doi:10.1155/2018/2102918Al Rahhal, M. M., Bazi, Y., Al Zuair, M., Othman, E., & BenJdira, B. (2018). Convolutional Neural Networks for Electrocardiogram Classification. Journal of Medical and Biological Engineering, 38(6), 1014-1025. doi:10.1007/s40846-018-0389-7He, R., Wang, K., Zhao, N., Liu, Y., Yuan, Y., Li, Q., & Zhang, H. (2018). Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01206Yildirim, O., Talo, M., Ay, B., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019). Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Computers in Biology and Medicine, 113, 103387. doi:10.1016/j.compbiomed.2019.103387SINGH, S. A., & MAJUMDER, S. (2019). A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK. Journal of Mechanics in Medicine and Biology, 19(04), 1950026. doi:10.1142/s021951941950026xByeon, Y.-H., Pan, S.-B., & Kwak, K.-C. (2019). Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics. Sensors, 19(4), 935. doi:10.3390/s19040935Clifford, G., Liu, C., Moody, B., Lehman, L., Silva, I., Li, Q., … Mark, R. (2017). AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017. 2017 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2017.065-469Redmond, S. J., Xie, Y., Chang, D., Basilakis, J., & Lovell, N. H. (2012). Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiological Measurement, 33(9), 1517-1533. doi:10.1088/0967-3334/33/9/1517Li, T., & Zhou, M. (2016). ECG Classification Using Wavelet Packet Entropy and Random Forests. Entropy, 18(8), 285. doi:10.3390/e18080285Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751-5757. doi:10.1016/j.eswa.2010.02.033Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P., & Rodriguez, B. (2018). Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of The Royal Society Interface, 15(138), 20170821. doi:10.1098/rsif.2017.0821Mincholé, A., & Rodriguez, B. (2019). Artificial intelligence for the electrocardiogram. Nature Medicine, 25(1), 22-23. doi:10.1038/s41591-018-0306-1Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48. doi:10.1016/j.neucom.2015.09.116Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002Zhao, Z., & Zhang, Y. (2018). SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00727Moeyersons, J., Smets, E., Morales, J., Villa, A., De Raedt, W., Testelmans, D., … Varon, C. (2019). Artefact detection and quality assessment of ambulatory ECG signals. Computer Methods and Programs in Biomedicine, 182, 105050. doi:10.1016/j.cmpb.2019.105050Clifford, G. D., Behar, J., Li, Q., & Rezek, I. (2012). Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiological Measurement, 33(9), 1419-1433. doi:10.1088/0967-3334/33/9/1419Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., & Tarassenko, L. (2014). Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2014.2338351Hayn, D., Jammerbund, B., & Schreier, G. (2012). QRS detection based ECG quality assessment. Physiological Measurement, 33(9), 1449-1461. doi:10.1088/0967-3334/33/9/1449Casey, S., Avalos, G., & Dowling, M. (2018). Critical care nurses’ knowledge of alarm fatigue and practices towards alarms: A multicentre study. Intensive and Critical Care Nursing, 48, 36-41. doi:10.1016/j.iccn.2018.05.004Nattel, S., Guasch, E., Savelieva, I., Cosio, F. G., Valverde, I., Halperin, J. L., … Camm, A. J. (2014). Early management of atrial fibrillation to prevent cardiovascular complications. European Heart Journal, 35(22), 1448-1456. doi:10.1093/eurheartj/ehu028Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B.-S., & Li, J. (2019). Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. IEEE Access, 7, 34060-34067. doi:10.1109/access.2019.2900719Petrėnas, A., Marozas, V., & Sörnmo, L. (2015). Low-complexity detection of atrial fibrillation in continuous long-term monitoring. Computers in Biology and Medicine, 65, 184-191. doi:10.1016/j.compbiomed.2015.01.01