A. Qureshi, M. Balmus, Steven E. Williams, G. Lip, D. Nordsletten, O. Aslanidi, A. Vecchi
Atrial fibrillation $(AF)$ is associated with a significantly increased risk of stroke due to the presence of three pro-thrombotic mechanisms known as Virchow's triad - blood stasis, endothelial damage and hypercoagulability - which primarily occur in the left atrial appendage $(LAA)$. Insilica evaluation of each factor can improve upon the current empirical stroke risk stratification for AF patients. Computational fluid dynamics simulations were performed on two patient-specific models of the left atrium, one in sinus rhythm $(SR)$ and one in $AF$ to quantify blood stasis and metrics of endothelial damage. Hypercoagulability was assessed by solving reaction-diffusion-convection equations for thrombin, fibrinogen and fibrin - three key clotting proteins, and varying initial concentrations of fibrinogen in accordance with clinical literature. An original grading system is proposed $(A= low, B = moderate, C=high$ risk) for each component of the triad to form a patient-specific risk profile. The $SR$ patient had a risk profile of $[A, B, A]$ showing a low-moderate risk of thrombus formation, while the $AF$ patient had $[C, B, C]$, indicating a very high risk of thrombus formation and increased potential for stroke. This novel modelling approach encapsulates all fundamental mechanisms of thrombus formation and may be used to improve stroke risk assessment for $AF$ patients.
{"title":"Modelling Virchow's Triad to Improve Stroke Risk Assessment in Atrial Fibrillation Patients","authors":"A. Qureshi, M. Balmus, Steven E. Williams, G. Lip, D. Nordsletten, O. Aslanidi, A. Vecchi","doi":"10.22489/CinC.2022.378","DOIUrl":"https://doi.org/10.22489/CinC.2022.378","url":null,"abstract":"Atrial fibrillation $(AF)$ is associated with a significantly increased risk of stroke due to the presence of three pro-thrombotic mechanisms known as Virchow's triad - blood stasis, endothelial damage and hypercoagulability - which primarily occur in the left atrial appendage $(LAA)$. Insilica evaluation of each factor can improve upon the current empirical stroke risk stratification for AF patients. Computational fluid dynamics simulations were performed on two patient-specific models of the left atrium, one in sinus rhythm $(SR)$ and one in $AF$ to quantify blood stasis and metrics of endothelial damage. Hypercoagulability was assessed by solving reaction-diffusion-convection equations for thrombin, fibrinogen and fibrin - three key clotting proteins, and varying initial concentrations of fibrinogen in accordance with clinical literature. An original grading system is proposed $(A= low, B = moderate, C=high$ risk) for each component of the triad to form a patient-specific risk profile. The $SR$ patient had a risk profile of $[A, B, A]$ showing a low-moderate risk of thrombus formation, while the $AF$ patient had $[C, B, C]$, indicating a very high risk of thrombus formation and increased potential for stroke. This novel modelling approach encapsulates all fundamental mechanisms of thrombus formation and may be used to improve stroke risk assessment for $AF$ patients.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"498 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130625188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aikaterini Vraka, J. Moreno-Arribas, Juan M Gracia-Baena, F. Ravelli, R. Alcaraz, J. J. Rieta
While pulmonary vein isolation (PVI) is the corner-stone of the paroxysmal atrial fibrillation (pAF) treatment, whether left (LPVI) and right PVI (RPVI) provoke equal atrial substrate modifications (ASMs), vastly assessed by P-waves, remains unexplored. Five-minute recordings from 40 pAF patients undergoing first-time PVI were extracted before PVI (B), after LPVI (L) and RPVI (R) at 1 kHz sampling rate. Signal-averaged P-wave features of duration, amplitude and area were calculated. Heartrate fluctuations (HRF) were mitigated for duration and area (HRDur,area). Results were compared between each transition (B-L: LPVI, L-R: RPVI) and between variations in values due to transitions with non-parametric tests. Duration $(Delta_{B-L}:-13.3%,p=0.001, Delta_{L-R}: +2.40%,p=0.558)$ and amplitude $Delta_{B-L}:-17.29%,p=0.055,Delta_{L-R}:+5.65%, p=0.319)$ got decreased after LPVI and slightly increased after RPVI. HRF mitigation mostly preserved these trends but lost statistical power (HRDur: $Delta_{B-L}: -10.54%,p=0.141,Delta_{L-R}: -5.52%,p=0.740)$. LPVI showed a significantly higher effect on duration than RPVI $(p < 0.0001)$. Variations observed in P-wave features after PVI stem principally from LPVI, which contributes significantly to the ASM. Studies focusing on ASM observation should implement and prioritize the analysis of LPVI recordings.
{"title":"Left Pulmonary Veins Isolation: The Cornerstone in Noninvasive Evaluation of Substrate Modification After Catheter Ablation of Paroxysmal Atrial Fibrillation","authors":"Aikaterini Vraka, J. Moreno-Arribas, Juan M Gracia-Baena, F. Ravelli, R. Alcaraz, J. J. Rieta","doi":"10.22489/CinC.2022.012","DOIUrl":"https://doi.org/10.22489/CinC.2022.012","url":null,"abstract":"While pulmonary vein isolation (PVI) is the corner-stone of the paroxysmal atrial fibrillation (pAF) treatment, whether left (LPVI) and right PVI (RPVI) provoke equal atrial substrate modifications (ASMs), vastly assessed by P-waves, remains unexplored. Five-minute recordings from 40 pAF patients undergoing first-time PVI were extracted before PVI (B), after LPVI (L) and RPVI (R) at 1 kHz sampling rate. Signal-averaged P-wave features of duration, amplitude and area were calculated. Heartrate fluctuations (HRF) were mitigated for duration and area (HRDur,area). Results were compared between each transition (B-L: LPVI, L-R: RPVI) and between variations in values due to transitions with non-parametric tests. Duration $(Delta_{B-L}:-13.3%,p=0.001, Delta_{L-R}: +2.40%,p=0.558)$ and amplitude $Delta_{B-L}:-17.29%,p=0.055,Delta_{L-R}:+5.65%, p=0.319)$ got decreased after LPVI and slightly increased after RPVI. HRF mitigation mostly preserved these trends but lost statistical power (HRDur: $Delta_{B-L}: -10.54%,p=0.141,Delta_{L-R}: -5.52%,p=0.740)$. LPVI showed a significantly higher effect on duration than RPVI $(p < 0.0001)$. Variations observed in P-wave features after PVI stem principally from LPVI, which contributes significantly to the ASM. Studies focusing on ASM observation should implement and prioritize the analysis of LPVI recordings.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123465481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Skoric, Y. D’Mello, Nathan Clairmonte, A. McLean, Siddiqui Hakim, Ezz Aboulezz, Michel A. Lortie, D. Plant
Wearable monitoring is important for the diagnosis, prevention, and treatment of cardiovascular diseases and overall cardiac health. A key indicator, Blood pressure (BP), currently relies on cuff-based devices for measurement that are cumbersome for ambulatory monitoring scenarios. Vibrational cardiography (VCG) is an unobtrusive, non-invasive tool which records cardiac vibrations on the surface of the chest. This work proposes using VCG in a novel method to estimate BP from a single point of contact. VCG was recorded by an inertial measurement unit on the xiphoid process of 62 subjects. A convolutional neural network was trained on the VCG waveforms to estimate systolic and diastolic BP. This resulted in an r-squared correlation coefficient of 0.86 and 0.89 and a mean-absolute-error of 3.4 mmHg and 2.2 mmHg for systolic and diastolic BP, respectively. Therefore, this work shows the applicability of using exclusively VCG for BP estimation. It affirms the value of VCG as an all-purpose health monitor, while also improving on the current techniques for continuous BP monitoring. This indicates the potential of VCG in many forms of wearable monitoring including remote healthcare, fitness, and wellness monitoring.
{"title":"Cuff-less Estimation of Blood Pressure from Vibrational Cardiography Using a Convolutional Neural Network","authors":"J. Skoric, Y. D’Mello, Nathan Clairmonte, A. McLean, Siddiqui Hakim, Ezz Aboulezz, Michel A. Lortie, D. Plant","doi":"10.22489/CinC.2022.110","DOIUrl":"https://doi.org/10.22489/CinC.2022.110","url":null,"abstract":"Wearable monitoring is important for the diagnosis, prevention, and treatment of cardiovascular diseases and overall cardiac health. A key indicator, Blood pressure (BP), currently relies on cuff-based devices for measurement that are cumbersome for ambulatory monitoring scenarios. Vibrational cardiography (VCG) is an unobtrusive, non-invasive tool which records cardiac vibrations on the surface of the chest. This work proposes using VCG in a novel method to estimate BP from a single point of contact. VCG was recorded by an inertial measurement unit on the xiphoid process of 62 subjects. A convolutional neural network was trained on the VCG waveforms to estimate systolic and diastolic BP. This resulted in an r-squared correlation coefficient of 0.86 and 0.89 and a mean-absolute-error of 3.4 mmHg and 2.2 mmHg for systolic and diastolic BP, respectively. Therefore, this work shows the applicability of using exclusively VCG for BP estimation. It affirms the value of VCG as an all-purpose health monitor, while also improving on the current techniques for continuous BP monitoring. This indicates the potential of VCG in many forms of wearable monitoring including remote healthcare, fitness, and wellness monitoring.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121536167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We participated in the George B. Moody Challenge 2022 to make a model which detects the presence or absence of murmurs from multiple heart sound recordings from multiple auscultation locations, as well as detecting the clinical outcomes from phonocardiogram (PCG) well. Our team, HCCL, developed a model with a visual approach for deriving a high-performance model. The model converts heart sound signals into spectrograms without requiring resampling or signal filtering. The result shows a weighted accuracy score of 0.69 (ranked 21th out of 40 teams) for the murmur detection classification on the hidden test data. For the clinical outcome identification task on the hidden test data, it shows a Challenge cost score of 11943 (ranked 6th out of 39 teams)
{"title":"Classification of Phonocardiogram Recordings Using Vision Transformer Architecture","authors":"Joonyeob Kim, Gibeom Park, B. Suh","doi":"10.22489/CinC.2022.084","DOIUrl":"https://doi.org/10.22489/CinC.2022.084","url":null,"abstract":"We participated in the George B. Moody Challenge 2022 to make a model which detects the presence or absence of murmurs from multiple heart sound recordings from multiple auscultation locations, as well as detecting the clinical outcomes from phonocardiogram (PCG) well. Our team, HCCL, developed a model with a visual approach for deriving a high-performance model. The model converts heart sound signals into spectrograms without requiring resampling or signal filtering. The result shows a weighted accuracy score of 0.69 (ranked 21th out of 40 teams) for the murmur detection classification on the hidden test data. For the clinical outcome identification task on the hidden test data, it shows a Challenge cost score of 11943 (ranked 6th out of 39 teams)","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114749079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
"Helena Puente-Díaz, R. García-Carretero, R. Goya-Esteban, Ó. Barquero-Pérez
Heart rate turbulence (HRT) is a physiological phenomenon used for cardiac risk stratification. Its alteration or absence indicates a significantly increased likelihood of mortality. However, the influence of the autonomic nervous system (ANS) on HRT needs to be further investigated. We propose a cause-effect relationship model to quantify the influence of the ANS. A set of 481 Holter-monitor recordings from different medical conditions were used, from THEW· acute myocardial infarction, coronary artery disease and end-stage renal disease. We proposed to model the relationship between HRT and ANS using Partial Least Squares Path Modeling (PLS-PM), a method for structural equation modeling that allows analyzing the relationships between the observed data and the latent variables. HRT parameters were estimated on individual ventricular premature complex (VPC) tachograms. ANS was assessed by heart rate variability indices computed from 3-min before VPC tachograms. The data set was split into low-risk and high-risk subgroups. SDN N, PLP, TS and TO were the most relevant variables. In low-risk, ANS activity was negatively related to HRT, while correlation between coupling interval and HRT on high-risk depends on the pathology. PLS-PM suggests that the influence of physiological variables on HRT is broken on high-risk. Results of the model are in agreement with the baroreflex hypothesis.
{"title":"Quantifying the Autonomic Nervous System Influence on Heart Rate Turbulence using Partial Least Squares Path Modeling","authors":"\"Helena Puente-Díaz, R. García-Carretero, R. Goya-Esteban, Ó. Barquero-Pérez","doi":"10.22489/CinC.2022.147","DOIUrl":"https://doi.org/10.22489/CinC.2022.147","url":null,"abstract":"Heart rate turbulence (HRT) is a physiological phenomenon used for cardiac risk stratification. Its alteration or absence indicates a significantly increased likelihood of mortality. However, the influence of the autonomic nervous system (ANS) on HRT needs to be further investigated. We propose a cause-effect relationship model to quantify the influence of the ANS. A set of 481 Holter-monitor recordings from different medical conditions were used, from THEW· acute myocardial infarction, coronary artery disease and end-stage renal disease. We proposed to model the relationship between HRT and ANS using Partial Least Squares Path Modeling (PLS-PM), a method for structural equation modeling that allows analyzing the relationships between the observed data and the latent variables. HRT parameters were estimated on individual ventricular premature complex (VPC) tachograms. ANS was assessed by heart rate variability indices computed from 3-min before VPC tachograms. The data set was split into low-risk and high-risk subgroups. SDN N, PLP, TS and TO were the most relevant variables. In low-risk, ANS activity was negatively related to HRT, while correlation between coupling interval and HRT on high-risk depends on the pathology. PLS-PM suggests that the influence of physiological variables on HRT is broken on high-risk. Results of the model are in agreement with the baroreflex hypothesis.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126018541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Bachi, M. Varanini, Magda Costi, D. Lombardi, F. Rangoni, L. Billeci
Noise reduction is a fundamental aspect of stress electrocardiogram $(ECG)$ recording. In this setting, muscular noise represents the main antagonist to signal quality. A possible solution to muscle noise in stress $ECG$ is to exploit the information redundancy in 12 - lead recordings to reduce noise while preserving the $ECG$ signal. Source Consistency Filtering $(SCF)$ is a spatial redundancy filter that follows this principle. In this paper, we compare the muscle noise rejection performance of conventional $25Hz$ and $40Hz$ low-pass filters (LPFs), the SC $F$ ‘ and a method based on singular value decomposition $(SVD)$ which exploits both the spatial and temporal correlation in the $ECG$ signal. Our results indicate that the $SCF$ can afford a $QRS$ complex distortion lower than that of a 40 $Hz$ lowpass filter while still maintaining a high noise rejection. The $QRS$ detection accuracy on the filtered $ECG$ was comparable for all methods except for the $SVD$ filter, which allowed a superior detection performance score in all the records.
{"title":"Multichannel ECG Filtering: Source Consistency Filtering, Eigenfiltering and Traditional Methods","authors":"L. Bachi, M. Varanini, Magda Costi, D. Lombardi, F. Rangoni, L. Billeci","doi":"10.22489/CinC.2022.168","DOIUrl":"https://doi.org/10.22489/CinC.2022.168","url":null,"abstract":"Noise reduction is a fundamental aspect of stress electrocardiogram <tex>$(ECG)$</tex> recording. In this setting, muscular noise represents the main antagonist to signal quality. A possible solution to muscle noise in stress <tex>$ECG$</tex> is to exploit the information redundancy in 12 - lead recordings to reduce noise while preserving the <tex>$ECG$</tex> signal. Source Consistency Filtering <tex>$(SCF)$</tex> is a spatial redundancy filter that follows this principle. In this paper, we compare the muscle noise rejection performance of conventional <tex>$25Hz$</tex> and <tex>$40Hz$</tex> low-pass filters (LPFs), the SC <tex>$F$</tex> ‘ and a method based on singular value decomposition <tex>$(SVD)$</tex> which exploits both the spatial and temporal correlation in the <tex>$ECG$</tex> signal. Our results indicate that the <tex>$SCF$</tex> can afford a <tex>$QRS$</tex> complex distortion lower than that of a 40 <tex>$Hz$</tex> lowpass filter while still maintaining a high noise rejection. The <tex>$QRS$</tex> detection accuracy on the filtered <tex>$ECG$</tex> was comparable for all methods except for the <tex>$SVD$</tex> filter, which allowed a superior detection performance score in all the records.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131839219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ilaria Marcantoni, Erica Iammarino, A. Sbrollini, M. Morettini, L. Burattini
Electrocardiographic alternans (ECGA) is the ABAB fluctuation of the electrocardiogram (ECG) and may manifest as P-wave/QRS-complex/T-wave alternans (PWA/QRSA/TWA). ECGA is a cardiovascular risk index, and its characterization may depend on the automatic identification method. Normal ranges (needed to define risk conditions) are still not available for the new enhanced adaptive matched filter (EAMF) method. Thus, the present study aims to provide them. EAMF was used to characterize ECGA (in terms of: amplitude, $mu V;$ area, $mu Vtimes ms$; and duration, number of beats) in 15-lead ECG from 52 healthy subjects (39/13 male/female), from the “PTB Diagnostic ECG Database”. Median ECGA values over leads and subjects were: $2 mu V, 200 mu Vtimes ms$, and 17 beats for PWA; $1 mu V, 80 mu Vtimes ms$, and 8 beats for QRSA; and $7 mu V, {1300} mu Vtimes ms$, and 49 beats for TWA. ECGA in females $(PWA:4 mu V, 350 mu Vtimes ms$ ,and 22 beats; QRSA: 1 $mu V, 80 mu V times ms$, and 11 beats; TWA: $10 mu V; 2000 mu Vtimes ms$, and 49 beats) was higher $(^{*}p < 0.05)$ than ECGA in males (PWA: $20 mu V^{*}, 200 mu Vtimes ms^{*}$, and $16 beats^{*}$; QRSA: $1 mu V, 80 mu Vtimes ms$, and 7 beats; TWA: $6mu V, 1150 mu Vtimes ms$, and 48 beats). Maximum ECGA values were observed in fundamental leads. The observed reference ECGA values seem reliable if comparing with pathological populations but are initial and analysis of wider datasets is needed.
心电图交替(Electrocardiographic alternating, ECGA)是心电图的ABAB波动，可表现为p波/QRS-complex/ t波交替(PWA/QRSA/TWA)。ECGA是一种心血管风险指标，其表征可能依赖于自动识别方法。对于新的增强自适应匹配滤波器(EAMF)方法来说，正常范围(定义风险条件所需)仍然不可用。因此，本研究旨在提供它们。用EAMF表征ECGA(振幅，$mu V;面积，$mu V乘以ms;52例健康受试者(男/女39/13)15导联心电图(PTB诊断心电图数据库)的持续时间、心跳次数。导联和受试者的ECGA值中位数为:2 mu V, 200 mu V乘以ms$， PWA为17次;$1 mu V， $ 80 mu V乘以ms$， QRSA为8次节拍;$7 mu V， {1300} mu V乘以ms$， TWA为49次。雌性ECGA $(PWA:4 mu V, 350 mu Vtimes ms$， 22次跳动;QRSA: 1 $ mu V, 80 mu V 乘以ms$， 11次节拍;环球航空:$10 mu V;2000 mu V 倍ms$， 49次跳动$(^{*}p < 0.05)$高于男性(PWA: 20 mu V倍ms$， 200 mu V倍ms$， 16 beats^{*}$;QRSA: $1 mu V， $ 80 mu V乘以ms$， 7次节拍;TWA: $6 mu V， $ 1150 mu V乘以ms$，和48次节拍)。在基本导联处观察到最大ECGA值。如果与病理人群比较，观察到的参考ECGA值似乎是可靠的，但这是初始的，需要对更广泛的数据集进行分析。
{"title":"Initial Reference Values of Electrocardiographic Alternans by Enhanced Adaptive Matched Filter","authors":"Ilaria Marcantoni, Erica Iammarino, A. Sbrollini, M. Morettini, L. Burattini","doi":"10.22489/CinC.2022.208","DOIUrl":"https://doi.org/10.22489/CinC.2022.208","url":null,"abstract":"Electrocardiographic alternans (ECGA) is the ABAB fluctuation of the electrocardiogram (ECG) and may manifest as P-wave/QRS-complex/T-wave alternans (PWA/QRSA/TWA). ECGA is a cardiovascular risk index, and its characterization may depend on the automatic identification method. Normal ranges (needed to define risk conditions) are still not available for the new enhanced adaptive matched filter (EAMF) method. Thus, the present study aims to provide them. EAMF was used to characterize ECGA (in terms of: amplitude, $mu V;$ area, $mu Vtimes ms$; and duration, number of beats) in 15-lead ECG from 52 healthy subjects (39/13 male/female), from the “PTB Diagnostic ECG Database”. Median ECGA values over leads and subjects were: $2 mu V, 200 mu Vtimes ms$, and 17 beats for PWA; $1 mu V, 80 mu Vtimes ms$, and 8 beats for QRSA; and $7 mu V, {1300} mu Vtimes ms$, and 49 beats for TWA. ECGA in females $(PWA:4 mu V, 350 mu Vtimes ms$ ,and 22 beats; QRSA: 1 $mu V, 80 mu V times ms$, and 11 beats; TWA: $10 mu V; 2000 mu Vtimes ms$, and 49 beats) was higher $(^{*}p < 0.05)$ than ECGA in males (PWA: $20 mu V^{*}, 200 mu Vtimes ms^{*}$, and $16 beats^{*}$; QRSA: $1 mu V, 80 mu Vtimes ms$, and 7 beats; TWA: $6mu V, 1150 mu Vtimes ms$, and 48 beats). Maximum ECGA values were observed in fundamental leads. The observed reference ECGA values seem reliable if comparing with pathological populations but are initial and analysis of wider datasets is needed.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"498 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131079555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Escalona, Sophie Magwood, Anna Hilton, N. McCallan
Heart rate variability (HRV) is a clinically important and prominent cardiovascular diseases diagnostic factor. Since HRV is a highly individualised measure, long-term continuous ECG and HRV tracking using a non-invasive armband-based wearable monitoring device is an appealing option for HRV trend-based indicator of general health. Therefore, we investigated the correlation between the bipolar arm-ECG Lead-1 (electrodes axis coplanar to chest and at axilla level) HRV measurements and their corresponding standard measurements from the standard chest ECG Lead I, using a 2 stage dB4 Wavelet-based denoising process supported by an iterative signal-averaged ECG optimal-thresholding adaptation algorithm on the arm-ECG signal, followed by a Pan-Tompkins QRS-detection algorithm. The conventional Pearson correlation coefficient was used as the main performance assessment metric. Four clinically common HRV time-domain metrics were studied: SDNN, RR-rms, RR-median and the interquartile-range value of normal-to-normal heartbeat intervals (IQRNN). The results revealed that RR-rms and RR-median HRV metrics from bipolar arm-ECG (Lead-1) closely correlated to the values measured from the standard Lead-I and present potential for clinical use.
{"title":"Feasibility of Wearable Armband Bipolar ECG Lead-1 for Long-term HRV Monitoring by Combined Signal Averaging and 2-stage Wavelet Denoising","authors":"O. Escalona, Sophie Magwood, Anna Hilton, N. McCallan","doi":"10.22489/CinC.2022.417","DOIUrl":"https://doi.org/10.22489/CinC.2022.417","url":null,"abstract":"Heart rate variability (HRV) is a clinically important and prominent cardiovascular diseases diagnostic factor. Since HRV is a highly individualised measure, long-term continuous ECG and HRV tracking using a non-invasive armband-based wearable monitoring device is an appealing option for HRV trend-based indicator of general health. Therefore, we investigated the correlation between the bipolar arm-ECG Lead-1 (electrodes axis coplanar to chest and at axilla level) HRV measurements and their corresponding standard measurements from the standard chest ECG Lead I, using a 2 stage dB4 Wavelet-based denoising process supported by an iterative signal-averaged ECG optimal-thresholding adaptation algorithm on the arm-ECG signal, followed by a Pan-Tompkins QRS-detection algorithm. The conventional Pearson correlation coefficient was used as the main performance assessment metric. Four clinically common HRV time-domain metrics were studied: SDNN, RR-rms, RR-median and the interquartile-range value of normal-to-normal heartbeat intervals (IQRNN). The results revealed that RR-rms and RR-median HRV metrics from bipolar arm-ECG (Lead-1) closely correlated to the values measured from the standard Lead-I and present potential for clinical use.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133597130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hassaan A. Bukhari, C. Sánchez, E. Pueyo, M. Potse
Parameter changes can cause long-term drift in membrane models. To reduce the cost of whole-heart simulations with such changes the stabilization can be performed in isolated-cell models, but it can then still take many beats to stabilize the full model. We hypothesized that differences in activation time leading to cycle length (CL) variability before the first beat contribute to this. To remove this variability we froze most state variables of the model until the sodium current activated. Simulations were performed with CL 400, 500, 600 and 1000 ms and modified Ten Tusscher-Panfilov 2006 dynamics. Isolated endocardial, mid-myocardial, and epicardial cells were simulated for 1000 beats. Their final states were then copied to a model of the whole human ventricles, which was run for 5 beats, with and without freezing. Stabilization of the full model took three to four beats. Freezing of the membrane state accelerated stabilization in some cell types but caused opposite drifts in others. Drifts were largest in the epicardial and mid-myocardial layers, and not in particular at their interfaces. Freezing of membrane state may help to accelerate stabilization but in our scenarios other types of drift dominate and may be aggravated by freezing, as it inhibits electrotonic interactions.
{"title":"Accelerating Stabilization of Whole-heart Models after Changes in Cycle Length","authors":"Hassaan A. Bukhari, C. Sánchez, E. Pueyo, M. Potse","doi":"10.22489/CinC.2022.388","DOIUrl":"https://doi.org/10.22489/CinC.2022.388","url":null,"abstract":"Parameter changes can cause long-term drift in membrane models. To reduce the cost of whole-heart simulations with such changes the stabilization can be performed in isolated-cell models, but it can then still take many beats to stabilize the full model. We hypothesized that differences in activation time leading to cycle length (CL) variability before the first beat contribute to this. To remove this variability we froze most state variables of the model until the sodium current activated. Simulations were performed with CL 400, 500, 600 and 1000 ms and modified Ten Tusscher-Panfilov 2006 dynamics. Isolated endocardial, mid-myocardial, and epicardial cells were simulated for 1000 beats. Their final states were then copied to a model of the whole human ventricles, which was run for 5 beats, with and without freezing. Stabilization of the full model took three to four beats. Freezing of the membrane state accelerated stabilization in some cell types but caused opposite drifts in others. Drifts were largest in the epicardial and mid-myocardial layers, and not in particular at their interfaces. Freezing of membrane state may help to accelerate stabilization but in our scenarios other types of drift dominate and may be aggravated by freezing, as it inhibits electrotonic interactions.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"363 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115428877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Juhola, H. Joutsijoki, R. Pölönen, K. Aalto-Setälä
Machine learning was applied to classify potential influence of two drugs on induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) on the basis of peak data detected from calcium transient signals of iPsC-CMs. The study shows that machine learning is capable to analyze such influence.
{"title":"Machine Learning of Drug Influence Based on iPSC Cardiomyocyte Calcium Transient Signals","authors":"M. Juhola, H. Joutsijoki, R. Pölönen, K. Aalto-Setälä","doi":"10.22489/CinC.2022.167","DOIUrl":"https://doi.org/10.22489/CinC.2022.167","url":null,"abstract":"Machine learning was applied to classify potential influence of two drugs on induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) on the basis of peak data detected from calcium transient signals of iPsC-CMs. The study shows that machine learning is capable to analyze such influence.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115489045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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