High Frequency QRS Components: A Comparative Analysis to Assess Myocardial Damage in Chronic Chagasic Patients Ezequiel de la Rosa#*1, Patricia Paglini-Oliva *☆†2, Elmer Andrés Fernández #*3 # Bioscience Data Mining Group, School of Engineering, Catholic University of Córdoba, Córdoba, Argentina. * CONICET, Argentina. 1 [email protected] 3 [email protected] ☆Instituto de Investigaciones en Ciencias de la Salud (INICSA), Córdoba, Argentina † Department of Biomedical Physics, Faculty of Medical Sciences, National University of Córdoba, Córdoba, Argentina 2 [email protected] Abstract— The aim of this work was to assess the effect of Keywords— High-frequency QRS, Chronic Chagas Disease, high-frequency QRS (HF-QRS) components over different Myocardial Damage. bandwidths and leads as a method to discern myocardial damage in a chronic Chagas disease cohort. We analysed 96 I. INTRODUCTION Chagasic subjects divided into three groups according to their High-frequency QRS components (HF-QRS) are one of cardiomyopathy damage degree and 11 healthy subjects. the most widely studied techniques in high- resolution ECG Signal-averaged ECG was obtained for each orthogonal lead (X-Y-Z) as well as the cardiac vector. Four bandwidths were (HRECG), as well as ventricular late potentials [1] and proposed in the analysis: 80-300Hz, 150-250 Hz, 40-80 Hz, and intra-QRS un-normal events [2]. Valuable studies have 40-300Hz. HF-QRS components were quantified in each assessed its clinical implications in different cardiopathies, bandwidth as the root-mean-square value of the filtered signals and novel methodologies have been proposed as a among the QRS endpoints (X-Y-Z and the cardiac vector were complementary diagnosis test. Myocardial infarction and individually assessed). Groups were compared using the acute myocardial ischemia have received, over the years, Mann-Whitney ‘U’ test. A ROC curve analysis was conducted the major attention in this field. Moreover, HF-QRS for each feature reflecting statistical significance (p<0.05) and components have proved to be more sensible to cardiac area under the curve values (AUC) were compared. HF-QRS disorders rather than other conventional parameters. For activity decreases as cardiac damage degree increases. All group comparison evidenced statistical significance. Overall, instance, Petterson et. al [3] determined that acute coronary the main effect was observed in 40-300 Hz (Y-lead), the artery occlusion could be detected with higher sensitivity bandwidth (channel) that achieved the highest AUC values using HF-QRS components instead of the 12-lead ECG. when discerning among groups. These results suggest HF-QRS Similarly, Conti et al. [4] obtained higher sensitivity in HF- could monitor cardiac damage progression in chronic QRS indexes for detecting chest pain. Chagasic cardiomyopathy. Nevertheless, the analysis of HF-QRS components as a tool to assess chronic Chagasic cardiomyopathy is still an Resumen— El objetivo del trabajo fue analizar las under explored issue. Chronic Chagas disease (ChD) componentes de alta frecuencia del QRS (HF-QRS) sobre disorders affect the conduction system in diverse forms [5], diferentes bandas frecuenciales y derivaciones para diferenciar grado de daño miocárdico en pacientes con Chagas crónico. Se from which bundle branch block is one of the most analizaron 96 sujetos Chagásicos divididos en tres grupos frequents. It is important to mention that surface ECG has según el daño cardíaco y 11 sujetos sanos. El ECG promediado been described as the most definitive test to assess the se obtuvo de derivaciones ortogonales (XYZ) y el vector Chagasic cardiomyopathy [5], thus non-invasive techniques cardíaco fue reconstruido. Cuatro bandas frecuenciales fueron play a key role for physicians. Hence, these allow us to analizadas: 80-300 Hz, 150-250 Hz, 40-80 Hz, y 40-300 Hz. Las hypothesize that the analysis of HF-QRS components in the componentes HF-QRS se estimaron en cada banda como el cardiac ChD form could provide important knowledge to valor cuadrático medio de las señales filtradas dentro de los assess myocardial damage degree and to monitor disease límites del QRS (X-Y-Z y el vector cardíaco fueron progression. Its physiological meaning has been associated considerados). Los grupos se contrastaron utilizando la prueba de Mann-Whitney ‘U’. Se realizó un análisis de curvas ROC with disorders in the cardiac action potential propagation. para cada parámetro que difirió estadísticamente (p<0.05) y Abboud et al. [6] suggested that altered HF-QRS las áreas bajo la curva (AUC) se compararon. La actividad de components could be related to fragmentation of the componentes HF-QRS decayó a medida que el daño cardíaco waveform over the myocardium, involving its propagation incrementaba. Todos los grupos evidenciaron significancia speed. Disruptions of ventricular activation could be due not estadística. En general, el mayor efecto se observó en 40-300 only to conduction system disorders, but also to ischemic or Hz (canal-Y), la banda (derivación) que obtuvo mayores AUC infarct areas [7]. para discernir entre grupos. Los resultados sugieren que la Overall, HF-QRS activity it is mainly assessed over the metodología podría utilizarse para evaluar progresión de daño 150-250 Hz bandwidth. However, in different studies cardíaco en sujetos Chagásicos. diverse lower/upper cutoff frequencies have been proposed. Bloomfield et al. [8] analyzed high-frequency content III. METHODS among 40-80Hz and also the 80-300 Hz bandwidth was explored [9]. A comparative analysis of the effect of acute A. HRECG Signal Processing myocardial ischemia over different bandwidths has been All signal processing analysis was developed under assessed in [7]. However, chronic ChD subjects have been Matlab® (The Mathworks Inc., Natick, Massachusetts, USA) humbly addressed. A preliminary study to characterize programming language. In order to assess HF-QRS spectral content in different bandwidths was reported in [10]. components in different bandwidths, a signal-averaged ECG Nevertheless, until now, comparison among HF-QRS (SAECG) was obtained for each orthogonal lead. SAECG is bandwidths to detect myocardial damage degree caused by a commonly used technique that allows very-low-amplitude the pathology was not evaluated. and high-frequency cardiac events assessment, such as In this work, a comparative analysis of HF-QRS ventricular late potentials and intra-QRS micro-potentials components over different bandwidths and leads is detection. The technique improves the signal-to-noise ratio conducted. Chagasic subjects with different cardiac damage when compared to the original record. are studied and bandwidths discriminants potential are An averaged, individual beat for each lead was obtained assessed by means of Receiver-Operating-Characteristic after ectopic and grossly noisy beats removal, beat (ROC) curves. alignment and averaging (--̅). All steps were conducted fulfilling the standard recommendations [1]. II. DATA SOURCE B. High-Frequency QRS components quantification 96 subjects from the ‘CARACAS’ database [11] with chronic Chagas disease and 11 healthy subjects were QRS waveform limits were estimated in each lead as well analysed in this work. Chagasic individuals were clinically as in the cardiac vector magnitude (CV) from averaged- classified, according to their myocardial damage degree, filtered leads (F(t)-F(t)-̅F(t)). into three groups as detailed in Table 1. For each subject, a The CV was computed as follows: 10-minute high resolution ECG record was obtained using the Frank-Starling (X-Y-Z) leads with the PREDICTOR () = () + () +̅() system (Corazonix Corporation, Oklahoma City, USA). Signals were acquired at 1000 Hz with an ADC resolution of 16 bits. Additionally, a medical history and four in which filtered channels are the results of applying a diagnosis tests (Machado-Guerrero serological test, Butterworth, bidirectional 4-order bandpass filter (40- conventional 12-lead ECG, echocardiography, and 24-hour 250Hz) that attenuates P and T waves and facilitates QRS Holter) were performed. All data was obtained under the endpoints identification [12]. QRS onset/offset were SEARCH project by the Simon Bolivar University, the detected in each lead and the CV using a recursive two- Central University of Venezuela and the University of moving window strategy as described in Laciar et al. [13]. Oklahoma, as stated in Mora et al. [11]. Although this technique was developed for CV endpoints estimation, in the individual leads the same technique has TABLE I ‘CARACAS’ DATABASE SUMMARY been applied but considering the absolute value of each lead, thus avoiding negative voltage values. G=Group; n=sample size; MG=Machado Guerrero Simultaneously, for HF-QRS components isolation, each serological test; ECG= 12-lead ECG; lead was bandpass filtered using the same filter Echo=Echocardiogram; Holter=24-hour Holter; configuration stated before but changing the cutoff Clinical M G n ECG Echo Holter frequencies. Four high-frequency bandwidths were Condition G considered in this study. QRS components were quantified 0 11 Healthy - Normal Normal Normal in each band as the root mean square (RMS) value of the Chronic components included within the QRS and delimited by the 1 41 Asymptomatic + Normal Normal Normal QRSon/QRSoff endpoints. HF-QRS values were computed ChD** as follows: Chronic Cardiac ChD Normal/ Normal/ 2 39 + Normal ={()} (weak/moderate Altered* PVB () damages) Chronic Cardiac where J=1,2,3,4 defines the considered bandwidth (BW1, Normal 3 16 ChD (severe + Altered* PVB/VT BW2, BW3, BW4); K is each of the averaged leads (--̅) /DEF damages) and the CV; w is the temporal window within QRS onset DEF=Diminished ejection fraction; PVB=Premature and QRS offset, and FJ indicates the filtered K signal in the Ventricular Beats; VT=Ventricular Tachycardia episodes; J bandwidth (cutoff frequencies fL and fH). *Altered ECG includes visible cardiac conduction defects Bandwidths BW1, BW2, BW3 and BW4 were computed such as bundle branch b lock; **G=1 was considered as with cutoff frequencies of {fL=80, fH=300Hz}, {fL=150, chronic asymptomatic ChD since despite being positive in fH=250Hz}, {fL=40, fH=80Hz} and {fL=40, fH=300Hz} the serological analysis, no alterations in the remaining tests respectively. were appreciated, as defined in [5] Fig. 1. Violin plots, boxplots and data distribution of all HF-QRS features. considered for differences among groups. IV. DATA ANALYSIS In order to compare and assess the discriminant potential All data analysis was done using R programming of the different HF-QRS features, a receiver operating language (https://www.r-project.org/). Comparative analysis characteristic’s curve analysis was conducted for each index of the 16 features computed for each record (4 bandwidths * that significant differed between two considered groups. 4 leads) was conducted. Exploratory analysis was Thus, the moving cutoff value of the discriminant rule performed by inspection of violin plots, boxplots and data assigns a record to a group if its feature value is below a distribution. Since some levels of heteroscedasticity can be threshold, otherwise falls into the remaining group. ROC appreciated, groups were contrasted using the non- analysis was performed using the ROCR library version 1.0- parametric Kruskal-Wallis test followed by Dunn’s multiple 5 [14]. Features were contrasted by means of the area under comparisons test. A significance level of 0.05 was the ROC curve (AUC). TABLE 2 P-VALUES FOR ALL GROUPS COMPARISON

Bandwidth Lead G0 vs G1 G0 vs G2 G0 vs G3 G1 vs G2 G1 vs G3 G2 vs G3 X* 0.6488 0.033 0.0012 0.01 0.0002 0.0438 Y* 0.1064 0.0004 0.0001 0.003 0.0001 0.0948 BW1 Z* 0.6058 0.0948 0.0014 0.0774 0.0002 0.0208 CVM* 0.4066 0.0148 0.0002 0.0138 0.0001 0.0208 X* 0.4162 0.014 0.0008 0.012 0.0004 0.07984 Y* 0.4176 0.0112 0.0006 0.0084 0.0004 0.1116 BW2 Z* 0.4172 0.1548 0.0054 0.3472 0.0058 0.0426 CVM* 0.632 0.0456 0.0016 0.02 0.0002 0.0652 X* 0.8482 0.0886 0.0016 0.021 0.0001 0.027 Y* 0.0728 0.0001 0.0001 0.001 0.0002 0.2334 BW3 Z* 0.7538 0.2272 0.015 0.0204 0.0004 0.0686 CVM* 0.4032 0.0146 0.0001 0.014 0.0001 0.0314 X* 0.9744 0.114 0.0008 0.018 0.0001 0.0086 Y* 0.1536 0.0001 0.0001 0.0002 0.0001 0.1346 BW4 Z* 0.6934 0.0504 0.0016 0.017 0.0002 0.0532 CVM* 0.4322 0.0112 0.0001 0.0074 0.0001 0.0096 *p<0.05 by means of the Kruskal-Wallis test; p<0.05 p<0.01 p<0.05 by means of the Dunn test. V. RESULTS damage ({G2vsG3}). Data distribution of each feature it is shown in Figure 1. VI. DISCUSSION AND CONCLUSION It can be observed that for all features, HF-QRS values decrease with myocardial damage degree. Thus, some of the In this work we evaluated the effects of high-frequency features reveal a progressive weakening of HF-QRS QRS features over healthy HRECG records and chronic components activity from the least (or none) cardiac Chagasic ones with different cardiac damage degree. Our damaged group (G0 and G1) to the most cardiac altered one results support the evidence of a high-frequency cardiac (G3). activity lessening in Chagasic subjects, with a possible Statistical analysis evidenced significant differences association between HF-QRS components and Chagasic among groups in many of the features considered. cardiomyopathy. In previous work using the same database, Excluding {G0vsG1}, at least one feature differed in each cardiac conduction system defects were also found. For group comparison, as it is shown in Table 2. The instance, Laciar et. al [12] analysed the dynamic/SAECG comparison between {G0vsG3} and {G1vsG3} (the groups QRS duration and the traditional indexes used for with none or weak cardiac damage and the most altered one) ventricular late potentials detection. Correa et al. [15] evidenced statistical differences for all the sixteen features. assessed myocardial damage through the vectorcardiogram, On the other hand, no differences were observed when and Pueyo et al. [16] characterized the QRS slopes in comparing healthy and chronic asymptomatic ChD chronic Chagasic subjects. Nevertheless, as far as the individuals ({G0vsG1} comparison). Only a borderline p- authors know, this work is the first one in which HF-QRS components effect over bandwidths and leads are value was achieved for (0.0728, Table 2.) In Figure 2 all AUC values are shown for each group considered in ChD. Hence, this analysis provides a valuable comparison. Overall, it can be observed that very insight to identify features with high discriminant competitive characteristics are obtained among bandwidths characteristics over patients suffering chronic ChD. and leads considered. According to Table 2, no evidences of myocardial It can be appreciated that features derived from BW4 damage were observed in the chronic asymptomatic have an outstanding performance in most group comparison Chagasic group (G1). These results are supported by and achieves the highest AUC values when comparing previous works with the same database [12,15], although in {G0vsG3}, {G1vsG2}, {G1vsG3} and {G2vsG3}. Pueyo et al. [16] and de la Rosa & Fernández [17] a When we consider the control group against chronic distinctive behaviour was found in G1 suggesting early conduction disorders. cardiac ChD ones, and When Chagasic groups were considered among them, a highlight. achieved the highest AUC (0.87) for progressive decline of HF-QRS activity was detected. This {G0vsG2} and achieved an AUC=0.97 for {G0vsG3}. It is important to point out that the greater effect can be observed, for instance, in most bandwidths discriminant performances were achieved in Y-lead when over the Y-lead (Figure 1). These results could have comparing G0 vs (G2/G3). important clinical implications since suggest that HF-QRS On the other hand, when Chagasic groups were compared features could be used to monitor myocardial damage among them, BW4 was the bandwidth with highest progression or evolution to determine forms of the disease. discriminant performances. {G1vsG2} comparison To conclude, HF-QRS components provided a valuable tool to assess Chagasic cardiomyopathy. Competitive achieved the highest AUC (0.75) in . The analysis of {G1vsG3} was better discerned using discriminant characteristics were found among features. Overall, the bandwidth {f =40, f =300} was identified as , with an AUC value of 0.88. Finally, L H the most susceptible to myocardial damage changes was the better feature in terms of AUC (0.75) achieving the greatest areas under the ROC curves. when comparing Chagasic groups with evidence of cardiac

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