A 64-Lead Body Surface Potential Mapping System

Salinet, João; Marques, Victor G; Mazzetto, Marcelo Oliveira; Camargo, Erick Dario León Bueno de; Pastore, Carlos Alberto; Cestari, Idágene Aparecida

doi:10.22489/cinc.2017.053-337

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…The analyses were expanded to the Welch approach aiming to observe the ability of both methods to estimate the atrial HDF. Additionally, to evaluate the robustness of both methods to lower spatial resolution BSPM lead layouts, the analyses of AF models was reproduced for subsets of the 567 points used for the analyses of computer models (here denominated as the high-resolution layout-HR) with various numbers of leads: 256 (Haissaguerre et al 2014), 127 (Vanheusden et al 2019), 67 (Guillem et al 2013), 64 (Salinet et al 2017), 32 and 16 (supplementary figure 1). For patient data, only the 32 and 16 lead layouts were used for comparison with the original 67 leads layout.…”

Section: Robustness Of the Methods To Noise And Reduction In Spatial ...mentioning

confidence: 99%

A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals

et al. 2020

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Objective: Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed.Approach: Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3-30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram and the robustness was tested on different protocols: increasing levels of white Gaussian noise, artificial DF harmonics presence and reduction of number of leads. 11 AF simulations and 12 AF patients are considered in the analysis. For each patient, intracardiac electrograms were acquired in 15 locations from both atria. The accuracy of both methods was assessed by calculating the absolute errors of the HDF B SP M with respect to the atrial HDF, either simulated or intracardially measured, and assumed correct if ≤ 1 Hz. The spatial distribution of the errors between torso DFs and atrial HDFs were compared with atria driving mechanisms location. Torso HDF regions, defined as portions of the maps with |DF −HDF B SP M | ≤ 0.5 Hz were identified and the percentage of the torso occupied these regions was compared between methods. Main results:The proposed method allowed a significant improvement on non-invasive estimation of the atria HDF (median relative error of 7.14% vs. 60.00%, p = 0.06), outperforming the Welch approach in correct estimations of atrial HDFs non-invasively for both cases: models (81.82% vs 45.45%) and patients (75.00% vs 66.67%). A low positive BSPM-DF maps correlation was seen between techniques (0.47 for models and 0.63 for patients), highlighting overall differences in DF distributions. The method was more robust to white Gaussian noise and harmonics and presented more consistent results in lead layouts with low spatial resolution (p = 0.99 vs. p = 0.94).Significance: Estimation of atrial HDFs using BSPM is improved by the proposed waveletbased algorithm, helping increase the non-invasive diagnostic ability in AF.

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Section: Robustness Of the Methods To Noise And Reduction In Spatial ...mentioning

confidence: 99%

A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals

et al. 2020

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“…The robustness of the HDF Atria estimations for both methods was assessed using the simulated data with different levels of added WGN and harmonic noise, here simulated by adding a sinusoidal wave in the frequency of the first harmonic of HDF Atria (SNR of 60, 30, 10, 5 and 1 dB). To evaluate the robustness of both methods to lower spatial resolution BSPM lead layouts, the analyses were reproduced for subsets of the original lead layouts: 256 [13], 127 [7], 67 [6], 64 [14], 32 and 16 for model data, and 32 and 16 leads for patients.…”

Section: Methodsmentioning

confidence: 99%

A Wavelet-Based Method for Non-Invasive Dominant Frequency Detection in Atrial Fibrillation

Marques¹,

Rodrigo²,

Guillem³

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed. Continuous wavelet transform (Gaussian mother wavelet) along 40 scales in the pseudo-frequency range of 3-30 Hz is performed in each BSPM signal. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram. The accuracy of both methods was assessed using the absolute errors between BSPM and atrial highest DFs (HDFs) and assumed correct if ≤ 1. The robustness of the methods was tested on different protocols: increasing levels of WGN, artificial DF harmonics presence, and reduction of the number of leads. 11 AF simulations and 12 AF patients are considered in the analysis. The proposed method outperformed the Welch approach, obtaining more correct estimations of atrial HDFs non-invasively in models (81.82% vs 45.45%) and patients (75.00% vs 66.67%) and being more robust to noise and reduction in spatial resolution, thus helping to increase the non-invasive diagnostic ability of BSPM in AF.

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“…Subsets of these points were selected to approximate lead layouts with different numbers of points: 252 [6], 131 [3], 67 [7], 64 [8], 32 and 16 (Fig. 2).…”

Section: Computer Models and Preprocessingmentioning

confidence: 99%

Effects of Reducing the Number of Leads from Body Surface Potential Mapping in Computer Models of Atrial Arrhythmias

Marques¹,

Rodrigo²,

Sánchez³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Body surface potential mapping (BSPM) can be an important tool in ablation therapy planning. Results obtained with high resolution (HR) computer models must be translated to realistic numbers of leads. This study aims to evaluate the impact on atrial tachycardia (AT), flutter (AFL) and fibrillation (AF) characterization by reducing the number of BSPM leads. 19 realistic computer simulations with 567 leads (HR) have been used to characterize the arrhythmias concerning the dominant frequencies (DF) and phase singularity point (SP) distributions. DF maps were generated combining Welch periodogram and activation detection with wavelet transform modulus maxima. Phase was calculated with Hilbert transform on signals filtered around the highest DF (±1Hz); dynamics of SPs were analyzed using histograms (heatmaps, HMs) and connecting SPs along time (filaments). The analyses were reproduced for 6 layouts with 252 to 16 leads and results were compared using the structural similarity index (SSIM), sensitivity and precision in SP detection and analyzing features extracted from the maps. SSIM was lower in AF than in AFL or AT for DF maps and HMs, but was in average above 0.6 for layouts with 32 leads or more. In HMs, a loss in spatial resolution with fewer leads is reflected in decreasing values for sensitivity and precision. Features from DF maps, filaments or HMs were statistically equivalent in all layouts. HR 252 leads 131 leads 67 leads 64 leads 32 leads 16 leads Frequency Analysis AT 4.06 ± 0.03 4.06 ± 0.03 4.06 ± 0.03 5.04 ± 0.97 5.04 ± 0.97 4.06 ± 0.03 4.06 ± 0.03 AFL 4.09 ± 0.12 4.09 ± 0.12 4.09 ± 0.12 4.09 ± 0.12 4.09 ± 0.12 4.09 ± 0.12 4.09 ± 0.12 f d r i ve (Hz) AF 5.58 ± 0.23 5.58 ± 0.23 5.58 ± 0.23 5.37 ± 0.30 5.58 ± 0.23 5.56 ± 0.23 5.59 ± 0.22 AT 0.76 ± 0.70 1.28 ± 0.78 1.10 ± 0.69 2.26 ± 2.28 1.89 ± 1.61 0.67 ± 0.66 0.70 ± 0.67 AFL 0.24 ± 0.49 0.24 ± 0.49 0.24 ± 0.49 0.24 ± 0.49 0.24 ± 0.49 0.24 ± 0.49 0.24 ± 0.49

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A 64-Lead Body Surface Potential Mapping System

Cited by 7 publications

References 15 publications

A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals

A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals

A Wavelet-Based Method for Non-Invasive Dominant Frequency Detection in Atrial Fibrillation

Effects of Reducing the Number of Leads from Body Surface Potential Mapping in Computer Models of Atrial Arrhythmias

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