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Automatic detection and mapping of double potentials in intracardiac electrograms

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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

Catheter ablation has become a very efficient strategy to terminate sustained cardiac arrhythmias like atrial flutter (AFlut). Identification of the optimal ablation spot, however, often proves difficult when scar from previous ablations is present. Although the application of electro-anatomical mapping systems allows to record thousands of intracardiac electrograms (EGMs) from each atrium, state-of-the-art techniques provide limited options for automatic signal processing. Goal of the presented research was the development of an algorithm to detect EGMs that present double potentials (DPs), as these often indicate functional or anatomical lines of block for cardiac excitation. Using an annotated database, we developed several features based on the morphological descriptors of DPs. These were used to train a binary decision tree which was able to detect DPs with a correct rate of over 90%.

Keywords: atrial flutter; double potential; intra-cardiac electrogram

1 Introduction

The curative therapy by catheter ablation is a promising treatment approach for drug-resistant atrial flutter (AFlut), with success rates in the order of 88–100% [1]. Systematic ablation, however, requires a detailed understanding of the mechanism of the presenting tachycardia. Conventional local activation time (LAT) mapping is the standard approach to comprehend the excitation pattern by analyzing intracardiac electrograms (EGMs). It neglects, however, the morphological peculiarities of individual EGMs.

Normal EGMs are expected to contain one deflection per basic cycle length (BCL) during AFlu. The presence of two distinct activity complexes (ACs) per BCL is termed double potential (DP), and has been shown to indicate the presence of functional or anatomical block [2]. Recent research has focused on the usability of non-contact [3] or epicardial [4] mapping to gain information about conduction block. The development of automatic approaches to detect these DPs is a challenging task and is addressed in this research.

2 Methods

2.1 Database of flutter EGMs

Clinical data were acquired during local activation time mapping of stable AFlut from eight patients (mean age 64 ± 6.8 years). Informed consent has been obtained from all individuals included in this study. A total of 2863 EGMs was available, including simultaneous ECG and an intracardiac reference EGM. All data were 1 s long and sampled at 2034.5 Hz. The EGMs were compiled in a database and annotated by an experienced scientist. QRS complexes were marked in the ECG, as well as the active segments of the measured EGMs. After inspection, 882 EGMs were discarded due to noise, ventricular far field or an inconsistent activation pattern. The remaining 1981 EGMs contained 154 signals labeled as DP.

2.2 Annotation of activity

The annotation of activity within the recorded EGMs was a central step in processing prior to DP detection. Within a previous project, five different methods were optimized and subsequently compared to achieve best possible agreement between automatically detected and manually annotated activity. These comprised an energy-based approach [5], methods using Hilbert or wavelet transform [7], the matched-filter, and a voltage-based technique. After optimization, each algorithm achieved correct rates over 90% [8].

As the activity detection was a fundamental step in processing, its performance was expected to strongly affect the detection of DPs. Therefore, the classification technique presented in the following was combined with each activity detection algorithm.

2.3 Feature definition

A variety of features were developed which reflected the morphological peculiarities of DPs.

The number of ACs was used as feature and determined within one BCL. In order to avoid the double counting of ACs located at the limits of the inspected cycle, the relative activity inside the cycle was assessed for each AC. Summation of these measures led to a total value which matched with the number of ACs in the signal. Figure 1 demonstrates this approach for both a prolonged EGM [total of 1.00 AC, (A)] and an EGM with two separate deflections [total of 1.98 ACs, (B)].

Figure 1: The number of ACs used as feature in two signals. The activity (red) is shown for each EGM (blue). The relative amount of activity of each AC inside the analyzed cycle (bounded by the vertical lines) is summed up. The total value reflects the number of ACs within one cycle.
Figure 1:

The number of ACs used as feature in two signals. The activity (red) is shown for each EGM (blue). The relative amount of activity of each AC inside the analyzed cycle (bounded by the vertical lines) is summed up. The total value reflects the number of ACs within one cycle.

To be more independent from the annotation of ACs, the total amount of covered cycle length was introduced as additional feature relative activity as proposed in previous work [5].

Since two distinct deflections may be located within one annotated AC, peaks of instantaneous energy were assessed. The concept of this approach is outlined in Figure 2. While the energy within ACs of normal EGMs typically showed one peak (A), multiple strong deflections within ACs of DPs caused several local maxima (B). Based on this observation, two features were generated. The number of peaks indicated the average number of peaks within the ACs. Also the distance between the peaks and the centroid of the energy was computed, assessed as peak to centroid distance.

Figure 2: Relationship between EGM morphology and the energy distribution within ACs. Multiple distinct deflections caused multiple peaks within an AC (B), while a single deflection translated into one peak (A). Both the number of peaks and the peak to centroid distance represent potential features for classification.
Figure 2:

Relationship between EGM morphology and the energy distribution within ACs. Multiple distinct deflections caused multiple peaks within an AC (B), while a single deflection translated into one peak (A). Both the number of peaks and the peak to centroid distance represent potential features for classification.

Adjacent ACs were frequently merged during the postprocessing step of activity detection. Thus annotated ACs were retrospectively inspected to assess the presence of baseline between deflections. First, the annotated activity prior to post-processing was analyzed and the duration of the longest segment of inactivity was measured as feature baseline I. Second, baseline II was computed as maximum duration of EGM segments with amplitudes below a low voltage threshold.

Figure 3: Demonstration of features which assess the presence of baseline within ACs. The initial result of activity annotation of an EGM (A) was inspected to detect discrete activities prior to merging [baseline I, (B)]. In addition, the EGM was evaluated directly to detect segments of low voltage [baseline II, (C)].
Figure 3:

Demonstration of features which assess the presence of baseline within ACs. The initial result of activity annotation of an EGM (A) was inspected to detect discrete activities prior to merging [baseline I, (B)]. In addition, the EGM was evaluated directly to detect segments of low voltage [baseline II, (C)].

Also the similarity of detected ACs was assessed using simple means like the mean AC duration and standard deviation of AC length. Analysis of these values was inspired by the observation that only in case of DPs a low similarity of ACs was expected.

2.4 Classification tree

A decision tree was chosen as classifier to distinguish between normal EGMs and DPs, with the Gini Diversity Index chosen as split criterion. Over-fitting was avoided by limiting the complexity of the tree to 10 branches. Also the minimum number of branch and leaf node observations were limited to 10 and 1, respectively.

2.5 Performance evaluation

As the number of normal EGMs was much larger than from the available DPs, a balancing of training data was required to gain a realistic performance evaluation of the classifier. Therefore random over-sampling of the minority class was performed. Cross-validation was performed using the leave-one-out technique, and considering the identification of a DP as positive. One decision tree was benchmarked for each activity detection method, as the latter was expected to play a major role for the outcome. Correct rate (CR), sensitivity (Se), specificity (Sp), as well as positive and negative predictive values (PPV and NPV) were computed as statistical performance measures.

3 Results

3.1 Classification performance

First, the suitability of the developed features and the classification approach was assessed. To exclude the effect of automatic activity detection, a decision tree was trained using the manual annotations. The CR computed by cross-validation was about 99%, confirming the suitability of the implemented method.

Subsequently, a classifier was trained for each automatic activity annotation algorithm and benchmarked. The results are summarized in Table 1. For the completely automatic approach, best performance was observed for the voltage-based method with about 90% accuracy.

After benchmarking, a final tree was induced using all data. Five features were considered in the resulting tree, being number of ACs, relative activity, peak to centroid distance, baseline II and mean AC duration.

Table 1:

Cross-validation performance of the decision trees for DP detection.

CRSeSpPPVNPV
Manual99.099.698.498.499.6
Energy79.769.290.287.674.5
Hilbert83.479.187.886.780.8
Wavelet78.765.591.788.872.7
Voltage90.591.389.689.891.2
Matched77.261.193.390.170.6

All values given in %.

3.2 Application in clinical mapping

The applicability of the developed classifier was demonstrated on a set of clinical data. Electrograms recorded during activation mapping of perimitral counter-clockwise atrial flutter were classified into normal EGMs or DPs. This process was included in the automatic signal analysis and the resulting information was projected back on the anatomy based on the location of each measurement point. The result is visualized in Figure 4, showing the LAT map at the top and the corresponding DP map below. A line of conduction block can be noticed on the anterior wall, causing a delay of excitation between the inferior and the superior part. Its position coincided with the region in which DPs were detected by automatic classification.

Figure 4: Maps demonstrating the clinical activation pattern (A) and the occurrence of DPs as detected by the presented algorithm (B). DPs were found primarily at a line of block along the anterior wall.
Figure 4:

Maps demonstrating the clinical activation pattern (A) and the occurrence of DPs as detected by the presented algorithm (B). DPs were found primarily at a line of block along the anterior wall.

4 Discussion

Within the presented research, a method for the automatic detection and annotation of DPs was developed and benchmarked. Despite their diagnostic relevance, this is the first work known to the authors addressing this issue.

A number of potentially relevant features was generated, inspired by the morphological properties of DPs. Their suitability for the differentiation between normal EGMs and DPs was confirmed by a resulting classification accuracy of 99%. In order to achieve a fully automatic approach, the DP classification was combined with a preceding activity detection. Each potential activity detection algorithm was therefore assessed as potential preprocessing step.

The voltage-based approach performed best with about 90% accuracy, and was subsequently applied to a patient specific set of clinical mapping data. The outcome correctly identified a line of block which was confirmed by visual inspection.

Although this approach was successfully demonstrated, additional features could help to further improve classification performance, as well optimizing the classifier type. In future, this method will be applied as novel diagnostic algorithm for the analysis of clinical flutter data [6]. Combination with different mapping systems and catheters will allow to identify critical cases in which DPs remain undetected and inspire further research.

Author’s Statement

Research funding: The work of Tobias Oesterlein is funded by the German Research Foundation (DO637/14-1). Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The research related to human use complies with all the relevant national regulations, institutional policies and was performed in accordance with the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee.

References

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Tobias Oesterlein et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0041
Keywords for this article
atrial flutter; double potential; intra-cardiac electrogram
Creative Commons
BY-NC-ND 4.0

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  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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