Dössel, Olaf

Gross, Carl

Lenis, Gustavo

Pollnow, Stefan

English

KITopen

Current Directions in Biomedical Engineering 2017; 3(2): 249–252Carl Gross*, Stefan Pollnow, Olaf Dössel and Gustavo LenisAutomatic feature extraction algorithms forthe assessment of in-vitro electricalrecordings of rat myocardium with ablationlesionsAbstract: Cardiac arrhythmias are a widely spread diseasein industrialized countries. A common clinical treatment forthis disease is radiofrequency ablation (RFA), in which highfrequency alternating current creates a lesion on the my-ocardium. However, the formation of the lesion is not en-tirely understood. To obtain more information about ablationlesions (ALs) and their electrophysiological properties, weestablished an in-vitro setup to record electrical activity ofrat myocardium. Electrical activity is measured by a circularshaped multielectrode array. This work was focused to gainmore information by developing algorithms to process themeasured electrical signals to collect different features, whichmay allow us to characterize an AL. First, pacing artefactswere detected and blanked. Subsequently, data were filtered.Afterwards, activations in atrial signals were detected usinga non-linear energy operator (NLEO) and templates of theseactivations were generated. Finally, we determined differentfeatures on each activation in order to evaluate changes ofunipolar as well as bipolar electrograms and considered thesefeatures before and after ablation. In conclusion, the majorityof the signal features delivered significant differences betweennormal tissue and lesion. Among others, a reduction in peakto peak amplitude and a diminished spectral power in the band0 to 100 Hz may be useful indicators for AL. These criteriashould be verified in future studies with the aim of estimatingindirectly the formation of a lesion.Keywords: ablation lesion, myocardium, rat, feature extrac-tion, in-vitro experiment, radiofrequency ablationhttps://doi.org/10.1515/cdbme-2017-0179*Corresponding author: Carl Gross: Institute of BiomdicalEngineering, Karlsruhe Institute of Technology (KIT), Kaiesrstr. 12,76128 Karlsruhe, Germany, e-mail: publications@ibt.kit.eduStefan Pollnow, Olaf Dössel, Gustavo Lenis: Institute ofBiomdical Engineering, Karlsruhe Institute of Technology (KIT),Kaiserstr. 12, 76128 Karlsruhe, Germany1 IntroductionAtrial fibrillation (AFib) is a massive problem in industrializedcountries [1]. Radiofrequency ablation (RFA) is a commonmethod to treat supraventricular tachycardia by creating inten-tionally ablation lesions (ALs). A high frequency alternatingcurrent heats up the tissue until myocardium is irreversiblydestroyed. Unfortunately, the recurrence rate of arrhythmiasis 35-40% after the minimal invasive treatment [2]. Both for-mation and geometry of the resulting AL are not entirely un-derstood. Furthermore, there is no existing evaluation method,which allows a statement on transmurality of ALs. To shedmore light on this concern, we established an in-vitro setup,in which we are able to analyze electrophysiological proper-ties of rat myocardium under well-defined conditions [3]. Inthis study, we developed automatic algorithms to gain moreinformation about the measured electrical signals and gener-ated features, which might allow us to evaluate ALs.2 Methods2.1 In-vitro RFA experimentsA setup combining optical mapping and electrical measure-ment technology was first designed by Keller [3]. Tissue waspositioned in a tissue bath with nutrient solution, in which itwas kept alive for several hours. In this study, we used theright atrium of a rat. The setup included a pacing electrodewith an adjustable frequency, usually 5 Hz was used. This elec-trode was able to overwrite the sine rhythm (4.5 ± 0.10 Hz)of the atrial myocardium. A thin ablation electrode with a di-ameter of 0.3 mm allowed to create an AL during a measure-ment without changing any other setting or moving the tis-sue. The electrical signals were recorded at a sampling rate of100 kHz with an eight-electrode array in circular shape, whichwas placed on the endocardial side of the myocardium. Forunipolar recordings, a reference electrode was located in thetissue bath. By combining adjacent electrodes, bipolar datawere obtained. A scheme of the measurement setup, focus-Open Access. © 2017 Carl Gross et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.250 | C. Gross et al.: Feature extraction for electrical recordingsFigure 1: Scheme of measurement setup for in-vitro experimentswith rat myocardium (pink). The myocardium was kept alive innutrient solution, in which a reference electrode was situated. Uni-and bipolar data were recorded with circular electrode array (red).ing on the recording of electrical data is shown in figure 1. Inthis paper, the word “activation” refers to the measured elec-trophysiological activity triggered by sine rhythm or pacing.For data analysis, we developed new algorithms, writtenin Matlab 2016b (MathWorks Inc.). These algorithms werebased on eight different measurements with changing settings.The program execution is presented as follows: At the begin-ning, measurement data were loaded into the program alongwith the matching setup configuration. First, pacing and RFAartifacts were removed and signals were filtered by an adaptivehigh and low pass filter. Afterwards, activations were detectedand features were generated. In the final step, these featureswere used to determine local activation time (LAT) and to cre-ate LAT maps. All parts will be explained in detail in the fol-lowing subsections.2.2 Artifacts removalFrom raw data, two different artifacts had to be removed,which occur depending on the chosen measurement configu-ration. The first artifacts were caused by the pacing electrode.By high pass filtering the unipolar signal at 1000 Hz, morethan 90 % of the tissue activity was removed and pacing peakswere detectable in the remaining signal using a 99 %-quantilethreshold. They were replaced by a cubic spline with a dy-namic width depending on the pacing duration. The locationsof the pacing peaks were saved and used to blank them in thebipolar signal as well.It was not feasible to record electrical activity during RFAdue to the high voltage and frequency of the ablation current.This unusable part was cut out and the signal got separatedin electrical signals before and after ablation. They were pro-cessed in the next steps concurrently to analyze signal charac-teristics before and after RFA.2.3 FilteringUnipolar as well as bipolar electrograms were analyzed infrequency domain using a Welch’s periodogram. 2.5 % and97.5 % of the power density were detected and their corre-sponding frequencies were chosen as cutoff frequency for thehigh and low pass filters. We implemented a third order Butter-worth high and low pass filter with median cutoff frequenciesat 0.6 Hz and 850 Hz.2.4 Template generationFor building templates from unipolar as well as bipolar signals,it was necessary to detect atrial activities. For this purpose, weimplemented two different functions using a nonlinear energyoperator (NLEO) [1] and the minimum of the derivative of thesignal, in the following called negative slope (NS). The result-ing signal of the NLEO was filtered with a 24 Hz low passfilter, so one NLEO peak corresponded to one activity. Theactivation detection was done only in one electrode as timereference. Index was used to cut out the exact same 100 mstime window, 30 ms before and 70 ms after this index, so anLAT offset is detectable in templates. The time windows wereall summed up and then averaged to build the template of anaverage activation in a channel. Both the final template and allactivations were saved for further data analysis.2.5 Feature extractionThe found activation time locations from the template algo-rithm were used to cut out each activation. We determinedsixteen different features in order to analyze both unipolarand bipolar signals: maximum amplitude, minimum ampli-tude, peak to peak amplitude (P2P), NS, peakness of the globalmaximum and global minimum, LAT using NS and NLEO,frequency bandwidth (5%- 95% of spectral signal power),50% power of spectrum, and frequency with highest power.Peakness was calculated with a least squares estimation al-gorithm (polyfit, Matlab 2016b), which fitted a parabola inthe detected parts of the activation. For further feature extrac-tion, the selected activity was squared and normalized to havean area under the curve of one. We assumed, that the result-ing curve has the properties of a probability density function.C. Gross et al.: Feature extraction for electrical recordings | 251Figure 2: Section of raw unipolar signal with pacing artifact andoffset (left). Processed and filtered section of the same signal(right).From this curve, we computed the centre of mass, variance intime, width in time, skewness in time, and kurtosis in time.In addition, we performed the Wilcoxon signed-rank test toevaluate the statistical significance of the changes of the abovementioned features before and after RFA.2.6 Local activation time mapWe created LAT maps to visualize the propagation directionof the excitation wavefront. The used electrode array had adiameter of 3.5 mm. Based on this, we created a 2D surfacewith evenly spread electrodes on a circle. The data betweenthe electrodes were interpolated using bilinear interpolation.Therefore, triangles had been formed with an LAT time oneach corner. The median LAT of each electrode was consid-ered as LAT of this tissue region.3 ResultsAll given results are median values with interquartile range. Infigure 2, a section of raw unipolar signal and processed signalis shown. In this example, pacing artifacts were removed andthe signal was filtered as described in the last section. Unipolarand bipolar templates before and after ablation are presentedin figure 3. After the Wilcoxon test, four features were definedto be significant. These were P2P, NS, the frequency band-width, and the 50 % value in power density spectrum (p-value< 10−6). The results of unipolar and bipolar features of ab-lated and non ablated tissue are shown in table 1 and in table2. Values are presented as range, because all electrodes wereconsidered for comparison. There were unipolar activations,which showed a low P2P (1 ± 0.1 mV), but showed the charac-teristics of non ablated myocardium in frequency bandwidth(6-296 Hz), figure 4 (2). As seen in table 1, P2P decreasedTable 1: Significant features of unipolar activity recordedabove ablated and non ablated tissue. Results are given inmedian ± interquartile range. Frequency bandwidth is medianfrequencies from 5% to 95% of the power density.feature non ablated ablatedpeak to peak amplitude[mV] 4.53 ± 2 0.65 ± 0.32max. negative slope [mV/ms] -4 ± 2.3 -0.08 ± 0.37frequency bandwidth [Hz] 3-350 0-7850% frequency [Hz] 66 ± 46 10 ± 1Table 2: Significant features of bipolar activity recordedabove ablated and non ablated tissue. Results are given inmedian ± interquartile range. Frequency bandwidth is medianfrequencies from 5% to 95% of the power density.feature non ablated ablatedpeak to peak amplitude [mV] 5.7 ± 2.4 0.13 ± 0.1max. negative slope [mV/ms] -2.8 ± 2.5 -0.05 ± 0.06frequency bandwidth [Hz] 3-481 0-14850% frequency [Hz] 113 ± 54 17 ± 12and frequency spectrum was shifted towards lower frequen-cies. Other features, e.g. time kurtosis or time skewness didnot show significance. Figure 5 shows an LAT map of non ab-lated tissue. The first registered LAT was on electrode 8 (bot-tom left). The difference between first and last registered LATwas 9.9 ms. Low LAT is represented in red and high LAT inlight blue, so the excitation wave propagated from the bottomleft to the right.4 DiscussionWe developed an automatic algorithm to process data recordedin an in-vitro experiment and to extract different features.Therefore, we tested the algorithm with different measure-Figure 3: Templates for unipolar signal (left) and bipolar signal(right). Before (blue) and after (red) radiofrequency ablation. Tem-plates were aligned by the local activation time.252 | C. Gross et al.: Feature extraction for electrical recordingsFigure 4: Templates of measurement with the electrode arrayhalve on ablated tissue and halve on non-ablated tissue. Templateof activity on non ablated tissue (1), ablated tissue (3), and elec-trode might be on border zone between ablation lesion and nonablated tissue (2). All templates were aligned by local activationtime in reference to electrode 1 (1) at 30 ms.ments with pacing, RFA, and different array positions. Themeasurements were preprocessed, filtered, activations weredetected and statistical features were determined. Significantfeatures were found and can be used in future studies. Thefeatures show, that on an AL the median P2P can decrease upto 90 %. Keller et al., also determined this reduction in P2Pand in NS after an RFA [4]. The median frequency bandwidthdecreases on an AL to 0-78 Hz. Avitall et. al came also to theconclusion, when performing ablation measurements with ringcatheters in canines with chronic AFib [5]. With the knowl-edge of the configuration of our setup and the exact positionof the used electrode array on the tissue, we interpreted thedata and found significant features, which could help to un-derstand ALs and their formation more in detail. It may befeasible to find a combination of features, which allow an ac-curate assessment of the formation of an AL. These indicatorscould be used to estimate indirectly lesion transmurality. In fu-ture studies, the presented features will be investigated for ALswith different geometries. In this context, histological or 3Dimaging methods are required to determine the microscopic aswell as the macroscopic structure of the tissue. The methodspresented in this paper could some day become standard inthe process of achieving higher success rates during ablationtherapy.Author’s Statement: Research funding: Stefan Pollnow issupported by a scholarship of the Karlsruhe School of Opticsand Photonics (KSOP) and this project was supported by theGerman ResearchFoundation (DFG grant DO 637/20-1). Con-flict of interest: Authors state no conflict of interest. InformedFigure 5: Local activaton time (LAT) map of a non ablated tissue.The crosses represent the electrodes on the edge of the patch.Data between electrodes was bilinearly interpolated.consent: Informed consent is not applicable. Ethical approval:The animal experiments were approved by the local committeefor animal welfare (35-9185.81/G-61/12).References[1] C. Schilling, M. Nguyen, A. Luik, et al. Non-Linear EnergyOperator for the Analysis of intracardial Electrograms. WorldCongress on Medical Physics and Biomedical Engineering.Munich, Germany. Springer, 2009, 872–875.[2] Shurrab M, Biase LD, Kaoutskaia A, Haj-Yahia S, Newman D,et al. Impact of contact force technology on atrial fibrillationablation:a meta-analysis.J Am Heart Assoc. 2015;4:1–9.[3] Keller, Matthias. Formation of Intracardiac Electrograms un-der Physiological and Pathological Conditions. KIT ScientificPublishing, 2014, 21:67-72.[4] Keller, Matthias Walter, et al. Characterization of radiofre-quency ablation lesion development based on simulated andmeasured intracardiac electrograms. IEEE Transactions onBiomedical Engineering, 2014:61, 2467-2478.[5] Avitall, Boaz, et al. Relationship between lesion formationand electrophysiological responses using catheters equippedwith mini-electrodes in chronic atrial fibrillation. Heart Rhythm,2017, 9-11.

Automatic feature extraction algorithms for the assessment of in-vitro electrical recordings of rat myocardium with ablation lesions

https://publikationen.bibliothek.kit.edu/1000134177/118152921

Automatic feature extraction algorithms for the assessment of in-vitro electrical recordings of rat myocardium with ablation lesions

Abstract

Similar works

Full text

Available Versions

KITopen