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Preprocessing of unipolar signals acquired by a novel intracardiac mapping system

  • Salina Huck EMAIL logo , Tobias Oesterlein , Armin Luik , Claus Schmitt , Reza Wakili and Olaf Dössel
Published/Copyright: September 30, 2016
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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

The novel high-density mapping system RhythmiaTM Medical (Boston Scientific, Marlborough, USA) allows a fast and automatic acquisition of intracardiac electrograms (EGMs). For recording the ORION mini-basket catheter is used. Due to the small electrode surface, the spatial averaging is smaller than with other commonly used mapping catheters. This results in a higher quality of unipolar signals. However, these are still corrupted by noise such as high frequency interference. Within this project, methods were developed and benchmarked that can be applied to detect and remove these undesired components. An algorithm was implemented to detect and eliminate artificial peaks in the spectrum of the EGM. The filtered signals showed improved quality in time domain. The performance of the spectral peak detection resulted in a median sensitivity of 92.1% and in a median positive predictive value of 91.9%.

Keywords: filtering; intracardiac mapping; unipolar electrogram

1 Introduction

Catheter ablation is a common treatment for atrial arrhythmias such as atrial flutter and atrial fibrillation. Prior to ablation, electro-anatomical mapping systems are applied for the identification of the arrhythmogenic sites. The novel high-density mapping system RhythmiaTM Medical (Boston Scientific, MA, USA) was installed in two centers. It allows an automatic and fast sequential recording of unipolar electrograms, using the ORION mini-basket catheter which consists of 64 electrodes. Due to the small spatial averaging which results from the small electrode surfaces, the quality of unipolar EGMs increases. This leads to a low noise level which is smaller than 0.01 mV [1]. However, unipolar signals still are disturbed by high frequency interference and far-fields. Thus, the preprocessing of the EGMs is a necessary aspect for further processing of the data. An export of the raw data which were recorded during mapping was enabled. Goal of this research is the preprocessing and evaluation of unipolar EMGs by the implementation of different filtering techniques. These have the requirement to automatically detect and eliminate artificial components in the spectrum of the EGM which may be different from lab to lab. The implementation of a simple low-pass filter is not sufficient since these artifacts also can be located in the lower frequency domain where the wanted signal is expected.

2 Methods

2.1 Clinical data

The evaluated and processed data in this research were derived from the RhythmiaTM Mapping System. Unipolar EGMs were acquired by use of the ORION mini-basket catheter. It consists of eight splines in which eight electrodes are arranged in an equal distance of 2.5 mm. The surface area of a single electrode is 0.4 mm2. The catheter can be deployed to a sphere with a maximal diameter of 22 mm. The localization occurs by a magnetic sensor and impedance sensing of the electrodes [2]. Five clinical data sets were recorded at Städtisches Klinikum in Karlsruhe and another five at German Heart Center in Munich. In total 28 maps were recorded in these ten data sets. Unipolar EGMs were recorded with a sample rate of 953.674 Hz.

2.2 Transformation to the frequency domain

The transformation of the EGMs to the frequency domain was the first necessary step. Therefore, the power spectral density (PSD) was calculated using Welch’s method [3]. The time signal was divided into overlapping segments with a window of 10 s and an overlap of 50%. For each segment the periodogram, an estimation of the PSD, was calculated. Then, each periodogram was windowed by the use of a Hamming window. Afterwards the averaged PSD of all segments was computed.

2.3 Detection of artificial peaks

In Figure 1 a unipolar signal recorded by the ORION mini-basket catheter (A) and the corresponding PSD (B) are illustrated. An overlay of noise is visible in the EGM. The PSD shows artificial discrete peaks at frequencies above 50 Hz. The signal of intracardiac EGMs is lying in the frequency domain up to 150 Hz. For this reason, all peaks above 70 Hz were not caused by physiological processes and can be considered as noise.

Figure 1 Unipolar EGM which is overlain with noise, recorded with ORION (A). A cutout of the corresponding PSD is illustrated in (B). Several artificial discrete peaks are visible.
Figure 1

Unipolar EGM which is overlain with noise, recorded with ORION (A). A cutout of the corresponding PSD is illustrated in (B). Several artificial discrete peaks are visible.

An automatic peak detection algorithm was developed for identification of these peaks. To this end, the PSD was scanned using a sliding window, with a window width of 2 Hz and a shift of 1 Hz. For each step, the median and the maximum was calculated. A peak was detected if the maximum exceeded a threshold T, which was defined as the product of a variable coefficient c, the p-quantile Qp of the relative heights in the PSD and the median within the window:

(1)T=c⋅Qp⋅median⁢(window)

Furthermore, the peak had to be located in the frequency domain above 70 Hz, to ensure that it was not caused by physiological processes. The relative height was defined as

(2)relative⁢height=maximum⁢(window)median⁢(window)

and was computed within a window width of 2 Hz for every second integer frequency in a range of 70 – 400 Hz. This resulted in N=400−702=165 considered values. During manual annotation a maximum number of peaks Npeaks = 20 was observed. The relative heights of those peaks differ from the remaining relative heights. On that account Qp was calculated for the threshold T with

(3)p=N−NpeaksN

An extended algorithm, the fundamental frequency (f0) calculation, was developed which additionally considered if these detected peaks were harmonics of f0. It was an iterative calculation with the basic idea that a candidate of f0 was the difference between two detected peaks.

2.4 Filtering

For peak suppression a Gaussian Notch filter with a band width of 1 Hz was used. The applied Gaussian Notch filter also filtered the harmonic frequencies of the notch frequency. This was the purpose of the f0 calculation. By clinical default, unipolar signals are examined in a frequency domain from 1 Hz - 300 Hz. Therefore, unipolar signals were filtered with a third order Butterworth high-pass filter with a cut-off frequency of 1 Hz and a third order Butterworth low-pass filter with a cut-off frequency of 300 Hz.

2.5 Evaluation of signal quality

To evaluate the quality of the signals the SNR defined as

(4)S⁢N⁢R=10⋅l⁢o⁢g10⁢(PsignalPnoise)⁢d⁢B

was examined. Only the quality of the measured activity of the endocardium was considered. Therefore, the SNR was calculated in time segments without ventricular far fields (VFF). These were identified by applying a QRS detection algorithm, developed by Lenis [4], to a simultaneously recorded reference ECG signal. Psignal was defined as the power of the signal after filtering and Pnoise as the power of the difference between the signal before filtering and after filtering.

2.6 Performance evaluation

2.6.1 Performance evaluation of peak detection

Statistical measures were computed for the evaluation of the peak detection performance. Therefore, the PSDs of the 28 considered maps were manually annotated. The peak detection algorithm was applied to the PSDs and respectively the sensitivity (Se) and the positive predictive value (PPV) were computed.

2.6.2 Performance evaluation of filtering

For the evaluation of the filtering the power P of a peak before and after filtering was assessed. Therefore, the area under the peak in a window of 2 Hz was calculated before and after filtering. The relative peak power defined as

(5)relative⁢peak⁢power=Pbefore−PafterPbefore⁢

quoted the quality of the filtering. In ideal case the power of the peak after filtering is zero. According to the definition, an ideal filtering resulted in a relative peak power of one.

3 Results

3.1 Performance evaluation

The manual annotation yielded artificial peaks at multiples of 40 Hz and 50 Hz. Additionally, single peaks occurred which differed between data recorded in different labs. The peak detection algorithm was optimized by applying the algorithm for various values of the coefficient c to all PSDs. For each value of c the averaged Se and the PPV was computed. The optimization resulted in a median PPV of 91.9% and a median sensitivity of 92.1%. The extended f0 calculation achieved a median Se and PPV of 100%. The filtering resulted a median relative peak power of 0.992 by the use of the peak detection algorithm and a median relative peak power of 0.991 using the f0 calculation. The evaluation of the quality of the EGM which is illustrated in Figure 3 resulted in an SNR of 1.3 dB.

3.2 Filtering

Resulting PSDs of an exemplary EMG are illustrated in Figure 2. At the top (A), a cutout of the PSD of the unfiltered signal is shown. Since most peaks appear at multiples of 40 Hz and 50 Hz, the assumption was made that these were harmonics of f0 of 40 Hz and 50 Hz. The middle plot (B) illustrates the PSD after filtering by the automatic peak detection. It is visible that all peaks above 70 Hz were detected and filtered. At the bottom (C), the PSD after filtering using the f0 calculation is illustrated. As can be seen, 40 Hz and 50 Hz, as well as the harmonic frequencies, were filtered. In Figure 3 the corresponding EGMs are plotted. At the top (A) the reference ECG is shown. The VFFs are clearly visible in the corresponding EGM.

Figure 2 The PSD before filtering (A), after filtering using the peak detection algorithm (B) and after filtering using the f0 calculation (C).
Figure 2

The PSD before filtering (A), after filtering using the peak detection algorithm (B) and after filtering using the f0 calculation (C).

Figure 3 The EGM before filtering (B), after filtering using the peak detection algorithm (C) and after filtering using the f0 calculation (D). The signals in (C) and (D) are filtered with a band-pass of 1 Hz – 300 Hz. The reference ECG is illustrated in (A).
Figure 3

The EGM before filtering (B), after filtering using the peak detection algorithm (C) and after filtering using the f0 calculation (D). The signals in (C) and (D) are filtered with a band-pass of 1 Hz – 300 Hz. The reference ECG is illustrated in (A).

4 Discussion

The preprocessing of the unipolar signals showed good results. The peak detection resulted in a median sensitivity of 92.1% and a PPV of 91.9% and the f0 calculation resulted in a median Se and PPV of 100%. The performance of the filtering was measured considering the relative peak power with an ideal value of one. The results were very good with a median of 0.992 using the peak detection and a median of 0.991 using the f0 calculation. The results in time domain looked quite well using both filtering techniques. Removal of the 40 Hz component does not seem reasonable due to the introduced artifact.

5 Outlook

In this research, the preprocessing techniques only were applied to ten data sets which were recorded in Munich and Karlsruhe. The methods should be applied to more data, preferable derived from other clinics. Additionally, the quality of the signal should be enhanced by preprocessing in the time domain. Artifacts such as VFFs or stimulation artifacts should be suppressed, since they strongly disturb the unipolar EGMs. Furthermore, the filtered data should be further processed. The preprocessed data can be analyzed with novel algorithms (5). Local activation time maps (LAT maps) can be generated by the processing of the filtered raw data. An exemplary LAT map which was generated by the processing of the raw data is illustrated in Figure 4.

Figure 4 LAT map generated out of the processed raw data. The left atrium is illustrated. A roof-dependent flutter circuit around the left pulmonary veins is visible. This was in agreement with the clinical diagnosis. The scale is specified in milliseconds.
Figure 4

LAT map generated out of the processed raw data. The left atrium is illustrated. A roof-dependent flutter circuit around the left pulmonary veins is visible. This was in agreement with the clinical diagnosis. The scale is specified in milliseconds.

Author’s Statement

Research funding: The author state no funding involved. 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 Salina Huck 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-2016-0058
Keywords for this article
filtering; intracardiac mapping; unipolar electrogram
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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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