Falling-edge, variable threshold (FEVT) method for the automated detection of gastric slow wave events in high-resolution serosal electrode recordings.

Department of Physics, Vanderbilt University, Nashville, TN, USA.
Annals of Biomedical Engineering (Impact Factor: 3.23). 12/2009; 38(4):1511-29. DOI: 10.1007/s10439-009-9870-3
Source: PubMed

ABSTRACT High resolution (HR) multi-electrode mapping is increasingly being used to evaluate gastrointestinal slow wave behaviors. To create the HR activation time (AT) maps from gastric serosal electrode recordings that quantify slow wave propagation, it is first necessary to identify the AT of each individual slow wave event. Identifying these ATs has been a time consuming task, because there has previously been no reliable automated detection method. We have developed an automated AT detection method termed falling-edge, variable threshold (FEVT) detection. It computes a detection signal transform to accentuate the high 'energy' content of the falling edges in the serosal recording, and uses a running median estimator of the noise to set the time-varying detection threshold. The FEVT method was optimized, validated, and compared to other potential algorithms using in vivo HR recordings from a porcine model. FEVT properly detects ATs in a wide range of waveforms, making its performance substantially superior to the other methods, especially for low signal-to-noise ratio (SNR) recordings. The algorithm offered a substantial time savings (>100 times) over manual-marking whilst achieving a highly satisfactory sensitivity (0.92) and positive-prediction value (0.89).

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    ABSTRACT: Gastric dysrhythmia continues to be of uncertain diagnostic and therapeutic significance. However, recent progress has been substantial, with technical advances, theoretical insights and experimental discoveries offering new translational opportunities. The discoveries that interstitial cells of Cajal (ICC) generate slow waves and that ICC defects are associated with dysmotility have reinvigorated gastric dysrhythmia research. Increasing evidence now suggests that ICC depletion and damage, network disruption and channelopathies may lead to aberrant slow wave initiation and conduction. Histological and high-resolution (HR) electrical mapping studies have now redefined the human ‘gastric conduction system’, providing an improved baseline for dysrhythmia research. The application of HR mapping to dysrhythmia has also generated important new insights into the spatiotemporal dynamics of dysrhythmia onset and maintenance, resulting in the emergence of new provisional classification schemes. Meanwhile, the strong associations between gastric functional disorders and electrogastrography (EGG) abnormalities (e.g. in gastroparesis, unexplained nausea and vomiting, and functional dyspepsia) continue to motivate deeper inquiries into the nature and causes of GI dysrhythmias. In future, technical progress in EGG methods, new HR mapping devices and software, wireless slow wave acquisition systems, and improved gastric pacing devices may achieve validated applications in clinical practice. Neurohormonal factors in dysrhythmogenesis also continue to be elucidated, and a deepening understanding of these mechanisms may open opportunities for drug design for treating dysrhythmias. However, for all translational goals, it still remains to be seen whether dysrhythmia can be corrected in a way that meaningfully improves organ function and symptoms in patients.This article is protected by copyright. All rights reserved.
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    ABSTRACT: High-resolution (HR) mapping employs multi-electrode arrays to achieve spatially-detailed analyses of propagating bioelectrical events. A major current limitation is that spatial analyses must currently be performed 'offline' (after experiments), compromising timely recording feedback and restricting experimental interventions. These problems motivated development of a system and method for 'online' HR mapping. HR gastric recordings were acquired and streamed to a novel software client. Algorithms were devised to filter data, identify slow wave events, eliminate corrupt channels, and cluster activation events. A graphical user interface animated data and plotted electrograms and maps. Results were compared against offline methods. The online system analyzed 256-channel serosal recordings with no unexpected system terminations with a mean delay 18 s. Activation time marking sensitivity was 0.92; positive predictive value was 0.93. Abnormal slow wave patterns including conduction blocks, ectopic pacemaking, and colliding wave fronts were reliably identified. Compared to traditional analysis methods, online mapping had comparable results with equivalent coverage of 90% of electrodes, average RMS errors of less than 1 s, and CC of activation maps of 0.99. Accurate slow wave mapping was achieved in near real-time, enabling monitoring of recording quality and experimental interventions targeted to dysrhythmic onset. This work also advances the translation of HR mapping toward real-time clinical application.
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