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 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

High resolution mapping of electrical activity is becoming an important technique for analysing normal and dysrhythmic gastrointestinal (GI) slow wave activity. Several methods are used to extract meaningful information from the large quantities of data obtained, however, at present these methods can only be used offline. Thus, all analysis currently performed is retrospective and done after the recordings have finished. Limited information about the quality or characteristics of the data is therefore known while the experiments take place. Building on these offline analysis methods, an online implementation has been developed that identifies and displays slow wave activations working alongside an existing recording system. This online system was developed by adapting existing and novel signal processing techniques and linking these to a new user interface to present the extracted information. The system was tested using high resolution porcine data, and will be applied in future high resolution mapping studies allowing researchers to respond in real time to experimental observations.

High-resolution (HR) mapping employs multielectrode arrays to achieve spatially detailed analyses of propagating bioelectrical events. A major current limitation is that spatial analyses must currently be performed “off-line” (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 off-line 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.

Clinical evaluation of cutaneous electrogastrograms (EGG) is important for understanding the role of slow waves in functional motility disorders and may be a useful diagnostic aid. An automated software package has been developed which computes metrics of interest from EGG and from slow wave recordings from the gastric mucosa and serosa in a reliable and efficient manner. In particular, the frequency and amplitude of the gastric slow waves were computed, after which signal integrity checks were performed to assess if the signals are valid. For validation, manual estimates of the frequency and amplitude were compared to automated estimates. The methods were packaged into a software executable which processes the data and presents the results in an intuitive graphical and a spreadsheet formats. Automated EGG analysis allows for clinical translation of bio-electrical analysis for potential diagnostics, as commonly used in the cardiac field.