GOAL: Analytic monitoring of electrophysiological data has become an essential component of efficient and accurate clinical care. In the gastrointestinal (GI) field, recent advances in high-resolution (HR) mapping are now providing critical information about spatiotemporal profiles of slow-wave activity in normal and disease (dysrhythmic) states. The current approach to analyze GI HR electrophysiology data involves the identification of individual slow-wave events in the electrode array, followed by tracking and clustering of events to create a spatiotemporal map. This method is labor and computationally intensive and is not well suited for real-time clinical use or chronic monitoring. METHODS: In this study, an automated novel technique to assess propagation patterns was developed. The method utilized time delays of the slow-wave signals which was computed through cross correlations to calculate velocity. Validation was performed with both synthetic and human and porcine experimental data. RESULTS: The slow-wave profiles computed via the time-delay method compared closely with those computed using the traditional method (speed difference: 7.2% ± 2.6%; amplitude difference: 8.6% ± 3.5%, and negligible angle difference). CONCLUSION: This novel method provides rapid and intuitive analysis and visualization of slow-wave activity. SIGNIFICANCE: This techniques will find major applications in the clinical translation of acute and chronic HR electrical mapping for motility disorders, and act as a screening tool for detailed detection and tracking of individual propagating wavefronts, without the need for comprehensive standard event-detection analysis.
GOAL: Analytic monitoring of electrophysiological data has become an essential component of efficient and accurate clinical care. In the gastrointestinal (GI) field, recent advances in high-resolution (HR) mapping are now providing critical information about spatiotemporal profiles of slow-wave activity in normal and disease (dysrhythmic) states. The current approach to analyze GI HR electrophysiology data involves the identification of individual slow-wave events in the electrode array, followed by tracking and clustering of events to create a spatiotemporal map. This method is labor and computationally intensive and is not well suited for real-time clinical use or chronic monitoring. METHODS: In this study, an automated novel technique to assess propagation patterns was developed. The method utilized time delays of the slow-wave signals which was computed through cross correlations to calculate velocity. Validation was performed with both synthetic and human and porcine experimental data. RESULTS: The slow-wave profiles computed via the time-delay method compared closely with those computed using the traditional method (speed difference: 7.2% ± 2.6%; amplitude difference: 8.6% ± 3.5%, and negligible angle difference). CONCLUSION: This novel method provides rapid and intuitive analysis and visualization of slow-wave activity. SIGNIFICANCE: This techniques will find major applications in the clinical translation of acute and chronic HR electrical mapping for motility disorders, and act as a screening tool for detailed detection and tracking of individual propagating wavefronts, without the need for comprehensive standard event-detection analysis.
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