Esophageal waveform analysis for detection and quantification of reflux episodes

Abstract

A system and method for automatically analyzing impedance and pH data from an esophageal probe includes a data collection system that collects and stores the output from the sensors for a certain period of time to locate reflux episodes in the waveforms. The data analysis system uses wavelet analysis to assist in locating bolus entry and exit points in the waveforms and to smooth waveforms for additional analysis.

1 . A method of locating bolus entry and bolus exit points on an impedance waveform, comprising: 
 applying a smoothing algorithm to the waveform;    locating singularities in the smoothed waveform;    determining whether the singularities are on negative slopes or positive slopes; and    identifying candidate bolus entry and exit points as singularities on negative slopes of the smoothed waveform followed by singularities on positive slopes of the smoothed waveform.    
     
     
         2 . The method of  claim 1  including: 
 performing wavelet transform calculations on the waveform to smooth the waveform and to detect occurrences of the singularities in the waveform.    
     
     
         3 . The method of  claim 2  including: 
 detecting the occurrences of the singularities from local maxima of absolute value of the wavelet transform.    
     
     
         4 . The method of  claim 3 , including: 
 using wavelet analysis to develop positive details and negative details of the waveform that correspond to positive slopes and negative slopes in the waveform;    locating local maxima of the negative details and local maxima of the positive details; and    determining the bolus entry points as corresponding to said local negative maxima and bolus exit points as corresponding to said local positive maxima.    
     
     
         5 . The method of  claim 1 , including identifying a transition point on the waveform by determining the Fourier transform of said wavelet, determining the Fourier transform of at least a portion of said waveform and computing said wavelet transformation using said Fourier transform of said wavelet.  
     
     
         6 . The method of  claim 4 , including comparing impedance between said local negative maxima of the details and said local positive maxima of the details to a threshold of gastric content, and determining that a candidate reflux episode has occurred at a time beginning at said local negative maxima and ending at a time corresponding to said positive maxima when said impedance is below said threshold of gastric content.  
     
     
         7 . The method of  claim 6 , including determining said threshold of gastric content by finding an average/mean gastric content impedance from data collected over a period of at least two hours.  
     
     
         8 . The method of  claim 4 , including determining an acid gastric content threshold by finding an average/mean gastric content impedance over a predetermined period of time when pH is measured as being below normal background pH, and determining a non-acid gastric content threshold by finding an average/mean gastric content impedance over a predetermined period of time when there is no supporting pH measurement showing such impedance is below normal background pH.  
     
     
         9 . The method of  claim 8 , including comparing impedance between said local negative maxima and said local positive maxima to said acid gastric content threshold and to said non-acid gastric content threshold, determining that a candidate acid reflux episode has occurred when said impedance is below said acid gastric content threshold.  
     
     
         10 . The method of  claim 9 , including adaptively changing the acid gastric content threshold and the non-acid gastric content threshold as new candidate episodes occur.