Method And Apparatus For Determining Central Aortic Pressure Waveform

• US 20090131804A1
• Filed: 03/15/2007
• Published: 05/21/2009
• Est. Priority Date: 03/15/2006
• Status: Active Grant

• Current Assignee: The Board of Trustees of Michigan State University (Michigan State University)
• Original Assignee: The Board of Trustees of Michigan State University (Michigan State University)
• Inventors: Ling, Qi, Li, Tongtong, Mukkamala, Ramakrishna, Swamy, Gokul
• Granted Patent: US 8,343,061 B2
• Time in Patent Office: Days
• US Class Current: 600/485
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence

Abstract

A method is provided for determining a central aortic pressure waveform. The method includes: measuring two or more peripheral artery pressure waveforms; analyzing the signals so as to extract common features in the measured waveforms; and determining an absolute central aortic pressure waveform based on the common features.

Claims

1-105. -105. (canceled)

106. A method for determining an absolute central aortic pressure waveform comprising:

measuring peripheral artery pressure signals or related signals at more than one peripheral location in an arterial tree of a subject;

modeling the arterial tree as a single input, multi-output system in which each output corresponds to one of the measured signals;

analyzing the measured signals so as to estimate a common input of the system to within an arbitrary scale factor; and

determining an absolute central aortic pressure waveform by scaling the estimated input based on the measured signals.

107. The method of claim 106 wherein channels of the single-input, multi-output system model of the arterial tree are first estimated to within an arbitrary scale factor and then an inverse of the estimated channels is applied to the measured signals so as to reconstruct the common input to within an arbitrary scale factor.

108. The method of claim 106 wherein channels of the single-input, multi-output system model of the arterial tree are characterized by linear and time-invariant impulse responses that are coprime.

109. The method of claim 108 wherein analyzing the measured signals further comprises applying multi-channel blind system identification to the measured signals to estimate parameters and order of the impulse responses and thereby estimate the impulse responses to within an arbitrary scale factor;

and estimating the input of the system to within an arbitrary scale factor by deconvolution.

110. The method of claim 109 further comprising the representation of the impulse responses with a set of basis functions with unknown parameters prior to the step of applying multi-channel blind system identification.

111. The method of claim 110 wherein the basis functions are truncated exponential varying sinusoids or truncated polynomials for finite impulse responses.

112. The method of claim 110 wherein the basis functions are complex exponentials for infinite impulse responses.

113. The method of claim 110 wherein the number of basis functions takes on an assumed value or is determined.

114. The method of claim 113 wherein the number of basis functions is determined through mean squared error analysis.

115. The method of claim 109 wherein a subset of the unknown parameters of the impulse responses takes on assumed values.

116. The method of claim 109 wherein the parameters and order of the impulse responses are estimated based on cross relations between pairs of measured signals.

117. The method of claim 109 wherein the parameters and order of the impulse responses are estimated based on properties of a channel subspace.

118. The method of claim 109 wherein the parameters of the impulse responses are estimated using least squares methods.

119. The method of claim 109 wherein the parameters of the impulse responses are estimated with the eigenvector method, an iterative two-step maximum likelihood method, an adaptive neural network, or a numerical search.

120. The method of claim 109 wherein the orders of the impulse responses take on assumed values or are determined from the measured signals.

121. The method of claim 120 wherein a maximum order of the impulse responses is determined.

122. The method of claim 120 wherein the orders are determined from the signals by singular value analysis, cross validation, cross validation-based criteria, or information-based criteria.

123. The method of claim 109 wherein single channel deconvolution is applied to one or more of the estimated impulse responses and corresponding measured output signals so as to result in multiple versions of the common input to within an arbitrary scale factor.

124. The method of claim 123 wherein the single channel deconvolution is achieved with Fourier methods or least squares methods.

125. The method of claim 123 further comprising selecting one of the multiple versions as the common input to within an arbitrary scale factor.

126. The method of claim 123 further comprising using an average or median of at least some of the multiple versions as the common input to within an arbitrary scale factor.

127. The method of claim 109 wherein multi-channel deconvolution is applied to one or more of the estimated impulse responses and corresponding measured signals so as to result in a single common input to within an arbitrary scale factor.

128. The method of claim 127 wherein the multi-channel deconvolution is achieved based on Bezout'"'"'s theorem or least squares methods.

129. The method of claim 108 wherein the common input of the system is estimated to within an arbitrary scale factor in one step by applying multi-channel blind system identification to the measured signals.

130. The method of claim 129 wherein the estimation is achieved with the input subspace method.

132. The method of claim 106 wherein the estimated input is scaled to have a mean value equal to that of the measured signal with the largest mean value.

133. The method of claim 106 wherein the estimated input is scaled to have a mean value equal to the mean or median of the mean values of select measured signals.

134. The method of claim 106 further comprising scaling the estimated impulse responses to have unity gain.

135. The method of claim 134 wherein the scaled estimated impulse responses are utilized to monitor local arterial functioning.

158. The method of claim 130 wherein multi-channel deconvolution is applied to one or more of the estimated absolute impulse responses and corresponding measured output signals so as to result in a single absolute common input.

159. The method of claim 158 wherein the multi-channel deconvolution is achieved based on Bezout'"'"'s theorem or least squares methods.

131. The method of claim 10606 wherein the estimated input is scaled to have a mean value equal to that of one of the measured signals.

136. A method for determining an absolute central aortic pressure waveform comprising steps of:

measuring peripheral artery pressure waveforms or related signals at more than one peripheral location in an arterial tree of a subject;

modeling the arterial tree as a single input, multi-output system in which each output corresponds to one of the measured signals;

constraining a property of the system;

analyzing the signals so as to estimate the absolute common input of the constrained system; and

determining the absolute central aortic pressure waveform from the estimated input.

137. The method of claim 136 wherein channels of the constrained single-input, multi-output system model of the arterial tree are first estimated and then an inverse of the estimated channels is applied to the measured signals so as to reconstruct the absolute common input.

138. The method of claim 136 wherein channels of the single-input, multi-output system model of the arterial tree are characterized by linear and time-invariant impulse responses that are coprime.

139. The method of claim 136 wherein one or more of the channels are constrained to unity gain.

140. The method of claim 138 wherein analyzing the measured signals further comprises applying multi-channel blind system identification to the measured signals to estimate parameters and order of the impulse responses and thereby estimate the absolute impulse responses;

and estimating the absolute input of the constrained system by deconvolution.

141. The method of claim 140 further comprising the representation of the impulse responses with a set of basis functions with unknown parameters prior to the step of applying multi-channel blind system identification.

142. The method of claim 141 wherein the basis functions are truncated exponential varying sinusoids or truncated polynomials for finite impulse responses.

143. The method of claim 141 wherein the basis functions are complex exponentials for infinite impulse responses.

144. The method of claim 141 wherein the number of basis functions takes on an assumed value or is determined.

145. The method of claim 144 wherein the number of basis functions is determined through mean squared error analysis.

146. The method of claim 145 wherein a subset of unknown parameters of the impulse responses takes on assumed values.

147. The method of claim 140 wherein the parameters and order of the impulse responses are estimated based on cross relations between pairs of measured signals.

148. The method of claim 140 wherein the parameters and order of the impulse responses are estimated based on properties of channel subspace.

149. The method of claim 140 wherein the parameters of the impulse responses are estimated using least squares methods.

150. The method of claim 140 wherein the parameters of the impulse responses are estimated with an eigenvector method, an iterative two-step maximum likelihood method, an adaptive neural network, or a numerical search.

151. The method of claim 140 wherein the orders of the impulse responses take on assumed values or are determined from the measured signals.

152. The method of claim 151 wherein a maximum order of the impulse responses is determined.

153. The method of claim 151 wherein the orders are determined from the measured signals by singular value analysis, cross validation, cross validation-based criteria, or information-based criteria.

154. The method of claim 140 wherein single channel deconvolution is applied to one or more of the estimated impulse responses and corresponding measured signals so as to result in multiple versions of the absolute common input.

155. The method of claim 154 wherein the single channel deconvolution is achieved with Fourier methods or least squares methods.

156. The method of claim 154 further comprising selecting one of the multiple versions as the absolute common input.

157. The method of claim 154 further comprising using an average or median of at least some of the multiple versions as the absolute common input.

160. The method of claim 138 wherein the absolute common input of the constrained system is estimated in one step by applying multi-channel blind system identification to the measured signals.

161. The method of claim 160 wherein the estimation is achieved with the input subspace method.

162. The method of claim 136, wherein the estimated absolute impulse responses are utilized to monitor local arterial functioning.

163. The method of claim 136 wherein an absolute central aortic pressure waveform is determined as the estimated common input.