referring to fig1 , an exemplified dynamic cerebral autoregulation assessment system 100 includes an analyzer 110 and one or more probes 120 , 130 that can be attached to a patient 10 . the probes 120 , 130 include transducers and sensors that are configured to measure physiological signals such as continuous beat - to - beat blood pressure ( bp ) and blood flow velocity ( bfv ) from the patient 10 . cerebral bfv can be measured using transcranial doppler ultrasound ( tcd ) under a non - stimulated condition or during sudden systematic bp changes induced by valsalva maneuver ( vm ), head - up tilt , or a sit - to - stand movement . the probes 120 , 130 can send sensing signals to the analyzer 110 in response to the physiological signals . the sensing signals are often in analog form . the analyzer 110 can include an analog - to - digital ( a / d ) converter for digitizing the sensing signals . the analyzer 110 also includes a computer processor that is configured to process and analyze the sensing signals after they are digitized by the a / d converter . an algorithm can be pre - stored in a computer memory in the analyzer 110 for analyzing the sensing signals . the analyzer 110 can also include necessary input / output devices that allow a user to enter instructions to process the data , and a display for displaying the raw sensing signals and data calculated from the sensing signals . one or more or all the analyzing steps described below can be automated . referring to fig2 , the disclosed ca assessment can include one or more of the following steps . physiological signals are first acquired ( step 210 ) from an individual using the ca assessment system 100 ( shown in fig1 ). the physiological signals can include bp and bfv signals . the bfv signals can include signals from left ( bfvl ) and right ( bfvr ) middle cerebral arteries ( mcas ). bp , and bfvl and bfvr signals can be measured from a healthy individual , shown in fig3 a , can be used as a control for pathological assessment . bp , and bfvl and bfvr signals can be measured from a patient having certain pathologic conditions such as diabetes , stroke , hypertension , aging , dementia , and traumatic brain injuries ( tbi ). bp , and bfvl and bfvr signals obtained from a diabetes patient are shown in fig3 b . the physiological signals ( e . g . blood pressure or blood flow ) from the healthy individual ( used as a control ) and the patient both show oscillatory pattern related to the dynamics of respiratory oscillations . the bp and bfv signals as shown in fig3 a and 3b are not stationary , that is , their amplitudes and frequencies vary over time . to analytically describe such non - stationary oscillations , amplitudes and phases ( or frequency ) need to be characterized at any instantaneous moment . conventional fourier transforms , however , cannot accurately describe non - stationary signals because these signals are non - periodic . in the present application , physiological signals can be decomposed into intrinsic mode functions ( imfs ) by the ensemble empirical mode decomposition technique . the blood pressure signals from both the healthy individual and the diabetes patient are decomposed into bp imfs ( step 220 ). for example , the blood pressure waveform obtained from the diabetes patient shown in fig3 b and 4a is decomposed into a plurality of bp intrinsic modes ( e . g . mode 1 to mode 12 ), as shown in fig4 b . mode 1 is obtained by tracing the envelope of local maxima and local minima in the blood pressure waveform . mode 1 is then subtracted from the blood pressure waveform to obtain a first residual signal . mode 2 is obtained by tracing the envelope of the maxima and minima in the first residual signal . mode 2 is then subtracted from the first residual signal to obtain a second residual signal . mode 3 is similarly calculated from the second residual signal . the above described decomposition steps are repeated to compute mode 4 to mode 12 of the bp imfs . the decomposition algorithm generally results in decreased oscillation frequencies from mode 1 to mode 12 in the intrinsic mode functions . the oscillation frequencies of the successive imfs approximately decrease by a factor of two . for control purpose , bp imfs are also obtained from the blood pressure waveform ( shown in fig3 a ) of a healthy individual . similarly , imfs can be obtained by decomposing the bfvl and bfvr signals ( shown in fig3 a ) obtained from the health individual . imfs can be obtained by decomposing the bfvl and bfvr signals ( shown in fig3 b ) measured from the diabetes patient ( step 230 ). the intrinsic mode functions ( bp , bfvl , or bfvr ) are usually characterized by frequency - amplitude modulation in a narrow frequency band . physiological or pathological processes are known to be associated with certain specific frequency ranges . for instance , spontaneous breathing oscillations normally exist in a frequency range of 0 . 1 ˜ 0 . 4 hz under baseline ( non - stimulated ) condition and in a frequency range of 0 . 1 ˜ 0 . 03 hz for oscillations induced by valsalva maneuver . a characteristic bp , bfvl or bfvr imf having characteristic frequency in the interesting frequency range can be selected for ca assessment ( step 240 ). for example , mode 6 in fig4 b is selected from the plurality of bp imfs for the healthy individual is shown in fig5 a ( the upper plot ). likewise , mode 6 is selected from the plurality of bp imfs for the diabetes patient is shown in fig5 b ( the upper plot ). the selection of characteristic bp , bfvl or bfvr imfs can be fully automated by determining the dominant oscillation frequencies in the imfs . for baseline ( non - stimulated ) conditions , the imfs that has characteristic frequencies between 0 . 1 hz and 0 . 4 hz can be automatically selected for ca assessment . similarly , characteristic bfvl and bfvr imfs having their respective characteristic frequencies in a predetermined frequency range can be selected from their respective bfvl and bfvr imfs for the healthy individual and diabetes patient ( step 250 ). the selected characteristic bp , bfvl and bfvr imfs from the healthy individual are jointly illustrated in fig5 a . the selected characteristic bp , bfvl and bfvr imfs from the diabetes patient are jointly illustrated in fig5 b . as shown in fig5 a and 5b , the characteristic bp , bfvl and bfvr imfs oscillations from the healthy individual and the diabetes patient both show oscillatory patterns . but the oscillatory patterns from the diabetes patient appear to be more irregular compared to the oscillatory patterns from the healthy individual . close scrutinizations of bp and fv signals in fig5 a and 5b can reveal that the bfvl or bfvr signals have significant phase shifts behind the bp signals for a healthy individual ( fig5 a ), whereas the bfvl or bfvr signals are much more synchronized to the bp signals for the diabetes patient . it is known in the medical diagnostics that pathologic impairments of dynamic cerebral autoregulation can significantly reduce this phase shift between bp and bfv . next , instantaneous phases between oscillations in the bp and bfv characteristic imfs are computed ( step 260 ). for the healthy individual , the instantaneous phases are separately calculated between characteristic bp and bfvl imfs ( fig6 a , upper plot ) and between characteristic bp and bfvr imfs ( fig6 a , lower plot ). for the diabetes patient , the instantaneous phases are also calculated between characteristic bp and bfvl imfs ( fig6 b , upper plot ) and between characteristic bp and bfvr imfs ( fig6 b , lower plot ). next , indices or “ bio - markers ” for ca are derived from the instantaneous phases of bp and bfv oscillations ( step 270 ). it is known that ca typically lead to faster recovery in bfv in response to bp fluctuations for normal control subject , however , the phases of bfv oscillations follow bp oscillations more closely for patients under pathological conditions compared to healthy individuals . in other words , the bfv oscillations have smaller phase shift behind the bp oscillations for these pathological conditions . referring to fig6 a , average bfv - bp phase shifts are in the range of 60 - 70 degrees for the healthy individual . in contrast , referring to fig6 b , average bfv - bp phase shifts for the diabetes patient are between approximately 10 - 20 degrees , which are significantly lower than the phase shifts for healthy individuals . average bfvl - bp and bfvr - bp phases can thus be used as indices or “ bio - markers ” for ca assessment . a pathological condition such as diabetes can be determined if the computed bfvl - bp or bfvr - bp phase is below a pre - determined threshold ( step 280 ). for example , in the examples shown in fig3 a through fig6 b , diabetes condition can be determined in association with a patient if the computed average of the bfvl - bp or bfvr - bp phases is below 30 , 40 , or 50 degrees . other suitable indices or “ bio - markers ” in bfv - bp phases for ca assessment can also include standard deviations , medians , maxima , minima , and so on . in general , a pathological condition can be determined when a ca index exceeds or falls below a predetermined condition . it should be understood that the above described systems and methods are applicable to different physiological signals and pathological conditions from the examples described above . for example , the disclosed methods and systems are suitable for pathological conditions such as diabetes , stroke , hypertension , aging , dementia , and traumatic brain injuries ( tbi ). the physiological signals can be measured under normal conditions or by the valsalva maneuver . moreover , physiological signals other than blood pressure and blood flow velocity can be suitable to disclosed systems and methods . phase differences from three or more separate physiological signals can be used for ca assessment and pathological determination .
a method for dynamic cerebral autoregulation assessment includes acquiring a blood pressure signal having a first oscillatory pattern from a first individual , acquiring a blood flow velocity signal having a second oscillatory pattern from the first individual , decomposing the bp signal into a first group of intrinsic mode functions , decomposing the bfv signal into a second group of imfs , determining dominant oscillatory frequencies in the first group of imfs , automatically selecting a first characteristic imf from the first group of imfs that has its associated dominant oscillatory frequency in a predetermined frequency range , automatically selecting a second characteristic imf from the second group of imfs , calculating a time sequence of instantaneous phase difference between the first characteristic imf and the second characteristic imf , computing an average of the instantaneous phase difference in the time sequence , and identifying a pathological condition in the first individual .