Patent Application: US-201013805803-A

Abstract:
a method and system of patient monitoring , the system performs short term acquisition of the plethysmographic waveform of a patient from a portable blood oxygenation level monitoring device and establishes whether the patient has an episode of atrial fibrillation or has a normal sinus rhythm or any other not - specific rhythm irregularity . such classification is implemented directly in the device , suitable for at home use , and the result of the classification is displayed automatically using a three - state , traffic - light indicator .

Description:
the method for the detection of af episode is based on the estimation of the heart rhythm irregularity , specifically on two indexes extracted from the beat - to - beat interval series obtained from pulsoximetric waveform . acquisition of the pulseoximetric signal / waveform from the finger . estimation of the time - occurrence of each beat from the pulseoximetric waveform . construction of the beat - to - beat series . beat validation to reduce artifacts characterization of pulseoximetric waveform , by morphological analysis of the ascendent and descendent tracts of a single pulse removal of beat intervals with values lower than 0 . 3 s and longer than 1 . 5 s coefficient of variation of the first differences of beat - to - beat interval series shannon entropy of the beat - to - beat interval series estimation of the mahalanobis distance from population with sinus rhythm and with af . with reference to fig1 , the device according to the present invention substantially carries out a process comprising the following steps : 1 . recording of the pulseoxymetric waveform at the finger ; 2 . estimation of the time - occurrence of each beat , by adaptive thresholding and parabolic interpolation ; 3 . beat validation : evaluation of the ascendant and descendent tracts of any detected beat ; 4 . construction of the beat - to - beat interval series 5 . estimation of quantitative indexes : 6 . classification of the heart rhythm by the estimation of the mahalanobis distance from population with sinus rhythm and with af . artifacts are rejected using a parametric modeling of the pulse waveform contour . each detected beat is segmented into three sections . the similarity of the first section to a linear segment and the similarity of the third segment to an exponential decay are used to discriminate proper beats from artifacts . similarity is assessed using the goodness of fit ( r - squared ) with linear and exponential interpolating functions . if the r - squared of the linear and exponential tracts are lower than a reference level , the beat is categorized as artifact and neglected . beat validation : evaluation of the ascendant and descendent tracts of any detected beat referring to fig2 and 3 , showing a sketch of pulse contours , respectively with the interpolating linear function ( fig2 ) and exponential function ( fig3 ), it is useful to point out the following validation steps . step 1 . each detected beat is segmented into three sections . step 2 . the similarity of the first section to a linear segment is - evaluated using linear fitting . similarity is quantified using the goodness of fit ( r - squared ) with a linear function ( fig2 ) step 3 . the similarity of the third segment to an exponential decay is evaluated using and exponential fitting . similarity is quantified using the goodness of fit ( r - squared ) with an exponential interpolating function ( fig3 ). step 4 . if the r - squared of the linear and exponential tracts are both lower than a reference level , the beat is categorized as artifact . known parameters based on the morphological analysis of the ecg signal were excluded ( e . g . p - wave detection ). it is known that there are several parameters aimed to detect af by analyzing the irregularity of the heart rhythm . some of these parameters does not suit a short - term detection since they requires a relatively large number of beats , others require significant computational effort / memory occupation . such methods have been excluded . among the other known parameters a novel combination of two of them has been adopted , by empirical testing . such peculiar combination has not been used previously . the performances of the proposed method have been verified on a population of 61 patients ( 43 with nsr , 14 with af and 4 with other arrhythmias ) coefficient of variation is the ratio between the standard deviation for the series of the first derivatives of the beat - to - beat interval series ( σδpp ) and the mean value of the beat - to - beat interval series ( μpp ): (( pp =√([( 1 )/ nσ i ( i = 1 ) n [([( pp ] i i −] μ ( pp )] 2 ): standard deviation of the series of the first derivatives of the beat - to - beat interval series μ ( pp =( 1 )/( n − 1 ) σ i ( i = 1 ) ( n − 1 ) [ ( pp i i ]: mean of the series of the first derivative of the beat - to - beat interval series [( pp ] i i = pp i ( i + 1 )− pp i i : series of the first derivatives of the beat - to - beat interval series conventionally , cv of a temporal series is computed as the ratio between its standard deviation and its mean value . in this case , since the man value of the first derivatives of the beat - to - beat interval series ( δpp ) is close to zero , the computation is made dividing for the mean value of the beat - to - beat interval series ( pp ). the entropy is computed from the shannon formula , that implies the estimation of the probability density function of the beat - to - beat interval series ( by construction of the histogram ). p k = is the probability of occurrence of a beat - to - beat interval , estimated as the ratio n k / n , with n k representing the number of beat - to - beat intervals within the k th bin . the bin amplitude have to be chosen sufficiently higher than the time resolution of the pp series , to obtain a reliable estimate of the histogram density , but not too large to avoid disruption of relevant information for the rhythm discrimination . the bin amplitude has been set to 16 . 6 ms . rhythm classification is based on the measure of the distance of the couple of values cv and en for a patient respect to the values characterizing a population of patients in atrial fibrillation and of subjects in sinus rhythm . instead of the euclidean distance , the mahalanobis distance is used since it takes into account the parameters &# 39 ; dispersion within the population and their mutual correlation . classification in nsr , af or other arrhythmia is achieved by the following criteria : if the mahalanobis distance from the af is lower than a properly selected value , rhythm is classified as af , else if the mahalanobis distance from nsr is lower than a properly selected value , rhythm is classified as normal , else the properly selected values of the mahalanobis distances have been chosen on the basis of the result from the clinical validation of the method . from a set of values representative of a given population , the mahalanobis distance is computed as x = set of parameters of the patient to be classified ( cv and en ) μ = set of the mean value of the parameters in the reference population ( sinus rhythm or atrial fibrillation ) [ μcv μen ]. d s ( i )= distance of the patient i respect to the population in sinus rhythm d af ( i )= distance of the patient i respect to the population in atrial fibrillation μcv s = mean of parameter cv for the population of patients in sinus rhythm μen s = mean of parameter en for the population of patients in sinus rhythm μcv af = mean of parameter cv for the population of patients in atrial fibrillation μen af = mean of parameter en for the population of patients in atrial fibrillation coefficients of the inverse of covariance matrix , for the population in sinus rhythm coefficients of the inverse of covariance matrix , for the population in atrial fibrillation the performances of the proposed method have been verified on a population of 61 patients with an history / suspect of af . heart rhythm diagnosis was performed by an expert cardiologist , before each signal collection . 43 patients had nsr , 14 af and 4 other arrhythmias . a short - term pulseoximetric signal was collected at the finger , using a commercial pulseoximeter , which may have a connection with a laptop pc for signal storage and further analysis . beat validation showed high sensitive and specificity (& gt ; 95 %) in discriminating proper beats from artifacts . the method classified correctly 43 out of 43 patient with sinus rhythm , 14 out of 14 patient with af , 3 out of 4 patients with other arrhythmias . one patient with a supraventricular tachycardia was classified as normal sinus rhythm . in terms of sensitivity ( se ) and specificity ( sp ) of af detection , the method shows a sp = 100 % and se = 100 %. the accuracy was 98 . 4 %. 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