Patent Application: US-36780706-A

Abstract:
a method and system is invented for automated continuous monitoring and real - time analysis of body sounds . the system embodies a multi - sensor data acquisition system to measure body sounds continuously . the sound signal processing functions utilize a unique signal separation and noise removal methodology by which authentic body sounds can be extracted from cross - talk signals and in noisy environments , even when signals and noises may have similar frequency components or statistically dependent . this method and system combines traditional noise canceling methods with the unique advantages of rhythmic features in body sounds . by employing a multi - sensor system , the method and system perform cyclic system reconfiguration , time - shared blind identification and adaptive noise cancellation with recursion from cycle to cycle . since no frequency separation or signal / noise independence is required , this invention can provide a robust and reliable capability of noise reduction , complementing the traditional methods . the invention further includes a novel method by which pattern recognition of groups of key parameters can be used to diagnosis physical conditions associated with body sounds , with confidence intervals on the diagnostic criterion to indicate accuracy of diagnosis .

Description:
the following description of the preferred embodiment ( s ) is merely exemplary in nature and is in no way intended to limit the invention , its application , or uses . fig1 shows an overview of the body sound analyzer system 100 modules and processes including inputs and output devices . the invention includes a sound acquisition module which consists of several vital sign sensors 30 for measuring body sounds continuously which have their signals acquired by a data acquisition module 130 , that is connected to a computer 190 . data acquisition module 130 and computer 190 may be embedded in a single multi - sensor body sound analyzer system as shown in fig7 . the sensors 30 can be any type of acoustic sensors that are sufficiently sensitive and have satisfactory signal / noise ratios . typical acoustic sensors include , but not limited to , special microphones , electronic stethoscopes , small accelerometers , and special - purpose body sound sensors . as shown in fig1 the sensors will be placed on auscultation sites on a patient 10 for targeted body sounds , such as tracheal , bronchial , heart , etc ., and for noise references . sound waves acquired by the sensors will then be processed using the body sound signal processing system 95 . in order to obtain noise measurements that represent lumped impact of distributed and multi - source noises on the lung sensors , noise reference sensors are placed on the patient 10 in the vicinity of the sound sensors 30 . sound waves acquired by the sensors 30 are then fed into an analog / digital data acquisition module 130 for signal input , scaling , sampling rate synchronization , and other signal conditioning . the data acquisition module 130 is then connected to a computer 190 which implements the body sound signal processing system 95 . as shown in fig7 , the systems of 130 and 190 may be embedded into one hardware unit . shown in fig1 , sound signals and noise references are then inputted to the following consecutive function modules 95 : a filter module for removing off - band noise 40 , an adaptive noise cancellation module to remove independent noise 50 , a noise cancellation and signal separation module 65 for removing in - band and other noises and separating sound signals to overcome signal interference , a pattern recognition module 80 and diagnosis module 90 for diagnosis . the processed sound signals and parameters are then sent to a display and storage module 250 for audio replay or graphical display , as well as data storage for future utility . shown in fig8 is an overview of the hardware modules that includes the body sound signal processing system 95 associated input and output devices and hardware . as shown in fig8 , the sensors 30 may include sensors measuring lung sounds , heart sounds and even brain and body oxygen sensors . the invention can perform its signal processing on any suitable signals . the physician 20 can be apprised of the invention processing results via speaker 35 , digital audio output 36 , or headphones 34 . the system of the invention can become an integral device in healthcare operations by interface with among others automated cpr devices 260 , automated oxygenation and ventilation devices 270 or other computing devices 190 . as shown in fig7 , the signals are first conditioned and synchronized by the data acquisition module 130 . to obtain authentic lung sounds , signals are filtered to remove off - band 40 and independent noises 50 . the time - shared noise cancellation module 60 and signal separation using cyclic system reconfiguration method module 70 embody the methodology for cyclic system reconfiguration and adaptive channel identification for removing in - band and correlated noises 65 . the adaptive individualized pattern recognition module 80 employs a stochastic pattern recognition algorithm that extracts key parameters for characterizing sound patterns with quantitative confidence levels . then , the real - time individualized optimal diagnosis module 90 identifies abnormal respiratory conditions and diseases . finally , the graphical display 250 and storage modules 170 provide a user interface for sound pattern feedback and display , information storage , and output of diagnoses . also shown in fig7 are several lung sound sensors 30 ( that can be special microphones , accelerometers , electronic stethoscopes , or specially - designed mems acoustic sensors ) on auscultation sites such as tracheal and bronchial , and one or more noise reference sensors . fig1 is a combined signal separation and noise cancellation module . fig9 show a block diagram of the model structure , configurations and including function modules for signal separation and noise removal . when two or more body sounds must be measured simultaneously , their transmission channels are typically those shown in fig1 , in which both signal interference and noise corruption in body sound transmission channels are present . to obtain authentic body sounds , the transmission channels are simplified to those shown in fig1 . fig1 shows the diagram for the main system reconfiguration method that identifies signal transmission channels iteratively , separates body sounds , and removes noises . using the heart and lung sound separation as an example , fig1 ( a ) shows that when both heart and lung sound are near zero , the sensor measurements are used to identify noise transmission channels . when the lung sound is near zero , the system removes noise and then identify the heart - to - lung interference channel ghl in fig1 ( b ). similarly , when the heart sound is near zero , the system removes noise and identify the lung - to - heart interference channel glh in fig1 ( c ). once all transmissions are identified , fig1 ( d ) shows that the system first removes noises and then separate heart and lung sound by inverting the transmission system . this framework is general and can be used for other body sounds as well . when only one body sound must be extracted from noise - corrupted measurements , this function module fig1 is in effect . fig1 shows the block diagram of the method incorporated in this module of the invention for representation of distributed noise sources with a lumped noise source near the reference sensor . this module treats the measurement from the reference sensor as a virtual noise source in which the distributed noise sources are replaced by a lumped noise source y 2 , as shown in fig1 . then the problem of noise cancellation is reduced to identification of the virtual noise channel and the noise free target signal can be approximately extracted . this module is shown in fig1 . the function blocks of this module are shown in fig1 . the key parameters in both the time domain and frequency domain are first extracted . the parameters are time sequences . they are averaged over a moving window to reduce randomness . then individualized histograms are generated to capture their statistical properties . the histograms serve as data points to generate in real - time parameter distribution functions that are unique to a patient . this module is shown in fig1 . the function blocks of this module are shown in fig1 . diagnosis is performed in real - time . diagnosis regions are generated recursively , by incorporating information from new parameter values of sound samples . the diagnosis regions are used to decide if an abnormal sound sample has been found . the decision is based on an optimal decision strategy that minimizes decision errors . then the diagnosis regions are updated by the new data . fig1 is a typical respiratory sound where for signal processing with an embodiment of the present invention , a ventilation or breathing cycle is divided into three stages : inhale ( ti ), exhale ( te ), and transitional pause ( t - ti - te ). fig1 is a diagram showing a time domain comparison of results for noise cancellation using anc and using the method of time - shared anc that shows deterioration of noise cancellation efficiency in lung sound analysis when correlations exist in accordance with an embodiment of the present invention . fig1 is a diagram of showing an illustration of noise impact on lung sound patterns . fig1 ( a ) is a typical normal breathing sound and fig1 ( b ) an expirational wheeze . the top figures in fig1 are the raw data . due to low - frequency noises from sensor contact surfaces , the breathing patterns are not obvious . a high - pass filter is used to eliminate the noise under 200 hz . after filtering , the difference between normal and wheeze lung sounds can be clearly seen from their time domain waveforms . in frequency domain analysis , the wheeze can be further characterized by a substantial narrowing of the spectrum , shifting of the center frequency ( towards low pitch in this example ), etc . for this example , sounds are obviously very clean with minimum noise corruption . sound patterns are significantly altered when noise artifacts are present . fig1 ( c ) shows the corrupted wheeze signal , both in its time - domain waveform and frequency - domain spectrum . it is apparent that in a noisy environment , the time - domain waveforms of a wheeze are distorted to the point that it is no longer possible to recognize sound patterns in accordance with an embodiment of the present invention . fig2 is a diagram showing a time domain comparison of results for noise cancellation using anc versus using the method of time - shared anc on wheeze sounds in accordance with an embodiment of the present invention . fig2 is a diagram showing a frequency domain comparison of results for noise cancellation using anc versus using the method of time - shared anc in accordance with an embodiment of the present invention . the noise spectrum overlaps with the lung sound spectrum . the estimated lung sound restores the power spectrum of the original lung sound . the results for anc compare the spectra of the measured lung sound , estimated lung sound and original lung sound ( the top plot of fig2 ( a )). anc can only reduce noises that are not correlated with the lung sound in spectra , as shown in the bottom plot of fig2 ( a ). time - shared anc provides a more effective noise reduction in spectra , as shown in fig2 ( b ). it can cancel most noises no matter if they are correlated with lung sounds or not . fig2 is a diagram showing characteristics for normal and abnormal lung sounds . to understand what variables might be useful to capture pattern changes in lung sounds , we illustrate some typical normal and abnormal lung sound waveforms and their frequency spectra during inhale and exhale in fig2 . for example , the wheeze can be clearly characterized by a substantial narrowing of spectrum , shifting of center frequency ( towards low pitch in this example ), and power imbalance between inspiration and expiration . in accordance with an embodiment of the present invention . fig2 is a diagram showing a histograms of sample points of sound parameters giving a quantitative analysis on parameter vector distributions . it is noted that when noise level increases sound patterns have larger deviations and have a pattern shifting as well . as discussed before , inherent noises result in pattern shifting which cannot be eliminated by stochastic averaging . reduction of impact from inherent noises must be done by noise cancellation techniques , which will be discussed later . on the other hand , increased sensor noises result in larger deviations . averaging can be used when the size of data samples becomes larger . fig2 is a diagram showing simulations were performed on identification errors of the recursive least - squares algorithm . three cases were compared : ( 1 ) the input signal u ( k ) is uncorrelated with the disturbance signal d ( k ); ( 2 ) u ( k ) is correlated with d ( k ) of a moderate level ; ( 3 ) the correlation between u ( k ) and d ( k ) is more severe than the second case . fig2 illustrates the trajectories of identification errors . the results clearly demonstrate that higher correlations between u ( k ) and d ( k ) lead to larger estimation errors and slower convergence rates . this simulation explains why our time - shared anc method is more accurate and efficient in accordance with an embodiment of the present invention . fig2 is a diagram showing parameter data points on normal sound and wheeze in accordance with an embodiment of the present invention . fig2 is a diagram showing noise impact on normal sound and wheeze in accordance with an embodiment of the present invention . fig2 is a diagram showing confidence regions for pattern recognition in accordance with an embodiment of the present invention . fig2 is a diagram showing mean trajectories of parameters without noise cancellation in accordance with an embodiment of the present invention . fig2 is a diagram showing pattern recognition after noise reduction by time - shared adaptive noise cancellation in accordance with an embodiment of the present invention . fig3 is a diagram showing measured heart and lung sounds and the signals after off - band noise filtering . it reveals that off - band noise removal is not sufficient to clarify these signals . fig3 is a diagram showing measured heart and lung sounds and the signals after noise cancellation and signal separation by using the cyclic system reconfiguration method of this invention . it reveals the effectiveness of the signal separation and noise cancellation method of this invention . the description of the invention is merely exemplary in nature and , thus , variations that do not depart from the general design of the invention are intended to be within the scope of the invention . such variations are not to be regarded as a departure from the intent and scope of the invention . 65 combined cyclic system reconfiguration method for signal separation and noise cancellation 220 special feature based signal separation that is disease specific n . gavriely , y . palti , and g . alroy : “ spectral characteristics of normal breath sounds ”, j . appl . physiol ., vol . 50 , no . 2 , pp . 320 - 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