in the following detailed description an aditester in accordance with an embodiment of the invention , is schematically shown in fig1 and its components and operation are discussed with reference to the figure . the aditester is shown being used to determine a value for adi and therefrom a diagnosis for the presence and severity of osa for a person responsive to snoring sounds that the person makes during sleep . details of the operation of the aditester in generating a feature set that characterizes the person &# 39 ; s snoring sounds and determining an osa diagnosis for the person in accordance with an embodiment of the invention are discussed with reference to a flow diagram shown in fig2 . fig1 schematically shows an aditester 20 operating to determine possible presence and severity of osa in a person 100 sleeping , optionally , in a bedroom 102 of his own house . aditester 20 comprises a microphone 22 , optionally placed on a night table 104 near person 100 and a computer system 24 . microphone 22 registers sounds made by the person during sleep and sounds that are not made by the person that reaches the microphone . sounds that are made by the person comprise for example , snoring sounds , breathing , coughing and voice sounds , and sounds that are produced by motion of the person , such as bed creaking and blanket rustling sounds . sounds that are not made by the person may comprise street sounds and sounds originating in other rooms of the person &# 39 ; s house that reach the bedroom and sounds made by appliances , such as a whirring sound made by an overhead fan 106 in bedroom 102 . sounds not made by the person may also include sounds made by another person ( not shown ) in the bedroom . for convenience of presentation , sounds that are registered by microphone 22 that are not respiratory sounds ( snoring and breathing sounds ) made by person 100 are referred to as background sounds . microphone 22 transmits the sounds that it registers as a “ sleep sound signal ” schematically represented by a waveform 23 , to computer system 24 . computer system 24 processes the sleep sound signal to identify snoring sounds therein and provide a value for adi for the person responsive to the snoring sounds . the computer system is optionally configured having computer executable instruction sets referred to as a snore detector 25 , a feature extractor 26 , and an adi / osa modeler 27 and optionally comprises a memory 28 in which it stores sleep sound signal 23 that the computer system receives from microphone 22 . snore detector 25 processes the sleep sound signal stored in the memory to identify snoring sounds therein . feature extractor 26 processes snoring sounds identified by snore detector 25 to determine features , in accordance with an embodiment of the invention , that characterize the snoring sounds and may be used to determine a value for adi for person 100 . adi / osa modeler 27 uses the features provided by the feature extractor to determine a value for adi and therefrom a diagnosis as to presence and severity of osa for person 100 . computer system 24 may comprise a smart phone pc , a laptop , and / or a work book located in the home of person 100 that stores and executes the instruction sets defining snore detector 25 , feature extractor 26 , and adi / osa modeler 27 . however , computer system 24 is not limited to being housed in a single computer , or a computer located in a same room with person 100 . computer system 24 may be a distributed system having components and executable instruction sets located in different servers , and may be partially or completely based on access to servers via the internet , that is partially or completely “ cloud based ”. for example , memory 28 may be located close to microphone 22 and directly coupled to the microphone to receive and store sleep sound signal 23 . snore detector 25 , extractor 26 and adi / osa modeler may be connected to memory 28 and each other by the internet and reside and function in different internet servers . aspects and functioning of aditester 20 , snore detector 25 , feature extractor 26 , and adi / osi modeler 27 in determining if person 100 suffers from osa , and if so a severity of the osa , are discussed below with reference to a flow diagram 200 shown in fig2 a . in a block 202 aditester 20 is turned on and microphone 22 is registering sounds made in or reaching room 102 and transmitting analog electronic signals that form sleep sound signal 23 to computer system 24 . the computer system converts sound signal 23 from an analog signal to a digital signal and optionally stores the digital sleep sound signal in memory 28 . hereinafter , unless otherwise specified , reference to sleep sound signal 23 is assumed to reference the digital form of the sleep sound signal . sleep sound signal 23 includes background sounds , such as background sounds noted above , and respiratory sounds made by person 100 during a period in which the person is asleep . a sleep period , for which an associated sleep sound signal 23 is acquired , may have different durations , and may of course have duration of a nominal full night &# 39 ; s sleep of 6 - 8 hours . the sleep sound signal may include electromagnetic interference from power lines and appliances in a neighborhood of aditester 20 . in a block 204 , snore detector 25 processes sleep sound signal 23 to distinguish and identify snoring sounds in the sound signal . any of various methods and algorithms may be used by the snore detector to identify snoring sounds . in an embodiment of the invention , snore detector 25 first filters sleep sound signal 23 to remove readily identifiable interference , such as electromagnetic interference generated at frequencies of alternating currents in power lines and appliance transformers , from sleep sound signal 23 . thereafter , snore detector 25 processes the filtered sleep sound signal 23 to identify portions , hereinafter referred to as “ audio events ”, of the filtered sleep sound signal 23 having energy and duration that indicate that the audio events are candidates for being “ snore signals ”, which represent snoring sounds made by person 100 . in an embodiment of the invention , for a portion of sleep sound signal 23 to be considered an audio event , the portion may be required to exhibit energy greater than a determined threshold energy e th and have a duration “ τ d ” greater than a minimum duration τ dmin and less than a maximum duration τ dmax . to determine a value for e th the sleep sound signal is segmented into consecutive , optionally partially overlapping sound frames having duration equal to about 30 ms ( milliseconds ). an energy for each sound frame is optionally , determined to be equal to a sum of squared amplitudes of sleep sound signal 23 in the frame , or an average of the squared amplitudes in the frame . in an embodiment of the invention a value for e th is determined for each of a plurality of relatively long “ windows ” of time into which sleep sound signal 23 is divided responsive to lower and upper bound energies e l and e u respectively determined for sleep sound signal 23 . a time window may have duration equal to hundreds or thousands of times that of the sound frames into which sleep signal 23 is segmented . optionally e l and e u are determined from a frequency distribution of frames in the sleep sound signal as a function of frame energy . in an embodiment of the invention , e l is an energy greater than an energy at which the distribution is maximum and for which the distribution falls to a fraction of the maximum . optionally , the fraction is equal to about 0 . 10 . optionally , e u is a multiple of e l determined to provide a reasonable upper limit to a value determined for e th . for a given time window , a candidate threshold energy “ ce th ” for threshold energy e th of the window is determined from a frequency distribution of frames in the window as a function of frame energy . optionally , ce th is an energy equal to a factor times an energy greater than an energy at which the frequency distribution is maximum , and for which the distribution falls to a fraction of its maximum . optionally , the factor is equal to about 1 . 3 . optionally , the fraction is equal to about 0 . 10 . in an embodiment of the invention , the threshold energy e th for the given time window is set equal to e l if ce th & lt ; e l ; is set equal to e u if ce th & gt ; e u ; and is set equal to ce th if e l ≦ ce th ≦ e u . a portion of sleep sound signal 23 in a given time window is determined to be an audio event in accordance with an embodiment of the invention if the portion comprises a plurality of consecutive sound frames : that have cumulative duration τ d satisfying the constraint τ dmin ≦ τ d ≦ τ dmax ; that have a peak energy “ e p ” greater than e th ; and for which none of the frames have energy less than a threshold energy “ e r ”. in an embodiment of the invention , e r is equal to 0 . 5 ( e th + e wm ), where e wm is a minimum energy exhibited by frames in the given window . optionally , τ dmin , is equal to about 0 . 2 s ( seconds ) and τ dmax is equal to about 2 . 5 s . for each audio event that is determined to be a candidate snore signal , snore detector 25 generates a feature set and uses a gaussian mixture model ( gmm ) classifier to determine responsive to the feature set , if the candidate snore signal is to be classified as a snore signal . in an embodiment of the invention the feature set that snore detector 25 generates for a snore signal candidate comprises a set of “ n ” linear predictor coefficients ( lpc ), and the candidate &# 39 ; s pitch density ; average pitch value ; total energy ; duration , and rise time . optionally , n is equal to 12 . in an embodiment of the invention the gmm classifier comprises two gaussian density models , one having order n s for snore signal candidates that represent snoring sounds and one having order n b for snore signal candidates that represent background sounds . optionally , n s and n b are equal to 3 and 11 , respectively . a set of gmm parameters that define the gmm classifier are optionally determined as a gmm parameter set that maximizes a likelihood of the feature sets for the models . the feature sets acquired for each of a plurality of training snore signal candidates that are known to represent a snoring sound or a background sound . in a block 206 snore signals identified by snore detector 25 in sleep sound signal 23 acquired for person 100 are processed by feature extractor 26 to define a feature set for sleep sound signal 23 that may be used to provide a value for adi and therefrom a diagnosis of osa for the person , in accordance with an embodiment of the invention . in an embodiment of the invention , feature extractor 26 generates a feature set comprising five features , f 1 , f 2 , f 3 , f 4 , and f 5 , for sleep sound signal 23 . feature f 1 , referred to as a “ mel - cepstability ” of sleep sound signal 23 , is a function of mel - frequency cepstral coefficients ( mfcc ) determined from the log power spectra as functions of frequency measured in the mel - frequency scale of snore signals identified by snore detector 25 in sleep sound signal 23 . the mel scale is a perceptual scale of frequencies , measure in “ mels ”, that maps frequency conventionally measured in hz to a perceptual scale for which pairs of pitches having a same difference in mels are perceived by a human as having a same difference in frequency , or pitch . a frequency of 1000 hz has a value in mels equal to 1000 . a frequency “ f hz ” in hz has a frequency f mel in mels defined by a formula : let an s - th snore signal of a total of “ s ” snore signals identified in sleep sound signal 23 acquired for person 100 have a time dependent amplitude represented by a s ( t ). then a power spectrum p ( f hz ) of a s ( t ) as a function of frequency in hz may be written : p ( f hz )=| f { a s ( t )}| 2 , 2 ) where f { a s ( t )} is a fourier transform of a s ( t ). filtering p ( f hz ) with a mel - frequency filterbank comprising k mel - frequency filters , provides a discrete mel - frequency power spectrum p ( k , f mel ) for a s ( t ) having k values . p ( k , f mel )=( mel k | f { a s ( t )}≡ 2 ), k = 1 → k . 3 ) x k ( s )={ log ( mel k | f { a s ( t )}| 2 )}, k = 1 → k , 4 ) then a discrete cosine transform ( dct ) of the x k ( s ) generates optionally k mel - frequency cepstral coefficients c i ( s ) for a s ( t ), where c i ( s )= σ k = 1 k = k x k ( s ) cos [ i ( k − ½ ) π / k ], i = 1 → k . in accordance with an embodiment of the invention , feature f 1 , that is mel - cepstability , is a sum of the variances of the mfcc c i ( s ) optionally normalized to an average energy “ e ” of the s snore signals in sleep sound signal 23 acquired for person 100 . in symbols , f 1 may be defined by an expression , f 1 = melcepstability = σ s = 1 s = s σ i = 1 i = k [ c i ( s )− c i ( s )] 2 / e . 6 ) in equation 6 ) c i ( s ) is an average value for c i ( s ) over all s snore signals identified in sleep sound signal 23 , and is defined by an expression , c i ( s )= σ s = 1 s = s c i ( s ), i = 1 → k . 7 ) e ( s )= σ k = 1 k = k x k ( s ) 2 , and the average snore energy e may be written 8 ) feature f 2 is optionally equal to an average of variances in energy for groups of snore signals in sleep sound signal 23 . a group of snore signals in accordance with an embodiment of the invention comprises a sequence of snores in sleep sound signal 23 for which a time delay between an end of a snore in the group and a next subsequent snore in the group is less than or equal to a maximum time lapse “ τ g ”. optionally , 37 τ g is equal to one minute . assume that sleep sound signal 23 comprises “ g ” snore signal groups , and a g - th group contains “ s g ” snore signals . if the variation in snore signal energy in a given group g is vare ( g ), an average energy of snore signals in the group is ē ( g ), and an s - th snore signal in the group has energy e ( s , g ), then var e ( g )= σ s = sg s = sg [ e ( s , g )− ē ( g )] 2 / s g , 10 ) f 2 = var e ( g ) =( 1 / g ) σ g = 1 g = g var e ( g ). 11 ) in an embodiment of the invention , feature f 3 is equal to a number of snore groups in sleep sound signal 23 whose variance , vare ( g ), in the group energy is greater than a threshold variance , “ vare ( g ) th .” in symbols , f 3 = σ g = 1 g = g bool { var e ( g )& gt ; var e ( g ) th }/ g . 12 ) feature f 4 is a count n q of a number of silent periods , referred to a “ quiet hiatuses ”, which are indicative of substantially total suspension of breathing in sleep sound signal 23 that are located between two audio events , whether or not at least one of the audio events is classified as a snore signal . in accordance with an embodiment of the invention , to be considered a silent period an absence of sound from person 100 is required to have duration “ τ q ” greater than a minimum duration “ τ qmin ” and less than a maximum duration equal to “ τ qmax ”. in symbols , if a ( t ) s the time dependent amplitude of sleep sound signal 23 , the f 4 count n q of quiet hiatuses may be defined by an expression : f 4 = n q = σ s = 1 s = s bool {( τ qmin & lt ; τ q & lt ; τ qmax )| a s ( t )≦ a sb } 13 ) in an embodiment of the invention τ qmin is equal to about 10 seconds and τ qmax is equal to about 90 seconds . a sb is substantially equal to a background noise level that may exist when person 100 is not making any respiratory sounds . feature f 5 is optionally equal to a mean of the pitch density of all snore signals identified in sleep sound signal 23 acquired for person 100 . in an embodiment of the invention a pitch density for an s - th snore signal is determined by segmenting the snore signal into “ f ” frames having duration equal to 30 ms ( milliseconds ) and determining a maximum of an autocorrelation function for each frame . the pitch density pd ( s ) for the s - th snore signal is equal to a fraction of the frames in the snore signal for which a maximum of the autocorrelation function is greater than a threshold value “ r ”. if the autocorrelation function of a given “ f - th ” frame in the s - th snore signal is represented by rii ( s , f ) and a number of frames in the snore signal is equal to f , then , pd ( s )= σ f = 1 f = f bool { max rii ( s , f )& gt ; r }/ f , 14 ) and if an average of pd ( s ) over all snore sounds in sleep sound signal 23 is pd , then f 5 = pd = σ s = 1 s = s pd ( s )/ s . 15 ) in a block 208 adi / osa calculator 27 processes the features f 1 . . . f 5 to generate a value for adi . in an embodiment the invention , adi is determined as a linear function of the features in accordance with an equation : adi = α o + α 1 f 1 + α 2 f 2 + α 3 f 3 + α 4 f 4 + α 5 f 5 . 16 ) optionally coefficients α o . . . α 5 are determined to provide a best fit to measurements of ahi acquired for a training group of persons that includes persons who do not exhibit osa and persons who exhibit osa characterized by different degrees of severity . optionally values for ahi for the persons are determined from psg studies . a best fit is optionally determined by a least squares analysis . by way of a numerical example , features f 1 , f 2 , f 3 , f 4 , and f 5 as defined above may assume values in the following ranges : f 1 : [ 0 to 0 . 5 ], f 1 : [ 0 to 1 . 5 ], f 1 : [ 0 to 1 ], f 1 : [ 0 to 500 ], and f 1 : [ 0 to 1 ]. 17 ) best fit values for α o . . . α 5 determined from a training group of about 90 people may have values , α o =− 3 , α 1 = 128 . 1 , α 2 = 18 . 8 , α 3 = 14 . 9 , α 4 = 0 . 0075 , and α 5 =− 48 . 0143 . 18 ) adi determined in accordance with equation 16 ) for the numerical values given in expressions 17 ) and 18 ) was found to be able to distinguish whether a person had : no or a clinically insignificant case of osa ; a mild case of osa ( ahi index greater than 10 and less than 30 ); or a severe case of osa ( ahi index greater than 30 ). classification of osa using adi in accordance with an embodiment of the invention was found to agree well with classifications provided by values of ahi determined by psg . the confusion matrix below indicates correlation between the adi index determined in accordance with an embodiment of the invention and an ahi determined by psg . rows in the table are labeled with a diagnosis of osa , “ no osa ”, “ mild osa ”, or “ severe osa ”, determined by psg . for each row , a diagnosis of osa determined in accordance with the adi index is given in columns headed “ no osa ”, “ mild osa ”, or “ severe osa ”. from the matrix it is seen that the adi and ahi indices give a same diagnosis 87 % of the time for people with no osa and 84 % of the time for people with severe osa . for mild cases of osa agreement falls to about 56 % but the two indices will agree 78 % of the time as to whether or not a person has osa . it is noted that whereas in the above description of embodiments of the invention , a linear regression function is used to provide a value of adi and a diagnosis of osa , practice of embodiments is not limited to linear regression classifiers . non linear regression functions , support vector functions of f 1 . . . f 5 , and any of various other regression methods may be used in accordance with an embodiment of the invention to detect and classify cases of osa . in the description and claims of the present application , each of the verbs , “ comprise ” “ include ” and “ have ”, and conjugates thereof , are used to indicate that the object or objects of the verb are not necessarily a complete listing of components , elements or parts of the subject or subjects of the verb . descriptions of embodiments of the invention in the present application are provided by way of example and are not intended to limit the scope of the invention . the described embodiments comprise different features , not all of which are required in all embodiments of the invention . some embodiments utilize only some of the features or possible combinations of the features . variations of embodiments of the invention that are described , and embodiments of the invention comprising different combinations of features noted in the described embodiments , will occur to persons of the art . the scope of the invention is limited only by the claims .
an embodiment of the invention provides a method of diagnosing obstructive sleep apnea , the method comprising : acquiring a sleep sound signal comprising sounds made by a person during sleep ; detecting a plurality of snore sounds in the sleep sound signal ; determining a set of mel - frequency cepstral coefficients for each of the snore sounds ; determining a characterizing feature for the sleep sound signal responsive to a sum of the variances of the cepstral coefficients ; and using the characterizing feature to diagnose obstructive sleep apnea in the person .