Patent Description:
The present application claims priority from <CIT>.

Any references to methods, apparatus or documents of the prior art are not to be taken as constituting any evidence or admission that they formed, or form part of the common general knowledge.

One common malady is the sleep disorder of Obstructive Sleep Apnea syndrome (OSA). The prevalence of OSA in adults varies from <NUM>-<NUM>% in males and <NUM>-<NUM>% in females [<NUM>]. At present over <NUM>% of OSA patients remain undiagnosed [<NUM>]. OSA is characterized by a repetitive upper airway collapse during sleep. Full closure of the upper airway is termed "apnea" and partial closure is termed "hypopnea". The average number of apnea and hypopnea events per-hour of sleep is termed the Apnea-Hypopnea Index (AHI). AHI is a major clinical severity measure for OSA.

The current standard for OSA diagnosis is Polysomnography (PSG)[<NUM>]. PSG requires continuous monitoring of multiple physiological signals over the course of a night. Physical contact of sensors with the patient is essential for these measurements. The several hours of PSG data are manually reviewed by an expert sleep technician. Reviewing PSG data is a labor intensive, time consuming and expensive process. PSG is also inconvenient to patients, especially the pediatric population, and results are subjective and unsuitable for population screening.

In the past several researchers have attempted to use patient sounds for diagnosis of maladies related to dysfunctions of the respiratory system. For example, the patient sounds may include snoring sounds used to diagnose OSA. Other maladies, such as pneumonia, asthma, bronchitis, croup and chronic obstructive pulmonary disease (COPD), Tracheobronchomalacia (TBM) or cystic fibrosis also cause characteristic patient sounds. Many of the existing methods depend on the identification of segments of the patient sound that are characteristic of the malady in question. For example, in the case of the malady being OSA then snore segments from the overnight sound data are identified. Hence if the snore segmentation algorithm fails to identify any snore segments or if the patient did not snore then results of the test will be indeterminate. Furthermore, procedures for identifying sounds that are characteristic of a malady of interest, such as snore sounds for OSA diagnosis, or a cough sound for pneumonia diagnosis, in a lengthy patient sound recording are computationally expensive and may be inaccurate. Therefore there is a need for an improved method of diagnosing a malady which does not rely on identification of sounds that are characteristic of a malady of interest in segments of the patient sounds.

<CIT> discloses an apparatus for diagnosing sleep disorders such as OASHS from snore sounds. The apparatus includes a segmentation module coupled to a data logger to provide segments of the digitized audio signal to a Snore Segment Identifier. A total airways response (TAR) module, pitch calculator and MFCC calculator are each coupled to an output side of the snore segment identifier module. Each of these modules is respectively arranged to calculate pitch, bispectrum, diagonal slice and MFCC parameters for the snore segments received from the snore segment identifier (<NUM>). Similarly, the NGI calculator produces a non-Gaussianity index for the digitized audio signal. A classification module is arranged to process the calculated parameters and compare a resulting diagnosis probability to a predetermined threshold value. The results of this comparison are then indicated on video display, which communicates with the classification module via display controller and bus. For example, if the results of the comparison are over threshold then display is driven to indicate "OS AHS is present".

<CIT> discloses a method of operating a computational device to process patient sounds. The method comprises the steps of: extracting features from segments of said patient sounds; and classifying the segments as cough or non-cough sounds based upon the extracted features and predetermined criteria; and presenting a diagnosis of a disease related state on a display under control of the computational device based on segments of the patient sounds classified as cough sounds.

According to a first aspect of the present invention there is provided a method of operating one or more electronic processors to diagnose a maladay of the respiratory system of a patient comprising:.

According to a preferred embodiment of the present invention the forming of the test vector based upon the deviations scores of the MFCCs includes applying a comparator to each of the deviation scores. For example, the comparator may comprise a set of instructions executed by the one or more processors to implement a decision routine.

In an embodiment the output of the routine is a "<NUM>" signal if the deviation score is above a threshold or a "<NUM>" signal if the deviation score is equal to or below the threshold.

Preferably the method further includes forming components of the test vector for each of the MFCCs by producing sums of outputs from the comparator. In an embodiment the method includes producing the sums of the outputs from the comparator for each MFCC over all of the epochs.

The method may include averaging each of the sums of the outputs over all of the epochs.

In a preferred embodiment of the invention the method includes reducing dimensionality of the test vector. For example, the method may include removing all but a subset of components of the test vector previously adjudged to be statistically significant for production of the malady signal from the pre-trained decision machine.

Preferably the method includes forming the test vector on the basis of the entire digital audio signal.

In one embodiment of the invention the probability distribution is a Gaussian distribution and the deviation from a probability distribution score is a non-Gaussianity Score (NGS) or non-Gaussianity "Index" though other distributions may also be used and measures of deviation from those distributions may also be used.

For example, other embodiments may involve computing a KS test (Kolmogorov-Smirnov) test statistic in the place of the Chi-squared test statistic.

Another embodiment of the invention may make use of a Lilliefors test for normalcy with the Gaussian distribution.

According to a second aspect of the present invention there is provided an apparatus for diagnosing the presence of a malady of the respiratory system of a patient comprising:.

According to a third aspect, there is provided a computer readable medium bearing tangible, non-transitory machine readable instructions for execution by one or more electronic microprocessors including instructions for performing the method of the first aspect.

The distribution may be a Gaussian distribution and the deviation from probability distribution score assembly is a non-Gaussianity score (NGS) assembly and the deviation score is a non-Gaussianity Score or "index". It will be realized that other distributions are also useable and some of these other distributions are described toward the end of this specification.

To aid understanding of the invention, a method for diagnosing a malady of a patient from sounds of the patient is disclosed, that is not presently covered by the claims as granted. The method includes the steps of:.

For example, the malady may comprise OSA or a disease state such as pneumonia or another malady that causes a change from normal patient sounds, such as, pneumonia, asthma, bronchitis, croup and chronic obstructive pulmonary disease (COPD), Tracheobronchomalacia (TBM) or cystic fibrosis.

The features may be one or more of pitch, entropy, formants, a Gaussianity or other probability distribution measure and higher-order spectra-based features.

The method may involve computing a Chi-squared test statistic between a MFCC distribution and a target probability distribution and using the computed test statistic directly as a feature to input to the decision machine.

The method may alternatively involve computing p-values for a Chi-squared test statistic between a MFCC distribution and the target distribution and use the p-value directly as a feature to feed the decision machine.

The target distribution may be a Gaussian distribution.

Alternatively, the method may involve computing a KS test (Kolmogorov-Smirnov) test statistic in the place of the Chi-squared test statistic.

The method may make use of a Lilliefors test for normalcy with the Gaussian distribution.

To further aid understanding, there is also provided a method for diagnosing OSA of a patient, that is not presently covered by the claims as granted. The method for diagnosing OSA of a patient includes the steps of:.

An additional method for diagnosing OSA of a patient is also provided, that is not present covered by the claims as granted, the additional method including the steps of:.

Preferred features, embodiments and variations of the invention may be discerned from the following Detailed Description which provides sufficient information for those skilled in the art to perform the invention. The Detailed Description is not to be regarded as limiting the scope of the preceding Summary of the Invention in any way. The Detailed Description will make reference to a number of drawings as follows:.

Referring initially to <FIG> there is shown a diagnostic device <NUM> in the form of a unique combination being a computational device in the form of a smart phone in combination with a diagnostic application software product. The diagnostic device <NUM> includes at least one microprocessor <NUM> that accesses an electronic memory <NUM>. The electronic memory <NUM> includes an operating system <NUM> such as the Android operating system or the Apple iOS operating system, for example, for execution by the microprocessor <NUM>. The electronic memory <NUM> also includes the diagnostic application software product or "App" <NUM> according to a preferred embodiment of the present invention. The diagnostic App <NUM> includes instructions that are executable by the microprocessor <NUM> in order for the diagnostic device <NUM> to process sounds from a patient <NUM> and present a diagnosis of a malady such as OSA to a clinician <NUM> by means of LCD touch screen interface <NUM>. In the exemplary embodiment that will be primarily discussed reference will be made to a malady being OSA and thus the device will be referred to as OSA diagnostic device <NUM>. However in other embodiments the device <NUM> may be configured by App <NUM> to diagnose other maladies of the respiratory system such as pneumonia, asthma, bronchitis, croup and chronic obstructive pulmonary disease (COPD), Tracheobronchomalacia (TBM) or cystic fibrosis. The App <NUM> includes instructions for the microprocessor <NUM> to implement a trained predictor or decision machine, which in the presently described preferred embodiment of the invention comprises a specially trained Logistic Regression Model <NUM>. It will be realised that in other embodiments of the invention other suitable decision machines may be used, such as an artificial neural network or a Bayesian decision machine and thus the invention is not limited to the use of an LRM only.

The microprocessor <NUM> is in data communication with a plurality of peripheral assemblies <NUM> to <NUM>, as indicated in <FIG>, via a data bus <NUM>. Consequently, if required the diagnostic device <NUM> is able to establish voice and data communication with a voice and/or data communications network <NUM> via WAN/WLAN assembly <NUM> and radio frequency antenna <NUM>.

Although the OSA diagnostic device <NUM> that is illustrated in <FIG> is provided in the form of a smartphone it might equally be some other computational device such as a laptop, or tablet in combination with a software product containing instructions to implement a method according to an embodiment of the invention such as will be described.

In a preferred embodiment the OSA diagnostic device <NUM> is programmed with App <NUM> so that it operates as a decision device that requires no external sensors, physical contact with patient <NUM> or communication network <NUM>.

In use the nominal distance from the microphone <NUM> of device <NUM> to the face of patient <NUM> is set to about <NUM>, but may vary between <NUM> to <NUM> due to patient movements.

Referring now to <FIG> and <FIG>, there is shown a flowchart of a method according to a preferred embodiment of the present invention, which the OSA diagnosis device <NUM> implements under the control of the instructions that are coded into the OSA diagnostic App <NUM> in order to make a diagnosis of whether or not patient <NUM> is suffering from a malady, being OSA in the present exemplary embodiment. The health carer <NUM> can then use the diagnosis to provide appropriate therapy, for example a positive pressure airway device or other suitable therapy to alleviate the OSA.

At box <NUM> of <FIG>, the microprocessor <NUM> operates the LCD screen <NUM> to display a prompt for a user, e.g. clinician <NUM>, to commence recording the in-air sounds <NUM> of patient <NUM>.

The breathing sound <NUM> of patient <NUM> is recorded by the diagnostic device <NUM> and <FIG> shows the diagnostic device <NUM> displaying a recording commencement screen <NUM> on LCD touch screen <NUM> for the clinician <NUM> to enter the recording parameters. The recording parameters are a patient ID number, and the "Timeout", i.e. the duration of the recording that is to be made and also the analogue to digital sample rate to be used. In the present instance the duration that has been selected is <NUM> hours and the sample rate that is to be used is <NUM>. <FIG> shows the screen <NUM> that is displayed once clinician <NUM> presses the "Record" button in screen <NUM> of <FIG>.

As the recording proceeds an audio file is stored in an electronic storage assembly such as either memory <NUM> or secondary memory <NUM>, which is typically a Secure Digital (SD) memory card. The audio file may be stored in a compressed format such as MP3 or in a non-compressed format such as a WAV or FLAC file. The pros and cons of using a compressed format as opposed to an uncompressed format will be discussed later in this specification. Depending on the hardware configuration the selection of the sample rate may alter a sample rate parameter in Audio interface <NUM> or alternatively the analog-to-digital conversion may be made at <NUM> in the audio interface <NUM> and then down-sampled by the microprocessor <NUM> in accordance with instructions in OSA Application <NUM>.

The procedure that microprocessor <NUM> uses to make a diagnosis of a malady, which in the present example is OSA, and which comprises instructions that make up App <NUM> is illustrated in the flowchart of <FIG> and <FIG> which will now be further described.

Three exemplary sample distributions are shown in <FIG> parts (a), (b) and (c). It will be visually observed that the plot of part (a) appears to match a Gaussian "bell curve" whereas that of part (c) does not and that of part (b) is intermediate. The normal probability plot is a plot of the midpoint probability positions of a given data segment versus the theoretical quantiles of a normal distribution. If the distribution of the data under consideration is normal, the plot will be linear. Other probability distributions will lead to plots that deviate from linearity, with the particular nature and amount of deviation depending on the actual distribution itself. <FIG> parts (a), (b), (c) show a normal (Gaussian) distribution as a linear dashed line on which corresponding plots from each of Figures <NUM> (a), (b) and (c) have been superimposed showing the increasing deviation from Gaussianity.

The increase in the NGS score is therefore a quantitative measure of the deviation from Gaussianity, of MFCC component values in an epoch. As previously mentioned methods of NGS computation are known in the prior art and are centered on computing the normal probability plot for each of the MFCCs for each epoch. Detailed methods of NGS computation can be found in<NPL>. <FIG> graphically represents NGS scores, normalized on a vertical scale of zero to one for each of three MFCCs (C1, Ci and C12) for each epoch <NUM>,.

At box <NUM> the microprocessor <NUM> implements a comparator and compares each NGS ξc(i) that was computed in box <NUM> against a threshold η to define Lc(i) using (<NUM>).

At box <NUM> microprocessor <NUM> computes an MFCC-Index vector, Ψc for all MFCC components using (<NUM>).

At box <NUM> microprocessor <NUM> produces a reduced dimension test vector Ψsc-test by removing some components from Ψc. As will be described, the components of Ψc that are removed have been previously judged to have little, or no, influence on the diagnosis.

At box <NUM> the test vector is applied to the pre-trained LRM <NUM>. It will be realised that other types of decision machines or "classifiers" can be used as well. The output of the LRM is a signal that represents a number that is "<NUM>" or very close thereto and so indicates a diagnosis of "OSA present" or "<NUM>" or very close thereto and so indicates a diagnosis of "no OSA present".

At box <NUM> the microprocessor <NUM> operates LCD touch screen interface <NUM> to present the diagnosis screen <NUM> in respect of patient <NUM> to the carer <NUM> as shown in <FIG>. Diagnosis screen <NUM> includes a message <NUM> indicating the diagnosis of OSA in the particular patient <NUM>.

In order to create the trained Logistic Regression Machine (LRM) <NUM> the Inventors initially recorded sounds from Q=<NUM> patients including individuals with symptoms such as daytime sleepiness, snoring, tiredness lethargy etc. and who were suspected of OSA. It will be realised that a similar procedure is followed in order to train the LRM for detection of other maladies and that in that case sounds would be recorded from patients suffering from the malady in question.

The steps that have previously been described in relation to boxes <NUM> to <NUM> of <FIG> and <FIG> were then performed in respect of each of the Q patients in the database and a feature matrix M of the size Q × Ψc was formed. Q represents the total number of patients and Ψc represents a feature vector from each patient.

As previously discussed, App <NUM> includes instructions for implementation of a logistic-regression model (LRM) as the "pattern classifier" or "decision machine" for classifying test patient sounds as suffering from a malady being OSA in the exemplary embodiment. It will be realized that in other embodiments of the invention other types of decision machine may also be used such as trained neural nets, Bayesian decision machines and support vector machines and that other maladies, such as those that have previously been referred to may be the subject of the training of the pattern classifier or decision machine.

The LRM that is implemented by App <NUM> in the present embodiment of the invention is the best LRM that could be determined by the methodology that the Inventors have devised and which will now be described.

An LRM is a generalized linear model, which uses several independent features to estimate the probability of a categorical event (dependent variable). In the present case, the dependent variable Y is assumed to be equal to 'one' (Y=<NUM>) for 'OSA' subjects and 'zero' for 'non-OSA subjects. OSA and non-OSA subjects were defined using <NUM> different AHI thresholds, AHI = [<NUM>; <NUM>; <NUM>;]. These AHI thresholds are routinely used in the clinical practice to define the severity of OSA as follows:.

As is known in the prior art, an LRM model is derived using a regression function to estimate the probability Y given the independent features in Ψc as follows: <MAT> <MAT>.

In (<NUM>), β<NUM>, is called the intercept and β<NUM>, β<NUM> and so on are called the regression coefficients of independent variables. To select the optimal decision threshold λ from Y (that subject is OSA if Y> λ; non-OSA otherwise) the Receiver-Operating Curve (ROC) analysis was used.

The Inventors used a K-fold cross validation (KCV) technique for the LRM design, setting K=<NUM>. In KCV technique, subject population in the database is randomly partitioned into K-equal size non-overlapping subsamples. Then of the K subsamples, data from subjects in K-<NUM> subsamples are used to train the LRM model and data from subjects in the remaining one subsample is used to test the model. This process is systematically repeated K times such that each patient in the database is used to test the model exactly one time. At the end of this process, we end up with K different LRM models. To evaluate the performance of the designed K LRMS, performance measures such as Sensitivity (Sn), Specificity (Sp), Accuracy (Ac), Positive Predicted Value (PPV) and Negative Predicted Value (NPV) were computed.

Feature selection is a technique of selecting a subset of features for building a robust classifier. Optimal feature selection requires the exhaustive search of all possible subsets of features. However, it is impractical to do so when large numbers of features are used as candidate features. Therefore, an alternative approach was used based on p-value to determine significant features. During LRM design, a p-value can be computed for each feature to indicate how significant that feature is to the model. Important features have low p-value. The Inventors used this property of an LRM to select a reasonable combination of features that facilitate the classification, in the model during the training phase. The technique that was used consisted of computing the mean p-value associated with Ψc for K LRM models. Then selecting the features with mean p-value less than a threshold pths. Let Ψsc be the feature vector with subset of the selected MFCC component index and Mfs (of size Q × Ψsc) be the feature matrix computed from selected features.

Once the significant features were known and selected they were used to build a new set of LRMs, following K-fold cross validation (K=<NUM>) as previously described. At the end of this process, Kfs number of LRMs were produced using the selected features.

As previously mentioned, the Inventors used breathing sound data from Q=<NUM> subjects. According to AHI severity these subjects were divided into four groups namely:.

Table <NUM> sets out the demographic details of the subjects in the database for four subject groups.

<FIG> shows plots for mean (a) Age; (b) Body Mass Index (BMI) and (c) Neck Circumference (NC) with <NUM>% confidence interval for subject groups. One way analysis of variance (ANOVA) statistical test showed no significant difference between the mean Age of subjects among the groups. Quite interestingly mean BMI of the severe OSA subjects was significantly higher than for non-OSA subjects. Similarly mean NC of the non-OSA subjects is significantly lower than mean NC of mild OSA and severe OSA.

One of the Inventors' objectives was to evaluate the effect of data compression on the classifier performance. For this the nocturnal breathing sound audio data was recorded from subjects in raw audio data format, WAV format. Then using Adobe Audition™ the data was converted into FLAC (loss-less audio format) and Mp3 (lossy audio data format).

The average length of the audio data recordings from Q = <NUM> subjects were <NUM> hours and <NUM> minutes with standard deviation of <NUM> hour and <NUM> minutes. The average size of an audio data recording with Fs = <NUM>, were, WAV file = <NUM>±<NUM> Giga bytes, FLAC file = <NUM>±<NUM> Giga bytes and that of MP3 file = <NUM>±<NUM> Giga bytes. On average size of a FLAC audio data file with Fs = <NUM> was <NUM>±<NUM>% smaller than that of WAV file and Mp3 audio data file was <NUM>±<NUM>% smaller than that of WAV file.

The Inventors investigated a snore sound waveform and its spectrogram using different audio file formats and at different sampling rates. They found no difference between the WAV file format and the FLAC file format and no difference in the time domain or in the frequency domain at all the sampling rates. With respect to the Mp3 audio file, no obvious changes could be seen in the time domain signal however a clear attenuation of the higher frequencies could be seen in the spectrogram. However high frequency attenuation could only be seen at Fs = <NUM> and was not present at Fs = <NUM> or <NUM>.

As previously discussed the LRM were trained using Ψc feature vectors which were derived from MFCC and NGS following a K-fold cross validation technique to classify patients into OSA and non-OSA. The LRM were trained to classify patients into OSA and non-OSA at different AHI thresholds of [<NUM>; <NUM>; <NUM>;]. The LRM were initially trained using all features and then the LRM models were retrained using a selected sub-set of features.

Table <NUM> gives the test classification results for OSA diagnosis at different AHI thresholds optimized for epoch lengths. These results are for audio data sampled at Fs = <NUM>,<NUM>.

It will be observed from Table <NUM> that there is no difference in classification accuracy between WAV and FLAC audio data at all the AHI thresholds. When selected features are used for model training, WAV and FLAC audio format have classification sensitivities/specificities of <NUM>/<NUM>%, <NUM>/<NUM>% and <NUM>/<NUM>% respectively at AHI = <NUM>, <NUM> and <NUM>. Classification results using Mp3 audio data format were slightly lower than WAV/FLAC audio data format. The sensitivities/specificities of the Mp3 data was <NUM>/<NUM>%, <NUM>/<NUM> and <NUM>/<NUM>% respectively at AHI = <NUM>, <NUM> and <NUM>.

<FIG> shows the boxplot of test classification Sensitivity and Specificity for the three types of audio data file format. To generate this graph, results of LRM from a file format, at different AHI thresholds and at different Fs were pooled together. According to <FIG>, the LRM show no significance (p = <NUM>) difference in classification performance when trained using MFCC features computed with different file format.

As previously discussed, the patient sounds may be resampled with different sampling frequencies Fs = [<NUM>; <NUM>; <NUM>; <NUM>; <NUM>; <NUM>; <NUM>; <NUM>; <NUM>;] Hz. Note that audio data is initially recorded at Fs = <NUM>. MFCC features were then computed with resampled data. <FIG> illustrates the variation in model test classification performance (sensitivity and specificity) with different Fs. Results in <FIG> are from selected MFCC features. <FIG> shows the mean test classification Sensitivity and Specificity with <NUM>% confidence interval, achieved for audio data with different sampling rate Fs. To generate the graph in <FIG>, results at specific Fs from three file formats at different AHI thresholds were pooled together. According to <FIG> and <FIG> a gross variation in sensitivity and specificity with change in data sampling rate can be seen. In general, Sensitivity initially increases with decrease in Fs, reaching at its peak at Fs = <NUM>. It then starts decreasing, reaching its lowest value at Fs = <NUM> across all file formats. Note that though a decrease in Sensitivity can be seen at lower Fs, however this decrease is insignificant. In Specificity generally remained stable when Fs decreased from <NUM> to <NUM>, then it decreased at Fs = <NUM>. Then from Fs = <NUM> it starts increasing up-to Fs = <NUM> and then gain starts decreasing reaching lowest at Fs = <NUM>. The decrease in Specificity was significant but only with respect to Fs = <NUM>.

The results indicate that methods according to embodiments of the present invention can classify patients into OSA and non-OSA at different AHI threshold with a high accuracy.

In the past several researchers [<NUM>-<NUM>] have attempted to use snoring sounds to diagnose OSA and many of the existing methods [<NUM>-<NUM>, <NUM>] have depended on the identification of snore segments from the overnight sound data. Hence if the snore segmentation algorithm fails to identify any snore segments or if the patient did not snore then results of the test will be indeterminate.

In contrast to those previous methods that have relied on detection of snore segments in the patient sound for subsequent diagnosis of OSA, preferred embodiments of the invention described herein capture the instantaneous characteristics of the upper airway present in continuous recordings of the breath sound.

Furthermore, preferred embodiments of the invention make use of MFCC features for the diagnosis of OSA via measuring the amount of deviation of MFCC features from Gaussianity in a given sound segment ("epoch"). This approach has the advantage of better performance, robustness against AHI variation and low computational complexity as it does not depend on identifying snore segments from breath sound data.

The Inventors' results also illustrate that it is possible to record the patient sounds, i.e. the sounds of the patient breathing, with a compressed audio format and at a low sampling rate without compromising on classification accuracies. The results show that it is possible to achieve a sensitivity/specificity of <NUM>/<NUM> %, <NUM>/<NUM> % and <NUM>/<NUM> % respectively at AHI threshold of <NUM>, <NUM> and <NUM>, with breath sound data recorded using Mp3 file format at Fs = <NUM> (<FIG>). With these settings the memory space required to record breath sound data of <NUM> hours duration will be < <NUM> megabytes which is important where it is desired to store lengthy sounds from a number of patients. The automated, non-contact and mobile technology according to preferred embodiments of the present invention provides an excellent tool for population screening.

Previously in <FIG> a block diagram of an OSA diagnostic device <NUM> according to a preferred embodiment of the present invention was provided and discussed. It is also possible in another embodiment of the invention to provide a dedicated OSA diagnostic device rather one that is comprised of a specially programmed microprocessor based apparatus such as a specially programmed smartphone.

A dedicated OSA diagnostic apparatus <NUM> for diagnosing the presence of Obstructive Sleep Apnea (OSA) of a patient <NUM> is illustrated in <FIG>. The apparatus includes a system clock <NUM> for synchronizing the various modules of the apparatus. A microphone <NUM> is coupled via an anti-aliasing filter <NUM> to an analog-to-digital converter (ADC) <NUM>. The output from the ADC <NUM> is received by an electronic storage assembly <NUM>, which stores a digitized audio file of patient sounds from the audio interface ADC <NUM>. A pre-emphasis assembly <NUM> is coupled to the output of the data storage assembly <NUM> for applying pre-emphasis to the digitized audio signal.

The apparatus <NUM> includes an epoch identification assembly <NUM> that is coupled to an output side of the pre-emphasis assembly <NUM> to process the digitized audio file and identify a number of epochs in the audio file. A sub-segment identification assembly <NUM> is provided that is arranged to process the digitized audio file and identify a plurality of sub-segments therein for each of the epochs.

The sub-segment ID assembly <NUM> and the Epoch ID Assembly <NUM> provide respective outputs to the Mel-Frequency Cepstral Coefficient generator <NUM> which processes the digitized audio file from the pre-emphasis assembly <NUM> to produce a multiplicity of mel-frequency cepstral coefficients (MFCCs) signals for each of the sub-segments.

A non-Gaussianity Score calculation assembly <NUM> is provided that is responsive to the Mel-Frequency Cepstral Coefficient generator and which is arranged to process the MFCC signals from the MFCC generator <NUM> for each of the sub-segments to produce NGS scores for each of the MFCCs signals for each epoch as identified by the Epoch ID Assembly <NUM>. In other embodiments of the invention a deviation from probability distribution score calculation assembly may be used to calculate a score for deviation from another distribution other than Gaussian.

The output from the NGS calculator <NUM> is passed to a comparator <NUM> which compares each of the MFCCs to a threshold value and respectively outputs a "<NUM>" or a "<NUM>" if the MFCC value is below or above threshold.

The output from the comparator is summed and averaged by Sum-and-Average block <NUM> to produce an initial test-vector which is subsequently reduced in dimension by Component Reduction assembly <NUM> to produce a reduced MFCC feature test vector. The reduced MFCC feature test vector is then passed to a decision machine block <NUM> which generates an OSA / non-OSA signal in response to the reduced MFCC feature test vector.

The apparatus <NUM> includes a human-machine interface including diagnostic display <NUM> that is coupled to the decision machine block <NUM> and which is arranged to present the OSA diagnosis to a human.

Whilst the previous discussion focused on a method and apparatus according to a preferred embodiment of the invention that uses deviation from Gaussian distribution, other measures of deviation from a known statistical distribution may also be used in other embodiments of the present invention and some of these are listed below. In other embodiments App <NUM> may include instructions for microprocessor <NUM> to implement each of the following statistical techniques as an alternative to determining deviation from Gaussian distribution.

Results on the above methods <NUM>-<NUM> are set forth below.

In general terms, a method according to an embodiment of an aspect of the present invention comprises a method for diagnosing a malady of a patient from sounds of the patient. The malady may be OSA or a respiratory disease such as pneumonia or some other impairment from normal health that results in changes to the sounds that a patient produces. The method includes the steps of initially making a digital recording of the sounds of the patient and that may be done with a contactless microphone as previously discussed. The digital recording is processed by one or more suitably programmed electronic processors to extract a multiplicity of features for sub-segments of each of a number epochs of the digital recording. Features comprising MFCCs have been discussed in detail but other features can also be used in other embodiments such as pitch, entropy, formants, NGS and higher-order spectra-based features. The features are suitably stored in an electronic data storage apparatus such as an electronic or magnetic storage device or server or network accessible storage. The method then involves operating the processors for determining deviation scores from a probability distribution for each epoch based on the extracted multiplicity of features which are retrieved from the storage. In the preferred embodiment the probability distribution that is used is the Gaussian distribution but other distributions can also be used and have been previously mentioned in the results tabled above. The one or more processors then generate a test vector derived from the deviation scores which is then applied to a pre-trained decision machine which is implemented by the processors or on another data network accessible hardware platform. The decision machine that has primarily been discussed is a LRM but other decisions machines such as artificial neural networks, Bayesian decision machines, support vector machines, might also be used.

Finally a diagnosis of malady on the basis of the output from the decision machine is presented on a display under control of the processors, for example to a clinician in order that suitable therapy can be applied to the patient if a malady has been found to be present. For example, therapy may involve administration of antibiotics (for patients suffering from pneumonia), application of controlled air pressure (for patients suffering from OSA) and other appropriate therapies based upon the diagnosis.

In compliance with the statute, the invention has been described in language more or less specific to structural or methodical features. The term "comprises" and its variations, such as "comprising" and "comprised of" is used throughout in an inclusive sense and not to the exclusion of any additional features. It is to be understood that the invention is not limited to specific features shown or described since the means herein described herein comprises preferred forms of putting the invention into effect. The invention is, therefore, claimed in any of its forms or modifications within the proper scope of the appended claims appropriately interpreted by those skilled in the art.

Throughout the specification and claims (if present), unless the context requires otherwise, the term "substantially" or "about" will be understood to not be limited to the value for the range qualified by the terms.

Claim 1:
A method of operating one or more electronic processors (<NUM>) to diagnose the presence of a malady of the respiratory system of a patient comprising:
accessing with said processors (<NUM>) a digital audio signal (<NUM>) of sounds of the patient (<NUM>) in an electronic storage assembly;
identifying a number of epochs of the digital audio signal;
identifying a plurality of sub-segments for each of the epochs;
for each sub-segment of each of the epochs determining an associated multiplicity of mel-frequency cepstral coefficients, MFCCs;
determining deviation scores from a probability distribution for each of the epochs in respect of each of the multiplicity of MFCCs;
forming a test vector for the patient based upon the deviations scores from the probability distribution of the MFCCs;
applying the test vector to a pre-trained decision machine stored in said electronic storage assembly to thereby generate a malady signal indicating malady or non-malady for the patient; and
controlling a display responsive to the one or more electronic processors (<NUM>) to display a message corresponding to the malady signal.