Patents by Inventor Udantha Abeyratne
Udantha Abeyratne has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11864880Abstract: A method for diagnosing one or more diseases of the respiratory tract for a patient including the steps of: acquiring cough sounds from the patient; processing the cough sounds to produce cough sound feature signals representing one or more cough sound features from the cough segments; obtaining one or more disease signatures based on the cough sound feature signals; and classifying the one or more disease signatures to deem the cough segments as indicative of one or more of said diseases; wherein the step of obtaining the one or more disease signatures based on the cough sound feature signals includes applying the cough sound features to each of one or more pre-trained disease signature decision machines, each said decision machine having been pre-trained to classify the cough sound features as corresponding to either a particular disease or to a non-disease state or as corresponding to first particular disease or a second particular disease different from the first particular disease.Type: GrantFiled: December 20, 2018Date of Patent: January 9, 2024Assignee: THE UNIVERSITY OF QUEENSLANDInventors: Udantha Abeyratne, Vinayak Swarnkar
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Patent number: 11783947Abstract: A method of automatically diagnosing pneumonia in a patient includes using an input/output interface device to obtain values of two or more diagnostic parameters of the patient from a caregiver for the patient. The method includes using a processor coupled to the input/output interface to apply the two or more diagnostic parameters to an electronic memory storing precompiled pneumonia diagnostic models to identify an optimal diagnostic model for making a diagnosis. The values of the two or more diagnostic signs are applied to the identified optimal diagnostic model to generate a diagnosis output. The input/output interface device is operated in accordance with the diagnosis output to indicate the presence or absence of pneumonia in the patient to the caregiver. The caregiver may use the diagnosis to provide appropriate care to the patient. The pneumonia diagnostic models are derived from investigation of a population of pneumonia positive and non-pneumonia subjects.Type: GrantFiled: September 26, 2017Date of Patent: October 10, 2023Assignee: UNIVERSITY OF QUEENSLANDInventors: Udantha Abeyratne, Keegan Kosasih
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Publication number: 20220280065Abstract: A method for stratifying severity of asthma of a patient initially comprises receiving acoustic data corresponding to sounds of the patient from an acoustic sensor and identifying, by a processor, at least one cough sound in the acoustic data. With or without the patient being present, the method further involves determining, by operation of the processor, one or more overall cough sound feature values of the at least one cough sound for each of one or more characteristic features. The overall cough sound feature values are then applied to a classifier that is implemented by the processor and which has been pre-trained with a training set of characteristic feature values from a population of asthmatic and non-asthmatic subjects. The method then involves monitoring an output from the pre-trained classifier to deem the patient cough sound as indicating one of a number of degrees of severity of asthma.Type: ApplicationFiled: August 19, 2020Publication date: September 8, 2022Inventors: Udantha ABEYRATNE, Vinayak SWARNKAR, Paul Anthony PORTER
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Publication number: 20210076977Abstract: A method for diagnosing one or more diseases of the respiratory tract for a patient including the steps of: acquiring cough sounds from the patient; processing the cough sounds to produce cough sound feature signals representing one or more cough sound features from the cough segments; obtaining one or more disease signatures based on the cough sound feature signals; and classifying the one or more disease signatures to deem the cough segments as indicative of one or more of said diseases; wherein the step of obtaining the one or more disease signatures based on the cough sound feature signals includes applying the cough sound features to each of one or more pre-trained disease signature decision machines, each said decision machine having been pre-trained to classify the cough sound features as corresponding to either a particular disease or to a non-disease state or as corresponding to first particular disease or a second particular disease different from the first particular disease.Type: ApplicationFiled: December 20, 2018Publication date: March 18, 2021Inventors: Udantha ABEYRATNE, Vinayak SWARNKAR
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Patent number: 10575751Abstract: An apparatus is provided for detecting Macro Sleep Architecture states of a subject such as WAKE, NREM and REM sleep from a subject's EEG. The apparatus includes an EEG digital signal assembly of modules arranged to convert analogue EEG signals into digital EEG signals. A bispectrum assembly is responsive to the EEG digital signal assembly and converts the digital EEG signals into signals representing corresponding bispectrum values. A bispectrum time series assembly, in electrical communication with an output side of the bispectrum assembly, generates at least one bispectrum time series for a predetermined frequency. A macro-sleep architecture (MSA) assembly is responsive to the bispectrum time series assembly and is arranged to produce classification signals indicating classification of segments of the EEG signals into macro-sleep states of the subject.Type: GrantFiled: November 27, 2009Date of Patent: March 3, 2020Assignee: THE UNIVERSITY OF QUEENSLANDInventors: Udantha Abeyratne, Vinayak Swarnkar
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Publication number: 20200027558Abstract: A method of automatically diagnosing pneumonia in a patient includes using an input/output interface device to obtain values of two or more diagnostic parameters of the patient from a carer for the patient. The method includes using a processor coupled to the input/output interface to apply the two or more diagnostic parameters to an electronic memory storing a plurality of precompiled pneumonia diagnostic models to identify an optimal diagnostic model for making a diagnosis. The values of the two or more diagnostic signs are applied to the identified optimal diagnostic model to generate a diagnosis output. The input/output interface device is operated in accordance with the diagnosis output to indicate the presence or absence of pneumonia in the patient to the carer. The carer may use the diagnosis to provide appropriate care to the patient. The pneumonia diagnostic models are derived from investigation of a population of pneumonia positive and non-pneumonia subjects.Type: ApplicationFiled: September 26, 2017Publication date: January 23, 2020Inventors: Udantha ABEYRATNE, Keegan KOSASIH
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Patent number: 10007480Abstract: A parameter quantifying deviation from Gaussianity distribution of a patient's sounds, in the form of a non-Gaussianity distribution index, may be used to assist in diagnosis of sleep dysfunction such as OSAHS. A method for diagnosing a sleeping disorder of a subject includes processing a digitized audio signal from the subject with at least one electronic processor. The processing includes estimating a parameter quantifying deviation from Gaussianity distribution of the audio signal. in the form of a non-Gaussianity Index (NGI). The NGI value is then applied to a diagnostic model. The presence of a sleeping disorder is then indicated based on the output of the diagnostic model.Type: GrantFiled: October 20, 2014Date of Patent: June 26, 2018Assignee: THE UNIVERSITY OF QUEENSLANDInventors: Udantha Abeyratne, Asela Samantha Karunajeewa, Houman Ghaemmaghami
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Publication number: 20150039110Abstract: A parameter quantifying deviation from Gaussianity distribution of a patient's sounds, in the form of a non-Gaussianity distribution index, may be used to assist in diagnosis of sleep dysfunction such as OSAHS. A method for diagnosing a sleeping disorder of a subject includes processing a digitized audio signal from the subject with at least one electronic processor. The processing includes estimating a parameter quantifying deviation from Gaussianity distribution of the audio signal. in the form of a non-Gaussianity Index (NGI). The NGI value is then applied to a diagnostic model. The presence of a sleeping disorder is then indicated based on the output of the diagnostic model.Type: ApplicationFiled: October 20, 2014Publication date: February 5, 2015Inventors: Udantha ABEYRATNE, Asela Samantha KARUNAJEEWA, Houman GHAEMMAGHAMI
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Patent number: 8880207Abstract: An apparatus for diagnosing sleep disorders such as OASHS from snore sounds includes a segmentation module (126) coupled to a data logger (124) to provide segments of the digitized audio signal to a Snore Segment Identifier (128). A total airways response (TAR) module (130), pitch calculator (132), and MFCC calculator (134) are each coupled to an output side of the snore segment identifier module (128). 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 (128). Similarly, the NGI calculator (136) produces a non-Gaussianity index for the digitized audio signal. A classification module (144) is arranged to process the calculated parameters and compare a resulting diagnosis probability to a predetermined threshold value.Type: GrantFiled: December 10, 2009Date of Patent: November 4, 2014Assignee: The University of QueenslandInventors: Udantha Abeyratne, Asela Samantha Karunajeewa, Houman Ghaemmaghami
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Publication number: 20120004749Abstract: An apparatus for diagnosing sleep disorders such as OASHS from snore sounds includes a segmentation module (126) coupled to a data logger (124) to provide segments of the digitized audio signal to a Snore Segment Identifier (128). A total airways response (TAR) module (130), pitch calculator (132) and MFCC calculator (134) are each coupled to an output side of the snore segment identifier module (128). 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 (128). Similarly, the NGI calculator (136) produces a non-Gaussianity index for the digitized audio signal. A classification module (144) is arranged to process the calculated parameters and compare a resulting diagnosis probability to a predetermined threshold value.Type: ApplicationFiled: December 10, 2009Publication date: January 5, 2012Applicant: The University of QueenslandInventors: Udantha Abeyratne, Asela Samantha Karunajeewa, Houman Ghaemmaghami
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Publication number: 20110301487Abstract: An apparatus is provided for detecting Macro Sleep Architecture states of a subject such as WAKE, NREM and REM sleep from a subject's EEG. The apparatus includes an EEG digital signal assembly of modules arranged to convert analogue EEG signals into digital EEG signals. A bispectrum assembly is responsive to the EEG digital signal assembly and converts the digital EEG signals into signals representing corresponding bispectrum values. A bispectrum time series assembly, in electrical communication with an output side of the bispectrum assembly, generates at least one bispectrum time series for a predetermined frequency. A macro-sleep architecture (MSA) assembly is responsive to the bispectrum time series assembly and is arranged to produce classification signals indicating classification of segments of the EEG signals into macro-sleep states of the subject.Type: ApplicationFiled: November 27, 2009Publication date: December 8, 2011Applicant: The University of QueenslandInventors: Udantha Abeyratne, Vinayak Swarnkar