Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.