US-12622592-B1 - Characterising tinnitus using functional near-infrared spectroscopy

US12622592B1US 12622592 B1US12622592 B1US 12622592B1US-12622592-B1

Abstract

Disclosed is a method for characterising tinnitus in a subject using functional near-infrared spectroscopy (fNIRS). The method comprises receiving data comprising fNIRS signals indicative of cortical activity in one or more regions of the subject's brain at a processing device. The received data is processed using the processor device by inputting one or more feature values into a model, where the feature values include one or more features of the received data. The model is configured to provide one or more classification results based on the one or more feature values, the classification results being indicative of at least one characteristic of tinnitus in the subject. Also disclosed is a system for applying the disclosed method.

Inventors

Mehrnaz Shoushtarian
James Fallon
Collette McKay
SHREYASI DATTA

Assignees

THE BIONICS INSTITUTE OF AUSTRALIA

Dates

Publication Date: 20260512
Application Date: 20210903
Priority Date: 20200904

Claims (17)

1 . A method for characterising tinnitus in a subject using functional near-infrared spectroscopy (fNIRS), the method comprising: receiving data at a processor device, the received data comprising fNIRS signals indicative of cortical activity in one or more regions of the subject's brain; and processing the received data using the processor device, the processing comprising: inputting, into a model, one or more feature values including one or more features of the received data, wherein the model is configured to provide one or more classification results based on the one or more feature values, the one or more classification results being indicative of at least one characteristic of tinnitus in the subject, wherein the received data comprises evoked response data comprising fNIRS signals indicative of cortical activity in at least one region of the subject's brain resulting from a plurality of discrete stimuli delivered to the subject in sequence, wherein the sequence includes one or more non-stimulus interval periods, wherein the one or more feature values include one or more features of the evoked response data, and wherein the plurality of discrete stimuli comprises at least one of a plurality of discrete auditory stimuli and a plurality of discrete visual stimuli.
2 . The method of claim 1 , wherein the classification results include one or more of: a presence or absence of tinnitus in the subject; a severity rating of tinnitus in the subject; quantification of loudness of the tinnitus; and quantification of annoyance produced by the tinnitus.
3 . The method of claim 1 , wherein the model comprises a trained model, and wherein the model has been trained with an artificial intelligence (AI) algorithm based on a previous one or more feature values mapped to subjective measures of characteristics of tinnitus.
4 . The method of claim 3 , wherein the model provides classification results using a classification algorithm selected from the group including: Naïve Bayes; K-nearest neighbour (KNN); Rule Induction; Artificial Neural Networks (ANN), and multi-level hierarchical classification.
5 . The method of claim 1 , further comprising applying a therapy for treating tinnitus and, through the processing of the received data, detecting a change in the one or more characteristics of the tinnitus as a result of applying the therapy.
6 . The method of claim 1 , comprising determining a quality of each fNIRS signal and removing signals of inadequate quality prior to processing of the received data.
7 . The method of claim 1 , wherein the fNIRS signals comprise signals indicative of changes in oxyhaemoglobin (O 2 Hb) concentration in the subject's brain and/or signals indicative of changes in deoxyhaemoglobin (HHb) concentration in the subject's brain.
8 . The method of claim 1 , wherein the received data comprises: resting-state data comprising fNIRS signals indicative of cortical activity in two or more regions of the subject's brain while the subject is at rest, and wherein processing the data further comprises determining at least one resting-state functional connectivity measure between the at least two regions of the subject's brain based on the resting-state data, and wherein the one or more feature values include one or more features of the at least one resting-state functional connectivity measure.
9 . The method of claim 1 , wherein the model is configured to provide a prognostic measure indicative of whether a proposed therapy for treating tinnitus in the subject is likely to be effective.
10 . The method of claim 9 , wherein a quality of each fNIRS signal is determined based on one or more of a level of signal gain and a level of cardiac signal content.
11 . A non-transitory machine readable storage medium comprising instructions configured to cause a processor device to execute the method of claim 1 .
12 . A system for characterising tinnitus in a subject using functional near infrared spectroscopy (fNIRS), the system comprising: a processor device, configured to: receive data, the received data comprising fNIRS signals indicative of cortical activity in one or more regions of the subject's brain; and process the received data, wherein the processing comprises: inputting, into a model, one or more feature values including one or more features of the received data, wherein the model is configured to provide one or more classification results based on the one or more feature values, the one or more classification results being indicative of at least one characteristic of tinnitus in the subject, wherein the received data comprises evoked response data comprising fNIRS signals indicative of cortical activity in at least one region of the subject's brain resulting from a plurality of discrete stimuli delivered to the subject in sequence, wherein the sequence includes one or more non-stimulus interval periods, wherein the one or more feature values include one or more features of the evoked response data, and wherein the plurality of discrete stimuli comprises at least one of a plurality of discrete auditory stimuli and a plurality of discrete visual stimuli.
13 . The system of claim 12 , wherein the received data comprises: resting-state data comprising fNIRS signals indicative of cortical activity in two or more regions of the subject's brain while the subject is at rest, and wherein processing the data further comprises determining at least one resting-state functional connectivity measure between the at least two regions of the subject's brain based on the resting-state data, and wherein the one or more feature values include one or more features of the at least one resting-state functional connectivity measure.
14 . The system of claim 12 , further comprising: a fNIRS system configured to measure a level of cortical activity in at least two regions of the subject's brain.
15 . The system of claim 12 , the system further comprising an auditory stimulator configured to deliver the auditory stimulus to the subject and/or a visual stimulator configured to deliver the visual stimulus to the subject.
16 . The system of claim 12 , wherein the fNIRS system comprises a multi-channel fNIRS system, wherein each channel is defined by a source-detector pair.
17 . The system of claim 16 comprising one or more channels configured to be positioned over each of the frontal, left and right temporal and occipital regions of the subject's brain.

Description

TECHNICAL FIELD The present disclosure relates to methods and systems for characterisation of tinnitus. BACKGROUND Tinnitus is a medical condition characterised by hearing unwanted sounds that are not present externally. Chronic tinnitus is a debilitating condition which affects 6-20% of adults and can severely impact their quality of life. Approximately 20% of adults with tinnitus experience it in a severe form, along with associated symptoms such as depression, cognitive dysfunction and stress. Despite its wide prevalence, there is currently no clinically used objective test for assessment of tinnitus. In general, clinical assessment of tinnitus relies on subjective feedback from individuals, which may be inaccurate. Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims. SUMMARY According to one aspect of the present disclosure, there is provided a method for characterising tinnitus in a subject using functional near-infrared spectroscopy (fNIRS), the method comprising: receiving data at a processor device, the received data comprising fNIRS signals indicative of cortical activity in one or more regions of the subject's brain; andprocessing the received data using the processor device, the processing comprising: inputting, into a model, one or more feature values including one or more features of the received data,wherein the model is configured to provide one or more classification results based on the one or more feature values, the one or more classification results being indicative of at least one characteristic of tinnitus in the subject. Functional near infrared spectroscopy (fNIRS, also known as Optical Tomography) is a non-invasive optical imaging technique, which may be used to measure changes in haemoglobin (Hb) concentrations within cortical regions of the brain. Cortical brain activity may be inferred from these measurements. The fNIRS signals may comprise signals indicative of changes in oxyhaemoglobin (O2Hb) concentration and/or signals indicative of changes in deoxyhaemoglobin (HHb) concentration in the subject's brain. In some embodiments, the fNIRS signals may be indicative of activity in regions of the subject's brain including one or more of the frontal, left temporal, right temporal and occipital cortical regions. In some embodiments, the regions of the subject's brain include each of the frontal, left temporal, right temporal and occipital cortical regions of the subject's brain. In some embodiments, the classification results may include a presence or absence of tinnitus in the subject. Additionally or alternatively, the classification results may include a severity rating of tinnitus in the subject. In some embodiments, the severity rating may categorise the tinnitus as either slight to mild tinnitus or moderate to severe tinnitus. In other embodiments, the severity rating may be selected from a greater number of categories. For example, the possible ratings may include slight tinnitus, mild tinnitus, moderate tinnitus and severe tinnitus, or other categories. In some embodiments, the severity rating may be selected from a range of severities. For example, the severity rating may be expressed on a numerical scale. In some embodiments, the model may further provide a quantification (e.g., as perceived by the subject) of loudness of the tinnitus and/or annoyance produced by the tinnitus. Providing a quantification of loudness and/or annoyance may be useful, for example, in assessing the impact of the tinnitus on the quality of life of the subject. Providing a quantification of loudness and/or annoyance may also be useful in developing of treatments for tinnitus and in defining parameters for assessing the relative success of such treatments. Feature values may be extracted from the received data using one or more methods. In some embodiments, feature values are extracted from the received data using Information Gain. Information Gain is a measure of entropy in the data, enabling identification of channels and O2Hb/HHb features with the most relevant information for classification. Information Gain may be used, for example, to select the most relevant features by ranking them based on their weight or importance in classification. In other embodiments, alternative features selection methods may be used. For example, the features values may be extracted using one or more of Gini index, SVM (Support Vector Machine) weight, wrapper method, or other suitable methods (for example, different entropy methods). In some embodiments, the model may comprise an algorithm. In some embodiments, the model may comprise a trained model. The model may have been trained with an