Nose bridge external electrical stimulation olfaction induction device and method

Abstract

The invention discloses a nose bridge external electrical stimulation olfaction induction device and method, and belongs to the technical field of biomedical engineering. According to the method, the problems of unstable stimulation intensity, high subject adaptability, high subjectivity in olfactory judgment and lack of objective quantitative indexes due to the fact that odor stimulation is generally used in an existing method are solved, olfactory related electroencephalogram activities are induced by applying electrical stimulation to the outside of the nose bridge, traditional odor stimulation is replaced, and the sensitivity of the olfactory is improved. The stability and controllability of olfactory stimulation are improved; according to the method, electroencephalogram signals of historical subjects and evaluation results of the historical subjects are classified through a linear discriminant analysis model, so that corresponding injury levels and causes are quickly classified according to component characteristics, amplitudes and incubation periods of olfactory evoked potentials OERPs of the current subjects, and therefore, the injury levels and the causes are more accurately classified; potential subjects can be found in the early stage of diseases, and early screening and intervention are achieved; interference of human factors is avoided, and objectivity and accuracy of diagnosis are improved.

Description

Technical Field

[0001] The present invention relates to the field of biomedical engineering technology, and in particular to a device and method for inducing olfactory sensation by external electrical stimulation of the nose bridge. Background Art

[0002] Olfactory evoked potentials (OEPs) are a type of brain electrical activity induced by odor stimulation that objectively reflects olfactory function. Olfactory function testing is important in clinical diagnosis, such as Alzheimer's disease and Parkinson's disease. External electrical stimulation of the nasal bridge is a non-invasive stimulation method, and electrical stimulation technology has been widely used in neuroscience research, such as transcranial electrical stimulation, and has potential application value.

[0003] However, in the existing technology, traditional olfactory detection methods usually use odor stimulation, such as olfactory recognition tests. Although they can obtain the subject's sense of smell, there are still problems such as unstable stimulation intensity and poor adaptability of the subject. In addition, the judgment of the olfactory condition is highly subjective and lacks objective quantitative indicators.

[0004] Therefore, the existing needs are not met, and we propose an external electrical stimulation olfactory induction device and method for the nose bridge. Summary of the Invention

[0005] The purpose of the present invention is to provide an external electrical stimulation olfactory induction device and method for the nose bridge, which induces olfactory-related EEG activity by applying electrical stimulation to the outside of the nose bridge, replacing traditional odor stimulation and improving the stability and controllability of olfactory stimulation; classifying the EEG signals of historical subjects and their evaluation results through a linear discriminant analysis model, so that the corresponding injury level and cause can be quickly classified according to the olfactory evoked potential (OERPs) component characteristics and its amplitude and latency of the current subject, thereby more accurately classifying the injury level and cause, so that potential subjects can be discovered in the early stages of the disease, and early screening and intervention can be achieved; avoiding interference from human factors, improving the objectivity and accuracy of diagnosis, and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A device for inducing olfactory sensation by electrical stimulation outside the nose bridge, comprising: a data acquisition unit and a data analysis unit, wherein the data analysis unit comprises: a component extraction module, a component classification module and a component calculation module;

[0008] The data acquisition unit is configured to use bipolar electrodes and an EEG cap to induce and record EEG signals related to olfaction;

[0009] The component extraction module is configured to average and superimpose the preprocessed EEG signals, highlight the olfactory evoked potential (OERPs) component, use time-frequency analysis technology to capture the frequency changes of the OERPs component at different time points, and identify the olfactory evoked potential (OERPs) component;

[0010] The component calculation module is configured to calculate the amplitude and latency of the olfactory evoked potential (OERPs) component to obtain the subject's olfactory function;

[0011] The component classification module is configured to classify EEG signals based on a linear discriminant analysis model, distinguish EEG signals related to and unrelated to olfactory stimulation, and record component features corresponding to EEG signals related to olfactory stimulation.

[0012] Furthermore, the data acquisition unit includes:

[0013] an electrical stimulation module configured to apply controllable electrical stimulation to the outside of the nose bridge using bipolar electrodes to induce brain electrical activity related to smell;

[0014] an EEG acquisition module, configured to record EEG signals of a subject based on an EEG cap;

[0015] The control module is configured to control the electrical stimulation parameters and the EEG signal acquisition frequency.

[0016] Furthermore, the component extraction module includes:

[0017] A signal segmentation module is configured to segment the overall EEG signal into multiple time windows, taking the stimulation start point as a reference and intercepting the signal segments within a specified time range before and after the stimulation;

[0018] A signal alignment module is configured to align the time point of each time window with the stimulus onset point.

[0019] Furthermore, the component extraction module further includes:

[0020] an average superposition module configured to add the EEG signals of multiple time windows at each time point and then remove the total number of time windows to obtain an average signal;

[0021] The component identification module is configured to search for specific waveform features from the averaged and superimposed EEG signals and identify the olfactory evoked potential (OERPs) components.

[0022] Furthermore, the component calculation module includes:

[0023] The component measurement module is configured to calculate the vertical distance from the baseline to the maximum peak to obtain the amplitude; calculate the time interval from the onset of the stimulus to the maximum peak of the component to obtain the latency;

[0024] The component analysis module is configured to analyze the amplitude to obtain the intensity of the brain's response to the olfactory stimulus in the EEG signal; analyze the latency to obtain the speed of the brain's response to the olfactory stimulus in the EEG signal;

[0025] The statistical analysis module is configured to evaluate the olfactory function of the subject based on the latency and amplitude of the olfactory evoked potential (OERPs) component.

[0026] Furthermore, the component classification module includes:

[0027] a sample classification module configured to classify historical EEG signals and their signal features, labeling EEG signals related to olfactory stimulation as one category and labeling EEG signals unrelated to olfactory stimulation as another category as data samples;

[0028] A model building module is configured to build a linear discriminant analysis model, and use the linear discriminant analysis model to learn the time-frequency characteristics of the EEG signal related to the olfactory stimulation;

[0029] The model training module is configured to capture EEG signals related to olfactory stimulation from data samples and distinguish EEG signals not related to olfactory stimulation based on the learning ability of the linear discriminant analysis model.

[0030] Furthermore, the component classification module further includes:

[0031] a damage quantification module configured to define damage classification standards based on the assessment results;

[0032] The disorder classification module is configured to define different types of olfactory disorders and classify different types of olfactory disorders and their corresponding component characteristics, damage levels, and causes based on a linear discriminant analysis model.

[0033] Furthermore, the data analysis unit further includes:

[0034] a preprocessing module configured to perform filtering and artifact removal on the collected EEG signals;

[0035] The function interpretation module is configured to evaluate the subject's olfactory function based on the latency and amplitude of the olfactory evoked potential (OERPs) component; and generate a visual report of the evaluation results and feed it back to the doctor-patient interaction terminal.

[0036] Furthermore, the stimulation intensity of the electrical stimulation module is 0.1-5 mA, the frequency is 1-100 Hz, the duration is 100-500 ms, and the olfactory evoked potential (OERPs) components include: N1 waveform, P2 waveform and P3 waveform.

[0037] A method for inducing olfactory sensation by external electrical stimulation of the nose bridge comprises the following steps:

[0038] Bipolar electrodes are used to apply controlled electrical stimulation to the outside of the nose bridge to induce brain electrical activity related to smell;

[0039] Recording EEG signals through an EEG cap;

[0040] The EEG signals were filtered, artifact removed, and segmented, and then averaged and superimposed to highlight and identify the olfactory evoked potential (OERPs) components.

[0041] Extract EEG signals related to olfactory stimulation, calculate the amplitude and latency of the olfactory evoked potential (OERPs) component, and evaluate the subject's olfactory function;

[0042] The EEG signals are analyzed based on the linear discriminant analysis model to distinguish between EEG signals related to and unrelated to olfactory stimulation; the corresponding cause of the disease and the level of olfactory damage are obtained based on the EEG signals related to olfactory stimulation.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. In the present invention, olfactory-related EEG activity is induced by applying electrical stimulation to the outside of the nose bridge, and the EEG signals of the subjects are recorded by wearing a standard EEG cap, thereby replacing traditional odor stimulation and improving the stability and controllability of olfactory stimulation.

[0045] 2. In the present invention, the EEG signals of historical subjects and their evaluation results are classified through a linear discriminant analysis model, so that the corresponding injury level and cause can be quickly classified according to the olfactory evoked potential (OERPs) component characteristics, amplitude, and latency of the current subject, thereby more accurately classifying the injury level and cause, enabling the detection of potential subjects in the early stages of the disease and achieving early screening and intervention; avoiding interference from human factors and improving the objectivity and accuracy of diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the olfactory induction device for external electrical stimulation of the nose bridge of the present invention;

[0047] Figure 2 This is a flow chart of the method for inducing olfactory sensation by external electrical stimulation of the nose bridge of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] In order to solve the technical problems in the existing technology, traditional olfactory detection methods usually use odor stimulation, such as: olfactory identification test. Although it can obtain the subject's olfactory status, it still has problems such as unstable stimulation intensity and poor subject adaptability. In addition, the judgment of olfactory status is highly subjective and lacks objective quantitative indicators. Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0050] A device for inducing olfactory sensation by external electrical stimulation of the nose bridge, comprising: a data acquisition unit and a data analysis unit, wherein the data acquisition unit is configured to use bipolar electrodes and an electroencephalogram (EEG) cap to induce and record EEG signals related to olfaction; the data acquisition unit comprises:

[0051] The electrical stimulation module is configured to apply controllable electrical stimulation to the outside of the nose bridge using bipolar electrodes to induce brain electrical activity related to smell; the parameters of the electrical stimulation module are configured as follows: stimulation intensity is 0.1-5mA, frequency is 1-100Hz, and duration is 100-500ms.

[0052] The EEG acquisition module is configured to record the EEG signal of the subject based on an EEG cap; for example, the electrodes are placed using the international 10 / 20 system, and the sampling rate is set to 250 Hz or higher to ensure the time resolution of the signal; and the EEG signal is synchronously recorded while applying electrical stimulation.

[0053] The control module is configured to control the electrical stimulation parameters and the EEG signal acquisition frequency, for example, by setting and adjusting the electrical stimulation parameters such as intensity, frequency, and duration through a user interface or automated script to ensure the safety and effectiveness of the stimulation; and synchronously controlling the acquisition of EEG signals to ensure the consistency and integrity of the data.

[0054] In one embodiment, it is assumed that the electrical stimulation parameters are: intensity: 1 mA; frequency: 10 Hz; duration: 200 ms; the sampling rate of EEG acquisition is 250 Hz; the electrical stimulation module and the EEG acquisition module are started at the same time, and the EEG signals of the subject under electrical stimulation conditions are recorded.

[0055] The beneficial effects achieved by the above content are: by applying electrical stimulation to the outside of the nose bridge to induce olfactory-related EEG activity, combined with wearing a standard EEG cap to record the subject's EEG signals, thereby replacing traditional odor stimulation and improving the stability and controllability of olfactory stimulation.

[0056] The data analysis unit includes: pre-processing module, component extraction module, component classification module, component calculation module and function interpretation module;

[0057] The preprocessing module is configured to filter the collected EEG signals using a 0.1-30 Hz bandpass filter to remove high-frequency noise and low-frequency drift; then use independent component analysis to remove components related to artifacts such as eye movements and electromyography, and retain EEG signals related to olfactory stimulation.

[0058] The component extraction module is configured to average and superimpose the preprocessed EEG signals, highlight the olfactory evoked potential (OERPs) components, and use time-frequency analysis technology to capture the frequency changes of the OERPs components at different time points to identify the OERPs components. The OERPs components include: N1 waveform, P2 waveform, and P3 waveform. The component extraction module includes:

[0059] The signal segmentation module is configured to segment the overall EEG signal into multiple time windows. Each window corresponds to an olfactory stimulation event. Based on the stimulation starting point, the signal segment within the specified time range before and after the stimulation is intercepted, such as: -100ms to 500ms.

[0060] The signal alignment module is configured to align the time point of each time window with the stimulus onset point, so that the olfactory evoked potential (OERPs) components in multiple time windows are at the same time position for subsequent averaging and superposition operations.

[0061] The average superposition module is configured to add the EEG signals of multiple time windows at each time point, and then remove the total number of time windows to obtain an average signal.

[0062] The component identification module is configured to search for specific waveform features from the averaged and superimposed EEG signals to identify the olfactory evoked potential (OERPs) components; it uses time-frequency analysis technology to capture the frequency changes of the olfactory evoked potential (OERPs) components at different time points, thereby identifying the required olfactory evoked potential (OERPs) components, including: N1 waveform, P2 waveform and P3 waveform.

[0063] The component calculation module is configured to calculate the amplitude and latency of the olfactory evoked potential (OERPs) component to obtain the olfactory function of the subject; the component calculation module includes:

[0064] The component measurement module is configured to obtain the maximum peak values of N1, P2, and P3 in the averaged and superimposed EEG signals, calculate the vertical distance from the baseline (0 μV) to the maximum peak value to obtain the amplitude, and calculate the time interval from the onset of the stimulus (0 ms) to the maximum peak value of the component to obtain the latency; as shown in the following table:

[0065] OERPs Features Amplitude incubation period N1 -3μV to -10μV 100ms P2 +5μV to +15μV 200ms P3 +10μV to +20μV 300ms

[0066] Table 1. OERPs composition

[0067] The component analysis module is configured to analyze amplitude: compare the amplitudes of the OERPs components of different subjects or under different conditions. A larger amplitude indicates a stronger brain response to the olfactory stimulus, thereby obtaining the strength of the brain's response to the olfactory stimulus in the EEG signal; analyze latency: compare the latencies of the OERPs components of different subjects or under different conditions. A shorter latency indicates a faster brain response to the olfactory stimulus, thereby obtaining the speed of the brain's response to the olfactory stimulus in the EEG signal.

[0068] The statistical analysis module is configured to evaluate the subject's olfactory function based on the latency and amplitude of the olfactory evoked potential (OERPs) component. For example, in subjects with normal olfactory function, the latency of the OERPs component is shorter and the amplitude is larger; whereas in subjects with impaired olfactory function, the latency may be prolonged and the amplitude may be reduced, as shown in the following table:

[0069]

[0070]

[0071] Table 2. Comparison of OERPs components between normal and impaired olfaction

[0072] The component classification module is configured to classify the EEG signals based on the linear discriminant analysis model, distinguish the EEG signals related to and unrelated to the olfactory stimulation, and record the component features corresponding to the EEG signals related to the olfactory stimulation; the component classification module includes:

[0073] The sample division module is configured to divide historical EEG signals and their signal features, marking EEG signals related to olfactory stimulation as one category, such as: 1, and marking EEG signals not related to olfactory stimulation as another category, such as: 0, as data samples.

[0074] The model building module is configured to build a linear discriminant analysis model, which is used to learn the time-frequency characteristics of EEG signals related to olfactory stimulation. The linear discriminant analysis model projects high-dimensional data into a low-dimensional space by finding the projection direction of the maximum inter-class variance, thereby achieving classification.

[0075] The model training module is configured to capture EEG signals related to olfactory stimulation from data samples and distinguish EEG signals not related to olfactory stimulation based on the learning ability of the linear discriminant analysis model.

[0076] The damage quantification module is configured to define damage classification standards based on the assessment results, as shown in the following table:

[0077]

[0078] Table 3. Olfactory function impairment grading standard

[0079] The disorder classification module is configured to define different types of olfactory disorders and classify different types of olfactory disorders and their corresponding component characteristics, damage level, and etiology based on the linear discriminant analysis model. For example, the olfactory disorder caused by Alzheimer's disease in Table 3 may have specific OERPs component characteristics, such as reduced P2 amplitude, and clinical manifestations include decreased ability to recognize electrical stimulation attention.

[0080] The functional interpretation module is configured to evaluate the subject's olfactory function based on the latency and amplitude of the olfactory evoked potential (OERPs) component; and generate a visual report of the evaluation results and feed it back to the doctor-patient interaction terminal. The visual report includes: the waveform of the OERPs component, the values of the latency and amplitude, as well as the corresponding damage level and cause.

[0081] The beneficial effects achieved by the above content are: by classifying the EEG signals of historical subjects and their evaluation results through the linear discriminant analysis model, it can quickly classify the corresponding injury level and cause according to the component characteristics, amplitude and latency of the current subject's olfactory evoked potential (OERPs), thereby more accurately classifying the injury level and cause, enabling it to detect potential subjects in the early stages of the disease and achieve early screening and intervention; avoiding interference from human factors and improving the objectivity and accuracy of diagnosis.

[0082] In order to better demonstrate the use of an external nasal bridge electrical stimulation olfactory induction device, the present application provides an external nasal bridge electrical stimulation olfactory induction method, comprising the following steps:

[0083] Bipolar electrodes are used to apply controllable electrical stimulation to the outside of the nose bridge to induce olfactory-related EEG activity, and the EEG signals are recorded using an EEG cap.

[0084] The EEG signals were filtered, artifact removed, and segmented, and then averaged and superimposed to highlight and identify the olfactory evoked potential (OERPs) components.

[0085] Extract EEG signals related to olfactory stimulation, calculate the amplitude and latency of the olfactory evoked potential (OERPs) component, and evaluate the subject's olfactory function;

[0086] The EEG signals are analyzed based on the linear discriminant analysis model to distinguish between EEG signals related to and unrelated to olfactory stimulation; the corresponding cause of the disease and the level of olfactory damage are obtained based on the EEG signals related to olfactory stimulation.

[0087] Working principle: By using bipolar electrodes and an EEG cap, electrical stimulation is applied to the outside of the nose bridge to induce olfactory-related EEG activity, and the subject's EEG signals are recorded; the EEG signals are then preprocessed and component extracted, and the component characteristics, amplitude, and latency of the olfactory evoked potentials (OERPs) are analyzed using a linear discriminant analysis model to quickly classify the corresponding injury level and cause, thereby improving the detection efficiency and practicality of existing induction devices.

[0088] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0089] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A device for electrically stimulating the sense of smell outside the nose bridge, characterized in that: The device comprises: a data acquisition unit and a data analysis unit, wherein the data analysis unit comprises: a component extraction module, a component classification module and a component calculation module; The data acquisition unit is configured to use bipolar electrodes and an EEG cap to induce and record EEG signals related to olfaction; The component extraction module is configured to average and superimpose the preprocessed EEG signals, highlight the olfactory evoked potential (OERPs) component, use time-frequency analysis technology to capture the frequency changes of the OERPs component at different time points, and identify the olfactory evoked potential (OERPs) component; The component calculation module is configured to calculate the amplitude and latency of the olfactory evoked potential (OERPs) component to obtain the subject's olfactory function; The component classification module is configured to classify EEG signals based on a linear discriminant analysis model, distinguish EEG signals related to and unrelated to olfactory stimulation, and record component features corresponding to EEG signals related to olfactory stimulation.

2. The olfactory stimulation device for external electrical stimulation of the nose bridge according to claim 1, characterized in that: The data acquisition unit includes: an electrical stimulation module configured to apply controllable electrical stimulation to the outside of the nose bridge using bipolar electrodes to induce brain electrical activity related to smell; an EEG acquisition module, configured to record EEG signals of a subject based on an EEG cap; The control module is configured to control the electrical stimulation parameters and the EEG signal acquisition frequency.

3. The olfactory stimulation device for external electrical stimulation of the nose bridge according to claim 1, characterized in that: The component extraction module includes: A signal segmentation module is configured to segment the overall EEG signal into multiple time windows, taking the stimulation start point as a reference and intercepting the signal segments within a specified time range before and after the stimulation; A signal alignment module is configured to align the time point of each time window with the stimulus onset point.

4. The device for electrically stimulating olfactory sensation outside the nose bridge according to claim 3, characterized in that: The component extraction module further includes: an average superposition module configured to add the EEG signals of multiple time windows at each time point and then remove the total number of time windows to obtain an average signal; The component identification module is configured to search for specific waveform features from the averaged and superimposed EEG signals and identify the olfactory evoked potential (OERPs) components.

5. The olfactory stimulation device for external electrical stimulation of the nose bridge according to claim 1, characterized in that: The component calculation module includes: The component measurement module is configured to calculate the vertical distance from the baseline to the maximum peak to obtain the amplitude; calculate the time interval from the onset of the stimulus to the maximum peak of the component to obtain the latency; The component analysis module is configured to analyze the amplitude to obtain the intensity of the brain's response to the olfactory stimulus in the EEG signal; analyze the latency to obtain the speed of the brain's response to the olfactory stimulus in the EEG signal; The statistical analysis module is configured to evaluate the olfactory function of the subject based on the latency and amplitude of the olfactory evoked potential (OERPs) component.

6. The device for electrically stimulating olfactory sensation outside the nose bridge according to claim 1, characterized in that: The component classification module includes: a sample classification module configured to classify historical EEG signals and their signal features, labeling EEG signals related to olfactory stimulation as one category and labeling EEG signals unrelated to olfactory stimulation as another category as data samples; A model building module is configured to build a linear discriminant analysis model, and use the linear discriminant analysis model to learn the time-frequency characteristics of the EEG signal related to the olfactory stimulation; The model training module is configured to capture EEG signals related to olfactory stimulation from data samples and distinguish EEG signals that are not related to olfactory stimulation based on the learning ability of the linear discriminant analysis model.

7. The device for electrically stimulating olfactory sensation outside the nose bridge according to claim 6, characterized in that: The component classification module further includes: a damage quantification module configured to define damage classification standards based on the assessment results; The disorder classification module is configured to define different types of olfactory disorders and classify different types of olfactory disorders and their corresponding component characteristics, damage levels, and causes based on a linear discriminant analysis model.

8. The device for electrically stimulating olfactory sensation outside the nose bridge according to claim 1, characterized in that: The data analysis unit further includes: a preprocessing module configured to perform filtering and artifact removal on the collected EEG signals; The function interpretation module is configured to evaluate the subject's olfactory function based on the latency and amplitude of the olfactory evoked potential (OERPs) component; and generate a visual report of the evaluation results and feed it back to the doctor-patient interaction terminal.

9. The device for electrically stimulating olfactory sensation outside the nose bridge according to claim 2, characterized in that: The stimulation intensity of the electrical stimulation module is 0.1-5 mA, the frequency is 1-100 Hz, the duration is 100-500 ms, and the olfactory evoked potential (OERPs) components include: N1 waveform, P2 waveform and P3 waveform.

10. A method for inducing olfactory sensation by external electrical stimulation of the nose bridge, implemented by using the external electrical stimulation of the nose bridge olfactory sensation inducing device according to any one of claims 1 to 9, characterized in that: The following steps are involved: Bipolar electrodes are used to apply controlled electrical stimulation to the outside of the nose to induce brain electrical activity related to smell; Recording EEG signals through an EEG cap; The EEG signals were filtered, artifact removed, and segmented, and then averaged and superimposed to highlight and identify the olfactory evoked potential (OERPs) components. Extract EEG signals related to olfactory stimulation and calculate the amplitude and latency of the olfactory evoked potential (OERPs) component to evaluate the subject's olfactory function; The EEG signals are analyzed based on the linear discriminant analysis model to distinguish between EEG signals related to and unrelated to olfactory stimulation; the corresponding cause of the disease and the level of olfactory damage are obtained based on the EEG signals related to olfactory stimulation.

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