Alopecia diagnosis device and method based on scalp impedance detection and storage medium

Abstract

The invention relates to an alopecia diagnosis method and device based on scalp impedance detection, computer equipment and a storage medium, and the method comprises a plurality of electrode pairs which are respectively in contact with a to-be-detected scalp area; the excitation signal source is used for sequentially applying a plurality of differential alternating current excitation signals with preset characteristic frequencies to a to-be-detected scalp area through the plurality of electrode pairs; the signal acquisition module is used for synchronously acquiring differential voltage response signals of the to-be-detected area under each characteristic frequency; the data processing module is used for calculating an impedance value of each to-be-detected area under each preset characteristic frequency according to the excitation signal and the voltage response signal, and generating an impedance spectrum; and the diagnosis module is used for identifying the alopecia area and evaluating the hair follicle state. According to the hair loss diagnosis method and device based on scalp impedance detection, the computer equipment and the storage medium, the defects that a traditional diagnosis method depends on subjective experience, and the state of deep hair follicle tissue cannot be quantified are overcome.

Description

Technical Field

[0001] This invention belongs to the field of medical aesthetics technology, and in particular relates to a hair loss diagnostic device, method and storage medium based on scalp impedance detection. Background Technology

[0002] Hair loss, especially androgenetic alopecia (AGA), has become a common disease affecting the quality of life of hundreds of millions of people worldwide. Accurate and objective early diagnosis and disease monitoring are prerequisites for effective intervention and treatment. However, the mainstream diagnostic technologies currently relied upon in the field of hair loss treatment have significant limitations in terms of objective quantification, in-depth information acquisition, and reproducibility.

[0003] Currently, clinical diagnosis mainly relies on the following traditional methods: visual observation and hair-pulling tests; dermoscopy (trigonoscopy): As a non-invasive surface imaging technique, dermoscopy can observe hair shaft morphology, hair follicle openings, and scalp microvessels. Although it provides magnified views, its essence is still morphological analysis based on surface optical images. Dermoscopy cannot provide direct quantitative data on key physiological and pathological information such as the vitality of deep hair follicles (dermal papilla, hair bulb), surrounding tissue microcirculation, and extracellular matrix composition. Its diagnosis still focuses on morphological description and is difficult to achieve true functional quantitative assessment. This method is highly dependent on the physician's subjective experience, lacks unified quantitative standards, and cannot accurately assess the microscopic state of hair follicles and potential hair loss activity, easily leading to misdiagnosis or missed diagnosis of early hair loss; scalp biopsy, histopathological examination can directly observe the hair follicle cycle, inflammatory infiltration, and fibrosis. However, it has inherent disadvantages such as invasiveness, sampling errors, and the inability to repeatedly monitor the same site dynamically, resulting in low patient acceptance and unsuitability for routine screening and long-term efficacy follow-up.

[0004] In recent years, with the development of artificial intelligence technology, some scalp image analysis systems based on computer vision have emerged. These systems analyze scalp photographs and use algorithms to identify parameters such as hair density and diameter. However, their technological foundation still rests on surface optical properties; they are a digital extension of traditional naked-eye observation and have not broken through the fundamental bottleneck of "only being able to analyze surface information accessible by visible light." For deeper biological information such as the functional state of hair follicles and the electrophysiological characteristics of scalp tissue, these methods inherently have limited acquisition capabilities.

[0005] Bioelectrical impedance analysis (BIA), a mature non-invasive detection method, has been widely used in fields such as human body composition analysis, brain impedance imaging, and skin electrophysiology research. Its principle involves applying a weak alternating current to biological tissue and measuring its impedance (including resistance and capacitance). This impedance characteristic is closely related to the tissue's structure, composition, water content, and cell membrane integrity. Currents of different frequencies can penetrate tissues to different depths; low-frequency currents mainly flow through the extracellular fluid, while high-frequency currents can penetrate the cell membrane, reflecting intracellular information. Therefore, the impedance spectrum contains rich information about the tissue's microstructure. Although this technology has proven its value in other medical fields, its application potential in the specific area of ​​hair loss diagnosis has not yet been effectively developed and systematically validated. Summary of the Invention

[0006] In view of this, the present invention provides a hair loss diagnostic device, method and storage medium based on scalp impedance detection, which solves the problems of existing hair loss diagnostic technology being highly subjective, relying on surface images and unable to non-invasively and quantitatively assess the health status of hair follicles and deep scalp tissues.

[0007] To achieve the above objectives, in a first aspect, the technical solution of the present invention to solve the technical problem is to provide a hair loss diagnosis method based on scalp impedance detection, comprising: multiple electrode pairs, each in contact with a scalp region to be detected; an excitation signal source, electrically connected to the multiple electrode pairs, for sequentially applying multiple differential AC excitation signals of preset characteristic frequencies to the scalp region to be detected through the multiple electrode pairs; a signal acquisition module, electrically connected to the multiple electrode pairs, for synchronously acquiring differential voltage response signals of the region to be detected at each characteristic frequency; a data processing module, electrically connected to the excitation signal source and the signal acquisition module, for calculating the impedance value of each region to be detected at each preset characteristic frequency based on the excitation signal and the voltage response signal, forming an impedance spectrum, and generating an impedance map of the scalp based on the impedance spectrum; and a diagnosis module, for identifying hair loss areas and assessing hair follicle status based on the impedance map.

[0008] In one specific embodiment, the preset characteristic frequencies include 1kHz, 10kHz, 50kHz, 100kHz, 200kHz, and 500kHz, and the excitation signal source is configured to output an excitation signal with a duration of 16ms at each characteristic frequency.

[0009] In one specific embodiment, the hair loss diagnostic device based on scalp impedance detection further includes a flexible probe cap, and the number of electrode pairs is 64, which are evenly arranged in a matrix on the flexible probe cap.

[0010] In one specific embodiment, the electrode pair includes a positive electrode and a negative electrode, the positive electrode being annular and the negative electrode being circular, and the positive electrode and the negative electrode being concentric; wherein, the outer diameter of the positive electrode is 5 mm and the diameter of the negative electrode is 1 mm.

[0011] In one specific embodiment, the excitation signal source is configured with an independent excitation channel for each electrode pair. Each excitation channel includes a signal generator, an amplifier circuit, and an output conditioning circuit that are electrically connected in sequence. The electrode pair is electrically connected to the output terminal of the output conditioning circuit, and the signal generator is electrically connected to the data processing module.

[0012] In one specific embodiment, the signal acquisition module includes multiple signal conditioning channels and at least one multi-channel analog-to-digital converter (ADC). Each signal conditioning channel includes an electrostatic discharge (ESD) protection circuit, an anti-aliasing filter circuit, a low-pass filter circuit, and a differential amplifier circuit connected in sequence. The electrode pair is electrically connected to the input terminal of the ESD protection circuit, and the output terminal of the differential amplifier circuit is electrically connected to at least one of the multi-channel ADCs. At least one of the multi-channel ADCs is electrically connected to the data processing module. The multi-channel ADCs are four 16-channel synchronous sampling ADCs to simultaneously acquire voltage response signals from 64 channels.

[0013] Secondly, this invention provides a hair loss diagnosis method based on scalp impedance detection, comprising: sequentially applying multiple differential AC excitation signals of preset characteristic frequencies to a scalp to be tested; synchronously acquiring differential voltage response signals of the to-be-tested area at each characteristic frequency; calculating the impedance value of the to-be-tested area corresponding to each electrode pair at each characteristic frequency based on the applied differential AC excitation signals and the acquired differential voltage response signals, and forming an impedance spectrum of the to-be-tested area; generating an impedance map of the scalp based on the impedance spectra of all to-be-tested areas and their corresponding spatial location information; and identifying hair loss areas and quantitatively assessing the health status of hair follicles by analyzing the impedance spectrum differences between different areas in the impedance map.

[0014] In one specific embodiment, the plurality of preset characteristic frequencies include 1kHz, 10kHz, 50kHz, 100kHz, 200kHz and 500kHz; the duration of the excitation signal applied at each characteristic frequency point is 16ms.

[0015] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a hair loss diagnosis method based on scalp impedance detection.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the hair loss diagnosis method based on scalp impedance detection.

[0017] Compared with the prior art, the hair loss diagnostic device, method and storage medium based on scalp impedance detection provided by the present invention have the following beneficial effects: By coordinating the setup of multiple electrode pairs, excitation signal sources, and signal acquisition modules, the electrical response signals of various scalp regions at multiple frequency points can be acquired quickly and synchronously. The data processing and diagnostic modules automatically calculate and generate intuitive impedance spectra, thereby achieving non-invasive, objective, and quantitative identification and assessment of hair loss areas. This effectively overcomes the shortcomings of traditional diagnostic methods that rely on subjective experience and cannot quantify the state of deep hair follicle tissue. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the structure of a hair loss diagnostic device based on scalp impedance detection provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the circuit connection for a single-channel electrode pair. Figure 3 This is a schematic diagram of the electrode pair. Figure 4 A flowchart illustrating the steps of a hair loss diagnosis method based on scalp impedance detection provided in the second embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Electrode pair; 11. Positive electrode; 12. Negative electrode; 2. Excitation signal source; 21. Signal generator; 22. Amplifier circuit; 23. Output conditioning circuit; 3. Signal acquisition module; 31. Multi-channel analog-to-digital converter; 32. Electrostatic protection circuit; 33. Anti-aliasing filter circuit; 34. Low-pass filter circuit; 35. Differential amplifier circuit; 4. Data processing module; 6. Flexible probe cap. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0020] It should be noted that all directional indications in the embodiments of this application are only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0021] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed in this application.

[0022] like Figures 1 to 3 As shown, the first embodiment of this application provides a hair loss diagnostic device based on scalp impedance detection, which includes multiple electrode pairs 1, an excitation signal source 2, a signal acquisition module 3, a data processing module 4, and a diagnostic module. The multiple electrode pairs 1 are respectively in contact with the scalp to be tested. The excitation signal source 2 is electrically connected to the multiple electrode pairs 1 and is used to sequentially apply multiple differential AC excitation signals with preset characteristic frequencies to the scalp to be tested through the multiple electrode pairs 1. The signal acquisition module 3 is electrically connected to the multiple electrode pairs 1 and is used to synchronously acquire the differential voltage response signal of the test area at each characteristic frequency. The data processing module 4 is electrically connected to the excitation signal source 2 and the signal acquisition module 3 and is used to calculate the impedance value of each test area at each characteristic frequency according to the excitation signal and the voltage response signal, form an impedance spectrum, and generate an impedance spectrum of the scalp according to the impedance spectrum. The diagnostic module is used to identify the hair loss area and evaluate the hair follicle status according to the impedance spectrum.

[0023] Understandably, the excitation signal source 2 may include multiple independent signal generation units, each connected to an electrode pair, capable of controlling the sequential output of multiple sinusoidal excitation signals with preset characteristic frequencies. The data processing module 4 controls the multiple preset characteristic frequencies output sequentially by the excitation signal source 2, which pass through multiple electrode pairs to reach the scalp detection area. After receiving the excitation signal, the detection area generates a voltage response signal for each characteristic frequency. The signal acquisition module 3 acquires the voltage response signal and transmits it back to the data processing module 4. The data processing module 4 calculates the impedance value of each detection area at each characteristic frequency based on the output excitation signal and the acquired voltage response signal, thereby forming an impedance spectrum. Based on the impedance spectrum and the location information of the scalp detection area, an impedance map is formed. The diagnostic module then identifies the hair loss area and assesses the hair follicle status based on the impedance map.

[0024] It should be noted that the excitation signal source 2 can be a centralized multi-output signal generator or multiple independent signal generation units synchronized by a unified clock source. Its core function is to controllably generate AC signals of specific frequencies and amplitudes. For example, it can be implemented using a direct digital frequency synthesizer (DDS) circuit, a programmable waveform generated by a microprocessor (MCU) combined with a digital-to-analog converter (DAC), or a combination of an analog oscillator circuit and a multiplexer.

[0025] Signal acquisition module 3 can be a highly integrated multi-channel synchronous sampling analog-to-digital converter (ADC) array, or a synchronous acquisition system composed of multiple ADC chips. Its core function is to synchronously or at high speed acquire multiple weak analog response signals in a time-division manner and digitize them. For example, a dedicated bioelectrical measurement analog front-end (AFE) chip can be used, or a circuit composed of multiple precision ADCs, differential amplifiers, and anti-aliasing filters can be used.

[0026] Data processing module 4 can be an embedded processor, microcontroller (MCU), digital signal processor (DSP), field-programmable gate array (FPGA), or any combination thereof. Its core function is to execute digital signal processing algorithms, performing tasks such as impedance calculation, spectrum and graph generation. For example, an FPGA can be used to achieve high-speed parallel real-time processing, or an "MCU+DSP" architecture can be used to handle control and complex calculations respectively.

[0027] Impedance spectrum refers to the curve or data set of the impedance value (usually including amplitude and phase) measured at a single scalp detection point (corresponding to one electrode pair) as a function of the excitation signal frequency. It reflects the characteristics of the scalp tissue at that point under different electrical scales.

[0028] Impedance spectrum refers to a two-dimensional or three-dimensional distribution map formed by arranging and visualizing the impedance spectrum or key characteristics (such as impedance amplitude at a specific frequency) of all detection points (electrode pair positions) according to their spatial location. It intuitively reflects the spatial differences in impedance characteristics of different areas of the scalp.

[0029] The diagnostic module can be a software algorithm integrated into the data processing module, or a diagnostic program running on a separate processor or remote server. Its core lies in its built-in impedance-based judgment logic or model. For example, it could be a threshold-based rule engine ("If the impedance of a certain region is lower than threshold X in the high-frequency band, it is marked as abnormal"), or it could be a trained machine learning model (such as a convolutional neural network) that directly receives the impedance spectrum and outputs the diagnostic results.

[0030] The basis for identifying areas of hair loss is that areas of hair loss (such as those with follicular atrophy or fibrosis) differ from normal, healthy scalp areas in terms of cell structure, tissue composition, and water content. These differences lead to measurable changes in their electrical impedance properties (conductivity and dielectric properties). Specifically, in impedance spectroscopy, the impedance spectrum of hair loss areas (especially the impedance amplitude, phase, or dispersion characteristics of specific frequency bands) will show statistically significant differences from those of normal areas.

[0031] Hair follicle condition includes, but is not limited to: hair follicle density, degree of hair follicle miniaturization, level of inflammation or fibrosis around the hair follicle, activity of dermal papilla cells in the hair follicle, and the metabolic microenvironment of the scalp tissue. By analyzing the correspondence model established between multi-frequency impedance spectra and these physiological and pathological conditions, an indirect quantitative assessment of hair follicle condition can be achieved.

[0032] In one embodiment, the preset characteristic frequencies include 1kHz, 10kHz, 50kHz, 100kHz, 200kHz, and 500kHz, and the excitation signal source 2 is configured to output an excitation signal with a duration of 16ms at each characteristic frequency.

[0033] Understandably, this set of frequency points was optimized. The 1kHz low-frequency impedance primarily reflects the extracellular fluid environment, while the 500kHz high-frequency impedance is more sensitive to intracellular structures, thus comprehensively characterizing the electrophysiological state of scalp tissue from macroscopic to microscopic levels. The 16ms stability duration for each frequency point balances the shortest time window required for signal establishment, high-precision sampling, and data processing. With this setup, the hardware platform can complete a rapid six-band impedance spectrum sweep measurement covering all detection points on the entire scalp within 96 milliseconds.

[0034] In one embodiment, the hair loss diagnostic device based on scalp impedance detection further includes a flexible probe cap 6, with 64 electrode pairs 1 arranged uniformly in a matrix on the flexible probe cap 6.

[0035] Understandably, by using a flexible probe cap 6 and 64 evenly arranged electrode pairs 1 in a matrix, a high-density, highly consistent synchronous electrical screening of the scalp area can be achieved while ensuring wearing comfort and fit. The flexible design allows the probe cap 6 to adapt to different head shapes, ensuring stable contact between each electrode pair 1 and the scalp, eliminating measurement errors caused by loosening. The matrix arrangement of the 64 electrode pairs enables the simultaneous acquisition of dense impedance spectrum data covering the entire scalp with a spatial resolution down to the square centimeter level in a single measurement. This not only comprehensively scans potential hair loss areas, avoiding missed diagnoses, but also accurately characterizes subtle gradient changes at the hair loss boundary through impedance spectroscopy, providing a hardware foundation for non-invasive, rapid, and quantitative hair loss diagnosis and tracking.

[0036] In this embodiment, electrode pair 1 is medical-grade silver chloride to ensure good biocompatibility and signal stability.

[0037] In one embodiment, electrode pair 1 includes a positive electrode 11 and a negative electrode 12, wherein the positive electrode 11 is annular and the negative electrode 12 is circular, and the positive electrode 11 and the negative electrode 12 are concentric.

[0038] Understandably, the concentric ring structure of the electrode pair allows for fundamental optimization of the current field distribution and signal acquisition mode through geometric design. This structure confines each measurement unit to a defined, symmetrical annular region, stabilizing and highly localizing the excitation current path. This achieves high spatial resolution and accurate detection of differences in the electrical properties of deep scalp tissues (such as hair follicles) while effectively suppressing measurement noise introduced by changes in contact impedance and crosstalk between adjacent channels. This self-contained differential measurement unit design ensures high accuracy, consistency, and comparability of the impedance spectrum data acquired from each detection point, providing a guarantee for generating reliable and clear scalp impedance diagnostic maps.

[0039] In one embodiment, the outer diameter D1 of the positive electrode 11 is 5 mm, and the diameter D2 of the negative electrode 12 is 1 mm.

[0040] Understandably, by defining the dimensions of the positive electrode 11 and the negative electrode 12, the electric field distribution and signal quality can be actively optimized through differentiated size design. The larger positive electrode (5mm) provides a stable, low-impedance current return path, enhancing the system's driving capability and reducing contact noise. The finer negative electrode (1mm) serves as a highly sensitive detection point, significantly increasing local current density and electric field gradient, making the measurement more sensitive to changes in the electrical properties of deep microstructures such as hair follicles. Thus, while ensuring measurement stability, the spatial resolution and signal sensitivity of single-point detection are significantly improved, enabling more accurate capture of subtle changes in the electrical properties of tissues caused by early hair loss.

[0041] In one embodiment, the excitation signal source 2 is configured with an independent excitation channel for each electrode pair 1. Each excitation channel includes a signal generator 21, an amplifier circuit 22, and an output conditioning circuit 23 that are connected in sequence. The electrode pair 1 is electrically connected to the output terminal of the output conditioning circuit, and the signal generator is electrically connected to the data processing module 4.

[0042] In this embodiment, the signal generator 21 is a direct digital frequency synthesizer (DDS), and the amplifier circuit 22 includes a first-stage in-phase amplifier unit and a first-stage in-phase amplifier unit. Their outputs are respectively used as differential inputs of the output conditioning circuit 23. The output conditioning circuit 23 includes a voltage divider circuit and / or a harmonic suppression filter circuit, which are used to adjust the amplitude at the output of the signal generator 21 using the voltage divider circuit and to purify the signal using the harmonic suppression filter circuit (such as filtering out harmonics and preventing high-frequency noise), thereby ensuring that the signal applied to the scalp is absolutely safe (amplitude is precisely controllable). The data processing module 4 programs the signal generator 21 through a digital interface to control the frequency, phase, and sweep timing of the output signal.

[0043] Understandably, signal generator 21 generates a raw sinusoidal electrical signal with a specific characteristic frequency according to the instructions of processing module 4. The raw signal is sent to amplifier circuit 22 for signal amplification and is converted into a pair of differential signals with equal amplitude and opposite phase. The differential signal enters output conditioning circuit 23, where voltage divider circuit is responsible for precisely adjusting the signal amplitude to a predetermined range that is safe for scalp tissue and suitable for measurement. At the same time, filter circuit further filters out any harmonics or noise that may exist in the signal, ensuring that the excitation signal waveform applied to the scalp is pure and stable. The final differential signal after conditioning is transmitted through the line to the positive and negative terminals of the corresponding electrode pair 1, thereby completing one excitation of the scalp area to be detected. During this process, data processing module 4 controls the signal generator to repeat the process sequentially at each preset characteristic frequency point, thereby achieving rapid, multi-frequency sweep excitation of the scalp tissue.

[0044] In one embodiment, the signal acquisition module 3 includes multiple signal conditioning channels and at least one multi-channel analog-to-digital converter 31. The input terminal of each signal conditioning channel is connected to an electrode pair 1, and the output terminal is connected to at least one multi-channel analog-to-digital converter 31, for conditioning the voltage response signal acquired by the electrode pair 1. Each signal conditioning channel includes an electrostatic discharge (ESD) protection circuit 32, an anti-aliasing filter circuit 33, a low-pass filter circuit 34, and a differential amplifier circuit 35, which are connected in sequence. Electrode pair 1 is electrically connected to the input terminal of ESD protection circuit 32. The output terminal of ESD protection circuit 32 is electrically connected to anti-aliasing filter circuit 33. The output terminal of anti-aliasing filter circuit 33 is electrically connected to differential amplifier circuit 35. The output terminal of differential amplifier circuit 35 is electrically connected to low-pass filter circuit 34. Low-pass filter circuit 34 is electrically connected to at least one multi-channel analog-to-digital converter (ADC) 31. At least one multi-channel ADC 31 is electrically connected to data processing module 4.

[0045] Furthermore, the multi-channel analog-to-digital converter consists of four 16-channel synchronous sampling analog-to-digital converters to simultaneously acquire voltage response signals from 64 channels.

[0046] Understandably, when the excitation signal is applied to the scalp through electrode pair 1, the same electrode pair 1 synchronously senses and acquires the weak differential voltage response signal generated by the scalp tissue. This response signal first enters the signal conditioning channel of the corresponding channel. It first passes through the electrostatic discharge protection circuit 32 to prevent electrostatic discharge from damaging subsequent precision circuits; then it enters the anti-aliasing filter circuit 33 (usually a bandpass filter) to filter out strong low-frequency interference (such as power frequency noise) and high-frequency noise outside the measurement frequency band (e.g., 1kHz-500kHz), preparing for subsequent amplification and digitization. The pre-filtered signal is sent to the differential amplifier circuit 35 to significantly amplify the weak differential signal while strongly suppressing interference shared by both input terminals, thereby significantly improving the signal-to-noise ratio. The amplified signal then passes through the low-pass filter circuit 34 (e.g., a 4th-order active low-pass filter) for further purification. The low-pass filter circuit 34 uses a cutoff frequency higher than the highest measurement frequency to smooth the signal and filter out residual high-frequency noise and possible harmonics, ensuring that the signal is pure and bandwidth-limited before entering the analog-to-digital converter. The fully conditioned analog signal is transmitted to the multi-channel analog-to-digital converter (ADC) array 31. Under the unified timing control of the data processing module 4, all channels of the multi-channel ADC 31 synchronously or in a high-speed time-division manner sample their respective input signals, accurately converting the analog voltage values ​​into digital codes. Finally, the multi-channel ADC 31 packages the obtained multi-channel digital response signals and transmits them to the data processing module 4 via a digital bus (such as SPI or parallel bus) for subsequent impedance calculation and analysis. This process is completed synchronously during the stable output period of each excitation frequency point and is repeated cyclically as the excitation frequency switches, thereby achieving high-fidelity, synchronous acquisition of the response signals of all 64 channels and all 6 characteristic frequency points.

[0047] In one embodiment, the data processing module 4 is a field-programmable gate array, which is configured to receive and process the response signals of all channels in real time during the stable period of the excitation signal at each frequency point, and synchronously calculate the impedance value of each channel at that frequency point.

[0048] Understandably, employing a field-programmable gate array (FPGA) as the core processor fully leverages its hardware parallel architecture and programmable characteristics. During each 16ms stabilization period at a specific frequency point, the array can synchronously receive digital response signals from all 64 acquisition channels and invoke its internally deployed parallel digital signal processing logic (such as a digital quadrature demodulator or a fast Fourier transform module) to perform real-time, independent computation on the data for each channel. This process does not rely on the serial instruction scheduling of a general-purpose processor, thus synchronously completing the impedance amplitude and phase calculations for all channels at the current frequency point within a very short time window. Its parallel real-time processing capabilities ensure the achievement of "completing a full scalp scan of 64 points and 6 frequency bands within 96 milliseconds," guaranteeing the efficiency and timeliness of the conversion from raw signal to impedance spectrum data, and providing a real-time and accurate data foundation for subsequent rapid spectrum generation and diagnosis.

[0049] like Figure 4 As shown, the second embodiment of this application provides a hair loss diagnosis method based on scalp impedance detection, which includes: S100, sequentially apply multiple differential AC excitation signals with preset characteristic frequencies to the scalp area to be detected; Specifically, 64 electrode pairs set on a flexible probe cap are used to apply excitation signals to different detection areas of the scalp. The excitation signals are generated by a direct digital frequency synthesizer controlled by a digital processing module (FPGA) program and are output cyclically according to preset characteristic frequencies.

[0050] S200, synchronously acquires the differential voltage response signal of the area to be detected at each characteristic frequency; Specifically, the differential voltage response signals of each detection area are synchronously acquired through the 64 electrode pairs. The signals acquired by each electrode pair are processed through an independent channel, which includes an electrostatic protection circuit, an anti-aliasing filter circuit, a differential amplifier circuit, and a low-pass filter circuit. The processed analog signals are synchronously sampled and converted into digital signals by four 16-channel analog-to-digital converters.

[0051] S300 calculates the impedance value of the detection area corresponding to each electrode pair at each characteristic frequency based on the applied excitation signal and the acquired voltage response signal, and forms the impedance spectrum of the detection area. Specifically, the data processing module receives and processes the digital response signals of all 64 channels in real time within the time window of each frequency point, and synchronously calculates the impedance value of each channel (i.e., each detection area) at the current frequency. After cycling through all the frequency points corresponding to the preset characteristic frequencies, the impedance spectrum of the impedance values ​​of multiple characteristic frequency points corresponding to each detection area is formed.

[0052] S400 generates an impedance spectrum of the scalp based on the impedance spectrum of all areas to be detected and their corresponding spatial location information. Specifically, the impedance spectra of the 64 detection areas (corresponding to 64 electrode pairs) are arranged and mapped according to the spatial position of each electrode pair on the probe cap to generate an impedance spectrum that can intuitively reflect the spatial distribution of impedance characteristics in different areas of the scalp.

[0053] S500 identifies hair loss areas and quantitatively assesses the health status of hair follicles by analyzing the differences in impedance spectrum between different regions in the impedance spectrum. Specifically, by comparing the impedance characteristics of suspected bald areas and normal scalp areas at different frequencies in impedance spectroscopy, health indicators such as hair follicle density and tissue composition can be assessed.

[0054] In one embodiment, the plurality of preset characteristic frequencies include 1kHz, 10kHz, 50kHz, 100kHz, 200kHz and 500kHz; the duration of the excitation signal applied at each characteristic frequency point is 16ms.

[0055] Specifically, each frequency point operates stably for 16ms, and a complete frequency sweep measurement of all 64 detection points is completed within 96ms.

[0056] The third embodiment of this application provides a computer device, which includes a memory and at least one processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0057] The fourth embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (Read-Only Memory). Memory includes ROM, magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application may include at least one of relational databases and non-relational databases. Non-relational databases may include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these. The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered to be within the scope of this specification.

[0059] Compared with existing technologies, the hair loss diagnostic device, method and storage medium based on scalp impedance detection provided by this invention can quickly and synchronously acquire the electrical response signals of various areas of the scalp at multiple frequency points through the coordinated setting of multiple electrode pairs, excitation signal source and signal acquisition module. The data processing and diagnostic module automatically calculates and generates intuitive impedance spectrum, thereby realizing non-invasive, objective and quantitative identification and evaluation of hair loss areas, effectively overcoming the defects of traditional diagnostic methods that rely on subjective experience and cannot quantify the state of deep hair follicle tissue.

[0060] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A hair loss diagnostic device based on scalp impedance detection, characterized in that, include: Multiple electrode pairs are individually contacted with the area of ​​the scalp to be tested; An excitation signal source is electrically connected to multiple electrode pairs, and is used to sequentially apply multiple preset characteristic frequency differential AC excitation signals to the scalp detection area through the multiple electrode pairs. The signal acquisition module is electrically connected to multiple electrode pairs respectively, and is used to synchronously acquire the differential voltage response signal of the area to be detected at each preset characteristic frequency; The data processing module is electrically connected to the excitation signal source and the signal acquisition module, respectively, and is used to calculate the impedance value of each area to be detected at each preset characteristic frequency according to the excitation signal and the voltage response signal, form an impedance spectrum, and generate an impedance spectrum of the scalp according to the impedance spectrum. The diagnostic module is used to identify areas of hair loss and assess the condition of hair follicles based on impedance spectroscopy.

2. The hair loss diagnostic device based on scalp impedance detection as described in claim 1, characterized in that: The preset characteristic frequencies include 1kHz, 10kHz, 50kHz, 100kHz, 200kHz, and 500kHz, and the excitation signal source is configured to output an excitation signal with a duration of 16ms at each characteristic frequency.

3. The hair loss diagnostic device based on scalp impedance detection as described in claim 1, characterized in that: The hair loss diagnostic device based on scalp impedance detection also includes a flexible probe cap, and the number of electrode pairs is 64, which are evenly arranged in a matrix on the flexible probe cap.

4. The hair loss diagnostic device based on scalp impedance detection as described in claim 1, characterized in that: The electrode pair includes a positive electrode and a negative electrode. The positive electrode is annular and the negative electrode is circular. The positive electrode and the negative electrode are concentric. The outer diameter of the positive electrode is 5 mm, and the diameter of the negative electrode is 1 mm.

5. The hair loss diagnostic device based on scalp impedance detection as described in claim 1, characterized in that: The excitation signal source is configured with an independent excitation channel for each electrode pair. Each excitation channel includes a signal generator, an amplifier circuit, and an output conditioning circuit that are connected in sequence. The electrode pair is electrically connected to the output terminal of the output conditioning circuit, and the signal generator is electrically connected to the data processing module.

6. The hair loss diagnostic device based on scalp impedance detection as described in claim 1, characterized in that: The signal acquisition module includes multiple signal conditioning channels and at least one multi-channel analog-to-digital converter. Each signal conditioning channel includes an electrostatic discharge (ESD) protection circuit, an anti-aliasing filter circuit, a low-pass filter circuit, and a differential amplifier circuit connected in sequence. The electrode pair is electrically connected to the input terminal of the ESD protection circuit. The output terminal of the ESD protection circuit is electrically connected to the anti-aliasing filter circuit. The output terminal of the anti-aliasing filter circuit is electrically connected to the differential amplifier circuit. The output terminal of the differential amplifier circuit is electrically connected to the low-pass filter circuit. The low-pass filter circuit is electrically connected to at least one of the multi-channel analog-to-digital converters. At least one of the multi-channel analog-to-digital converters is electrically connected to the data processing module. The multi-channel analog-to-digital converter is a set of four 16-channel synchronous sampling analog-to-digital converters to simultaneously acquire voltage response signals from 64 channels.

7. A method for diagnosing hair loss based on scalp impedance detection, characterized in that, include: Multiple differential AC excitation signals with preset characteristic frequencies are sequentially applied to the scalp area to be detected; The differential voltage response signal of the area to be detected at each characteristic frequency is acquired synchronously; Based on the applied excitation signal and the acquired voltage response signal, the impedance value of the detection area corresponding to each electrode pair at each characteristic frequency is calculated, and the impedance spectrum of the detection area is formed. Based on the impedance spectrum of all areas to be detected and their corresponding spatial location information, an impedance map of the scalp is generated; By analyzing the differences in impedance spectrum between different regions in the impedance spectrum, we can identify areas of hair loss and quantitatively assess the health status of hair follicles.

8. The hair loss diagnosis method based on scalp impedance detection as described in claim 7, characterized in that: The preset characteristic frequencies include 1kHz, 10kHz, 50kHz, 100kHz, 200kHz, and 500kHz; the duration of the excitation signal applied at each characteristic frequency point is 16ms.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 7 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 7 to 8.

Images