A high-throughput population detection device and method for heart rate variability

Abstract

This invention discloses a high-throughput heart rate variability (HRV) population detection device and method. The high-throughput HRV population detection device includes a physiological signal acquisition module, a storage chamber, a central management system, a data cable, and a charging power supply. The high-throughput HRV population detection method employs a data temporary storage and continuation technology to achieve phased acquisition and transmission of population HRV test data. The data temporary storage and continuation technology, used in conjunction with the wireless distributed terminal of the HRV population detection device, overcomes the limitations of subjective lag in psychological scales and low efficiency of objective methods in physical and mental health assessment. It enables high-throughput rapid detection of heart rate variability in large-scale populations and convenient data management, providing objective and accurate data support for screening physical and mental health risk groups. This makes the entire detection process easier to organize and operate, and ensures the accuracy and safety of the test results.

Description

Technical Field

[0001] This invention relates to the field of autonomic nervous system function detection, specifically a high-throughput population detection device and method for heart rate variability. Background Technology

[0002] Heart rate variability (HRV) represents the fluctuation of the RR interval between successive heartbeats. It is a good indicator of autonomic nervous system flexibility and can also be used to assess cardiac health. A wide range of HRV rhythms suggests our ability to adapt to physical and psychological challenges; emotions such as stress, anxiety, and depression can lead to autonomic nervous system dysfunction. Therefore, the time-domain, frequency-domain, and nonlinear characteristics of HRV are widely used in the assessment of physical and mental health risks.

[0003] In conducting mental and physical health risk assessments in non-medical settings such as schools and workplaces, there is often a need for efficient and high-throughput collection of group psychological data. The commonly used tool is psychological scales, which inevitably have subjectivity and time lag. From a technical perspective, compared to psychological scales, real-time collected HRV data is undoubtedly more objective and accurate. Currently, most HRV testing technologies and products only support single-person testing, such as contact-type ECG monitoring devices (application no. 201720050167.8 Heart Rate Variability Detection Device, application no. 202011422177.2 A Method and System for Detecting Heart Rate Variability), health monitoring bracelets (application no. 202311029159.1 A Health Monitoring Bracelet), pulse wave finger clip structure detection equipment (application no. 202310009020.4 A Finger Clip Structure and Heart Rate Variability Detector), and non-contact mattresses based on fiber optic sensing systems (application no. 201811345885.3 A Mattress Based on Non-Contact Detection of Heart Rate Variability and Its Detection Method, application no. 202311347549.3 A Method for Detecting Heart Rate Variability Based on Micro-Bend Fiber Optic Sensing). For group testing, multiple machines need to work simultaneously, or a single machine needs to work in cycles, increasing financial, human, and time costs. Technologies supporting simultaneous HRV detection for multiple individuals are still immature. Specifically: millimeter-wave radar technology (application number 202411251687.6, a non-contact multi-target heart rate variability detection method based on adaptive variational mode decomposition) is susceptible to interference from overlapping human figures or dynamic environments in dense scenes, severely affecting the accuracy of heart rate signal extraction; technologies based on facial iPPG recognition (application number 202310507863.7, a non-contact heart rate variability detection method and system, and application number 202410265323.7, fusion) are also problematic. Non-contact psychological stress detection methods for heart rate variability and vocal emotion (application number 202211497685.6: A non-contact, normalized heart rate variability estimation system) are significantly affected by changes in lighting and motion artifacts, and signal quality drops sharply when individuals' faces are obscured or they move in group settings. Radio frequency technology (application number 201510151330.5: Short-range wireless heart rate and heart rate variability detection method), while avoiding contact limitations, requires high sensor deployment accuracy, and is prone to introducing vibration noise in multi-person scenarios, affecting the reliability of HRV analysis. Furthermore, these technologies all require sophisticated hardware support, resulting in excessively high costs for large-scale deployment and complex debugging, hindering widespread adoption.

[0004] Application number 202122281601.2 discloses a heart rate variability detection device capable of simultaneously detecting multiple individuals. This device includes a central data processing module and multiple vital sign parameter acquisition modules, each comprising a blood oxygen acquisition unit and a blood oxygen processing unit. However, its multi-person detection is achieved by simply connecting multiple vital sign parameter acquisition modules in parallel. Each module requires a wired connection to the central data processing module for real-time data transmission, resulting in limited throughput and no flexible expansion. For larger-scale group detection, multiple devices still need to be deployed simultaneously for multi-center testing, making it essentially no different from single-person detection. Furthermore, the device is bulky and inconvenient to carry out, failing to meet the requirements for rapid deployment to the testing site and for conducting group tests in different scenarios.

[0005] Based on the above analysis, the existing technologies in the field of population HRV detection have the following core problems that urgently need improvement:

[0006] 1. It relies on a fixed location, which limits the layout of the venue;

[0007] 2. Low detection efficiency, which has significant limitations for high-throughput detection of 10 or more people, and cannot be easily expanded;

[0008] 3. When used for multi-person detection, it has drawbacks such as weak signal interference resistance, poor equipment expandability, and low real-time processing efficiency;

[0009] 4. Group testing can easily lead to organizational chaos and information mismatch.

[0010] 5. Regarding data transmission, existing wireless data transmission methods each have their limitations. Bluetooth cannot aggregate more than 10 terminals to a central terminal, data transmission is significantly limited by distance, and there is interference between multiple terminals. When multiple devices work simultaneously, WiFi also suffers from co-channel interference; insufficient channel resources lead to decreased data transmission rates, and in severe cases, even data loss or transmission errors. In long-distance or complex electromagnetic environments, WiFi signals are easily interfered with and attenuated, affecting transmission stability. NFC is suitable for reading small amounts of information, but not for large amounts of data.

[0011] Therefore, the present invention provides a high-throughput population detection device and method for heart rate variability. Summary of the Invention

[0012] To address the shortcomings of existing technologies, this invention provides a high-throughput heart rate variability population detection device and method to solve the aforementioned problems.

[0013] To achieve the above objectives, the present invention provides the following technical solution:

[0014] A high-throughput population detection device for heart rate variability includes a physiological signal acquisition module, a storage compartment, a central management system, and a charging power supply;

[0015] Multiple physiological signal acquisition modules are sequentially housed within a storage compartment. The storage compartment has multiple fixed acupoints that match the physiological signal acquisition modules. Each fixed acupoint has an internal interface corresponding to its magnetic suction interface, and an external interface is located on the back side of the same storage compartment. Multiple fixed acupoints are used to place the physiological signal acquisition modules. After a physiological signal acquisition module is placed in a fixed acupoint, its magnetic suction interface is tightly connected to the internal interface of the acupoint in the storage compartment. The internal interfaces are connected in parallel to the external interface, which is connected to a charging power supply. The external interface is then connected to the data transmission interface of the central management system via a data cable, transmitting the collected physiological signal data and the corresponding physiological signal acquisition module number to the central management system. The central management system performs statistical analysis on the physiological signal data of the group and provides a group status report. The charging power supply provides power to the multiple physiological signal acquisition modules.

[0016] Preferably, a cabin base may be added, with multiple fixed slots on the front or upper side of the cabin base. Each fixed slot has an internal interface corresponding to the external interface of a storage cabin. Multiple storage cabins are arranged side-by-side, and each storage cabin is movably inserted into a fixed slot on the front or upper side of the cabin base. The external interface of each storage cabin is connected to the internal interface of the cabin base for transmitting current and collecting physiological signal data. An external interface is also provided on the outer side of the cabin base. Multiple internal interfaces within the cabin base are connected in parallel to the external interface. The external interface is connected to a data cable and a charging power supply. The outer end of the data cable is connected to the data transmission interface of the central management system, transmitting the collected physiological signal data and the corresponding physiological signal acquisition module number to the central management system.

[0017] The cabin base also includes a power management module. The input end of the power management module is electrically connected to an external charging power source via a cable, and the output end of the power management module is electrically connected to multiple slot interfaces of the cabin base via cables. When it is detected that the slot interface / external interface and the acupoint interface / magnetic interface are all connected, the battery of the physiological signal acquisition module inside the cabin can be charged by an external charging power source through the external interface of the cabin base.

[0018] Preferably, the physiological signal acquisition module includes a detection sensor module, a management chip, a storage chip, a battery, a display screen, a switch, and a magnetic interface. The detection sensor module is one or more of an electrocardiogram acquisition module and a pulse wave acquisition module, and the detection sensor module can integrate multimodal sensors, including skin temperature acquisition sensors, skin conductance acquisition sensors, blood oxygen acquisition sensors, and respiratory acquisition sensors. The management chip and the storage chip are connected to the battery after the switch is turned on. The management chip acquires physiological signals through the detection sensor module, the display screen displays relevant signal information, and the storage chip stores physiological signal data. The magnetic interface is used to connect to the fixed acupoint of the storage compartment to realize data export and charging.

[0019] The battery of the physiological signal acquisition module supports wireless operation, and the base of the cabin can be efficiently charged when mains power is available. Specifically:

[0020] The power management module detects the battery status of the modules and prioritizes charging modules with low battery levels. Charging uses intermittent pulse charging to avoid battery overheating and extend battery life.

[0021] A single charge can support the module to work continuously for several hours, meeting the needs of multiple rounds of testing; charging and data transmission are time-division multiplexed to ensure that the testing process is not affected.

[0022] Preferably, the central management system includes a smart terminal and central management software running on the smart terminal. The central management software includes a data receiving module, a data processing module, a data analysis module, a database, and a report output module. The central management software can analyze the data from the physiological signal acquisition module of each subject, obtain HRV data and save it according to the database format, and output a test report; it can also perform risk classification of the group and mark the risk level; it can also generate a group report, store it locally or upload it to the cloud, and distribute it to the management department.

[0023] Furthermore, the specific workflow of the central management software is as follows:

[0024] The data receiving module parses the raw physiological signals and converts them into a standard format;

[0025] The data processing module employs a hybrid analog algorithm and multidimensional signal analysis: First, it identifies abnormal signals, such as artifacts or missed beats, by using voltage thresholds and beat frequency intervals. Then, based on the signal type, it selects one or more filtering methods, such as bandpass filtering to remove baseline drift, bandstop filtering to eliminate power frequency interference, and automatic gain control to suppress motion artifacts, to improve the signal-to-noise ratio.

[0026] The data analysis module calculates the HRV index: the time-domain parameters SDNN and RMSSD are obtained by statistically analyzing the RR interval variation; the frequency-domain parameters LF and HF power are calculated by fast Fourier transform or autoregressive model; the nonlinear parameters SD1 / SD2 and sample entropy are evaluated using the phase space reconstruction method to assess the complexity of the autonomic nervous system.

[0027] The database stores indicators by subject number, and the report output module generates individual or group reports, supports risk grading, and marks low, medium and high risks.

[0028] Furthermore, the algorithm of the data processing module specifically includes:

[0029] Abnormal signal identification, setting dynamic thresholds, and combining beat frequency trend analysis to automatically mark and interpolate missing data;

[0030] Filtering algorithm: The bandpass filtering range is usually set to 0.0033-0.04Hz for HRV ultra-low frequency components, 0.04-0.15Hz for low frequency components, and 0.15-0.4Hz for high frequency components. The bandstop filtering is for 50Hz power frequency interference. The algorithm uses the Butterworth filter and is implemented in real time through software.

[0031] Automatic gain control: dynamically adjusts sampling accuracy based on signal amplitude to reduce motion interference; for multi-mode signals, a blind source separation algorithm is used to enhance specificity.

[0032] Furthermore, the central management system supports data uploading to the cloud and remote management:

[0033] Group reports can be distributed to management departments via the network, and the system provides API interfaces to connect to third-party health platforms;

[0034] Extended features include real-time monitoring gating: through software upgrades, real-time data transmission of some modules is supported during the monitoring of key populations, while retaining the advantages of batch processing.

[0035] A high-throughput population heart rate variability detection method is proposed, wherein the method employs data temporary storage and continuation technology to achieve phased acquisition and transmission of population heart rate variability test data, the main phases of which include:

[0036] S1: The central management system enters the subject's information and assigns a number to correspond to the number of the physiological signal acquisition module;

[0037] S2: Each subject's physiological signal data is collected and stored through the physiological signal acquisition module, and the process stops after all valid data collection is completed.

[0038] S3: Place one or more physiological signal acquisition modules into the fixed acupoints of the storage compartment, and connect the physiological signal acquisition modules to the storage compartment through an interface;

[0039] S4: The storage compartment connects to the central management system, transmits the physiological signal data temporarily stored in the physiological signal acquisition module to the central management system, and clears the data in the physiological signal acquisition module in preparation for the next batch of data acquisition.

[0040] S5: The central management system records, analyzes, calculates, and manages the data in each physiological signal acquisition module, obtains and stores the HRV index parameters of each subject, including time domain, frequency domain, and nonlinear parameters;

[0041] S6: The central management system can retrieve or call up individual HRV index parameters and generate test reports, and can also perform statistical analysis on the situation of a group and give a group situation report.

[0042] In step S4, as a preferred option, the storage compartment containing the physiological signal acquisition module is inserted into the fixed slot of the compartment base, and the storage compartment and the compartment base are connected through an interface; the compartment base is connected to the central management system through a data cable.

[0043] The above technical solution produces the following technical effects:

[0044] The high-throughput heart rate variability (HRV) population detection device and method of this invention provide a highly efficient tool based on objective indicators for high-throughput screening of the physical and mental health of large populations. The detection process is convenient, data is secure, and cost-effective. Using the high-throughput HRV population detection device, autonomic nervous function assessment and physical and mental health screening and early warning can be completed within ten minutes for large populations in schools, military units, government agencies, and enterprises. A single-round throughput can meet the simultaneous testing needs of multiple individuals, and multi-round testing of larger populations is also possible. The data temporary storage and continuation technology used in the detection method prevents data identity mismatch, data loss, and crosstalk during batch testing, greatly reducing the labor costs and organizational complexity for operators. Furthermore, the use of the high-throughput HRV population detection device and method does not involve any easily damaged or consumable parts, further reducing detection costs.

[0045] Furthermore, the introduction of the central management system not only enables the real-time acquisition and orderly transmission of test data, but also provides a platform for data storage and analysis. Users can view test reports through smart terminals and perform further multi-dimensional data processing and analysis. Power management of the testing device eliminates the limitations imposed by the power supply at the testing site, greatly improving the user experience.

[0046] Beneficial effects

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] The data temporary storage and connection technology, used in conjunction with the wireless distributed terminal of the heart rate variability (HRV) population detection device, overcomes the limitations of subjective lag in psychological scales and low efficiency of objective methods in mental and physical health assessments. This enables high-throughput, rapid detection of HRV in large populations and convenient data management, providing objective and accurate data support for screening at-risk groups. System innovations in data acquisition, transmission, management, and equipment power management make the entire detection process easier to organize and operate, while ensuring the accuracy and security of the test results. Attached Figure Description

[0049] Figure 1 This is a flowchart of the high-throughput population detection method for heart rate variability of the present invention;

[0050] Figure 2 This is a schematic diagram of the system structure of the present invention;

[0051] Figure 3 This is a system structure block diagram of the finger clip-type physiological signal acquisition module used in Embodiment 2 of the present invention;

[0052] Figure 4 This is a schematic diagram of the system structure when using a handheld electrocardiogram and physiological signal acquisition module in Embodiment 4 of the present invention.

[0053] In the diagram: 1. Physiological signal acquisition module; 2. Storage compartment; 3. Compartment base; 4. Central management system; 5. Data cable; 6. Charging power supply. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example 1

[0056] As attached Figures 1-2 As shown, a high-throughput heart rate variability population detection device includes a physiological signal acquisition module 1, a storage compartment 2, a central management system 4, and a charging power supply 6.

[0057] Multiple physiological signal acquisition modules 1 are sequentially housed in a storage compartment 2. The storage compartment 2 has multiple fixed acupoints that match the physiological signal acquisition modules 1. Each fixed acupoint has an internal interface corresponding to a magnetic suction interface, and an external interface is located on the back side of the same storage compartment 2. Multiple fixed acupoints are used to place the physiological signal acquisition modules 1. After the physiological signal acquisition module 1 is placed in a fixed acupoint, its magnetic suction interface is tightly connected to the internal interface of the acupoint in the storage compartment 2. The internal interfaces are connected in parallel to the external interface, which is connected to a charging power supply 6. The external interface is then connected to the data transmission interface of the central management system 4 via a data cable, transmitting the collected physiological signal data and the corresponding physiological signal acquisition module 1 number to the central management system 4. The central management system 4 performs statistical analysis on the physiological signal data of the group and provides a group status report. The charging power supply 6 supplies power to the multiple physiological signal acquisition modules 1.

[0058] Preferably, a cabin base 3 can be added. The front side of the cabin base 3 has multiple fixed slots, and the fixed slots have internal interfaces corresponding to the external interfaces of multiple storage cabins 2. Multiple storage cabins 2 are arranged side by side, and each storage cabin 2 is movably inserted into the fixed slot on the front side of the cabin base 3. The external interfaces of the storage cabins 2 are connected to the internal interfaces of the slots of the cabin base 3 for transmitting current and collecting physiological signal data. The outer side of the cabin base 3 is also provided with an external interface. The multiple internal interfaces of the slots in the cabin base 3 are connected in parallel to the external interface. The external interface is connected to a data cable 5 and a charging power supply 6. The outer end of the data cable 5 is connected to the data transmission interface of the central management system 4, transmitting the collected physiological signal data and the corresponding physiological signal acquisition module 1 number to the central management system 4.

[0059] The cabin base 3 also includes a power management module. The input end of the power management module is electrically connected to an external charging power supply 6 via a cable, and the output end of the power management module is electrically connected to multiple slot interfaces of the cabin base 3 via cables. When it is detected that the slot interface / external interface and the acupoint interface / magnetic interface are all connected, the battery of the physiological signal acquisition module 1 in the storage cabin 2 can be charged by the external charging power supply 6 through the base external interface of the cabin base 3. The cabin base 3 provides a data transmission and charging solution for the device, ensuring stability during data transmission and charging, and avoiding problems such as data transmission interruption or charging failure caused by shaking or displacement.

[0060] The physiological signal acquisition module 1 includes a detection sensor module, a management chip, a storage chip, a battery, a display screen, a switch, and a magnetic interface. The detection sensor module is one or more of an electrocardiogram acquisition module and a pulse wave acquisition module, and the detection sensor module can integrate multimodal sensors, including skin temperature acquisition sensors, skin conductance acquisition sensors, blood oxygen acquisition sensors, and respiratory acquisition sensors. The management chip and storage chip are connected to the battery after the switch is turned on. The management chip acquires physiological signals through the detection sensor module, the display screen displays relevant signal information, and the storage chip stores physiological signal data. The magnetic interface is used to connect to the fixed acupoint of the storage compartment 2 to realize data export and charging.

[0061] This design of the physiological signal acquisition module 1 greatly enhances its functional versatility and ease of use. The combination of the ECG acquisition module and the pulse wave acquisition module can accurately capture the electrical activity of the heart and the pulsation of blood vessels, providing a rich and accurate data foundation for the analysis of heart rate variability. Furthermore, the integration of multimodal sensors, such as skin temperature sensors, skin conductance sensors, blood oxygenation sensors, and respiratory sensors, further expands the dimensions of detection. Changes in skin temperature can reflect the body's stress state and emotional fluctuations; skin conductance signals are related to the conductivity of the skin and can reflect the body's sympathetic nervous system activity; blood oxygen saturation is an important indicator for measuring the body's respiratory function and oxygen transport capacity; and respiratory signals are directly related to the body's respiratory system status. Through the collaborative work of these multimodal sensors, the physiological signal acquisition module 1 can comprehensively and holistically acquire the body's physiological information, providing multifaceted data support for subsequent physical and mental health assessments.

[0062] The management chip and storage chip connect to the battery after the switch is turned on, allowing the physiological signal acquisition module 1 to operate independently. As the core control unit, the management chip precisely coordinates the operation of each sensor module, ensuring the accuracy and stability of physiological signal acquisition. The storage chip is responsible for locally storing the acquired physiological signal data, avoiding the risk of data loss during transmission and providing a reliable data source for subsequent data processing and analysis. The display screen allows operators to view relevant signal information in real time, such as key indicators like heart rate and blood oxygen, facilitating timely understanding of the subject's physiological state. The magnetic interface cleverly solves the problems of data export and charging. By connecting to the fixed acupoints of the storage compartment 2, it not only enables rapid data export but also simultaneous charging, improving efficiency.

[0063] In practical group testing scenarios, operators can quickly equip each subject with a physiological signal acquisition module 1. Subjects can perform various physiological activities naturally without excessive interference during the data collection process, ensuring the authenticity and validity of the collected data. After collection, the physiological signal acquisition module 1 is placed in the storage compartment 2, and the data is transmitted systematically to subsequent devices via an interface, avoiding data corruption and loss. The entire process is simple to operate and well-organized, significantly improving the efficiency and quality of group testing.

[0064] The central management system 4 includes a smart terminal and central management software running on the smart terminal. The central management software includes a data receiving module, a data processing module, a data analysis module, a database, and a report output module. The central management software can analyze the data from the physiological signal acquisition module 1 of each subject, obtain HRV data and save it in the database format, and output a test report; it can also perform risk classification of the group and mark the risk level; it can also generate a group report, store it locally or upload it to the cloud, and distribute it to the management department.

[0065] The central management system 4, as the core data processing and control unit of the entire high-throughput heart rate variability population detection device, possesses powerful data processing capabilities and intelligent management functions. It can not only quickly and accurately receive data temporarily stored in all physiological signal acquisition modules 1, but also efficiently verify this data to ensure its integrity and accuracy. After data transmission is complete, the central management system 4 immediately issues a command, starting from the external interface of the base, sequentially passing through the internal interface of the card slot, the external interface of the cabin, the internal interface of the acupoint, and the magnetic interface, finally reaching the physiological signal acquisition module 1 to clear its stored physiological signal data. This process not only achieves timely data clearing but also allows the physiological signal acquisition module 1 to quickly re-enter standby acquisition mode, preparing for the next round of data acquisition. This intelligent data management and equipment control mechanism greatly improves the efficiency and continuity of population testing, reduces the need for manual intervention, lowers operational complexity, and thus further enhances the practicality and reliability of the entire detection device.

[0066] Preferably, the specific workflow of the central management software is as follows:

[0067] The data receiving module parses the raw physiological signals and converts them into a standard format;

[0068] The data processing module employs a hybrid analog algorithm and multidimensional signal analysis: First, abnormal signals, such as artifacts or missed beats, are identified by voltage thresholds and beat frequency intervals. Then, based on the signal type, one or more filtering processes are selected, including bandpass filtering to remove baseline drift, bandstop filtering to eliminate power frequency interference, and automatic gain control to suppress motion artifacts, to improve the signal-to-noise ratio.

[0069] Specifically, the calculation process of the abnormal signal identification and correction algorithm is as follows:

[0070] Input: Preliminary detected RR interval sequence ,in The number of heartbeats detected. The unit is milliseconds (ms).

[0071] Calculation steps:

[0072] 1. Dynamic threshold detection:

[0073] Calculate the arithmetic mean of the local RR intervals. and standard deviation .

[0074] Set dynamic threshold range: ,in This is an empirical coefficient, typically ranging from 2.5 to 3.0.

[0075] Judgment rule: If Then determine This is an outlier.

[0076] 2. Interpolation correction:

[0077] For marked outliers or missing beats, linear interpolation is performed using the nearest normal RR interval before and after them.

[0078] Correction formula: ;

[0079] Eliminate abnormal heartbeat intervals caused by motion artifacts, temporary signal loss, or misjudgment by detection algorithms to ensure signal quality for subsequent analysis.

[0080] Output: Corrected, continuous RR interval sequence (generally ).

[0081] Specifically, the calculation process of the digital filtering algorithm is as follows:

[0082] Input: Raw physiological signal time series data Such as ECG voltage sequences or PPG light intensity sequences, with a sampling frequency of .

[0083] Calculation steps: In a digital signal processor or software, the above analog filter can be converted into a digital filter (Z-domain) through bilinear transformation. The calculation formula is the transfer function (S-domain, second order):

[0084] ;

[0085] in, For Laplace variables, The cutoff angular frequency, The center frequency of the bandpass filter is typically located within the HRV effective frequency band. between.

[0086] Output: Pure physiological signal after filtering out baseline drift and high-frequency noise. .

[0087] inhibition ultra-low frequency baseline drift and High-frequency noise, such as electromyography interference, improves the signal-to-noise ratio and ensures the accuracy of QRS wave or pulse wave peak detection.

[0088] Specifically, the calculation process of the automatic gain control algorithm is as follows:

[0089] Input: The amplitude of the signal at the current moment. .

[0090] In the calculation process, the system continuously adjusts the gain through a feedback mechanism. This makes the output signal amplitude Stable at To adapt to dynamic changes in signal amplitude, such as those caused by loosening of the strap or movement, and to prevent signal saturation or excessive weakness, ensuring the stability of the acquired signal and increasing gain. The control formula is:

[0091] ;

[0092] in, and These are the new and old gain values, respectively. The desired target range.

[0093] Output: Adjusted signal amplitude .

[0094] The data analysis module calculates the HRV index: the time-domain parameters SDNN and RMSSD are obtained by statistically analyzing the RR interval variation; the frequency-domain parameters LF and HF power are calculated by fast Fourier transform or autoregressive model; the nonlinear parameters SD1 / SD2 and sample entropy are evaluated using the phase space reconstruction method to assess the complexity of the autonomic nervous system.

[0095] Specifically, the HRV calculation process includes:

[0096] The input is the preprocessed, clean RR interval sequence. The goal is to calculate various HRV indicators.

[0097] 1. Time Domain Analysis

[0098] SDNN (Standard deviation of all normal sinus intervals):

[0099] formula: ;

[0100] in: It is a sequence The mean of the intervals. SDNN quantifies the overall fluctuation range of the RR intervals, reflecting the total tension of the autonomic nervous system.

[0101] RMSSD (Root Mean Square of the Difference Between Adjacent RR Periods):

[0102] formula: ;

[0103] Among them, this indicator is sensitive to rapid changes between adjacent heartbeats and is a core time-domain parameter for assessing parasympathetic (vagus nerve) activity.

[0104] 2. Frequency Domain Analysis (Based on Fast Fourier Transform - FFT)

[0105] Input: The non-uniform RR interval series needs to be resampled into a uniform time series through interpolation. .

[0106] Power spectral density (PSD) calculation: For The power spectrum was obtained by performing an FFT. .

[0107] Frequency band power calculation:

[0108] Low-frequency power (LF): The unit is usually 1 This reflects the combined regulatory effect of the sympathetic and parasympathetic nervous systems.

[0109] High-frequency power (HF): ,unit It mainly reflects the activity of the parasympathetic nervous system.

[0110] LF / HF ratio: It is often used to characterize the balance between the sympathetic and parasympathetic nervous systems.

[0111] 3. Nonlinear Analysis

[0112] SD1 and SD2 (Poincaré diagram analysis):

[0113] Specific steps: Drawing and A scatter plot (Poincaré plot).

[0114] Calculation formula:

[0115] ;

[0116] in, It is a sequence of the differences between adjacent RR intervals. The variance is represented by SD1, which reflects short-term variability (parasympathetic nervous system), and SD2, which reflects long-term variability.

[0117] The formula for calculating sample entropy (SampEn) is:

[0118] ;

[0119] in: To match the length of the template, This is a similarity tolerance (usually 0.2 times the standard deviation of the time series). In dimension Below, the number of template matches. In dimension The number of template matches measures the complexity of the time series. The higher the entropy value, the more complex the heart rate dynamics and the stronger the system's regulatory ability.

[0120] The database stores indicators by subject number, and the report output module generates individual or group reports, supports risk grading, and marks low, medium and high risks.

[0121] Specifically, the risk classification method is as follows:

[0122] The system has a built-in HRV norm database for healthy individuals of the same age and gender, which includes the normal range of each indicator.

[0123] Risk classification rules (example):

[0124] Low risk (green): Key indicators such as SDNN, RMSSD, and HF are all within the normal range or better than normal levels.

[0125] Medium risk (yellow): One or two key indicators remain below the lower limit of the normal range.

[0126] High risk (red): Several key indicators are significantly lower than the norm, or the LF / HF ratio deviates abnormally for a sustained period.

[0127] Transforming complex physiological data into intuitive quantitative results of health risks provides decision support for large-scale screening and precise intervention.

[0128] Preferably, the algorithm of the data processing module specifically includes:

[0129] Abnormal signal identification, setting dynamic thresholds, and combining beat frequency trend analysis to automatically mark and interpolate missing data;

[0130] Filtering algorithm: The bandpass filtering range is usually set to 0.0033-0.04Hz for HRV ultra-low frequency components, 0.04-0.15Hz for low frequency components, and 0.15-0.4Hz for high frequency components. The bandstop filtering is for 50Hz power frequency interference. The algorithm uses the Butterworth filter and is implemented in real time through software.

[0131] Automatic gain control: dynamically adjusts sampling accuracy based on signal amplitude to reduce motion interference; for multi-mode signals, a blind source separation algorithm is used to enhance specificity.

[0132] Preferably, the central management system 4 supports data cloud uploading and remote management:

[0133] Group reports can be distributed to management departments via the network, and the system provides API interfaces to connect to third-party health platforms;

[0134] Extended features include real-time monitoring gating: through software upgrades, real-time data transmission of some modules is supported during the monitoring of key populations, while retaining the advantages of batch processing.

[0135] Preferably, the battery of the physiological signal acquisition module 1 supports wireless operation, and the base 3 can be efficiently charged when mains power is available.

[0136] Specifically:

[0137] The power management module detects the battery status of the modules and prioritizes charging modules with low battery levels. Charging uses intermittent pulse charging to avoid battery overheating and extend battery life.

[0138] A single charge can support the module to work continuously for several hours, meeting the needs of multiple rounds of testing; charging and data transmission are time-division multiplexed to ensure that the testing process is not affected.

[0139] A high-throughput population heart rate variability detection method employs data temporary storage and continuation technology to achieve phased acquisition and transmission of population heart rate variability test data. The main phases include:

[0140] S1: The central management system 4 inputs and numbers the subject information, so that it corresponds to the number of the physiological signal acquisition module 1;

[0141] S2: Each subject collects and stores physiological signal data through physiological signal acquisition module 1, and stops after valid data collection is completed;

[0142] S3: Place one or more physiological signal acquisition modules 1 into the fixed acupoints of the storage compartment 2, and connect the physiological signal acquisition modules 1 and the storage compartment 2 through an interface;

[0143] S4: The storage compartment 2 connects to the central management system 4, transmits the physiological signal data temporarily stored in the physiological signal acquisition module 1 to the central management system 4, and clears the data in the physiological signal acquisition module 1 in preparation for the next batch of data acquisition.

[0144] S5: The central management system 4 records, analyzes, calculates, and manages the data in each physiological signal acquisition module 1, obtains and stores the HRV index parameters of each subject, including time domain, frequency domain, and nonlinear parameters;

[0145] S6: The central management system 4 can retrieve or call up individual HRV index parameters and generate test reports, and can also perform statistical analysis on the situation of a group and give a group situation report.

[0146] The aforementioned detection device effectively solves the problems of organizational chaos and information mismatch during group testing. In step S1, the central management system 4 accurately enters and numbers the information of the test subjects, ensuring a one-to-one correspondence between each subject and the physiological signal acquisition module 1, thus avoiding information mismatch at the source. At each stage of data acquisition and transmission, the physiological signal acquisition module 1 independently collects and temporarily stores data, while the storage chamber 2 and the chamber base 3 play a role in orderly storage and stable transmission, allowing the entire group testing process to proceed smoothly. Finally, the central management system 4 comprehensively processes the data, providing not only individual test reports but also statistical analysis of the group situation, greatly improving the efficiency and accuracy of group heart rate variability detection.

[0147] Preferably, in step S4, the storage compartment 2, which has been placed into the physiological signal acquisition module 1, is inserted into the fixed slot of the compartment base, and the storage compartment 2 and the compartment base 3 are connected through an interface; the compartment base 3 is connected to the central management system 4 through a data cable 5.

[0148] This invention innovates in terms of the throughput, portability, scalability potential, and data management and transmission of the detection device. It establishes a new method for HRV group testing using data temporary storage and continuation technology. This method eliminates the need for real-time data transmission via wired or wireless means, removing the limitations on the number of detection terminals in group testing and addressing the problem of crosstalk during the simultaneous collection and transmission of large amounts of detection data. It allows for flexible scalability and significantly reduces operator labor costs and organizational complexity.

[0149] Unlike the inefficiency and high time cost of single-target tests, this device is designed for large-scale population mental and physical health assessment scenarios such as schools, the military, government agencies, and enterprises. It features a simple process and high efficiency, being dozens of times more efficient than current heart rate variability (HRV) testing methods on the market. It can truly achieve single-center population HRV testing, enabling rapid and real-time collection, dynamic analysis, and screening of objective data. Combined with other physiological signals, such as respiration, skin temperature, and skin conductance, it can perform multimodal AI analysis to achieve early warning and precise intervention for mental health risks. It is an intelligent, cost-effective, and highly efficient means of managing mental and physical health.

[0150] Example 2

[0151] As attached Figure 3 As shown, to address the throughput issues in heart rate variability detection and the data management problems inherent in high-throughput detection, this invention designs a high-throughput heart rate variability population detection device based on multiple wireless distributed terminals, namely physiological signal acquisition modules 1. Each physiological signal acquisition module 1 is arranged in an orderly manner in a fixed acupoint within the storage compartment 2; and data management and power management are performed within the fixed acupoint.

[0152] Specifically, the high-throughput population heart rate variability detection device includes a physiological signal acquisition module 1, a storage chamber 2, a chamber base 3, a central management system 4, a data cable 5, and a charging power supply 6.

[0153] The physiological signal acquisition module 1 is in the form of a finger clip and integrates a pulse wave PPG, temperature, and respiration sensors, a management chip, a storage chip, a battery, a display screen, a switch, and a magnetic interface. The physiological signal acquisition module 1 can acquire pulse wave timing data and end-point temperature and respiration timing data. The storage compartment 2 consists of multiple fixed acupoints for placing the physiological signal acquisition module 1, an internal interface for each acupoint, and an external interface. Preferably, ten finger clip-type physiological signal acquisition modules 1 are placed in each fixed acupoint simultaneously. After the physiological signal acquisition module 1 is placed in the fixed acupoint, the magnetic interface of the physiological signal acquisition module 1 is ensured to be tightly connected to the internal interface of the acupoint in the storage compartment 2. The cabin base 3 consists of a power management module, a fixed slot for accommodating multiple storage cabins 2, an internal interface of the slot, and an external interface of the base. The internal interface of the slot and the external interface of the base are already connected. The fixed slot connects the external interface of the storage cabin 2 to the internal interface of the slot in the cabin base 3, and the external interface of the base is connected to the interface of the storage cabin 2. When the power management module detects that the internal interface / external interface and the internal interface / magnetic interface of the slot are all connected, it can perform intermittent high-throughput charging of the battery of the physiological signal acquisition module 1 in the storage cabin 2 through the external charging power supply 6. The external interface of the base on the cabin base 3 is connected to the central management system 4 through the data cable 5.

[0154] The central management system 4 consists of smart terminals and central management software. The central management software includes a data receiving module, a data processing module, a data analysis module, a database, and a report output module; the smart terminal can be a smart tablet, a smartphone, or a computer.

[0155] Example 3

[0156] As attached Figure 3 As shown, the working principle of the high-throughput heart rate variability (HRV) population detection method is based on data temporary storage and continuation technology. The specific workflow for HRV population detection of 40 students in a class includes:

[0157] 1. Input and display the subject information in the central management system 4, and match the subject information with the number on the physiological signal acquisition module 1 to ensure the identity and exclusivity of the data.

[0158] 2. The subject wears the physiological signal acquisition module 1 as required, turns on the switch, connects the battery, and the management chip in the physiological signal acquisition module 1 can collect physiological signals through the PPG sensor and temperature sensor in the finger clip. The display shows the relevant collected signal information. After collecting physiological data for a period of time, the RR interval, skin temperature, skin conductance and other physiological data of 5 minutes of rest are preferred and stored in the storage chip. The acquisition is automatically displayed and the subject has completed the test.

[0159] 3. The testers placed the completed physiological signal acquisition modules 1 into the acupoints of the storage chamber 2 according to their numbers and fixed them in place. Multiple storage chambers 2 were inserted into the slots of the chamber base 3 to ensure that the corresponding interfaces were connected.

[0160] 4. After connection, if the base 3 of the cabin is connected to an external power source, it can charge the finger clip through the external charging power source 6. At the same time, the data cable 5 connects to the smart terminal of the central management system 4. The central management software of the central management system 4 reads the data in each physiological signal acquisition module 1 and stores it in the computer database, and clears the data in the physiological signal acquisition module 1.

[0161] 5. The data receiving module, data processing module, data analysis module, database, and report output module of the central management system software analyze, calculate, and manage the physiological signal acquisition data of each subject's finger clip. A hybrid simulation algorithm is used to identify abnormal signals through voltage thresholds and beat frequency intervals. Bandpass filtering, bandstop filtering, and automatic gain control are selected as needed to filter out baseline drift, power frequency interference, electromyography, artifacts, and other interferences. This yields HRV index parameters for each subject, such as time-domain features (SDNN, RMSSD, etc.), frequency-domain features (LF / HF ratio, etc.), and nonlinear features (SD1, SD2, sample entropy, etc.). Multidimensional signal analysis is performed, and the data is then saved in a database format and an individual test report is output and stored.

[0162] 6. At the same time, risk classification of the entire group is carried out and the risk level is marked. A class-wide report can be generated and stored or uploaded to the cloud and forwarded to the school and education management department.

[0163] Example 4

[0164] As attached Figure 4 As shown, the physiological signal acquisition module 1 of the high-throughput heart rate variability population detection device includes a handheld ECG and blood oxygen sensor, a management chip, a storage chip, a battery, a display screen, a switch, and a magnetic interface. As a wireless distributed terminal, it can collect ECG time-series data and terminal blood oxygenation data for each subject. The storage compartment 2 includes fixed acupoints for placing the physiological signal acquisition modules 1, an internal interface of the acupoints, and an external interface. Each compartment can accommodate ten physiological signal acquisition modules 1, which are arranged in an orderly manner in their respective fixed acupoints. After being placed in the fixed acupoints, the magnetic interface is ensured to be tightly connected to the internal interface of the acupoint in the storage compartment 2 so that data management and power management can be completed in the fixed acupoints.

[0165] Multiple storage compartments 2 are connected in parallel to the same compartment base 3. The compartment base 3 is equipped with a power management module, a fixed slot for placing multiple storage compartments 2, an internal interface of the slot, and an external interface of the base. The internal interface of the slot and the external interface of the base are connected. The fixed slot connects the external interface of the storage compartment 2 to the internal interface of the slot in the compartment base 3, and the external interface of the base is connected to the interface of the storage compartment 2. When the power management module detects that the internal interface / external interface and the internal interface / magnetic interface of the slot are all connected, it can perform intermittent high-throughput charging of the battery of the handheld ECG in the storage compartment 2 through an external charging power supply 6. The external interface of the base on the compartment base 3 is a USB data cable 5 connected to the central management system 4.

[0166] The central management system 4 consists of smart terminals and central management software. The central management software includes a data receiving module, a data processing module, a data analysis module, a database, and a report output module; the smart terminal can be a smart tablet, a smartphone, or a computer.

[0167] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, and variations made within the spirit and principles of this application should be included within the scope of protection of this application.

[0168] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0169] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or high-throughput heart rate variability population detection apparatus and method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or high-throughput heart rate variability population detection apparatus and method.

Claims

1. A high-throughput heart rate variability population detection device, characterized in that, It comprises a physiological signal acquisition module (1), a storage cabin (2), a central management system (4) and a charging power supply (6); A plurality of physiological signal acquisition modules (1) are sequentially stored in the storage cabin (2), a plurality of fixed acupoints matched with the physiological signal acquisition modules (1) are arranged on the storage cabin (2), an acupoint inner interface is arranged in the fixed acupoint corresponding to the magnetic attraction interface, an outer cabin interface is arranged on the back of the same storage cabin (2), the fixed acupoints are provided for placing the physiological signal acquisition modules (1), the magnetic attraction interface of the physiological signal acquisition module (1) is tightly connected with the acupoint inner interface of the storage cabin (2) after being placed in the fixed acupoint, the acupoint inner interfaces are connected in parallel with the outer cabin interface, the outer cabin interface is electrically connected with the data transmission interface of the central management system (4) and the charging power supply (6) through a cable, the physiological signal data collected and transmitted and the corresponding physiological signal acquisition module (1) number are transmitted to the central management system (4), the central management system (4) statistically analyzes the physiological signal data of the group, and gives a group situation report, and the charging power supply (6) supplies power for the plurality of physiological signal acquisition modules (1); The cabin base (3) is additionally arranged, a plurality of fixed clamping positions are arranged on the front side of the cabin base (3), a clamping inner interface is arranged in the fixed clamping position corresponding to the outer cabin interface of the plurality of storage cabins (2), the plurality of storage cabins (2) are arranged side by side, and the plurality of storage cabins (2) are correspondingly and movably inserted into the fixed clamping positions on the front side of the cabin base (3), the outer cabin interface of the storage cabin (2) and the clamping inner interface of the cabin base (3) are connected to transmit the collected physiological signal data, an outer base interface is further arranged on the outer side of the cabin base (3), the plurality of clamping inner interfaces in the cabin base (3) are connected in parallel with the outer base interface, the outer base interface is connected with an output interface arranged on the cabin base (3) through an internal cable, a data line (5) is connected to the output interface, the data line (5) is connected to the data transmission interface of the central management system (4) at the outer end, and the physiological signal data collected and transmitted and the corresponding physiological signal acquisition module (1) number are transmitted to the central management system (4).

2. The high-throughput heart rate variability population detection device of claim 1, wherein, The cabin base (3) further comprises a power management module, the input end of the power management module is electrically connected with an external charging power supply (6) through a cable, the output end of the power management module is electrically connected with the clamping inner interfaces of the plurality of cabin bases (3) through a cable, when the clamping inner interfaces and the outer cabin interfaces, the acupoint inner interfaces and the magnetic attraction interfaces are all connected, the batteries of the physiological signal acquisition modules (1) in the storage cabins (2) are charged by the external charging power supply (6) through the outer base interface of the cabin base (3).

3. The high-throughput heart rate variability population detection device of claim 1, wherein, The physiological signal acquisition module (1) comprises a detection sensor module, a management chip, a storage chip, a battery, a display screen, a switch and a magnetic interface. The detection sensor module is one or more of an electrocardio acquisition module and a pulse wave acquisition module, and the detection sensor module can integrate multiple modal sensors, including a skin temperature acquisition sensor, a skin electricity acquisition sensor, a blood oxygen acquisition sensor and a respiration sensing acquisition sensor. The management chip and the storage chip are connected to the battery after the switch is turned on. The management chip collects physiological signals through the detection sensor module. The display screen displays relevant signal information, and the storage chip stores physiological signal data.

4. The high-throughput heart rate variability population detection device of claim 1, wherein, The battery of the physiological signal acquisition module (1) supports wireless operation, and the cabin base (3) realizes efficient charging when there is commercial power. Specifically: The power management module detects the state of the module battery and preferentially charges the low-power module. The charging is intermittent pulse charging to avoid battery heating and prolong the service life. Single charging supports continuous operation of the module for several hours, meeting the requirements of multiple rounds of detection. Charging and data transmission are time-multiplexed to ensure that the detection process is not affected.

5. The high-throughput heart rate variability population detection device of claim 1, wherein, The central management system (4) comprises an intelligent terminal and a central management software running on the intelligent terminal. The central management software comprises a data receiving module, a data processing module, a data analysis module, a database and a report output module. The central management software analyzes the data of the physiological signal acquisition module (1) of each subject, obtains HRV data and saves them in a database format, and outputs a detection report. The risk level of a group is classified, and the risk level is marked. A group report is formed, stored locally or uploaded to the cloud, and distributed to the management department.

6. The high-throughput heart rate variability group detection device according to claim 5, wherein In the present application, The specific working process of the central management software is as follows: The data receiving module analyzes the original physiological signals and converts them into a standard format. The data processing module adopts a hybrid simulation algorithm and multi-dimensional signal analysis. First, abnormal signals are identified by voltage threshold and beat frequency time interval. Abnormal signals are artifacts or missed beats. Then, one or more filtering processes are selected according to the signal type to filter out baseline drift, eliminate power frequency interference and suppress motion artifacts by automatic gain control, thereby improving the signal-to-noise ratio. The data analysis module calculates the HRV indicators. The time domain parameters SDNN and RMSSD are obtained by statistical RR interval variation. The frequency domain parameters LF and HF power are calculated by fast Fourier transform or autoregressive model. The nonlinear parameters SD1 / SD2 and sample entropy are evaluated by phase space reconstruction method to assess the complexity of the autonomic nervous system. The database stores the indicators according to the subject number, and the report output module generates individual or group reports, supports risk classification, and marks low, medium and high risks.

7. The high-throughput heart rate variability population detection device of claim 5, wherein, The central management system (4) supports data cloud uploading and remote management: The group report is distributed to the management department through the network, and the system provides an API interface to access a third-party health platform. The extended functions include real-time monitoring gating. Through software upgrade, the system supports real-time data transmission of some modules during key population monitoring, while retaining the advantages of batch processing.

8. The high-throughput heart rate variability population detection device of claim 5, wherein, The algorithm of the data processing module specifically comprises: Abnormal signal recognition, setting dynamic threshold, combining beat frequency trend analysis, automatically marking and interpolating missing data; Filtering algorithm: the band-pass filtering range is set to HRV low-frequency component 0.04-0.15 Hz and high-frequency component 0.15-0.4 Hz, and the band-stop filtering is for 50 Hz power frequency interference; the algorithm adopts Butterworth filter, and real-time processing is realized through software; Automatic gain control: dynamically adjusting sampling accuracy according to signal amplitude, reducing motion interference; for Multi-modal signals, blind source separation algorithm is used to enhance specificity.

9. The method of detecting by the high-throughput heart rate variability population detection device according to claim 1, wherein, The detection method adopts data temporary storage and continuous connection technology to realize phased acquisition and transmission of group heart rate variability test data, including: S1: the central management system (4) inputs the test personnel information and numbers, so as to correspond to the numbers of the physiological signal acquisition module (1); S2: each testee acquires physiological signal data through the physiological signal acquisition module (1) and stores it, and stops after effective data acquisition is completed; S3: one or more physiological signal acquisition modules (1) are placed in the fixed acupoints of the storage cabin (2), and the physiological signal acquisition module (1) is connected with the storage cabin (2) through an interface; S4: the storage cabin (2) is connected with the central management system (4), the physiological signal data temporarily stored in the physiological signal acquisition module (1) is transmitted into the central management system (4), and the data in the physiological signal acquisition module (1) is emptied at the same time, so as to be ready for the next batch of data acquisition; S5: the central management system (4) records, analyzes, calculates and manages the data in each physiological signal acquisition module (1), obtains the HRV index parameters of each testee and stores them, including time domain, frequency domain and nonlinear parameters; S6: the central management system (4) retrieves or calls individual HRV index.

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