An automated closed-loop analysis method and system for multi-source electrophysiological recordings

Abstract

This invention relates to an automated closed-loop analysis method and system for multi-source electrophysiological recordings, comprising: identifying the file format of the input raw electrophysiological data file and extracting the core electrophysiological information, and converting the core electrophysiological information into standardized data objects; using multiple detection strategies to detect the standardized data at each detection point to obtain each detection event, assigning a quality score to each detection event and labeling them in descending order of score; performing statistical analysis on each core calculation indicator of the detection event to obtain statistics, finding a continuous time window representing the global characteristics of each statistics based on the local changes of each statistics over time, generating standardized visualization charts based on the data from the time windows; and using a statistical-signal dual-objective optimization intelligent window selection algorithm to generate standardized visualization charts based on representative windows, thereby establishing a systematic multi-dimensional quality assessment and error quantification system.

Description

Technical Field

[0001] This invention relates to the field of biomedical informatics technology, and in particular to an automated closed-loop analysis method and system for multi-source electrophysiological recordings. Background Technology

[0002] Patch-clamp techniques are fundamental to neuroscience research, but the highly fragmented formats of electrophysiological data (such as ABF, HEKA, CED, etc.) pose challenges to data analysis. Existing analysis workflows suffer from the following problems: 1) Fragmented workflows, resulting in low efficiency and error-prone switching between multiple tools; 2) Lack of systematic and objective data quality and error assessment; 3) Inconsistencies between representative images used for presentation and data segments used for statistical analysis, posing a risk of misleading results; 4) Analysis results are limited to data presentation and cannot effectively guide experimental optimization, relying on researchers' trial-and-error experience.

[0003] Existing technologies employ two main approaches: First, commercial analytics software (such as Clampfit, pCLAMP, and Spike2) is used. These typically support only their own or a few data formats, providing basic event detection and statistical functions, but lack a systematic error quantification module, resulting in a disconnect between analysis results and experimental optimization. Second, open-source analytics toolkits / scripts (such as Neo, ephyviewer, and lab-developed code) are used. These offer greater flexibility, but usually only address analysis problems specific to certain formats or stages, resulting in incomplete workflows, low standardization, and an inability to achieve cross-platform closed-loop optimization.

[0004] The drawbacks of existing technologies for acquiring and analyzing electrophysiological data include: Strong format dependence: Tools are tied to specific data formats, leading to high costs associated with equipment replacement or processing historical data. Discontinuous workflow: Analysis steps are fragmented, relying on manual intervention and data transformation. Lack of quality assessment: There is a lack of objective, quantitative data quality and error analysis systems. Disconnect between statistics and visualization: Statistical results and representative graphs may come from different data segments, making consistency difficult to guarantee. Lack of experimental guidance: Analysis software cannot translate identified problems into specific, actionable suggestions for experimental optimization. Poor standardization and reproducibility: The analysis workflow is highly dependent on operator experience, resulting in poor comparability of results from different platforms and personnel. Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art by providing an automated closed-loop analysis method for multi-source electrophysiological recordings. It establishes a universal analysis framework decoupled from the underlying data format to achieve one-click automated processing of multi-source electrophysiological data. Employing a statistical-signal dual-objective optimized intelligent window selection algorithm, it automatically selects representative time windows based on core computational indicators. Based on these representative windows, it generates standardized visualization charts with fixed physical dimensions, resulting in a systematic multi-dimensional quality assessment and error quantification system.

[0006] According to a first aspect of the present invention, an automated closed-loop analysis method for multi-source electrophysiological recordings is provided, comprising: Step 1: After identifying the file format of the input raw electrophysiological data file, extract the core electrophysiological information and convert the core electrophysiological information into a standardized data object; Step 2: Using multiple detection strategies, the standardized data at each detection point is detected to obtain each detection event. After quality scoring for each detection event, it is labeled as qualified, suspicious, or abnormal according to the score. The detection strategies include: threshold method, slope method, and template matching method. Step 3: Statistical analysis is performed on each core calculation indicator based on the type of the detected event label to obtain statistical quantities. Based on the local changes of each statistical quantity over time, a continuous time window representing the global characteristics of the statistical quantity is found. Based on the data in the time window, a standardized visualization chart is generated as the analysis result.

[0007] Based on the above technical solution, the present invention can also be improved as follows.

[0008] Optionally, step 1 includes: The standardized data object is obtained by constructing a parsing plugin with a built-in / extensible format; the core electrophysiological information includes: time series signal, sampling rate, channel units, and stimulus labels.

[0009] Optionally, after obtaining the standardized data object in step 1, the method further includes: performing interference removal processing and visualization effect optimization processing on the standardized data object to obtain analysis layer signals and display layer signals; the analysis layer signals are used for data detection and analysis, and the display layer signals are used to generate the analysis results of the standardized visualization charts; The interference removal process includes: baseline correction, trend removal, light filtering, and zero-phase digital filtering; The optimized visualization process includes: slow drift removal, noise smoothing, and dynamic range adjustment.

[0010] Optionally, in step 2, The threshold method monitors whether the signal amplitude exceeds the set baseline; the slope method monitors the first derivative of the rising edge of the signal to determine whether the signal has a sudden change; the template matching method slides a standard synaptic current curve across the input signal and calculates the correlation coefficient to monitor whether the shape of the signal is abnormal. Step 2 further includes: integrating the results of each detection strategy using a confidence-weighted fusion algorithm to obtain each detection event; Each detection event is scored based on signal-to-noise ratio, baseline stability, and waveform characteristics.

[0011] Optionally, step 3 includes: Step 301, calculate the core calculation indicators including: event frequency, median event amplitude, and average decay time constant; Step 302, construct the cost function : ; Where ω is a candidate time window of length T, It is the deviation between the median of the indicator within the window and the global median. , and The preset weighting coefficients, It is the variance of the signal within that window. This is a penalty factor that accumulates deductions based on the number of events marked as suspicious or abnormal within the window; Step 303: Find the cost function that makes the cost function The smallest continuous time period is used to obtain the continuous time window.

[0012] Optionally, step 3 may be followed by: Step 4: Construct a six-dimensional error quantification model. The six-dimensional error quantification model is used to determine the observation indicators and related experimental factors corresponding to the errors in each dimension. The input electrophysiological raw data file is quantitatively diagnosed by monitoring each observation indicator and related experimental factors. The six-dimensional error quantification model includes: environmental / reagent preparation error model, electrode state error model, tissue state error model, cell state error model, recording system error model, and analysis algorithm error model.

[0013] Optionally, the observation indicators of the environmental / reagent preparation error model include: baseline root mean square noise and 50Hz power frequency, and the associated experimental factors include: shielding effectiveness and internal fluid activity; The observation indicators of the electrode state error model include: the initial value of the series resistance and its offset over time; the related experimental factors include: electrode tip blockage and electrode offset. The observation indicators of the tissue state error model include: spontaneous fluctuation power spectrum of non-target frequency band and background noise intensity, and the associated experimental factors include: brain slice activity and non-specific receptor background. The observation indicators of the cell state error model include: the absolute amplitude and variance of the clamping current, and the associated experimental factors include: cell membrane integrity and sealing quality. The observed indicators of the recorded system error model include: the residual capacitance transient signal after a voltage step, and the related experimental factors include: Axopatch 200B compensation knob adjustment; The observed metrics of the analysis algorithm error model include: multi-strategy fusion detection confidence and dynamic fitting residuals, and the associated experimental factors include: template matching accuracy and signal-to-noise ratio.

[0014] Optionally, step 4 further includes: normalizing and mapping the observation indicators of the error quantification model in each dimension, determining the weight of each observation indicator according to the experiment type, and calculating the weighted sum of each observation indicator to obtain the comprehensive quality score of the input electrophysiological raw data file.

[0015] Optionally, step 4 may be followed by: Step 5: Construct an error pattern-optimization measure knowledge base, which records experimental operation suggestions ordered by priority for each dimension of error determined based on historical data. When the observed values ​​of the error quantification model in each dimension are lower than the preset safety threshold, experimental operation suggestions are issued based on the error pattern-optimization measures knowledge base, ordered by priority.

[0016] According to a second aspect of the present invention, an automated closed-loop analysis system for multi-source electrophysiological recording is provided, comprising: an input unit, a core processing unit, and an intelligent decision-making unit; The input unit is used to identify the file format of the input raw electrophysiological data file, extract the core electrophysiological information, and convert the core electrophysiological information into a standardized data object. The core processing unit is used to detect the standardized data of each detection point using multiple detection strategies to obtain each detection event. After scoring each detection event, it is labeled as qualified, suspicious, or abnormal according to the score. The detection strategies include: threshold method, slope method, and template matching method. The intelligent decision-making unit is used to perform statistical analysis on each core calculation indicator based on the type of the detected event label to obtain statistical quantities, find a continuous time window representing the global characteristics of each statistical quantity based on the local changes of each statistical quantity over time, and generate standardized visualization charts based on the data in the time window.

[0017] This invention provides an automated closed-loop analysis method and system for multi-source electrophysiological recordings. It establishes a universal analysis framework decoupled from the underlying data format, enabling one-click automated processing of multi-source electrophysiological data. A separate dual-channel signal processing architecture is designed to optimize event detection accuracy and result visualization. A systematic multi-dimensional quality assessment and error quantification system is constructed. An innovative statistical-window mapping mechanism fundamentally ensures the consistency between statistical features and visualization results. An intelligent experimental correction suggestion engine is developed, forming a closed-loop feedback from data analysis to experimental optimization, improving experimental efficiency and data quality. Attached Figure Description

[0018] Figure 1 A flowchart for automated closed-loop analysis of multi-source electrophysiological recordings provided by this invention; Figure 2 This invention provides a structural block diagram for automated closed-loop analysis of multi-source electrophysiological recordings. Detailed Implementation

[0019] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0020] Figure 1 A flowchart of an automated closed-loop analysis method for multi-source electrophysiological recordings provided by this invention is shown below. Figure 1 As shown, the analytical method includes: Step 1: After identifying the file format of the input raw electrophysiological data file, extract the core electrophysiological information and convert the core electrophysiological information into a standardized data object (SDO).

[0021] Electrophysiological recording data files are time-series data files containing raw or pre-processed electrophysiological signals, and their formats include, but are not limited to: Axon Binary Format (ABF), HEKA (.dat / .pgf), CED Spike2 (.smr / .son), Neurodata Without Borders (.nwb), and custom binary formats.

[0022] Step 2: Standardized data from each detection point are tested using multiple detection strategies to obtain each detection event. Each detection event is then scored for quality and labeled as qualified, questionable, or abnormal according to the score. The detection strategies include: threshold method, slope method, and template matching method. Step 3: Statistical analysis is performed on each core calculation indicator based on the type of event labeling to obtain statistics. Based on the local changes of each statistics over time, a continuous time window representing the global characteristics of the statistics is found. Based on the data of the time window, standardized visualization charts are generated to analyze the results.

[0023] This invention provides an automated closed-loop analysis method for multi-source electrophysiological recordings. By establishing a general analysis framework decoupled from the underlying data format, it achieves one-click automated processing of multi-source electrophysiological data. It adopts an intelligent window selection algorithm with statistical-signal dual-objective optimization, and automatically selects representative time windows based on core calculation indicators. Based on the representative windows, it generates standardized visualization charts with fixed physical sizes, thus establishing a systematic multi-dimensional quality assessment and error quantification system.

[0024] Example 1

[0025] Embodiment 1 provided by this invention is an embodiment of automated closed-loop analysis for multi-source electrophysiological recordings provided by this invention, combined with... Figure 2 As can be seen, embodiments of this analytical method include: Step 1: After identifying the file format of the input raw electrophysiological data file, extract the core electrophysiological information and convert the core electrophysiological information into a standardized data object (SDO). In one possible embodiment, step 1 includes: A parsing plugin with a built-in / extensible format is constructed to obtain standardized data objects; the core electrophysiological information includes: time series signals, sampling rate, channel units, and stimulation labels.

[0026] In practice, the input electrophysiological raw data files are multi-source heterogeneous electrophysiological raw data files, such as .abf, .dat, .smr, etc.

[0027] The parsing plugin automatically identifies the file format of the raw electrophysiological data file and extracts core information such as time series signals, sampling rate, channel units, and stimulation labels from the raw electrophysiological data file, converting them into standard data objects within the system.

[0028] This parsing plugin can be implemented by encapsulating existing basic parsers (such as Neo and pyABF) into a "dynamically extensible plugin architecture," establishing an automatic mapping mechanism from multi-source data to a unified standard object, achieving hardware independence. It decouples from specific recording devices or software, providing format-independent standardized input for subsequent processing.

[0029] In one possible embodiment, after obtaining the standardized data object in step 1, the method further includes: performing interference removal processing and visualization effect optimization processing on the standardized data object to obtain analysis layer signals and display layer signals.

[0030] The analysis layer signals are processed to preserve the original characteristics of events and maximize event detection accuracy, and are used for data detection and analysis. The display layer signals are processed to optimize visualization effects and are used to generate standardized visualization charts for the analysis results.

[0031] In practice, after the standardized data object is input, it enters two parallel processing paths: the analysis layer and the display layer. The algorithm parameters of the two paths are independent of each other and do not interfere with each other.

[0032] The interference removal process includes: baseline correction, trend removal, light filtering, and zero-phase digital filtering; In practice, zero-phase digital filtering, such as Python's `filtfilt`, is used. Standardized data objects undergo baseline correction, trend removal, and light filtering to eliminate slow drift interference. Simultaneously, zero-phase digital filtering maximizes the preservation of the original dynamic characteristics of the events (such as the signal's rise slope and decay constant), preparing for subsequent accurate detection.

[0033] The analysis layer signal first identifies signal components through multi-resolution / modal decomposition, then removes noise components for reconstruction, achieving simultaneous baseline correction and denoising. This can also be achieved through wavelet transform or analog decomposition.

[0034] Wavelet transform: Wavelet thresholding is used for denoising (such as sym or db wavelet basis). Through multi-resolution analysis, high-frequency white noise is filtered out while the singularity characteristics of the rising edge of the event are preserved, ensuring that the calculation of the attenuation constant is not distorted.

[0035] Mode decomposition: Adaptive variational mode decomposition (VMD) is employed. The signal is decomposed into several intrinsic mode functions, and adaptive signal purification is achieved by discarding extremely low-frequency (trend term) and extremely high-frequency (noise term) modes.

[0036] The process of optimizing visualization effects includes: slow drift removal, noise smoothing, and dynamic range adjustment.

[0037] In practice, slow drift removal is used to handle extremely low-frequency fluctuations caused by electrode polarization, tissue peristalsis, or temperature drift, establishing a "horizontal" reference baseline. Noise smoothing, such as using Gaussian filtering or Savitzky-Golay filtering, handles "glitch" caused by electronic thermal noise or 50 / 60 Hz power frequency interference, smoothing lines while maintaining signal contours. Dynamic range adjustment calculates the effective amplitude range of the signal, such as 5–95%, maps the current value to the pixel coordinate system of the drawing area, and scales the signal to a suitable amplitude for display.

[0038] Standardized data objects undergo slow drift removal, noise smoothing, and dynamic range adjustment in sequence to optimize visualization and facilitate the generation of clear, aesthetically pleasing, publication-quality charts.

[0039] The display layer signal focuses on publishing-level visual optimization, which can also be achieved through morphological processing and nonparametric regression methods.

[0040] Morphological processing: A top-hat transformation is employed. Wide baseline fluctuations are located and stripped using structuring elements (length corresponding to physical time, e.g., 500 ms). This method is more physically targeted than the rolling median method for removing nonlinear drift.

[0041] Nonparametric regression: LOESS local weighted regression or Gaussian process regression is used for smoothing, which can automatically adjust the smoothing intensity according to the local data density, eliminating noise spikes without generating phase shift.

[0042] Step 2: Standardized data from each detection point are tested using multiple detection strategies to obtain each detection event. Each detection event is then scored for quality and labeled as qualified, questionable, or abnormal according to the score. The detection strategies include: threshold method, slope method, and template matching method. In one possible embodiment, in step 2, multiple detection strategies, such as thresholding, slope analysis, and template matching, are employed in parallel on the analysis layer signal.

[0043] The threshold method monitors whether the signal amplitude exceeds the set baseline. In practice, when monitoring the amplitude, if the signal exceeds N times the standard deviation of the baseline, it is marked as a candidate.

[0044] The slope method uses the first derivative of the signal rising edge to determine whether the signal has a sudden change. In practice, the slope method captures the first derivative of the signal rising edge and is very sensitive to small but rapid changes.

[0045] Template matching involves sliding a standard synaptic current curve across the input signal and calculating the correlation coefficient to monitor whether the signal shape becomes abnormal. In practice, the standard synaptic current curve in the template matching method can be a double exponential function.

[0046] Step 2 also includes: integrating the results of each detection strategy using a confidence-weighted fusion algorithm to obtain each detection event; Specifically, the confidence-weighted fusion algorithm can be: in, Indicates the overall confidence level. , and These are the weights for the threshold method, slope method, and template matching method, respectively. , and The confidence scores are calculated using the threshold method, slope method, and template matching method, respectively.

[0047] In practice, a score matrix can be established for each detection point. If all three detection strategies provide a signal at the same time point, the confidence level of that point is the highest. Employing a confidence-weighted fusion algorithm effectively filters out isolated noise spikes, improving the robustness of event detection in complex noisy environments.

[0048] Each detection event is scored based on signal-to-noise ratio, baseline stability, and waveform characteristics.

[0049] In practice, automated three-level quality scoring and labeling is performed: qualified, questionable, and abnormal.

[0050] Step 3: Statistical analysis is performed on each core calculation indicator based on the type of event labeling to obtain statistics. Based on the local changes of each statistics over time, a continuous time window representing the global characteristics of the statistics is found. Based on the data of the time window, standardized visualization charts are generated to analyze the results.

[0051] In one possible implementation, a standardized representative image with a fixed physical size scale and publication-grade style (e.g., 200ms corresponds to a specific pixel length) is generated using the display layer signal and a selected representative window, ensuring that the visualization results are consistent with the statistical conclusions and that there is direct comparability between different images.

[0052] In one possible embodiment, step 3 includes: Step 301: Calculate the core metrics, including event frequency, median event amplitude, and average decay time constant. Step 302, construct the cost function : ; Specifically, statistical analysis is performed on events based on their pass / fail rating. Event frequency is defined as the number of events per unit time (e.g., per second). The median amplitude is the median amplitude value of all events; this indicator is insensitive to outliers and more robustly reflects the amplitude of typical events. The mean decay time constant reflects channel dynamics. A statistical-window mapping relationship is then established: a quantitative correspondence is established between key statistical features (such as event frequency and median amplitude) and specific time periods in the original records to ensure that the visualization results accurately reflect the statistical conclusions.

[0053] Where ω is a candidate time window of length T, It is the deviation between the median of the indicator within the window and the global median. , and The preset weighting coefficients, It is the variance (noise level) of the signal within that window. This is a penalty factor that accumulates points based on the number of events marked as suspicious or abnormal within the window.

[0054] In one possible embodiment, to address the arbitrariness and potential bias of traditional manual selection of representative segments, this invention employs a multi-objective optimization model. Automatically search for the most representative time window in the entire record. This ensures that the visualization results accurately reflect the conclusions of the statistical analysis.

[0055] First, extract the global feature vector through the full analysis layer signal. This vector serves as the "anchor point" for window filtering, ensuring that the selected segments do not deviate from the overall experimental conclusions.

[0056] The calculation method includes statistical analysis of all events marked as "qualified" within the entire record. Indicators include: global frequency, global amplitude median, and global decay dynamics.

[0057] Quantification of statistical matching degree (weight 30%): measuring the local window Consistency with the global mean.

[0058] The system iterates through the signals using a sliding window, calculating the mean of events within each window. The smaller the difference between the mean and the mean, the higher the score. The higher.

[0059] Quantification of signal quality (weight 40%): Signal quality not only determines the readability of charts, but also relates to the error model. and .

[0060] Quantitative metric 1: Local signal-to-noise ratio stability. This involves calculating the stability of the non-event region within the calculation window. .

[0061] Quantitative metric 2: Baseline drift rate. The slope is calculated by performing a first-order linear fit on the window baseline. . The closer a value is to 0, the more level the baseline is, and the higher the score.

[0062] Quantitative Indicator 3: Burst Removal Rate. The number of abnormal transition points within the statistical window that are not identified as events but whose amplitude exceeds the background noise.

[0063] Event distribution uniformity quantification (weight 30%): Avoid selecting events that are too crowded (such as burst releases) or too sparse extreme segments.

[0064] Calculate the ratio of the standard deviation to the mean of the inter-event intervals (IEI) within the window. System search interval coefficient of variation. The segment that best approximates the expected physiological range. If the pattern of events conforms to the distribution model for this cell type, then a score is awarded. Reached its peak.

[0065] For abnormal events (such as events with excessive amplitude) in the window, a point deduction strategy is adopted instead of direct elimination. The window is allowed to contain a small number of imperfect events, but the quality problem is reflected by adjusting the score.

[0066] Step 303, find the cost function The smallest continuous time period is used to obtain the continuous time window. That is, the algorithm finds the continuous time window that best represents the global characteristics of each statistic, ensuring that the representative graph can represent the global average level and is also the cleanest segment in the whole.

[0067] This represents a leap from "experience-based manual selection" to "algorithmic target optimization." Through a three-pronged evaluation of statistical features, signal quality, and distribution logic, it fundamentally ensures the impartiality of scientific research images, guaranteeing that the final representative images are the "optimal subset" of the entire record in terms of probability distribution, thus resolving the long-standing risk of selection bias in electrophysiological research.

[0068] In one possible embodiment, step 3 is followed by: Step 4: Construct a six-dimensional error quantification model. The six-dimensional error quantification model is used to determine the observation indicators and related experimental factors corresponding to the errors in each dimension. By monitoring each observation indicator and related experimental factors, quantitative diagnosis of the input electrophysiological raw data file is performed. Error source tracing analysis: By applying the six-dimensional error quantification model and decoupling analysis of the physical characteristics of the raw signal, quantitative diagnosis of data quality is achieved.

[0069] The six-dimensional error quantification model includes: environmental / reagent preparation error model, electrode state error model, tissue state error model, cell state error model, recording system error model, and analysis algorithm error model.

[0070] The following table 1 shows: Table 1. Mapping of the six core dimensions to experimental error In one possible implementation, the observed metrics of the environmental / reagent preparation error model include: baseline root mean square noise and 50 Hz power frequency, and the associated experimental factors include: shielding effectiveness and internal fluid activity. The reagent preparation error quantification model analyzes abnormal event amplitude distribution (such as overall offset or abnormal dispersion) and combines metadata such as internal liquid ion composition, osmotic pressure, and pH to automatically diagnose whether there are deviations in internal liquid ion concentration, osmotic pressure, or pH.

[0071] The observation indicators of the electrode state error model include: the initial value of the series resistance and its offset over time. The related experimental factors include: electrode tip blockage and electrode offset. The electrode condition error model is based on the correlation analysis between electrode impedance and baseline noise and event detection rate, and automatically identifies electrode tip conditions such as electrode contamination, damage or poor sealing.

[0072] The observational indicators of the tissue state error model include: spontaneous fluctuation power spectrum and background noise intensity in the non-target frequency band, and the associated experimental factors include: brain slice activity and non-specific receptor background. The tissue state error model identifies brain slice hypoxia or mechanical damage by detecting abnormal high-frequency event patterns and baseline instability features (such as burst discharges) and combining parameters such as slice time and perfusion conditions.

[0073] The observation indicators of the cell state error model include: the absolute amplitude and variance of the clamping current, and the associated experimental factors include: cell membrane integrity and sealing quality. The cell state error model quantifies the systematic shift of cell state decay to statistical results by tracking changes in input resistance (Rin), capacitance (Cm), and series resistance (Rs) in real time.

[0074] The observed indicators for recording the system error model include: the residual capacitance transient signal after a voltage step, and the related experimental factors include: the adjustment of the Axopatch 200B compensation knob; Record system error models to automatically detect deviations introduced by environmental factors such as improper amplifier settings (e.g., filter cutoff frequency) and temperature fluctuations.

[0075] The observed metrics for analyzing the algorithm error model include: multi-strategy fusion detection confidence and dynamic fitting residuals, while the associated experimental factors include: template matching accuracy and signal-to-noise ratio.

[0076] The algorithm error model is analyzed to quantify the false positive / false negative rate of event detection and the representativeness bias of window selection.

[0077] In one possible embodiment, step 4 further includes: normalizing and mapping the observation indicators of the error quantification model for each dimension, determining the weight of each observation indicator according to the experiment type, and calculating the weighted sum of each observation indicator to obtain the comprehensive quality score of the input electrophysiological raw data file.

[0078] In practice, normalization mapping is achieved by using membership functions. Map the original indicators of each dimension to Interval score The specific method can be: (1) Descent-type exponential operator (used for) , , ): ,in These are physical measurement values. This is the physiological threshold for this indicator. This is the steepness coefficient. It is applicable to parameters whose quality monotonically decreases as the specification increases (such as noise and leakage current).

[0079] (2) Gaussian central operator (used for) , , ): ,in It is an ideal physical center. It is applicable to parameters with an optimal value range (such as dynamic time constant and compensation offset).

[0080] (3) Stepped threshold operator (for fast filtering): Used to set the "bottom line" of the experiment. When the index exceeds the fatal threshold, the score is directly reduced to zero (i.e., Grade C judgment).

[0081] Dynamic weight injection can automatically load weight vectors based on the predicted experiment type (such as IPSC). For example, increasing the IPSC value when recording. (Environment) and (Cell) weight.

[0082] Weighted calculation can be performed by Generate a comprehensive score from 0 to 100.

[0083] In one possible embodiment, step 4 is followed by: Step 5: Construct an error pattern-optimization measure knowledge base. The error pattern-optimization measure knowledge base records experimental operation suggestions in order of priority for each dimension of error determined based on historical data.

[0084] When the observed values ​​of the error quantification model in each dimension are lower than the preset safety threshold, experimental operation suggestions are issued based on the error pattern-optimization measures knowledge base, ordered by priority.

[0085] By comparing the scores of each dimension, the item with the lowest score is... This is determined to be the root cause of the current experiment. For example, if... If the value is significantly lower than expected, the system will determine that the equipment compensation is "misaligned" and trigger an optimization instruction to generate targeted experimental optimization suggestions.

[0086] Experimental optimization suggestions are based on error quantification results, using a rule engine (or combined with a machine learning model) for matching and reasoning. This engine has a built-in "error pattern - optimization measure" knowledge base, which can automatically generate targeted, priority-based specific experimental operation suggestions (e.g., "significant baseline drift; it is recommended to check the perfusion system and extend the cell stabilization time to X minutes"). The engine can be triggered not only after the experiment but also in a threshold-triggered mode. That is, when the real-time monitoring value of the Comprehensive Quality Score (GQS) falls below a preset safety threshold for a consecutive number of data blocks, the current analysis is immediately interrupted and the highest-priority optimization suggestion is displayed.

[0087] This refers to automatically converting errors and quality issues identified in data analysis into specific, actionable experimental parameters or operational suggestions to guide the next round of experiments, forming a continuous improvement cycle of "analysis-evaluation-optimization-experiment".

[0088] In one possible implementation, a structured analysis report package can be output. Key experimental optimization suggestions directly guide parameter adjustments and optimizations for the next round of experiments through a closed-loop feedback loop. When new experiments conducted following the suggestions generate data, it is input back into the system, thus forming a data-driven, continuous improvement positive loop of "analysis → optimization → experiment → reanalysis," ultimately achieving a step-by-step improvement in experimental quality.

[0089] Specifically, addressing the limitation of traditional analysis tools that "only diagnose but do not treat," this invention constructs an intelligent suggestion system based on a combination of rule engines and machine learning, transforming data analysis conclusions into actionable guidance for experimental optimization.

[0090] Error type-optimization measure mapping rule base: An expert database based on IF-THEN-ACTION logic to achieve accurate mapping from error patterns to operation instructions.

[0091] Pattern Matching (IF): Based on the six-dimensional score of module 6. For example, IF S5 (device item) < 0.6 AND signal rising edge overshoot.

[0092] Causal determination (THEN): Mapping physical failure. For example, if determined to be due to excessive Rs compensation or improper Lag settings.

[0093] Execution Command (ACTION): Generates specific suggestions. For example, a suggestion might be: Adjust the Lag knob of the Axopatch 200B clockwise.

[0094] Priority dynamic sorting algorithm: To ensure that the most "remedial" solutions are pushed, the system uses an improvement potential model to sort the suggestions.

[0095] Sorting score Error severity The degree to which the current error term deviates from the standard value ( The higher the score, the more serious the problem.

[0096] Feasibility of repair : Software / knob adjustable items (such as compensation adjustment) have a high weight (0.9); irreversible items (such as cell death, electrode blockage) have a low weight (0.1).

[0097] Expected improved returns : Simulate and calculate the improvement in Global Quality Score (GQS) after this fix.

[0098] Prioritize solutions that address the most critical issues and offer significant quality improvements with minimal knob adjustments.

[0099] Feedback loop: Record signal changes after suggestions are issued. If the score rises after the action is taken, the system strengthens the correlation weight of the "error-measure".

[0100] Co-evolution: By continuously accumulating repair trajectories under different cell types, the accuracy of recommendations is iteratively optimized.

[0101] This engine endows the software system with the intelligence of an "experimental collaborator," which can not only identify problems but also provide specific, prioritized improvement solutions, significantly accelerating the scientific research iteration process.

[0102] Example 2

[0103] Embodiment 2 provided by this invention is an embodiment of an automated closed-loop analysis system for multi-source electrophysiological recordings provided by this invention. Figure 2 This invention provides a structural diagram of an automated closed-loop analysis system for multi-source electrophysiological recordings, combined with... Figure 2As can be seen, the embodiment of this analysis system includes: an input unit, a core processing unit, an intelligent decision-making unit, and an output unit. The output is connected to new experimental inputs through a closed-loop feedback circuit.

[0104] The input unit is used to identify the file format of the input raw electrophysiological data file, extract the core electrophysiological information, and convert the core electrophysiological information into standardized data objects. The core processing unit is used to detect standardized data at each detection point using various detection strategies to obtain each detection event. After scoring each detection event, it is labeled as qualified, suspicious, or abnormal according to the score. The detection strategies include: threshold method, slope method, and template matching method.

[0105] In one possible embodiment, the core processing unit comprises five sequentially executed core algorithm modules, forming an automated analysis pipeline: a general parsing and standardization module, a dual-channel signal preprocessing module, a multi-strategy event detection and quality labeling module, a statistical analysis and time-series mapping module, and a standardized visualization generation module.

[0106] The intelligent decision-making unit is used to perform statistical analysis on various core calculation indicators based on the type of event labeling to obtain statistical quantities. Based on the local changes of each statistical quantity over time, it finds a continuous time window that represents the global characteristics of the statistical quantity. Based on the data in the time window, it generates standardized visualization charts and analysis results.

[0107] It is understood that the automated closed-loop analysis system for multi-source electrophysiological recordings provided by this invention corresponds to the automated closed-loop analysis method for multi-source electrophysiological recordings provided in the foregoing embodiments. The relevant technical features of the automated closed-loop analysis system for multi-source electrophysiological recordings can be referred to the relevant technical features of the automated closed-loop analysis method for multi-source electrophysiological recordings, and will not be repeated here.

[0108] This invention provides an embodiment of an automated closed-loop analysis system for multi-source electrophysiological recordings, representing a systematic upgrade from single-point analysis to end-to-end quality control: A six-dimensional error tracing system was constructed, which can systematically identify and quantify errors: Reagent preparation error: By analyzing the abnormal distribution of event amplitude and baseline, potential deviations in internal liquid ion concentration, osmotic pressure, or pH can be inferred.

[0109] Electrode fabrication error: Based on the impedance-noise correlation model, the impact of electrode tip condition (such as contamination or damage) on recording quality is automatically assessed.

[0110] Tissue state error: Identifying hypoxia or mechanical damage in brain slices through abnormal patterns of event frequency (such as burst discharge).

[0111] Cell health error: Based on the trends of input resistance (Rin) and capacitance (Cm), the systematic bias of cell state decay on statistical results is quantified.

[0112] Record system errors: Automatically detect deviations introduced by environmental factors such as improper amplifier settings (e.g., filter cutoff frequency) and temperature fluctuations.

[0113] Analysis of algorithm errors: quantification of false positive / false negative rate in event detection and representativeness bias in window selection.

[0114] A paradigm shift from passive processing to proactive optimization: An active control loop of real-time quality monitoring, early warning, and optimization suggestions was established. Signal quality indices (such as signal-to-noise ratio and baseline stability) were calculated in real time, and an early warning was issued immediately when the indices fell below the threshold. Based on statistical analysis of the current batch, targeted optimization suggestions were generated (such as "electrode impedance is too high, re-drawing is recommended" or "internal fluid osmotic pressure needs calibration"). Error analysis results from historical data were used to guide parameter settings in subsequent experiments, forming a data-driven experimental design optimization.

[0115] Through algorithmic quality assessment criteria and automated representative window selection mechanism; Multi-dimensional quantitative scoring of baseline stability, event quality, and other dimensions ensures objective and comparable quality assessments. The intelligent window selection algorithm comprehensively considers statistical matching degree, signal quality, and event distribution, and deducts penalty terms to automatically select the most representative 5-second window, ensuring that the visualization results truly reflect statistical characteristics. Fixed physical dimensions are achieved during image generation, ensuring direct comparability between different images and meeting publication standards.

[0116] From isolated analysis to integrated platform architecture innovation, providing end-to-end unified solutions: Multi-format adaptive parsing: Supports intelligent parsing and standardization of multiple electrophysiological file formats such as ABF, HEKA, and CED.

[0117] Dual-channel parallel processing: The analysis layer and the display layer are separated, which balances analysis accuracy and visualization effect.

[0118] Multi-strategy event detection fusion: It integrates multiple detection strategies such as thresholding, slope, and wavelet transform to improve the robustness of detection in complex noisy environments.

[0119] Closed-loop feedback mechanism: The analysis results are directly transformed into experimental optimization suggestions to guide the adjustment of experimental parameters in the next round.

[0120] The focus has shifted from laboratory tools to research infrastructure, designed as a scalable research infrastructure. Modular architecture: The script functions are designed in a modular way, which makes it easy to add new error analysis dimensions or detection algorithms.

[0121] Open API interface: Supports integration with other experimental equipment (such as perfusion systems, temperature controllers) or data platforms (such as Laboratory Information Management System LIMS).

[0122] Knowledge accumulation mechanism: As the amount of data used increases, the system can continuously optimize its error diagnosis model and optimization suggestion rule base, forming collective intelligence.

[0123] For the first time, a quantitative correlation was established between the physicochemical parameters of the experimental preparation process and the statistical characteristics of the final electrophysiological data, making "data quality issues" traceable to specific experimental operation steps and providing a scientific basis for precise optimization.

[0124] This invention provides an automated closed-loop analysis method and system for multi-source electrophysiological recordings. It establishes a universal analysis framework decoupled from the underlying data format to achieve one-click automated processing of multi-source electrophysiological data; designs a separate dual-channel signal processing architecture to optimize event detection accuracy and result visualization; constructs a systematic multi-dimensional quality assessment and error quantification system; innovates a statistical-window mapping mechanism to fundamentally ensure the consistency between statistical features and visualization results; and develops an intelligent experimental correction suggestion engine to form a closed-loop feedback from data analysis to experimental optimization, improving experimental efficiency and data quality.

[0125] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0126] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0131] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An automated closed-loop analysis method for multi-source electrophysiological recordings, characterized in that, The analytical method includes: Step 1: After identifying the file format of the input raw electrophysiological data file, extract the core electrophysiological information and convert the core electrophysiological information into a standardized data object; Step 2: Using multiple detection strategies, the standardized data at each detection point is detected to obtain each detection event. After quality scoring for each detection event, it is labeled as qualified, suspicious, or abnormal according to the score. The detection strategies include: threshold method, slope method, and template matching method. Step 3: Statistical analysis is performed on each core calculation indicator based on the type of the detected event label to obtain statistical quantities. Based on the local changes of each statistical quantity over time, a continuous time window representing the global characteristics of the statistical quantity is found. Based on the data in the time window, a standardized visualization chart is generated as the analysis result.

2. The analytical method according to claim 1, characterized in that, Step 1 includes: The standardized data object is obtained by constructing a parsing plugin with a built-in / extensible format; the core electrophysiological information includes: time series signal, sampling rate, channel units, and stimulus labels.

3. The analytical method according to claim 1, characterized in that, After obtaining the standardized data object in step 1, the method further includes: performing interference removal processing and visualization effect optimization processing on the standardized data object to obtain analysis layer signals and display layer signals; the analysis layer signals are used for data detection and analysis, and the display layer signals are used to generate the analysis results of the standardized visualization charts; The interference removal process includes: baseline correction, trend removal, light filtering, and zero-phase digital filtering; The optimized visualization process includes: slow drift removal, noise smoothing, and dynamic range adjustment.

4. The analytical method according to claim 1, characterized in that, In step 2, The threshold method monitors whether the signal amplitude exceeds the set baseline; the slope method monitors the first derivative of the rising edge of the signal to determine whether the signal has a sudden change; the template matching method slides a standard synaptic current curve across the input signal and calculates the correlation coefficient to monitor whether the shape of the signal is abnormal. Step 2 further includes: integrating the results of each detection strategy using a confidence-weighted fusion algorithm to obtain each detection event; Each detection event is scored based on signal-to-noise ratio, baseline stability, and waveform characteristics.

5. The analytical method according to claim 1, characterized in that, Step 3 includes: Step 301, calculate the core calculation indicators including: event frequency, median event amplitude, and average decay time constant; Step 302, construct the cost function : ; Where ω is a candidate time window of length T, It is the deviation between the median of the indicator within the window and the global median. , and The preset weighting coefficients, It is the variance of the signal within that window. This is a penalty factor that accumulates deductions based on the number of events marked as suspicious or abnormal within the window; Step 303: Find the cost function that makes the cost function The continuous time window is obtained by finding the smallest continuous time period.

6. The analytical method according to claim 1, characterized in that, Step 3 is followed by: Step 4: Construct a six-dimensional error quantification model. The six-dimensional error quantification model is used to determine the observation indicators and related experimental factors corresponding to the errors in each dimension. The input electrophysiological raw data file is quantitatively diagnosed by monitoring each observation indicator and related experimental factors. The six-dimensional error quantification model includes: environmental / reagent preparation error model, electrode state error model, tissue state error model, cell state error model, recording system error model, and analysis algorithm error model.

7. The analytical method according to claim 6, characterized in that, The observed indicators of the environmental / reagent preparation error model include: baseline root mean square noise and 50Hz power frequency, and the associated experimental factors include: shielding effectiveness and internal fluid activity; The observation indicators of the electrode state error model include: the initial value of the series resistance and its offset over time; the related experimental factors include: electrode tip blockage and electrode offset. The observation indicators of the tissue state error model include: spontaneous fluctuation power spectrum of non-target frequency band and background noise intensity, and the associated experimental factors include: brain slice activity and non-specific receptor background. The observation indicators of the cell state error model include: the absolute amplitude and variance of the clamping current, and the associated experimental factors include: cell membrane integrity and sealing quality. The observed indicators of the recorded system error model include: the residual capacitance transient signal after a voltage step, and the related experimental factors include: Axopatch 200B compensation knob adjustment; The observed metrics of the analysis algorithm error model include: multi-strategy fusion detection confidence and dynamic fitting residuals, and the associated experimental factors include: template matching accuracy and signal-to-noise ratio.

8. The analytical method according to claim 6, characterized in that, Step 4 further includes: normalizing and mapping the observation indicators of the error quantification model in each dimension, determining the weight of each observation indicator according to the experimental type, and calculating the weighted sum of each observation indicator to obtain the comprehensive quality score of the input electrophysiological raw data file.

9. The analytical method according to claim 1, characterized in that, Step 4 is followed by: Step 5: Construct an error pattern-optimization measure knowledge base, which records experimental operation suggestions ordered by priority for each dimension of error determined based on historical data. When the observed values ​​of the error quantification model in each dimension are lower than the preset safety threshold, experimental operation suggestions are issued based on the error pattern-optimization measures knowledge base, ordered by priority.

10. An automated closed-loop analysis system for multi-source electrophysiological recordings, characterized in that, The analysis system includes: an input unit, a core processing unit, and an intelligent decision-making unit; The input unit is used to identify the file format of the input raw electrophysiological data file, extract the core electrophysiological information, and convert the core electrophysiological information into a standardized data object. The core processing unit is used to detect the standardized data of each detection point using multiple detection strategies to obtain each detection event. After scoring each detection event, it is labeled as qualified, suspicious, or abnormal according to the score. The detection strategies include: threshold method, slope method, and template matching method. The intelligent decision-making unit is used to perform statistical analysis on each core calculation indicator based on the type of the detected event label to obtain statistical quantities, find a continuous time window representing the global characteristics of each statistical quantity based on the local changes of each statistical quantity over time, and generate standardized visualization charts based on the data in the time window.

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