A method and system for monitoring vital signs of an aeromedical evacuation patient

Abstract

The application discloses an aviation medical rescue patient vital sign monitoring method and system, relates to the medical sign detection technical field, and comprises the following steps: acquiring a physiological signal sequence and an aircraft operation parameter sequence; according to the physiological signal sequence and the aircraft operation parameter sequence, feature information of an abnormal disturbance is extracted, the feature information of the abnormal disturbance comprises a starting time, a duration, a frequency range or an amplitude; according to the feature information of the abnormal disturbance, the physiological signal sequence and the aircraft operation parameter sequence, time synchronization processing is carried out, and the correlation between the feature information of the abnormal disturbance and the aircraft operation parameter sequence is obtained; according to the correlation, the source of the abnormal disturbance is determined; if the source of the abnormal disturbance contains environmental interference, each physiological signal to be processed in the physiological signal sequence is subjected to differential processing, and the corresponding target physiological signal is obtained. The application realizes vital sign monitoring and improves accuracy.

Description

Technical Field

[0001] This invention relates to the field of medical vital sign detection technology, and in particular to a method and system for monitoring the vital signs of patients in air medical rescue. Background Technology

[0002] In the field of medical care, especially in challenging environments like air ambulance, continuous monitoring of patients' vital signs is crucial for ensuring their safety. However, the unique operating conditions of aircraft, such as the continuous mechanical vibrations generated by rotors and engines, and the electromagnetic interference emitted by airborne communication and navigation equipment, can severely affect the quality of acquired physiological signals. To address this signal interference problem, existing methods identify and filter out known interference signals with specific characteristics. After the monitoring equipment acquires the raw physiological signals, the program analyzes them, removing portions that match preset interference characteristics, thus outputting a relatively clear physiological waveform and reading.

[0003] However, in actual rescue and transport processes, the condition of critically ill patients is inherently unstable, and sudden emergencies may occur at any time during transport. Physical activity caused by changes in the patient's own pathological state will also produce strong motion artifacts in physiological signals. When a patient experiences epilepsy or chills, the signal artifacts generated by their muscle tremors may be very similar in frequency and amplitude to the mechanical vibration artifacts of the helicopter itself. In such cases, existing methods struggle to effectively distinguish the source of disturbance in the signal, potentially misinterpreting pathological state changes as environmental interference and filtering them out, or misinterpreting flight attitude changes as physiological abnormalities of the patient, resulting in low detection accuracy.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0005] The main objective of this invention is to propose a method and system for monitoring the vital signs of patients in air medical rescue, which can combine abnormal disturbance characteristic information to analyze the source of abnormal disturbances in order to achieve vital sign monitoring, thereby improving accuracy.

[0006] On one hand, embodiments of the present invention provide a method for monitoring the vital signs of patients in air medical rescue, including the following steps:

[0007] Acquire physiological signal sequences and aircraft operational parameter sequences;

[0008] Based on the physiological signal sequence and the aircraft operating parameter sequence, feature information of the abnormal disturbance is extracted, including start time, duration, frequency range or amplitude;

[0009] Based on the characteristic information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence, time synchronization processing is performed to obtain the correlation between the characteristic information of the abnormal disturbance and the aircraft operating parameter sequence;

[0010] Based on the aforementioned correlation, the source of the abnormal disturbance is determined;

[0011] If the source of the abnormal disturbance includes environmental interference, then each physiological signal to be processed in the physiological signal sequence is differentiated to obtain the corresponding target physiological signal.

[0012] In some embodiments, extracting feature information of abnormal disturbances based on the physiological signal sequence and the aircraft operating parameter sequence includes:

[0013] Identify abnormal perturbations in the physiological signal sequence;

[0014] Based on the aircraft operating parameter sequence, determine the characteristic range of environmental interference generated by the aircraft during the occurrence of abnormal disturbances;

[0015] Identify physiological signal components in the abnormal disturbance that do not match the characteristic range of the environmental interference;

[0016] Feature information of the abnormal perturbation is extracted from the physiological signal components.

[0017] In some embodiments, the step of performing time synchronization processing based on the characteristic information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence to obtain the correlation between the characteristic information of the abnormal disturbance and the aircraft operating parameter sequence includes:

[0018] The physiological signal sequence and the aircraft operating parameter sequence are compared for morphological similarity to obtain the morphological similarity comparison results.

[0019] Based on the characteristic information of the abnormal disturbance and the morphological similarity comparison results, the degree of morphological correlation between the abnormal disturbance in the physiological signal sequence and the aircraft operating parameter sequence is determined;

[0020] The degree of morphological correlation is used as the correlation between the characteristic information of the abnormal disturbance and the sequence of aircraft operating parameters.

[0021] In some embodiments, determining the source of the abnormal disturbance based on the correlation includes:

[0022] After the motion sensor is deployed, an environmental interference reference signal is collected through the motion sensor, which is deployed at the physiological signal collection location;

[0023] The environmental interference reference signal is separated from the physiological signal to be processed using an adaptive filtering method to obtain the residual physiological signal;

[0024] The residual physiological signals are subjected to feature analysis to obtain residual physiological signal features;

[0025] The source of the abnormal disturbance is determined based on the residual physiological signal characteristics and the correlation.

[0026] In some embodiments, determining the source of the abnormal disturbance based on the correlation includes:

[0027] Collect rotor speed and engine speed;

[0028] Calculate the rotor frequency based on the rotor speed;

[0029] Calculate the engine frequency based on the engine speed;

[0030] Calculate the trend of mechanical vibration variation based on the rotor frequency and the engine frequency;

[0031] Calculate the disturbance frequency range based on the rotor frequency and the engine frequency;

[0032] Based on the correlation, the dominant frequency of physiological signal perturbation is determined;

[0033] If the trend of change of the dominant frequency of the physiological signal disturbance is the same as the trend of change of the mechanical vibration, and the dominant frequency of the physiological signal disturbance is within the disturbance frequency range, then it is determined that the source of the abnormal disturbance includes environmental interference.

[0034] In some embodiments, the differential processing of each physiological signal to be processed in the physiological signal sequence to obtain the corresponding target physiological signal includes:

[0035] The physiological signal to be processed is decomposed into multiple signal components using a preset signal analysis method. The signal components have different frequency characteristics. The preset signal analysis method includes multi-resolution analysis or time-frequency analysis.

[0036] Aircraft operational status information is extracted from the aircraft operational parameter sequence, and the aircraft operational status information is time-synchronized with the physiological signal to be processed.

[0037] Based on the aircraft's operational status information and the frequency characteristics of the signal components, the source of the signal components is determined. The source of the signal components includes environmental interference and the patient's own pathological activities.

[0038] The signal components whose source is environmental interference are suppressed in order to retain the signal components whose source is the patient's own pathological activity;

[0039] The target physiological signal is obtained by reconstructing multiple signal components after suppression.

[0040] In some embodiments, determining the source of the signal component based on the aircraft operating status information and the frequency characteristics of the signal component includes:

[0041] Based on the frequency characteristics of the signal components, the target correlation between the signal components and the aircraft operating status information is calculated, and the target correlation includes time domain correlation and frequency domain correlation.

[0042] The source of the signal component is determined based on the target correlation.

[0043] In some embodiments, the method further includes:

[0044] The confidence level is obtained by performing a confidence assessment on the target physiological signal;

[0045] Based on the confidence level, the intensity of the inhibition treatment is adjusted, and the target physiological signal is updated;

[0046] Based on the updated target physiological signals, pathological activity indication information is generated.

[0047] In some embodiments, adjusting the intensity of the inhibition treatment and updating the target physiological signal based on the confidence level includes:

[0048] The intensity of the suppression treatment is divided into multiple candidate intensity levels;

[0049] Based on the confidence level, one intensity level is selected as the target intensity level from the plurality of candidate intensity levels;

[0050] The intensity of the suppression treatment is adjusted according to the target intensity level;

[0051] Based on the adjusted intensity of the suppression process, the signal components whose source is environmental interference are suppressed, and the target physiological signal is updated.

[0052] On the other hand, embodiments of the present invention provide a vital signs monitoring system for air medical rescue patients, including:

[0053] The data acquisition module is used to acquire physiological signal sequences and aircraft operating parameter sequences;

[0054] The disturbance identification module is used to extract the feature information of abnormal disturbances based on the physiological signal sequence and the aircraft operating parameter sequence. The feature information of abnormal disturbances includes start time, duration, frequency range or amplitude.

[0055] The time synchronization module is used to perform time synchronization processing based on the feature information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence to obtain the correlation between the feature information of the abnormal disturbance and the aircraft operating parameter sequence.

[0056] The source determination module is used to determine the source of the abnormal disturbance based on the correlation.

[0057] The signal processing module is used to perform differential processing on each physiological signal to be processed in the physiological signal sequence if the abnormal disturbance source includes environmental interference, so as to obtain the corresponding target physiological signal.

[0058] The embodiments of this application include at least the following beneficial effects: First, the embodiments of this application acquire physiological signal sequences and aircraft operating parameter sequences. Then, based on the physiological signal sequences and aircraft operating parameter sequences, feature information of abnormal disturbances is extracted. Next, based on the feature information of abnormal disturbances, physiological signal sequences, and aircraft operating parameter sequences, time synchronization processing is performed to obtain the correlation between the feature information of abnormal disturbances and the aircraft operating parameter sequences. Finally, based on the correlation, the source of abnormal disturbances is determined. If the source of abnormal disturbances includes environmental interference, each physiological signal to be processed in the physiological signal sequence is differentiated to obtain the corresponding target physiological signal. Thus, it is possible to combine the feature information of abnormal disturbances to analyze the source of abnormal disturbances in order to achieve vital sign monitoring, thereby improving accuracy.

[0059] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart of a method for monitoring the vital signs of patients in air medical rescue according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the structure of an air medical rescue patient vital signs monitoring system according to an embodiment of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0064] In the field of medical care, especially in challenging environments like air ambulance services, continuous monitoring of patients' vital signs is crucial for ensuring their safety. However, the unique operating conditions of aircraft, such as the continuous mechanical vibrations from rotors and engines, and the electromagnetic interference emitted by onboard communication and navigation equipment, can severely affect the quality of collected physiological signals. Existing monitoring equipment typically incorporates signal processing programs to filter out these known environmental interferences. A more challenging issue is that when patients move due to changes in their condition (such as epileptic seizures or severe chills), the resulting signal artifacts can have frequencies and amplitudes very similar to the aircraft's own environmental interference. This makes it difficult for monitoring systems to distinguish whether signal disturbances originate from the external environment or from the patient's own pathological changes. This can lead to the erroneous filtering of critical physiological signals reflecting worsening conditions or misinterpreting environmental noise as pathological abnormalities, resulting in false alarms and seriously impacting medical decisions and patient safety.

[0065] For example, in air medical evacuation missions, such as when using medical rescue helicopters to transport critically ill patients, continuous monitoring of the patient's vital signs is a crucial aspect of ensuring their safety. Typically, multiple physiological parameter sensors, such as ECG electrodes, pulse oximeters, and blood pressure cuffs, are attached to the patient. These sensors are connected to onboard monitoring equipment via cables. The monitoring equipment can collect and display key information such as the patient's heart rate, ECG waveform, blood oxygen saturation, and blood pressure in real time, allowing accompanying medical personnel to monitor changes in the patient's condition and providing early warning data for ground hospital preparations. The goal of the entire process is to achieve stable, accurate, and continuous observation of the patient's vital signs in the turbulent and confined cabin environment.

[0066] However, aircraft themselves are complex sources of interference. During flight, helicopters generate strong and continuous mechanical vibrations from their rotors, engines, and fuselage structure. These vibrations are transmitted through the cabin floor and stretcher, ultimately reaching the patient's body and connected sensors. This mechanical vibration directly introduces numerous motion artifacts into weak physiological electrical signals such as electrocardiograms and blood oxygen saturation, causing severe distortion or even unrecognizable waveforms on monitors. Heart rate and blood oxygen saturation values ​​also exhibit large, irregular fluctuations, significantly impacting the judgment of medical personnel. Simultaneously, onboard communication equipment, navigation systems, and other electronic devices radiate strong electromagnetic waves during operation. These waves also interfere with the sensitive physiological signal acquisition process, further reducing the reliability and validity of the monitoring information.

[0067] To address signal interference caused by the flight environment, existing monitoring equipment typically incorporates a signal processing program. The core function of this program is to identify and filter out known interference signals with specific characteristics. For example, the program pre-sets vibration signal characteristics related to helicopter rotor speed and engine frequency, as well as electromagnetic interference characteristics related to airborne radio communication frequencies. When the monitoring equipment acquires raw physiological signals, the program first analyzes the signals, separating out portions that match the preset interference characteristics, thus outputting a relatively clean and clear physiological waveform and reading. This processing method is indeed effective in dealing with fixed and predictable environmental interference, allowing medical personnel to obtain reliable vital sign data during most stable flight phases.

[0068] However, a more challenging situation arises during actual rescue and transport procedures. The condition of critically ill patients is inherently unstable, and sudden emergencies can occur at any time during transport. For example, a patient with a traumatic brain injury might suddenly experience epilepsy, leading to frequent convulsions and tremors throughout the body; or a patient in shock due to excessive blood loss might experience severe chills due to hypothermia. These physical activities caused by changes in the patient's own pathological state will also produce strong motion artifacts in the physiological signals.

[0069] This raises a new and deeper problem. When a patient experiences a seizure or chills, the signal artifacts generated by their muscle tremors can be very similar in frequency and amplitude to the mechanical vibration artifacts of a helicopter. In this situation, the signal processing program in the monitoring equipment, originally designed to filter out environmental interference, faces a "recognition dilemma." It cannot effectively distinguish whether the disturbance in the signal originates from "external aircraft vibration" or "internal patient lesions." As a result, the program may mistakenly treat key signal features indicating a rapid deterioration of the condition caused by seizures or chills as ordinary environmental interference and filter them out. The final result presented to medical staff may be a seemingly smooth, but actually masking, "normal" ECG waveform. Conversely, the program may also misinterpret transient, intense vibrations caused by special flight attitude changes (such as sudden strong turbulence) as physiological abnormalities in the patient, triggering false alarms and leading to unnecessary interventions and wasted time.

[0070] In the context of air medical rescue, the signal processing programs built into existing monitoring equipment are unable to effectively identify and distinguish motion artifacts caused by the patient's own pathological causes (such as epilepsy or chills) when filtering out environmental interference such as aircraft vibration and electromagnetic interference. These artifacts have similar signal characteristics to environmental interference and may lead to the erroneous filtering of key physiological signals reflecting the deterioration of the condition as environmental noise, or the misjudgment of environmental noise as pathological signals, resulting in low detection accuracy.

[0071] In view of this, this application acquires physiological signal sequences and aircraft operating parameter sequences, extracts feature information of abnormal disturbances based on both, and then performs time synchronization processing to obtain the correlation between abnormal disturbances and aircraft operating parameters. Based on this correlation, the source of the abnormal disturbance is determined; if it is determined to be environmental interference, the physiological signals are processed differentially. This method effectively solves the problem in existing technologies where it is difficult to distinguish between environmental interference (such as mechanical vibration and electromagnetic interference) generated by aircraft operation and physiological signal artifacts caused by the patient's own pathological activities (such as epilepsy and chills) in an aeromedical rescue environment. Through accurate judgment and targeted processing of the disturbance source, this application avoids the erroneous filtering of key physiological signals reflecting the deterioration of the patient's condition or the misjudgment of environmental interference as physiological abnormalities, thereby significantly improving the accuracy and reliability of vital sign monitoring data.

[0072] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0073] Figure 1 This is an optional flowchart of a method for monitoring the vital signs of patients in air medical rescue provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0074] Step S101: Obtain physiological signal sequences and aircraft operation parameter sequences;

[0075] Step S102: Extract the feature information of the abnormal disturbance based on the physiological signal sequence and the aircraft operation parameter sequence. The feature information of the abnormal disturbance includes the start time, duration, frequency range or amplitude.

[0076] Step S103: Based on the characteristic information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence, perform time synchronization processing to obtain the correlation between the characteristic information of the abnormal disturbance and the aircraft operating parameter sequence;

[0077] Step S104: Determine the source of the abnormal disturbance based on the correlation;

[0078] Step S105: If the source of the abnormal disturbance includes environmental interference, then perform differential processing on each physiological signal to be processed in the physiological signal sequence to obtain the corresponding target physiological signal.

[0079] Steps S101 to S105 shown in the embodiments of this application can combine abnormal disturbance feature information to analyze the source of abnormal disturbance in order to achieve vital sign monitoring, thereby improving accuracy.

[0080] In some embodiments, steps S101-S105 may first acquire physiological signal sequences and aircraft operating parameter sequences. For example, physiological signal sequences can be acquired in real time using various physiological sensors connected to the patient, such as an electrocardiogram (ECG) sensor for acquiring ECG signals, an electroencephalogram (EEG) sensor for acquiring EEG signals, and an electromyography (EMG) sensor for acquiring EMG signals. These sensors convert analog signals into digital signals and transmit them to the data processing unit in time-series format. Aircraft operating parameter sequences can be acquired using the aircraft's built-in flight data recorder (FDR) or a dedicated aircraft condition monitoring system. For example, relevant operating parameters can be read from the aircraft's bus system (such as ARINC 429 or MIL-STD-1553B), or acquired using devices such as vibration sensors and accelerometers installed at different locations on the aircraft. These data are also recorded and transmitted in time-series format.

[0081] Understandably, physiological signal sequences refer to continuously collected patient vital sign data over a period of time, such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), respiratory waveforms, and pulse waves. These signals are typically stored and processed in the form of digital sequences. Aircraft operating parameter sequences refer to aircraft operating status data recorded within the same time period, such as flight speed, altitude, attitude angles (pitch, roll, yaw), engine speed, rotor speed, and vibration sensor data. These parameters can reflect the characteristics of environmental disturbances generated by the aircraft.

[0082] Then, based on the physiological signal sequence and the aircraft operating parameter sequence, the characteristic information of the abnormal disturbance is extracted. This characteristic information includes the start time, duration, frequency range, or amplitude. For example, spectral analysis of the physiological signal sequence can identify abnormal energy peaks occurring within a specific frequency range, and their corresponding start time, duration, and amplitude can be recorded. Alternatively, a threshold can be set; when the instantaneous amplitude or rate of change of the physiological signal exceeds a preset threshold, it is marked as an abnormal disturbance, and its characteristic information is extracted. Furthermore, the aircraft operating parameter sequence can be combined; for example, when the aircraft experiences severe vibration, the presence of corresponding abnormal fluctuations in the physiological signal sequence can be analyzed simultaneously to extract abnormal disturbance characteristics related to aircraft operation.

[0083] Next, based on the characteristic information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence, time synchronization processing is performed to obtain the correlation between the characteristic information of the abnormal disturbance and the aircraft operating parameter sequence. Time synchronization processing is a key step to ensure that abnormal disturbances in the physiological signals and aircraft operating parameters can be accurately correlated. For example, a precise timestamp alignment method can be used to establish a one-to-one correspondence between data points in the physiological signal sequence and the aircraft operating parameter sequence. If an abnormal disturbance is detected in the physiological signal sequence at a certain time point, the operating status of the aircraft operating parameter sequence at the same time point or in a similar time period can be searched to analyze whether there is a correlation between the two. This correlation can be qualitative, such as "when the aircraft experiences turbulence, high-frequency artifacts appear in the physiological signals"; or it can be quantitative, such as measuring the degree of correlation between abnormal disturbances in the physiological signals and specific aircraft operating parameters by calculating a correlation coefficient.

[0084] Finally, based on the correlation, the source of the abnormal disturbance is determined. For example, if an abnormal disturbance in the physiological signal is found to be highly correlated with drastic changes in aircraft engine speed, and its frequency characteristics are consistent with the engine vibration frequency range, it can be preliminarily determined that the source of the abnormal disturbance includes environmental interference. Conversely, if the abnormal disturbance has a weak correlation with aircraft operating parameters, or its characteristics do not match known environmental interference characteristics, it may indicate that the disturbance originates from the patient's own pathological activity. Based on the determined source of the abnormal disturbance, if the source includes environmental interference, each physiological signal to be processed in the physiological signal sequence is differentially processed to obtain the corresponding target physiological signal. The purpose of differential processing is to filter out environmental interference while preserving the patient's true physiological information to the greatest extent possible. For example, based on the determined environmental interference characteristics, signal processing techniques such as adaptive filtering, wavelet denoising, and independent component analysis (ICA) can be used to selectively suppress or remove components in the physiological signal caused by environmental interference. Unlike traditional methods, this embodiment only performs differentiated processing after determining that the source of abnormal disturbance includes environmental interference. This avoids misjudging signals caused by the patient's own pathological activities as environmental interference and filtering them out incorrectly, thereby obtaining more accurate and reliable target physiological signals and providing medical staff with precise patient vital sign data.

[0085] Through the above technical solution, this embodiment introduces aircraft operating parameter sequences as a key basis for determining the source of abnormal disturbances. By employing time synchronization processing and correlation analysis, it accurately distinguishes the source of interference, avoiding the erroneous filtering out of key signal features reflecting a rapid deterioration of the patient's condition, caused by changes in the patient's own pathological state, as ordinary environmental interference. Simultaneously, it avoids misinterpreting instantaneous strong vibrations caused by specific flight attitude changes as physiological abnormalities in the patient. In this way, this embodiment provides more accurate and reliable patient vital sign data, significantly improving the safety and effectiveness of air medical rescue, providing medical personnel with precise decision-making basis, and thus better protecting patient safety in the complex and ever-changing air medical rescue environment.

[0086] In some embodiments, step S102, extracting feature information of abnormal disturbances based on physiological signal sequences and aircraft operating parameter sequences, may include, but is not limited to, the following steps:

[0087] Identify abnormal perturbations in physiological signal sequences;

[0088] Based on the aircraft operating parameter sequence, determine the characteristic range of environmental disturbances generated by the aircraft during the occurrence of abnormal disturbances;

[0089] Identify physiological signal components in abnormal disturbances that do not match the characteristic range of environmental interference;

[0090] Extract feature information of abnormal perturbations from physiological signal components.

[0091] In some embodiments, abnormal perturbations in the physiological signal sequence can be identified first. For example, various signal processing techniques can be employed. For instance, a preset threshold can be set, and changes in the amplitude, frequency, or morphology of the physiological signal exceeding this threshold are identified as abnormal perturbations. Statistical methods, such as abnormal changes in indicators like mean, variance, kurtosis, or skewness, can also be used to identify perturbations. Machine learning models can also be employed, trained on historical data to identify abnormal patterns in physiological signals.

[0092] Then, based on the aircraft's operating parameter sequence, the characteristic range of environmental disturbances generated by the aircraft during abnormal disturbances is determined. For example, the aircraft's operating status within a specific time period can be analyzed, such as parameters like flight altitude, speed, attitude, engine speed, rotor speed, vibration level, or cabin pressure. From this, the types of environmental disturbances that may occur during this period and their corresponding characteristic ranges can be inferred, such as specific vibration frequency ranges or pressure fluctuation amplitudes. When an aircraft takes off, lands, turns, or encounters turbulence, its operating parameters change significantly, and these changes may lead to specific environmental disturbances, the characteristic range of which can be determined in advance through experiments or modeling.

[0093] Further identification of physiological signal components within abnormal disturbances that do not match the characteristic range of environmental interference is necessary to distinguish components of the physiological signal caused by environmental interference from those caused by the patient's own physiological activities. Identified abnormal disturbances in physiological signals can be compared with the characteristic range of environmental interference determined based on aircraft operating parameters. If the characteristics of the abnormal disturbances in physiological signals (such as frequency, amplitude, and duration) are inconsistent with or do not match the characteristic range of environmental interference, then this portion of the physiological signal is considered more likely to originate from the patient's own physiological activities rather than environmental interference. For example, if aircraft vibration is mainly concentrated in a low-frequency range, while the abnormal disturbances in physiological signals exhibit high-frequency characteristics, then this high-frequency component is identified as a physiological signal component that does not match the environmental interference.

[0094] Finally, feature information of abnormal perturbations is extracted from the physiological signal components. This feature information may include, but is not limited to, the start time, duration, frequency range, or amplitude of the abnormal perturbation. This feature information is an important basis for subsequent determination of the source of abnormal perturbations and for differentiated processing.

[0095] This embodiment identifies abnormal disturbances in physiological signal sequences and simultaneously combines these with aircraft operational parameter sequences to determine the characteristic range of environmental interference. This allows for the differentiation between abnormal disturbances in physiological signals and environmental interference generated by aircraft operation. By identifying physiological signal components that do not match the characteristic range of environmental interference, false disturbances caused by aircraft operation can be effectively eliminated, ensuring that the subsequently extracted abnormal disturbance feature information more accurately reflects the patient's physiological state. This avoids misjudging environmental interference as patient physiological abnormalities and improves the accuracy of monitoring.

[0096] Through the above technical solution, this embodiment can effectively distinguish between environmental interference generated by aircraft operation and the patient's actual physiological abnormalities. This significantly improves the accuracy and reliability of the extracted abnormal disturbance feature information, reduces the false alarm rate caused by environmental interference, and thus provides a purer and more accurate data foundation for subsequent abnormal disturbance source judgment and physiological signal processing, ensuring the effectiveness of monitoring the patient's vital signs during air medical rescue.

[0097] In some embodiments, in step S103, time synchronization processing is performed based on the characteristic information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence to obtain the correlation between the characteristic information of the abnormal disturbance and the aircraft operating parameter sequence. This may include, but is not limited to, the following steps:

[0098] Morphological similarity comparison was performed on physiological signal sequences and aircraft operating parameter sequences to obtain morphological similarity comparison results;

[0099] Based on the characteristic information of abnormal disturbances and the morphological similarity comparison results, the degree of morphological correlation between abnormal disturbances in physiological signal sequences and aircraft operating parameter sequences is determined.

[0100] The degree of morphological correlation is used as a characteristic information of abnormal disturbances and the correlation between them and the sequence of aircraft operating parameters.

[0101] In some embodiments, morphological similarity comparisons can be performed on physiological signal sequences and aircraft operational parameter sequences to obtain morphological similarity comparison results. For example, a correlation can be established by analyzing the similarity of the shape, pattern, or trend of the two sequences in the time dimension. This can be achieved using various signal processing techniques; for instance, cross-correlation analysis can be used to quantify the similarity of the two sequences at different time lags, or the Dynamic Time Warping (DTW) algorithm can be used to find the optimal nonlinear time alignment path between the two sequences, thereby calculating their morphological distance. Furthermore, time-frequency analysis methods such as wavelet coherence analysis can be used to evaluate the synchronicity and similarity of the two sequences at different frequency scales. The morphological similarity comparison results are indicators that quantify this degree of similarity, such as cross-correlation coefficients, DTW distance, or coherence coefficients.

[0102] Then, based on the characteristic information of the anomalous disturbances and the morphological similarity comparison results, the degree of morphological correlation between the anomalous disturbances in the physiological signal sequence and the aircraft operating parameter sequence is determined. The characteristic information of the anomalous disturbances, such as their start time, duration, frequency range, or amplitude, can be used to limit the scope or weight of the comparison, making the comparison process more targeted. For example, if it is known that the anomalous disturbances mainly occur within a specific frequency range, the comparison can focus on the morphological features within that frequency range. The degree of morphological correlation is a quantification of the morphological matching degree between the anomalous disturbances in the physiological signal sequence and the aircraft operating parameter sequence; the higher the value, the stronger the morphological correlation between the two. Finally, the degree of morphological correlation is used as the correlation between the characteristic information of the anomalous disturbances and the aircraft operating parameter sequence.

[0103] This embodiment, by comparing the morphological similarity of physiological signal sequences and aircraft operating parameter sequences, can delve deeper into the intrinsic relationship between the two in the temporal dimension. Traditional time synchronization processing may only focus on whether the time points of events are consistent, ignoring the morphological characteristics of the signals themselves. By introducing morphological similarity comparison, it is possible to more accurately identify whether there is a structural or pattern-based correspondence between abnormal disturbances in physiological signals and changes in aircraft operating parameters. For example, if a specific oscillation pattern appears in the physiological signal, and a vibration pattern with a similar frequency and amplitude also appears in the aircraft operating parameters, then morphological similarity comparison can effectively capture this deep-seated correlation, thereby providing a more reliable basis for subsequent determination of the source of abnormal disturbances.

[0104] Through the above technical solution, this embodiment can establish a more accurate and refined correlation between abnormal physiological signal disturbances and aircraft operating parameters. This correlation analysis based on morphological similarity helps distinguish between physiological signal changes caused by aircraft environmental interference and those caused by the patient's own pathological activities, thereby improving the accuracy and reliability of determining the source of abnormal disturbances. This effectively avoids misjudgments caused by simple time synchronization, providing more precise data support for monitoring patient vital signs in air medical rescue.

[0105] In some embodiments, in step S104, determining the source of the abnormal disturbance based on correlation may include, but is not limited to, the following steps:

[0106] After deploying motion sensors, environmental interference reference signals are collected through the motion sensors, which are deployed at the physiological signal collection locations.

[0107] An adaptive filtering method is used to separate the environmental interference reference signal from the physiological signal to be processed, thereby obtaining the residual physiological signal;

[0108] The residual physiological signals were analyzed to obtain their characteristics.

[0109] The source of the abnormal disturbance was determined based on the characteristics and correlations of the residual physiological signals.

[0110] In some embodiments, relying solely on the correlation between physiological signal sequences and aircraft operating parameter sequences may not accurately distinguish between environmental disturbances and abnormalities caused by the patient's own physiological activities, especially in the complex and ever-changing environment of aeromedical care. Environmental disturbances may share characteristics with certain physiological signals, leading to misjudgments. Therefore, introducing and separating environmental disturbance reference signals can improve the accuracy of identifying the source of abnormal disturbances. After deploying motion sensors, environmental disturbance reference signals can be collected first. A motion sensor is a device capable of sensing and measuring the motion or vibration of an object, such as an accelerometer, gyroscope, or vibration sensor. Deployed at the physiological signal acquisition location, it aims to ensure that the collected environmental disturbance reference signals accurately reflect the environmental disturbances affecting the physiological signals. For example, if the physiological signal to be processed is an electrocardiogram (ECG) signal, the motion sensor can be fixed to the patient's chest or near the ECG electrodes to capture environmental noise or vibration signals directly caused by aircraft operation (such as engine vibration, rotor rotation, airflow disturbances, etc.). Environmental disturbance reference signals refer to these signals directly generated by aircraft operation that are similar to environmental disturbance components that may exist in the physiological signals.

[0111] Then, an adaptive filtering method is used to separate the environmental interference reference signal from the physiological signal to be processed, obtaining the residual physiological signal. Adaptive filtering is a signal processing method that automatically adjusts filter parameters based on the statistical characteristics of the input signal, aiming to effectively separate the target signal or interference signal from a mixed signal. In practical applications, algorithms such as Least Mean Square (LMS) and Recursive Least Squares (RLS) can be used as specific implementations of the adaptive filtering method. By using the environmental interference reference signal as a reference input, the adaptive filter can estimate and remove the environmental interference component from the physiological signal to be processed, thereby obtaining the residual physiological signal. The residual physiological signal is the physiological signal after environmental interference suppression; its environmental interference component is significantly suppressed, thus more closely resembling the pure patient's physiological signal.

[0112] Next, feature analysis is performed on the residual physiological signals to extract key information related to the patient's pathological activities. Feature analysis can include time-domain analysis (e.g., calculating mean, variance, peak value, waveform characteristics, etc.), frequency-domain analysis (e.g., analyzing spectrum, power spectral density, dominant frequency, etc.), or time-frequency analysis (e.g., wavelet transform, short-time Fourier transform, etc.). This yields residual physiological signal characteristics, such as QRS complex characteristics of ECG signals, periodic characteristics of respiratory signals, or pulse wave characteristics of blood oxygen saturation signals, which can more accurately reflect the patient's true physiological state. Finally, by combining these residual physiological signal characteristics and the correlation between the feature information of abnormal disturbances and the aircraft operating parameter sequence, the source of the abnormal disturbance can be comprehensively determined.

[0113] This embodiment deploys motion sensors at physiological signal acquisition locations to directly acquire environmental interference reference signals. An adaptive filtering method is then used to separate these reference signals from the physiological signals to be processed, resulting in a purer residual physiological signal. This direct environmental interference measurement and removal mechanism allows for more accurate identification of any remaining abnormalities in the residual physiological signal during subsequent feature analysis. Furthermore, by combining the correlation between the original physiological signal anomalies and aircraft operating parameters, dual verification can be performed, effectively distinguishing whether the abnormal disturbance originates from environmental interference or the patient's own pathological activity. This method avoids misjudgments that may result from relying solely on correlation, significantly improving the accuracy and reliability of determining the source of abnormal disturbances.

[0114] To illustrate this technical solution more clearly, a specific example is used below. Suppose that during an air medical evacuation mission, real-time monitoring of the patient's electrocardiogram (ECG) signal is required. To accurately distinguish the impact of helicopter vibration on the ECG signal from abnormalities in the patient's own cardiac activity, a miniature accelerometer can be placed on the patient's chest near the ECG electrodes as a motion sensor. This accelerometer continuously collects vibration signals generated during helicopter flight, serving as a reference signal for environmental interference. Simultaneously, the ECG acquisition device acquires the patient's raw ECG signal as the physiological signal to be processed. In the data processing stage, an adaptive filter based on the LMS algorithm is used, with the vibration signal collected by the accelerometer as the reference input, to filter the raw ECG signal, separating and removing vibration interference components to obtain the residual ECG signal. Subsequently, QRS complex detection and heart rate variability analysis are performed on the residual ECG signal to obtain its characteristics. If the residual ECG signal shows a clear abnormality (e.g., persistent arrhythmia), and this abnormality is not significantly correlated with aircraft operating parameters such as helicopter engine speed or rotor frequency, it can be determined that the abnormal disturbance mainly originates from the patient's own pathological activity. Conversely, if the original ECG signal contains anomalies that are highly correlated with the helicopter's vibration frequency, but after adaptive filtering, the anomalies in the residual ECG signal are significantly suppressed or eliminated, it can be determined that the abnormal disturbances mainly originate from environmental interference.

[0115] Through the above technical solution, this embodiment can effectively reduce the contamination of physiological signals by environmental noise by actively measuring and suppressing environmental interference, making subsequent monitoring and analysis of the patient's vital signs more accurate. This is crucial for air medical rescue scenarios, because during flight, environmental interference is complex and intense. Accurately identifying the source of abnormal disturbances can prevent environmental interference from being misjudged as a deterioration of the patient's condition, thereby reducing unnecessary medical interventions and ensuring a timely response to the patient's true pathological activities, significantly improving the quality and safety of air medical rescue.

[0116] In some embodiments, in step S104, determining the source of the abnormal disturbance based on correlation may include, but is not limited to, the following steps:

[0117] Collect rotor speed and engine speed;

[0118] Calculate the rotor frequency based on the rotor speed;

[0119] Calculate the engine frequency based on the engine speed;

[0120] Calculate the trend of mechanical vibration variation based on rotor frequency and engine frequency;

[0121] Calculate the disturbance frequency range based on the rotor frequency and engine frequency;

[0122] Based on the correlation, determine the dominant frequency of physiological signal perturbation;

[0123] If the trend of change of the dominant frequency of physiological signal disturbance is the same as that of mechanical vibration, and the dominant frequency of physiological signal disturbance is within the disturbance frequency range, then the source of abnormal disturbance is determined to include environmental interference.

[0124] In some embodiments, due to the complex environment of air medical evacuation, the sources of abnormal disturbances can be diverse, including the aircraft's own mechanical vibrations, airflow disturbances, electromagnetic interference, and the patient's own pathological activities. Failure to accurately distinguish these sources, particularly misinterpreting mechanical vibrations generated by the aircraft itself as physiological abnormalities, could lead to inaccurate assessments of the patient's vital signs or unnecessary interventions. To address this, rotor speed and engine speed can be collected first, exemplarily through real-time acquisition using speed sensors installed at corresponding locations on the aircraft. These sensors convert mechanical rotational motion into electrical signals, which are then acquired by the data acquisition system.

[0125] Then, based on the rotor speed, the rotor frequency is calculated, and based on the engine speed, the engine frequency is calculated. This can be achieved by converting the collected speed values ​​to revolutions per second (Hz), for example, dividing the speed (RPM) by 60 yields the frequency (Hz). Next, based on the rotor and engine frequencies, the trend of mechanical vibration variation is calculated. This can be done by analyzing the changes in rotor and engine frequencies over time, for example, by performing time-series analysis, trend fitting, or differential processing on these frequencies to capture their rising, falling, or stable trends. Based on the rotor and engine frequencies, the disturbance frequency range is calculated. This can be determined based on the typical operating frequency range of the rotor and engine, as well as their potential harmonic frequencies. This range represents the frequency interval within which aircraft mechanical vibrations may affect physiological signals. Finally, based on correlation, the dominant frequency of physiological signal disturbance is determined. This can be achieved by combining the correlation obtained after time synchronization processing to perform spectral analysis on abnormal disturbances in the physiological signal sequence to identify the frequency components with the most concentrated energy. If the trend of change of the dominant frequency of physiological signal disturbance is the same as that of mechanical vibration, and the dominant frequency of physiological signal disturbance is within the disturbance frequency range, then the source of abnormal disturbance is determined to include environmental interference.

[0126] To illustrate this technical solution more clearly, a specific example is used below. Assume a helicopter is performing a medical transport mission, and the patient's vital signs monitoring equipment is collecting electrocardiogram (ECG) signals. To accurately identify environmental interference, the system first collects the helicopter's rotor speed and engine speed in real time. For example, the rotor speed sensor and engine speed sensor output corresponding RPM data. Based on this speed data, the system calculates the rotor frequency and engine frequency in real time and further analyzes the changes of these frequencies over time to obtain the trend of mechanical vibration. Simultaneously, based on the helicopter model and operating characteristics, a disturbance frequency range is pre-determined, covering the vibration frequencies and harmonics that the rotor and engine may generate. When an abnormal disturbance is detected in the ECG signal, the system combines the previous time synchronization processing results (i.e., the correlation between the ECG signal disturbance and the aircraft's operating parameters) to perform spectral analysis on the ECG disturbance to determine its dominant frequency. Subsequently, the system compares the trend of the dominant frequency of this ECG disturbance with the calculated trend of mechanical vibration and checks whether the dominant frequency falls within the preset disturbance frequency range. For example, if the dominant frequency of the ECG signal disturbance is 15Hz, and its trend is consistent with the upward trend of the rotor speed, and 15Hz is within the frequency range that rotor vibration may produce, then the system will determine that the source of the abnormal disturbance includes environmental interference. In this way, ECG artifacts caused by helicopter vibration can be effectively distinguished from the patient's actual abnormal cardiac activity, thereby ensuring accurate assessment of the patient's vital signs.

[0127] Through the above technical solution, this embodiment can accurately identify environmental interference caused by vibrations from mechanical components such as aircraft rotors and engines on the patient's physiological signals. This accurate identification avoids misjudging environmental interference as the patient's own pathological activity, thereby improving the accuracy and reliability of vital sign monitoring. Furthermore, by clearly distinguishing environmental interference, more accurate input can be provided for subsequent differential processing of physiological signals, ensuring that the final target physiological signal more accurately reflects the patient's physiological state, avoiding the risk of misdiagnosis or missed diagnosis due to environmental interference, which is of great significance for decision-making in air medical care.

[0128] In some embodiments, step S105 involves differential processing of each physiological signal to be processed in the physiological signal sequence to obtain the corresponding target physiological signal, which may include, but is not limited to, the following steps:

[0129] Step S201: Decompose the physiological signal to be processed into multiple signal components using a preset signal analysis method. The signal components have different frequency characteristics. The preset signal analysis method includes multi-resolution analysis or time-frequency analysis.

[0130] Step S202: Extract aircraft operating status information from the aircraft operating parameter sequence. The aircraft operating status information is time-synchronized with the physiological signal to be processed.

[0131] Step S203: Based on the aircraft's operational status information and the frequency characteristics of the signal components, determine the source of the signal components. The sources of the signal components include environmental interference and the patient's own pathological activities.

[0132] Step S204: Suppress signal components whose source is environmental interference to preserve signal components whose source is the patient's own pathological activity.

[0133] Step S205: Reconstruct the multiple signal components after suppression processing to obtain the target physiological signal.

[0134] In some embodiments, the complexity and variability of environmental interference make it difficult to completely eliminate interference through simple differential processing, potentially leaving residual noise in the target physiological signal and affecting the accurate assessment of the patient's vital signs. To effectively separate environmental interference components from the physiological signal and obtain a purer and more reliable patient physiological signal, a pre-defined signal analysis method can be used to decompose the physiological signal to be processed into multiple signal components, each with different frequency characteristics. This pre-defined signal analysis method is a mathematical tool that decomposes a complex mixed signal into several simpler and easier-to-analyze components. For example, multi-resolution analysis methods, such as wavelet transform, can decompose the signal into different frequency sub-bands, thereby analyzing the local features of the signal at different scales; time-frequency analysis methods, such as short-time Fourier transform or Wigner-Ville distribution, can simultaneously reveal the signal's distribution characteristics in time and frequency. The aim is to effectively separate different frequency components (including the physiological signal itself, environmental interference, etc.) in the physiological signal to be processed, laying the foundation for subsequent interference identification and suppression.

[0135] Then, aircraft operational status information is extracted from the aircraft operational parameter sequence. This information may include, but is not limited to, parameters such as aircraft vibration frequency, engine speed, rotor speed, flight altitude, flight speed, and attitude angles. These parameters directly or indirectly reflect the characteristics of environmental disturbances generated by the aircraft at a specific moment. The aircraft operational status information and the physiological signals to be processed are time-synchronized, meaning that while acquiring the physiological signals, the corresponding aircraft operational parameters are also recorded, ensuring a temporal correspondence between the two for subsequent correlation analysis.

[0136] Then, based on the aircraft's operational status information and the frequency characteristics of the signal components, the source of the signal components is determined. The sources include environmental interference and the patient's own pathological activities. For example, by analyzing the frequency characteristics of each signal component and combining it with synchronized aircraft operational status information, it can be determined whether the component originates from environmental interference generated by the aircraft or from the patient's own physiological or pathological activities. For instance, if the frequency of a signal component is highly consistent with the vibration frequency of the aircraft engine or rotor, it can be identified as environmental interference; conversely, if its frequency characteristics match the frequency range of known human physiological signals (such as electrocardiogram, electroencephalogram, respiration, etc.), it can be identified as the patient's own pathological activity.

[0137] Finally, signal components originating from environmental interference are suppressed to retain those originating from the patient's own pathological activities. This maximizes the elimination or reduction of the impact of these interfering components on the target physiological signal. Suppression can be achieved using various techniques, such as setting thresholds, applying filters (e.g., notch filters, band-stop filters), or employing adaptive filtering algorithms. Suppression effectively removes environmental noise from the physiological signal. Furthermore, the suppressed signal components are reconstructed to obtain the target physiological signal. For example, the suppressed signal components (i.e., those with removed environmental interference) can be recombined to form a complete and pure physiological signal, more accurately reflecting the patient's true vital signs and providing reliable data support for medical care.

[0138] To illustrate this technical solution more clearly, a specific example is used below. Suppose that during an air medical evacuation mission, real-time monitoring of the patient's electrocardiogram (ECG) signal is required. First, the ECG signal is acquired as a physiological signal to be processed. Simultaneously, the aircraft's operating parameters, such as engine speed, rotor speed, and fuselage vibration frequency, are also acquired. Specifically, wavelet transform (a multi-resolution analysis method) is used to decompose the acquired ECG signal into multiple wavelet coefficients, each corresponding to a component of the ECG signal at a different frequency scale. For example, low-frequency components (which may include baseline drift and respiratory artifacts), mid-frequency components (which may include QRS complexes, P waves, T waves, and some mechanical vibration interference), and high-frequency components (which may include electromyographic interference, high-frequency noise, and some mechanical vibration interference) can be extracted from the aircraft operating parameter sequence. At the same time, the current engine vibration frequency is extracted to be 50Hz, and the rotor vibration frequency is extracted to be 10Hz. Next, the decomposed signal components are analyzed. If the dominant frequency of a signal component is concentrated around 10Hz or 50Hz, and its time-domain waveform shows a high correlation with aircraft vibration parameters, it can be determined that the signal component mainly originates from environmental interference generated by the aircraft. For example, a 10Hz signal component is identified as rotor vibration interference. Signal components whose frequency characteristics match normal ECG signals (e.g., the frequency range of the QRS complex is typically 10-30Hz) and have low correlation with aircraft operating parameters are identified as originating from the patient's own cardiac electrical activity. Subsequently, the signal components identified as environmental interference (e.g., the 10Hz rotor vibration component) are suppressed. This can be achieved by thresholding or zeroing the corresponding wavelet coefficients in the wavelet domain, or by designing a notch filter to filter out interference at specific frequencies. Finally, all the suppressed signal components are reconstructed using inverse wavelet transform to obtain a target ECG signal free of rotor vibration interference. This target ECG signal will more clearly display the patient's cardiac electrical activity, facilitating accurate diagnosis and assessment by medical personnel.

[0139] Through the above technical solution, this embodiment enables refined processing of vital sign signals of patients in air medical rescue, significantly improving the signal-to-noise ratio and accuracy of physiological signals. By decomposing, identifying the source of the signal, and selectively suppressing it, this embodiment can more thoroughly and intelligently filter out complex environmental interference generated during aircraft operation, ensuring that the acquired target physiological signals truly and accurately reflect the patient's physiological state. This avoids misjudgment or missed diagnosis due to the presence of interference signals, thereby providing medical personnel with more reliable decision-making basis and improving the quality and safety of air medical rescue.

[0140] In some embodiments, in step S203, determining the source of the signal component based on the aircraft operating status information and the frequency characteristics of the signal component may include, but is not limited to, the following steps:

[0141] Based on the frequency characteristics of the signal components, the target correlation between the signal components and the aircraft operational status information is calculated. The target correlation includes time domain correlation and frequency domain correlation.

[0142] The source of the signal components is determined based on the target relevance.

[0143] In some embodiments, the target correlation between the signal component and the aircraft operational status information can be calculated first based on the frequency characteristics of the signal component. Target correlation refers to an index used to measure the similarity or synchronization between the signal component and the aircraft operational status information in the time and frequency dimensions. Target correlation includes time-domain correlation and frequency-domain correlation. Time-domain correlation refers to the consistency between the trend of the signal component's change and the trend of the aircraft operational status information in the aircraft operational parameter sequence on the time axis. This can be quantified, for example, by calculating cross-correlation functions or dynamic time warping (DTW). Frequency-domain correlation refers to the similarity between the signal component and the aircraft operational status information on a specific frequency component on the frequency axis. This can be quantified, for example, by calculating coherence or spectral similarity. By comprehensively considering the correlation in the time and frequency domains, it is possible to more accurately determine whether the signal component is caused by environmental interference. For example, when calculating the target correlation, the signal component and the aircraft operational status information can be preprocessed, such as by normalization, detrending, or filtering, to eliminate the influence of irrelevant factors. Subsequently, for time-domain correlation, the cross-correlation coefficients of the two components under different time delays can be calculated, and the maximum correlation coefficient can be selected as the measure of time-domain correlation. For frequency-domain correlation, Fourier transforms can be performed on the signal components and aircraft operational status information to obtain their respective spectra, and then the similarity between the spectra can be calculated, such as Euclidean distance or cosine similarity.

[0144] Then, based on the target relevance, the source of the signal component is determined. For example, one or more relevance thresholds can be preset. If the calculated target relevance (including time-domain and frequency-domain relevance) exceeds the preset relevance threshold, the signal component is considered to be highly correlated with aircraft operational status information, thus indicating that its source is environmental interference. Conversely, if the relevance is below the threshold, the signal component is considered to primarily originate from the patient's own pathological activities.

[0145] This embodiment quantifies the degree of correlation between signal components and aircraft operational status information in both the time and frequency domains by calculating the target correlation between the two. Aircraft operational parameter sequences (such as vibration, noise, and attitude changes) typically generate environmental interference with specific time-frequency characteristics, which superimposes on physiological signals. By analyzing the time and frequency correlation between physiological signal components and these aircraft operational parameters, signal components caused by environmental interference can be effectively identified. For example, if a physiological signal component is highly correlated with the engine speed or rotor vibration frequency at a specific frequency, it can be inferred that this component mainly originates from environmental interference. This correlation-based judgment method makes the differentiation of signal component sources more accurate and objective, avoiding errors that may arise from judgments based solely on experience or a single feature.

[0146] Through the above technical solution, this embodiment can more accurately identify signal components caused by environmental interference in physiological signal sequences, thereby improving the extraction accuracy of patient's own pathological activity signals. This judgment method based on time-domain and frequency-domain correlation makes the determination of signal component sources more scientific and reliable, helping to effectively filter out interference in complex air medical rescue environments, obtain more realistic and clinically significant patient vital sign data, and provide accurate decision-making basis for medical personnel.

[0147] In some embodiments, the method further includes:

[0148] Step S301: Analyze the confidence level of the target physiological signal to obtain the confidence score;

[0149] Step S302: Adjust the intensity of the inhibition treatment based on the confidence level and update the target physiological signal;

[0150] Step S303: Generate pathological activity indication information based on the updated target physiological signals.

[0151] In some embodiments, due to the complexity and dynamism of environmental interference in aeromedical rescue environments, a single, preset suppression intensity may not be fully adaptable to all situations. This could result in residual interference in the obtained target physiological signal, or excessive suppression of the patient's own pathological activity signals, thereby affecting the accurate assessment of the patient's vital signs and potentially reducing the reliability of monitoring data, ultimately impacting the accuracy of medical decisions. Therefore, a confidence assessment of the target physiological signal can be performed first to obtain a confidence level. For example, the quality of the differentiated target physiological signal can be quantitatively or qualitatively judged. For instance, methods such as signal-to-noise ratio (SNR) analysis, spectral purity analysis, similarity comparison with known normal physiological signal templates, or classification of signal quality based on machine learning models can be used to calculate the confidence level. This confidence level can be a value between 0 and 1, representing the reliability of the signal, or a discrete level, such as "high," "medium," or "low." Its purpose is to provide a quantitative basis for subsequent signal optimization.

[0152] Then, based on the confidence level, the intensity of the suppression treatment is adjusted to update the target physiological signal. For example, the intensity of suppressing environmental interference signals used in the differentiated processing can be dynamically adjusted according to the assessed confidence level. For instance, if the confidence level is low, it indicates that significant environmental interference may still exist in the target physiological signal or that key physiological information is excessively suppressed. In this case, the intensity of the suppression treatment can be appropriately increased or decreased depending on the specific situation. Furthermore, if the low confidence level is due to excessive residual noise, the suppression intensity can be increased; if the low confidence level is due to signal distortion, it may be necessary to decrease the suppression intensity or adopt a different suppression strategy. The adjusted suppression intensity will be used to reprocess the original physiological signal to obtain an updated target physiological signal, aiming to obtain a higher quality signal.

[0153] Based on the updated target physiological signals, pathological activity indicators are then generated. For example, key information related to the patient's pathological activities can be extracted from the optimized and updated target physiological signals. For instance, heart rate variability (HRV) indicators and arrhythmia event indicators can be generated based on updated electrocardiogram (ECG) signals; respiratory rate and respiratory depth abnormality alerts can be generated based on updated respiratory signals; or hypoxemia warnings can be generated based on updated blood oxygen saturation signals. These indicators aim to provide healthcare professionals with more accurate and reliable patient vital signs, assisting them in diagnosis and decision-making.

[0154] To illustrate this technical solution more clearly, a specific example is used below. Suppose that during air medical evacuation, real-time monitoring of the patient's electrocardiogram (ECG) is required. First, the raw ECG signal sequence and the aircraft operating parameter sequence are acquired. In the initial differential processing stage, a preset signal analysis method (such as multi-resolution analysis) is used to decompose the ECG signal into multiple signal components. Based on the aircraft operating status information, most of the environmental interference components caused by aircraft vibration are identified and suppressed, resulting in the preliminary target ECG signal.

[0155] Subsequently, a confidence assessment can be performed on the initial target ECG signal. Specifically, the signal-to-noise ratio (SNR) of the signal can be calculated, and the morphological integrity of its QRS complex can be analyzed. If the assessment results show that the SNR is lower than a preset threshold, or that the QRS complex exhibits significant distortion, the confidence level of the target ECG signal is considered low. Based on this confidence level, the system will automatically adjust the intensity of the previous suppression processing. For example, if the low confidence level is due to residual aircraft vibration noise, the system will increase the suppression intensity for signal components in a specific frequency range; if the low confidence level is due to excessive suppression resulting in an excessively small QRS complex amplitude, the system will appropriately reduce the suppression intensity. The system will then reprocess the original ECG signal using the adjusted suppression intensity to obtain an updated, higher-quality target ECG signal. Ultimately, based on this updated target ECG signal, more accurate indications of pathological activity can be generated, such as precise heart rate, identification of arrhythmic events, or monitoring of ST segment deviation. This information will be directly used to assist medical personnel in assessing and intervening in the patient's cardiac condition.

[0156] Through the above technical solution, this embodiment can significantly improve the accuracy and reliability of patient vital sign monitoring in air medical rescue environments. By performing confidence assessment on the target physiological signals and dynamically adjusting the intensity of suppression processing, the problems of residual signal interference or over-suppression that may be caused by single suppression processing can be effectively solved, ensuring that the obtained target physiological signals are purer and more accurate. As a result, the pathological activity indication information generated based on the updated target physiological signals will be more accurate, providing medical personnel with more reliable clinical evidence, thereby improving the quality of air medical rescue and patient safety.

[0157] In some embodiments, step S302, adjusting the intensity of the inhibition treatment and updating the target physiological signal based on the confidence level, may include, but is not limited to, the following steps:

[0158] The intensity of the suppression treatment is divided into multiple candidate intensity levels;

[0159] Based on the confidence level, select one intensity level from multiple candidate intensity levels as the target intensity level;

[0160] Adjust the intensity of the suppression treatment according to the target intensity level;

[0161] Based on the intensity of the adjusted suppression processing, the signal components whose source is environmental interference are suppressed, and the target physiological signal is updated.

[0162] In some embodiments, simply adjusting the intensity may not precisely control the degree of removal of environmental interference, thus affecting the accuracy and reliability of the final target physiological signal. For example, when the confidence level is low, insufficient suppression intensity may leave behind a significant amount of interference; conversely, excessive suppression intensity may inadvertently damage the patient's own physiological signals. Therefore, the intensity of the suppression treatment can be divided into multiple selectable intensity levels. For instance, a series of discrete, selectable suppression levels can be pre-defined, such as multiple levels like "weak," "medium," and "strong," or even a finer numerical range. These levels are designed to provide flexible and controllable options for subsequent adaptive adjustments. It is understood that the intensity of the suppression treatment refers to the degree to which signal components originating from environmental interference are attenuated or removed, and this intensity can be quantified or categorized.

[0163] Then, based on the confidence level, one intensity level is selected as the target intensity level from multiple candidate intensity levels. For example, when the confidence level is high, a lower suppression intensity level may be selected to avoid overprocessing; when the confidence level is low, a higher suppression intensity level may be selected to more thoroughly remove interference. The intensity of the suppression process is then adjusted according to the target intensity level. Finally, based on the adjusted suppression intensity, signal components originating from environmental interference are suppressed to update the target physiological signal, which is considered to have higher accuracy and reliability.

[0164] To illustrate this technical solution more clearly, a specific example is used below. Assume the intensity of the suppression process is divided into five selectable intensity levels: very low, low, medium, high, and very high. Simultaneously, the confidence level of the target physiological signal is quantified as a value from 0 to 100. A mapping rule can be set, for example: when the confidence level is greater than 90, select the very low or low intensity level; when the confidence level is between 70 and 90, select the medium intensity level; when the confidence level is between 50 and 70, select the high intensity level; and when the confidence level is less than 50, select the very high intensity level. Specifically, if the confidence level of a physiological signal to be processed is 65 after a confidence assessment, the system will select the "high" intensity level as the target intensity level according to the preset rule. Subsequently, the signal processing module will suppress the environmental interference signal components identified in the physiological signal according to this "high" intensity level, and reconstruct the updated target physiological signal. This hierarchical adjustment method makes the suppression process more intelligent and adaptive, enabling precise intervention based on the actual signal quality.

[0165] Through the above technical solution, this embodiment enables refined and adaptive adjustment of the suppression intensity of environmental interference signal components. Compared to simple adjustments based solely on confidence levels, this embodiment significantly improves the accuracy and flexibility of the suppression process by introducing candidate intensity levels and intelligently selecting the target intensity level based on confidence levels. This allows for more effective removal of environmental interference while preserving the patient's true physiological signals to the maximum extent, thereby enhancing the accuracy and reliability of the target physiological signals and laying the foundation for generating more precise pathological activity indicators.

[0166] The beneficial effects of implementing the embodiments of the present invention include: First, the embodiments of this application obtain physiological signal sequences and aircraft operating parameter sequences. Then, based on the physiological signal sequences and aircraft operating parameter sequences, feature information of abnormal disturbances is extracted. Next, based on the feature information of abnormal disturbances, physiological signal sequences, and aircraft operating parameter sequences, time synchronization processing is performed to obtain the correlation between the feature information of abnormal disturbances and the aircraft operating parameter sequences. Finally, based on the correlation, the source of abnormal disturbances is determined. If the source of abnormal disturbances includes environmental interference, each physiological signal to be processed in the physiological signal sequence is differentiated to obtain the corresponding target physiological signal. Thus, it is possible to combine the feature information of abnormal disturbances to analyze the source of abnormal disturbances in order to achieve vital sign monitoring, thereby improving accuracy.

[0167] like Figure 2 As shown, this embodiment of the invention also provides a vital signs monitoring system for air medical rescue patients, including:

[0168] Data acquisition module 401 is used to acquire physiological signal sequences and aircraft operating parameter sequences;

[0169] The disturbance identification module 402 is used to extract the feature information of abnormal disturbances based on the physiological signal sequence and the aircraft operating parameter sequence. The feature information of abnormal disturbances includes the start time, duration, frequency range or amplitude.

[0170] The time synchronization module 403 is used to perform time synchronization processing based on the characteristic information of the abnormal disturbance, the physiological signal sequence and the aircraft operating parameter sequence, so as to obtain the correlation between the characteristic information of the abnormal disturbance and the aircraft operating parameter sequence.

[0171] Source determination module 404 is used to determine the source of abnormal disturbances based on correlation.

[0172] The signal processing module 405 is used to perform differential processing on each physiological signal to be processed in the physiological signal sequence if the abnormal disturbance source includes environmental interference, so as to obtain the corresponding target physiological signal.

[0173] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0174] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for monitoring the vital signs of patients in air medical rescue, characterized in that, Includes the following steps: Acquire physiological signal sequences and aircraft operational parameter sequences; Based on the physiological signal sequence and the aircraft operating parameter sequence, feature information of the abnormal disturbance is extracted, including start time, duration, frequency range or amplitude; Based on the characteristic information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence, time synchronization processing is performed to obtain the correlation between the characteristic information of the abnormal disturbance and the aircraft operating parameter sequence; Based on the aforementioned correlation, the source of the abnormal disturbance is determined; If the source of the abnormal disturbance includes environmental interference, then each physiological signal to be processed in the physiological signal sequence is differentiated to obtain the corresponding target physiological signal; The step of determining the source of the abnormal disturbance based on the correlation includes: Collect rotor speed and engine speed; Calculate the rotor frequency based on the rotor speed; Calculate the engine frequency based on the engine speed; Calculate the trend of mechanical vibration variation based on the rotor frequency and the engine frequency; Calculate the disturbance frequency range based on the rotor frequency and the engine frequency; Based on the correlation, the dominant frequency of physiological signal perturbation is determined; If the trend of change of the dominant frequency of the physiological signal disturbance is the same as the trend of change of the mechanical vibration, and the dominant frequency of the physiological signal disturbance is within the range of the disturbance frequency, then it is determined that the source of the abnormal disturbance includes environmental interference. The step of differentially processing each physiological signal to be processed in the physiological signal sequence to obtain the corresponding target physiological signal includes: The physiological signal to be processed is decomposed into multiple signal components using a preset signal analysis method. The signal components have different frequency characteristics. The preset signal analysis method includes multi-resolution analysis or time-frequency analysis. Aircraft operational status information is extracted from the aircraft operational parameter sequence, and the aircraft operational status information is time-synchronized with the physiological signal to be processed. Based on the aircraft's operational status information and the frequency characteristics of the signal components, the source of the signal components is determined. The source of the signal components includes environmental interference and the patient's own pathological activities. The signal components whose source is environmental interference are suppressed in order to retain the signal components whose source is the patient's own pathological activity; The target physiological signal is obtained by reconstructing multiple signal components after suppression.

2. The method according to claim 1, characterized in that, The step of extracting feature information of abnormal disturbances based on the physiological signal sequence and the aircraft operating parameter sequence includes: Identify abnormal perturbations in the physiological signal sequence; Based on the aircraft operating parameter sequence, determine the characteristic range of environmental interference generated by the aircraft during the occurrence of abnormal disturbances; Identify physiological signal components in the abnormal disturbance that do not match the characteristic range of the environmental interference; Feature information of the abnormal perturbation is extracted from the physiological signal components.

3. The method according to claim 1, characterized in that, The step of performing time synchronization processing based on the characteristic information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence to obtain the correlation between the characteristic information of the abnormal disturbance and the aircraft operating parameter sequence includes: The physiological signal sequence and the aircraft operating parameter sequence are compared for morphological similarity to obtain the morphological similarity comparison results. Based on the characteristic information of the abnormal disturbance and the morphological similarity comparison results, the degree of morphological correlation between the abnormal disturbance in the physiological signal sequence and the aircraft operating parameter sequence is determined; The degree of morphological correlation is used as the correlation between the characteristic information of the abnormal disturbance and the sequence of aircraft operating parameters.

4. The method according to claim 1, characterized in that, The step of determining the source of the abnormal disturbance based on the correlation includes: After the motion sensor is deployed, an environmental interference reference signal is collected through the motion sensor, which is deployed at the physiological signal collection location; The environmental interference reference signal is separated from the physiological signal to be processed using an adaptive filtering method to obtain the residual physiological signal; The residual physiological signals are subjected to feature analysis to obtain residual physiological signal features; The source of the abnormal disturbance is determined based on the residual physiological signal characteristics and the correlation.

5. The method according to claim 1, characterized in that, The step of determining the source of the signal component based on the aircraft operating status information and the frequency characteristics of the signal component includes: Based on the frequency characteristics of the signal components, the target correlation between the signal components and the aircraft operating status information is calculated, and the target correlation includes time domain correlation and frequency domain correlation. The source of the signal component is determined based on the target correlation.

6. The method according to claim 1, characterized in that, The method further includes: The confidence level is obtained by performing a confidence assessment on the target physiological signal; Based on the confidence level, the intensity of the inhibition treatment is adjusted, and the target physiological signal is updated; Based on the updated target physiological signals, pathological activity indication information is generated.

7. The method according to claim 6, characterized in that, The step of adjusting the intensity of the inhibition treatment and updating the target physiological signal based on the confidence level includes: The intensity of the suppression treatment is divided into multiple candidate intensity levels; Based on the confidence level, one intensity level is selected as the target intensity level from the plurality of candidate intensity levels; The intensity of the suppression treatment is adjusted according to the target intensity level; Based on the adjusted intensity of the suppression process, the signal components whose source is environmental interference are suppressed, and the target physiological signal is updated.

8. A vital signs monitoring system for air medical rescue patients, characterized in that, include: The data acquisition module is used to acquire physiological signal sequences and aircraft operating parameter sequences; The disturbance identification module is used to extract the feature information of abnormal disturbances based on the physiological signal sequence and the aircraft operating parameter sequence. The feature information of abnormal disturbances includes start time, duration, frequency range or amplitude. The time synchronization module is used to perform time synchronization processing based on the feature information of the abnormal disturbance, the physiological signal sequence, and the aircraft operating parameter sequence to obtain the correlation between the feature information of the abnormal disturbance and the aircraft operating parameter sequence. The source determination module is used to determine the source of the abnormal disturbance based on the correlation. The signal processing module is used to perform differential processing on each physiological signal to be processed in the physiological signal sequence to obtain the corresponding target physiological signal if the abnormal disturbance source includes environmental interference. The step of determining the source of the abnormal disturbance based on the correlation includes: Collect rotor speed and engine speed; Calculate the rotor frequency based on the rotor speed; Calculate the engine frequency based on the engine speed; Calculate the trend of mechanical vibration variation based on the rotor frequency and the engine frequency; Calculate the disturbance frequency range based on the rotor frequency and the engine frequency; Based on the correlation, the dominant frequency of physiological signal perturbation is determined; If the trend of change of the dominant frequency of the physiological signal disturbance is the same as the trend of change of the mechanical vibration, and the dominant frequency of the physiological signal disturbance is within the range of the disturbance frequency, then it is determined that the source of the abnormal disturbance includes environmental interference. The step of differentially processing each physiological signal to be processed in the physiological signal sequence to obtain the corresponding target physiological signal includes: The physiological signal to be processed is decomposed into multiple signal components using a preset signal analysis method. The signal components have different frequency characteristics. The preset signal analysis method includes multi-resolution analysis or time-frequency analysis. Aircraft operational status information is extracted from the aircraft operational parameter sequence, and the aircraft operational status information is time-synchronized with the physiological signal to be processed. Based on the aircraft's operational status information and the frequency characteristics of the signal components, the source of the signal components is determined. The source of the signal components includes environmental interference and the patient's own pathological activities. The signal components whose source is environmental interference are suppressed in order to retain the signal components whose source is the patient's own pathological activity; The target physiological signal is obtained by reconstructing multiple signal components after suppression.

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