Method and system for evaluating heart rehabilitation curative effect of breast cancer chemotherapy patient

Abstract

The invention relates to the technical field of evaluation of heart rehabilitation curative effects of breast cancer chemotherapy patients, in particular to a method and system for evaluating the heart rehabilitation curative effects of the breast cancer chemotherapy patients, and the method comprises the following steps: continuously obtaining heart rate variability data and skin conductance data of the breast cancer chemotherapy patients; based on the heart rate variability data and the skin conductance data, the activation state of the autonomic nervous system of the patient is judged; according to the activation state of the autonomic nervous system of the patient, quantifying the intensity and duration of the internal emotional state of the patient, and generating an emotional energy index; when the conventional physiological indexes of the patient fluctuate, determining a physiological fluctuation attribution result in combination with the emotional energy index to distinguish whether the physiological fluctuation is derived from heart function change or emotional influence; and evaluating the cardiac rehabilitation effect of the patient based on the physiological fluctuation attribution result. The accuracy of heart rehabilitation curative effect evaluation can be improved.

Description

Technical Field

[0001] This invention relates to the technical field of evaluating the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients, and specifically to a method and system for evaluating the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients. Background Technology

[0002] In the cardiac rehabilitation management of breast cancer patients undergoing chemotherapy, remote monitoring and assessment systems are typically used to accurately evaluate rehabilitation efficacy, integrating patients' physiological data with clinical examination results. However, fluctuations in patients' daily physiological indicators are often influenced by various non-medical factors, such as environmental changes, mental stress, or daily physical exertion. Existing assessment methods often struggle to accurately distinguish whether these physiological fluctuations stem from changes in cardiac function, the effectiveness of the rehabilitation program, or from these subtle, unidentified, and unquantified contextual factors. This limits the reliability of assessment results and hinders timely adjustments to the rehabilitation program. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned shortcomings by proposing a method and system for evaluating the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients.

[0004] The present invention adopts the following technical solution: A method for assessing the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients, comprising the following steps: Continuously acquire heart rate variability and skin conductance data from breast cancer patients undergoing chemotherapy; Based on heart rate variability data and skin conductance data, the activation status of the patient's autonomic nervous system is determined; Based on the activation state of the patient's autonomic nervous system, the intensity and duration of the patient's internal emotional state are quantified to generate an emotional energy index. When a patient’s routine physiological indicators fluctuate, the emotional energy index is used to determine the attribution of physiological fluctuations, which can be used to distinguish whether the physiological fluctuations are caused by changes in cardiac function or by emotional influences. The efficacy of cardiac rehabilitation was evaluated based on the attribution results of physiological fluctuations.

[0005] This technical solution integrates heart rate variability data and skin conductance data to quantify the emotional energy index and combines it with attribution analysis of fluctuations in conventional physiological indicators. This effectively distinguishes whether physiological fluctuations are caused by changes in cardiac function or by emotional influences, thus improving the accuracy of cardiac rehabilitation efficacy assessment.

[0006] This application also discloses a cardiac rehabilitation efficacy assessment system for breast cancer chemotherapy patients, applicable to the assessment of cardiac rehabilitation efficacy in breast cancer chemotherapy patients, the system comprising: The acquisition module continuously acquires heart rate variability and skin conductance data from breast cancer chemotherapy patients; The judgment module, based on heart rate variability data and skin conductance data, determines the activation state of the patient's autonomic nervous system; The generation module quantifies the intensity and duration of the patient's internal emotional state based on the activation state of the patient's autonomic nervous system, and generates an emotional energy index. The attribution module, when a patient’s routine physiological indicators fluctuate, combines the emotional energy index to determine the attribution results of the physiological fluctuations, which is used to distinguish whether the physiological fluctuations are caused by changes in cardiac function or by emotional influences. The assessment module evaluates the effectiveness of cardiac rehabilitation for patients based on the attribution results of physiological fluctuations.

[0007] This technical solution enables the automation and systematization of the aforementioned assessment methods through modular design, improving assessment efficiency and data processing capabilities, and providing a convenient and efficient tool for clinical applications.

[0008] This application, through comprehensive analysis of heart rate variability and skin conductance data, can more accurately capture patients' autonomic nervous activity and emotional state. This multi-dimensional and objective assessment method makes the attribution of physiological fluctuations more accurate and reliable.

[0009] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0010] Figure 1 This is a flowchart of a method for evaluating the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients according to the present invention; Figure 2 This is a schematic diagram of the structure of a cardiac rehabilitation efficacy assessment system for breast cancer chemotherapy patients according to the present invention. Detailed Implementation

[0011] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0012] This embodiment provides a method and system for evaluating the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients, combined with... Figure 1 and Figure 2 As shown.

[0013] refer to Figure 1 A method for assessing the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients, the method comprising the following steps: Continuously acquire heart rate variability and skin conductance data from breast cancer patients undergoing chemotherapy; Based on heart rate variability data and skin conductance data, the activation status of the patient's autonomic nervous system is determined; Based on the activation state of the patient's autonomic nervous system, the intensity and duration of the patient's internal emotional state are quantified to generate an emotional energy index. When a patient’s routine physiological indicators fluctuate, the emotional energy index is used to determine the attribution of physiological fluctuations, which can be used to distinguish whether the physiological fluctuations are caused by changes in cardiac function or by emotional influences. The efficacy of cardiac rehabilitation was evaluated based on the attribution results of physiological fluctuations.

[0014] The “heart rate variability data” mentioned in this application refers to a continuous time series of heartbeat intervals acquired by devices such as electrocardiography or photoplethysmography, which reflects the ability of the cardiac autonomic nervous system to regulate heart rate. This data typically includes time-domain metrics (such as SDNN, RMSSD) and frequency-domain metrics (such as LF, HF) to assess the activity of the sympathetic and parasympathetic nervous systems.

[0015] "Skin conductivity data" refers to changes in the resistance or conductivity of the skin surface, primarily reflecting sweat gland activity and regulated by the sympathetic nervous system. When an individual is emotionally agitated, tense, or under increased stress, the sympathetic nervous system is activated, sweat gland secretion increases, and skin conductivity rises accordingly.

[0016] The autonomic nervous system is the nervous system that regulates the functions of internal organs (such as heart rate, respiration, digestion, and blood pressure). It is divided into the sympathetic nervous system and the parasympathetic nervous system. The sympathetic nervous system is mainly responsible for the "fight or flight" response, keeping the body in an excited state; the parasympathetic nervous system is responsible for "rest and digestion," keeping the body in a relaxed state.

[0017] The "Emotional Energy Index" is a quantitative indicator proposed in this application, used to comprehensively reflect the intensity and duration of a patient's internal emotional state. The index is generated based on the activation state of the autonomic nervous system and aims to provide an objective and continuous assessment of emotional state.

[0018] "Routine physiological indicators" typically include heart rate, blood pressure, respiratory rate, body temperature, etc. These indicators are commonly used vital signs in clinical practice to monitor the patient's overall physiological condition.

[0019] "Physiological fluctuation attribution results" refers to the judgment results that, through the methods of this application, clearly attribute fluctuations in a patient's routine physiological indicators to changes in cardiac function or emotional influences.

[0020] "Cardiac rehabilitation efficacy" refers to the degree of improvement in cardiac function, quality of life, and related physiological indicators in breast cancer chemotherapy patients after receiving cardiac rehabilitation treatment.

[0021] This application provides a method for assessing the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients. The method first involves continuously acquiring heart rate variability (HRV) and skin conductance data from these patients. For example, HRV data can be collected in real-time and continuously using a smart wearable device, such as a smartwatch or a chest strap heart rate monitor. Simultaneously, the wearable device can also integrate a skin conductance sensor to synchronously acquire skin conductance data. This data can be transmitted to a local storage device or a cloud server for further processing.

[0022] Based on the acquired heart rate variability (HF) and skin conductance (SCR) data, the activation status of the patient's autonomic nervous system can be determined. Specifically, the relative activity of the sympathetic and parasympathetic nervous systems can be assessed by analyzing the ratio of high-frequency (HF) to low-frequency (LF) components in the HF data, and the frequency and amplitude of the skin conductance response (SCR) in the SCR data. For example, an elevated LF / HF ratio and frequent SCR responses may indicate that the sympathetic nervous system is activated.

[0023] Based on the activation state of the patient's autonomic nervous system, the intensity and duration of the patient's internal emotional state are quantified to generate an emotional energy index. For example, a threshold can be set; when the autonomic nervous system activation state exceeds this threshold, emotional energy begins to accumulate. The higher the activation intensity, the faster the accumulation rate; the longer the activation duration, the larger the accumulated value. The emotional energy index can be a dynamically changing value, reflecting the fluctuations in the patient's emotions in real time.

[0024] When a patient's routine physiological indicators fluctuate, the emotional energy index is used to determine the attribution of these fluctuations, distinguishing whether they stem from changes in cardiac function or emotional influences. For example, if a patient's heart rate or blood pressure is abnormally elevated, and the emotional energy index is also high and persists for a period of time, the physiological fluctuation may be attributed to emotional factors. Conversely, if the emotional energy index is low, but physiological indicators still fluctuate, it may be attributed to changes in cardiac function.

[0025] The effectiveness of cardiac rehabilitation is assessed based on the attribution of physiological fluctuations. For example, if a patient's physiological fluctuations during rehabilitation are more attributable to emotional influences than to changes in cardiac function, and cardiac function-related physiological indicators (such as ejection fraction and exercise tolerance) remain stable or improve, the rehabilitation can be considered effective. Conversely, if physiological fluctuations are frequently attributed to changes in cardiac function, adjustments to the rehabilitation program may be necessary.

[0026] The proposed method for evaluating the efficacy of cardiac rehabilitation in breast cancer chemotherapy patients can effectively distinguish whether physiological fluctuations are caused by changes in cardiac function or by emotional influences by incorporating an emotional energy index.

[0027] Specifically, this method first continuously acquires the patient's heart rate variability and skin conductance data, which are key indicators reflecting the activity of the autonomic nervous system. In-depth analysis of this data allows for the assessment of the patient's autonomic nervous system activation state, thus providing an objective basis for subsequent emotion quantification. Next, based on the activation state of the autonomic nervous system, this application innovatively introduces an emotion energy index, which quantifies the intensity and duration of the patient's internal emotional state, concretizing abstract emotions into measurable numerical values.

[0028] When a patient's routine physiological indicators fluctuate, such as increased heart rate, elevated blood pressure, or shortness of breath, this method no longer relies solely on the physiological indicators themselves but also incorporates an emotional energy index. If physiological fluctuations occur simultaneously with a high emotional energy index, the fluctuations are more likely to be attributed to emotional influences; conversely, if the emotional energy index is low, it is more likely to be attributed to changes in cardiac function. This attribution mechanism effectively addresses the problem of unclear attribution of physiological fluctuations in existing technologies, avoiding the misinterpretation of emotionally induced physiological responses as deterioration of cardiac function, or vice versa.

[0029] Ultimately, based on this precise attribution of physiological fluctuations, this method can more accurately assess the effectiveness of cardiac rehabilitation for patients. For example, if a patient's physiological fluctuations during rehabilitation are primarily caused by emotions, while cardiac function indicators remain stable or improve, it indicates that the rehabilitation program is effective. Conversely, if physiological fluctuations are frequently attributed to changes in cardiac function, it suggests that the rehabilitation program may need adjustment.

[0030] The steps for generating an emotional energy index, based on the activation state of the patient's autonomic nervous system, to quantify the intensity and duration of the patient's internal emotional state include: Analyze accelerometer data, assess equipment contact status, and obtain equipment contact status information; Continuously acquire raw waveforms of photoplethysmography, heart rate variability data, raw skin conductance data, and device contact status information; The original waveform of the photoplethysmography is filtered, and the waveform morphology parameters of the original waveform are analyzed. Calculate time-domain and frequency-domain indices for heart rate variability data; Filter the raw skin conductance data to identify skin conductance response characteristics; The waveform morphology parameters, time domain indicators, frequency domain indicators, skin conductance response characteristics, and device contact status information are input into the real-time discrimination mechanism. By using a real-time discrimination mechanism, the correlation and temporal consistency between waveform morphology parameters, time domain indicators, frequency domain indicators, skin conductance response characteristics, and device contact status information are analyzed to determine the authenticity of autonomic nerve activation. When autonomic nerve activation is judged to be real, the instantaneous activation intensity value is calculated; An emotional energy index is generated by accumulating the instantaneous activation intensity value and the duration of the instantaneous activation intensity value.

[0031] The process of "analyzing accelerometer data, assessing device contact status, and obtaining device contact status information" refers to continuously monitoring the movement of a wearable device and its contact with the skin using an accelerometer built into the patient. For example, when the device experiences violent shaking or detachment, the accelerometer data will exhibit abnormal patterns. By analyzing these patterns, it can be determined whether the device is in good contact condition, and corresponding device contact status information can be generated. The purpose is to identify and eliminate physiological signal artifacts caused by poor device contact, ensuring the effectiveness of subsequent data collection.

[0032] "Continuously acquiring raw photoplethysmography waveforms, heart rate variability data, raw skin conductance data, and device contact status information" refers to the uninterrupted acquisition of multiple physiological signals throughout the monitoring process. Raw photoplethysmography waveforms can be acquired through optical sensors, reflecting changes in vascular volume; heart rate variability data can be derived from electrocardiogram (ECG) or raw photoplethysmography waveforms, reflecting cardiac autonomic regulation; raw skin conductance data can be acquired through skin electrodes, reflecting sweat gland activity. Simultaneously, combining this data with previously acquired device contact status information ensures the quality of the acquired data.

[0033] "Filtering the raw waveform of photoplethysmography (PPG) and analyzing its waveform morphology parameters" refers to preprocessing the raw PPG waveform signal to remove noise and artifacts, such as using bandpass filtering and notch filtering techniques. Various morphological parameters can be extracted from the filtered waveform, such as peak time, trough time, rise slope, fall slope, and pulse wave propagation time. These parameters are closely related to the physiological state of the cardiovascular system.

[0034] "Calculating time-domain and frequency-domain indices for heart rate variability data" refers to conducting in-depth analysis of heart rate variability data to quantify the activity of the autonomic nervous system. Time-domain indices include standard deviation and root mean square continuous difference, reflecting the short-term and long-term variability of heart rate; frequency-domain indices include low-frequency power, high-frequency power, and their ratio, reflecting the balance of sympathetic and parasympathetic nervous system activity, respectively.

[0035] "Filtering raw skin conductance data to identify skin conductance response characteristics" refers to preprocessing the raw skin conductance data signal to remove baseline drift and high-frequency noise. The filtered raw skin conductance data signal can identify the characteristics of the skin conductance response, such as response amplitude, latency, rise time, and recovery time. These characteristics are direct indicators of sympathetic nervous system activation.

[0036] "Inputting waveform morphology parameters, time-domain indicators, frequency-domain indicators, skin conductance response characteristics, and device contact status information into a real-time discrimination mechanism" refers to aggregating various processed and feature-extracted physiological signal data, along with device contact status information, into an intelligent discrimination system. This real-time discrimination mechanism can be a machine learning-based model, such as a support vector machine, neural network, or decision tree, or it can be a logic system based on expert rules.

[0037] "By analyzing the correlation and temporal consistency between waveform morphological parameters, time-domain indicators, frequency-domain indicators, skin conductance response characteristics, and device contact status information through a real-time discrimination mechanism, the authenticity of autonomic nerve activation can be determined." This refers to the real-time discrimination mechanism's comprehensive analysis of multiple input features. For example, when the morphological parameters of the original waveform recorded by photoplethysmography, the low-frequency / high-frequency ratio of heart rate variability data, and skin conductance response characteristics simultaneously show changes consistent with sympathetic nerve activation, and the device contact status information indicates that the device is worn properly, the discrimination mechanism will consider the autonomic nerve activation to be genuine. This multi-dimensional, multi-time-scale consistency analysis can effectively eliminate misjudgments caused by single signal artifacts or noise.

[0038] "Calculating the instantaneous activation intensity value when autonomic activation is determined to be genuine" means that once the real-time discrimination mechanism confirms that autonomic activation is genuine, the system will calculate a quantified instantaneous activation intensity value based on the degree of deviation of the current physiological signal characteristics. For example, the instantaneous intensity of the current emotional activation can be calculated using a preset algorithm or model based on the amplitude of skin conductance response, the increase in the low-frequency / high-frequency ratio of heart rate variability data, etc.

[0039] "Accumulating the emotional energy index based on the instantaneous activation intensity value and its duration" refers to accumulating the calculated instantaneous activation intensity value and its duration. For example, the instantaneous activation intensity value can be integrated or weighted and summed within a certain time window to obtain an index reflecting the cumulative effect of emotion, namely the emotional energy index. This index not only reflects the immediate intensity of emotion but also considers the impact of the duration of emotion on the patient's physiological and psychological state.

[0040] This application's solution effectively addresses the inaccuracies and susceptibility to artifacts inherent in basic solutions that rely solely on heart rate variability and skin conductance data to assess device contact status by introducing collaborative monitoring and refined processing of multi-source physiological signals (raw waveforms from photoplethysmography, heart rate variability data, and raw skin conductance data). Specifically, filtering the raw photoplethysmography waveforms and analyzing their morphological parameters provides supplementary information on autonomic nervous activity from a cardiovascular perspective; calculating time-domain and frequency-domain indices for heart rate variability data allows for a more comprehensive quantification of the balance of cardiac autonomic regulation; and filtering the raw skin conductance data and identifying its response characteristics directly reflects the activation level of the sympathetic nervous system. Furthermore, inputting these multi-dimensional physiological characteristics and device contact status information into a real-time discrimination mechanism analyzes the correlation and temporal consistency between these characteristics. It is precisely this multi-factor cross-validation and consistency assessment that reliably determines the authenticity of autonomic nervous activation. For example, when there is a malfunction in the device, even if some physiological signals fluctuate, the discrimination mechanism can identify that it is not a genuine autonomic nervous system activation. Once the autonomic nervous system activation is correctly identified, by calculating the instantaneous activation intensity value and accumulating it in conjunction with its duration, the intensity and duration of the patient's internal emotional state can be quantified more accurately and comprehensively, thereby generating a more reliable emotional energy index.

[0041] As a specific implementation, suppose a breast cancer chemotherapy patient wears a smart wearable device integrating an accelerometer, photoplethysmography (PPG) sensor, and skin conductivity sensor. During the patient's daily activities, the device continuously collects accelerometer data, raw PPG waveforms, heart rate variability (HRV) data, and raw skin conductivity data. First, the system analyzes the accelerometer data. For example, if it detects sudden and irregular movement of the device within a short period, it determines that the device may have poor contact or become detached, and generates device contact status information, marking it as "poor contact." Simultaneously, the raw PPG waveform is acquired and bandpass filtered to remove motion artifacts and baseline drift, and then waveform morphology parameters such as pulse wave propagation time and peak rise time are extracted. Heart rate variability data is used to calculate time-domain indicators such as standard deviation and root mean square continuity difference, as well as frequency-domain indicators such as the low-frequency / high-frequency ratio. The raw skin conductivity data is low-pass filtered to identify characteristics such as the amplitude and latency of the skin conductivity response. Subsequently, these waveform morphology parameters, time-domain indices, frequency-domain indices, skin conductance response characteristics, and device contact status information (e.g., "good contact") are input into a pre-trained real-time discrimination mechanism. This mechanism is a deep learning-based neural network model capable of learning complex correlations and temporal consistency patterns between different physiological signal features. For example, when a patient is suddenly startled, if the raw waveform morphology parameters of the photoplethysmography show vasoconstriction, a significantly increased low-frequency / high-frequency ratio in heart rate variability data, an increased amplitude of the skin conductance response, and the device contact status information shows "good contact," then the real-time discrimination mechanism will determine that autonomic activation is genuine. Once autonomic activation is determined to be genuine, the system calculates an instantaneous activation intensity value, for example, 0.7 (range 0-1), based on the amplitude of the skin conductance response, the change in the low-frequency / high-frequency ratio, etc. If this activation state lasts for 30 seconds, the system generates an emotional energy index based on the instantaneous activation intensity value of 0.7 and its duration of 30 seconds, using an accumulation algorithm (e.g., accumulating 0.7 per second, for a total of 21 units). In this way, even during chemotherapy, patients' emotional fluctuations can be accurately captured and quantified, providing a reliable basis for assessing the effectiveness of cardiac rehabilitation.

[0042] This application further proposes the following steps for cumulatively generating an emotional energy index: At the start of a patient's chemotherapy cycle or during the physiological recovery phase, heart rate variability and skin conductance data were collected in the patient's calm state to construct an individualized autonomic nervous system response baseline. The cumulative weight and time decay factor of the emotional energy index are dynamically adjusted according to the patient's chemotherapy cycle stage or physiological recovery stage. Based on the degree of deviation between the instantaneous activation intensity value and the individualized autonomic nervous system response baseline, the contribution of the instantaneous activation intensity value to the emotional energy index is dynamically adjusted. Combined with the cumulative weight of the dynamically adjusted emotional energy index and the time decay factor, the duration of the instantaneous activation intensity value corresponding to the adjusted contribution is accumulated to generate the emotional energy index.

[0043] Specifically, establishing an individualized autonomic nervous system response baseline involves continuously monitoring and collecting heart rate variability and skin conductance data when the patient begins chemotherapy or enters the physiological recovery period, in a calm and relaxed state. This data is used to establish a reference benchmark reflecting the patient's autonomic nervous system activity level under no emotional stimulation or low emotional load. This baseline is highly individualized and effectively eliminates the interference of individual physiological differences on emotional assessment.

[0044] The dynamic adjustment of the cumulative weight and time decay factor of the emotional energy index can be understood as parametrically adjusting the accumulation method of the emotional energy index based on the patient's current stage of the chemotherapy cycle (e.g., early, middle, or late stage) or physiological recovery stage (e.g., acute or chronic recovery). For example, in the early stage of chemotherapy, patients may be more sensitive to emotional stimuli. At this time, the cumulative weight can be increased and the time decay factor decreased, making the impact of emotional fluctuations on the emotional energy index more significant and longer-lasting. In the later stage of physiological recovery, these parameters can be adjusted accordingly to more accurately reflect the patient's actual emotional state.

[0045] In practical applications, dynamically adjusting the contribution of the instantaneous activation intensity value to the emotional energy index based on the degree of deviation between the instantaneous activation intensity value and the individualized autonomic nervous system response baseline involves comparing the real-time calculated instantaneous activation intensity value with a pre-constructed individualized autonomic nervous system response baseline. When the instantaneous activation intensity value deviates significantly from the baseline, it indicates strong emotional activation, and its contribution to the emotional energy index can be increased; conversely, if the deviation is small, the contribution is reduced accordingly. This adjustment ensures that the emotional energy index can more accurately reflect the intensity of an individual's emotional response under specific physiological conditions.

[0046] Furthermore, by combining the cumulative weights and time decay factors of the dynamically adjusted emotional energy index, the duration of the instantaneous activation intensity value corresponding to the adjusted contribution is accumulated to generate the emotional energy index. The purpose is to weight and accumulate the individualized and dynamically adjusted instantaneous activation intensity value according to its duration, combined with the cumulative weights and time decay factors of the current stage. This accumulation method comprehensively considers the intensity and duration of emotional activation, the individual's physiological baseline, and the specific physiological stage the patient is in, thereby generating a more accurate and clinically meaningful emotional energy index.

[0047] In some preferred embodiments, it is assumed that a breast cancer chemotherapy patient's autonomic nervous system is highly sensitive to external stimuli during the initial stages of chemotherapy. At the start of a chemotherapy cycle, heart rate variability and skin conductance data are collected in the patient's resting state to construct an individualized autonomic nervous system response baseline. When the patient experiences an emotional fluctuation during chemotherapy, resulting in a transient activation intensity value significantly higher than the individualized autonomic nervous system response baseline, the system dynamically adjusts the cumulative weight of the emotional energy index to a higher value based on the initial stage of chemotherapy, and sets a smaller time decay factor. Simultaneously, because the transient activation intensity value deviates significantly from the baseline, its contribution to the emotional energy index is also dynamically increased. For example, if the transient activation intensity value persists for 5 minutes, then the emotional activation during these 5 minutes will accumulate in the emotional energy index with a higher weight and a slower decay rate.

[0048] As the patient enters the physiological recovery phase, the sensitivity of their autonomic nervous system may decrease. At this time, the system dynamically adjusts the cumulative weight to a lower value and sets a larger time decay factor based on the recovery stage. If a transient activation of the same intensity occurs again, its contribution and cumulative effect on the emotional energy index will differ from that at the beginning of chemotherapy because its deviation from the individualized autonomic nervous system response baseline may be relatively small, and the cumulative weight and time decay factor have been adjusted. Through this dynamic adjustment, the emotional energy index can more accurately reflect the patient's true emotional load at different physiological stages, avoiding misjudgments caused by changes in physiological background, thus providing a more reliable basis for assessing the efficacy of cardiac rehabilitation.

[0049] The steps to determine the attribution of physiological fluctuations include: We collected routine physiological data, heart rate variability data, and skin conductance data from patients in a calm state to construct individualized physiological and emotional response baselines. The threshold for judging fluctuations in routine physiological indicators is dynamically adjusted based on the patient's chemotherapy cycle stage or physiological recovery stage, and in combination with individualized physiological baseline. The attribution threshold of the emotional energy index is dynamically adjusted based on the patient's chemotherapy cycle stage or physiological recovery stage, and in conjunction with the baseline of emotional response. When the fluctuation of routine physiological indicators exceeds the judgment threshold of the fluctuation of routine physiological indicators after dynamic adjustment, and the emotional energy index reaches or exceeds the attribution threshold of the emotional energy index after dynamic adjustment, the physiological fluctuation is judged to be attributed to the influence of emotions. When the fluctuation of routine physiological indicators exceeds the judgment threshold of the fluctuation of routine physiological indicators after dynamic adjustment, and the emotional energy index does not reach the attribution threshold of the emotional energy index after dynamic adjustment, the physiological fluctuation is judged to be attributed to changes in cardiac function.

[0050] Specifically, at the start of a patient's chemotherapy cycle or during the physiological recovery phase, routine physiological indicators, heart rate variability, and skin conductance data are collected from the patient in a calm state. This data is used to construct individualized physiological and emotional response baselines. The individualized physiological baseline reflects the patient's baseline physiological levels in a stress-free state, such as baseline heart rate, blood pressure, and respiratory rate; the individualized emotional response baseline reflects the patient's baseline activation level of the autonomic nervous system in a calm state, serving as a reference for subsequent emotional energy index assessments.

[0051] The thresholds for judging fluctuations in routine physiological indicators and attribution thresholds for emotional energy indices are not fixed but dynamically adjusted based on the patient's stage of chemotherapy or physiological recovery. For example, during the acute phase of chemotherapy, patients may have lower physiological tolerance, and even slight fluctuations in routine physiological indicators may be clinically significant; in this case, the thresholds will be tightened accordingly. Conversely, during the recovery phase, when the patient's physiological state tends to stabilize, the thresholds may be appropriately relaxed. This dynamic adjustment mechanism ensures personalized and clinically relevant assessments.

[0052] In practical applications, when fluctuations in a patient's routine physiological indicators are detected, and these fluctuations exceed the dynamically adjusted threshold for such fluctuations, the system further incorporates the emotional energy index for attribution analysis. Specifically, if the emotional energy index reaches or exceeds the dynamically adjusted attribution threshold, the physiological fluctuation is considered primarily attributable to emotional factors. Conversely, if the emotional energy index does not reach the dynamically adjusted attribution threshold, the physiological fluctuation is more likely attributable to changes in cardiac function.

[0053] This application's approach introduces individualized physiological and emotional response baselines, allowing the assessment of physiological fluctuations and the attribution of emotional impacts to no longer rely on universal, static thresholds. Instead, it fully considers the individual differences and dynamic physiological background of breast cancer chemotherapy patients. Specifically, the construction of individualized baselines provides personalized reference points for subsequent fluctuation assessments, avoiding misjudgments caused by individual differences. Simultaneously, the assessment and attribution thresholds are dynamically adjusted according to the chemotherapy cycle stage or physiological recovery stage, enabling the evaluation system to adapt to the physiological and psychological characteristics of patients at different treatment stages. For example, during chemotherapy-sensitive periods, even small physiological fluctuations may be identified and attributed, while during the recovery period, larger fluctuations may be required to be considered abnormal.

[0054] In some preferred embodiments, it is assumed that a breast cancer chemotherapy patient is in the second cycle of chemotherapy and his routine physiological indicators (such as heart rate and blood pressure) show a slight increase on a certain day.

[0055] First, at the start of the patient's chemotherapy cycle, heart rate, blood pressure, heart rate variability data, and skin conductance data were collected in the patient's calm state, and an individualized physiological baseline and emotional response baseline were constructed for the patient.

[0056] When patients enter the second cycle of chemotherapy, the system dynamically adjusts the threshold for judging fluctuations in routine physiological indicators based on the clinical characteristics of this stage and the individualized physiological baseline. For example, the threshold for judging increased heart rate is adjusted from 10% to 5%. At the same time, the attribution threshold for the emotional energy index is dynamically adjusted based on the emotional response baseline.

[0057] If the system detects that the patient's heart rate has increased by 6% (exceeding the dynamically adjusted 5% threshold) and their emotional energy index has also reached the dynamically adjusted attribution threshold (for example, if continuous monitoring reveals that the patient had obvious signs of autonomic nervous system activation before the heart rate increase, resulting in a higher accumulated emotional energy index), the system will determine that the increase in heart rate is mainly due to emotional influences, such as the patient experiencing anxiety due to concerns about the side effects of chemotherapy.

[0058] Conversely, if the heart rate also increases by 6%, but the emotional energy index does not reach the attribution threshold, the system will determine that the increase in heart rate is more likely to be attributed to changes in cardiac function, suggesting that further examination of cardiac function is needed.

[0059] In this way, this application can provide more refined and accurate attribution results of physiological fluctuations based on the patient's actual situation and stage.

[0060] The steps to determine the attribution of physiological fluctuations also include: Continuously monitor the instantaneous rate of change and duration of the emotional energy index, as well as the fluctuation range of routine physiological indicators; The contribution weight of emotional influence to fluctuations in routine physiological indicators is dynamically adjusted according to the patient's chemotherapy cycle stage or physiological recovery stage. Based on the patient's current cardiac function status, dynamically adjust the weighting of the contribution of cardiac function changes to fluctuations in routine physiological indicators. Based on the instantaneous rate of change and duration of the emotional energy index, as well as the fluctuation range of conventional physiological indicators, the contribution weight of the dynamically adjusted emotional influence on the fluctuation of conventional physiological indicators, and the contribution weight of the dynamically adjusted changes in cardiac function on the fluctuation of conventional physiological indicators, the relative contribution ratio of emotional influence on the fluctuation of conventional physiological indicators and the relative contribution ratio of changes in cardiac function on the fluctuation of conventional physiological indicators are calculated. Based on the relative contribution of emotional influence to fluctuations in routine physiological indicators and the relative contribution of changes in cardiac function to fluctuations in routine physiological indicators, multifactorial attribution of fluctuations in routine physiological indicators was performed, and the specific contribution values ​​of emotional influence and changes in cardiac function to physiological fluctuations were quantified.

[0061] Specifically, continuous monitoring of the instantaneous rate of change and duration of the emotional energy index, as well as the fluctuation range of routine physiological indicators, refers to acquiring, in real-time and continuously, the dynamic trend and duration of the patient's emotional energy index, as well as the real-time fluctuations of routine physiological indicators such as blood pressure, heart rate, and respiratory rate, through wearable devices or medical monitoring equipment. The aim is to capture subtle and real-time changes in the patient's physiological and emotional states, providing comprehensive dynamic data support for subsequent refined attribution.

[0062] The method dynamically adjusts the weighting of emotional influence on fluctuations in routine physiological indicators based on the patient's chemotherapy cycle or physiological recovery phase. This means that patients' sensitivity to emotional stimuli and the intensity of their physiological responses may differ significantly at different chemotherapy stages (e.g., early, middle, and late stages) or during the physiological recovery phase. For example, in the early stages of chemotherapy, patients may experience greater emotional fluctuations due to drug side effects and psychological stress; in this case, the weighting of emotional influence will be appropriately increased. Conversely, during the physiological recovery phase, as bodily functions gradually recover, the weighting of emotional influence may relatively decrease. The aim is to make the attribution of emotional influence more aligned with the patient's individualized physiological background.

[0063] In practical applications, the contribution weight of changes in cardiac function to fluctuations in routine physiological indicators is dynamically adjusted based on the patient's current cardiac function status. Specifically, this involves assessing the patient's cardiac function (e.g., ejection fraction, arrhythmia status) based on the results of echocardiography, electrocardiogram, and myocardial enzyme tests. When a patient's cardiac function is weak or there are potential risks, the contribution weight of changes in cardiac function to physiological fluctuations is increased, and vice versa. The aim is to ensure that cardiac function factors are reasonably and dynamically considered in attribution analysis.

[0064] Furthermore, calculating the relative contribution of emotional influence to fluctuations in conventional physiological indicators, and calculating the relative contribution of changes in cardiac function to fluctuations in conventional physiological indicators, involves establishing mathematical models or algorithms to comprehensively consider the instantaneous rate and duration of change in the emotional energy index, the amplitude of fluctuations in conventional physiological indicators, and the dynamically adjusted contribution weights of emotional influence and changes in cardiac function. This quantifies the percentage or proportion of each factor—emotional or cardiac—in causing physiological fluctuations. The aim is to provide a quantitative and intuitive attribution result, rather than a simple binary judgment.

[0065] Therefore, attributing fluctuations in routine physiological indicators to multiple factors and quantifying the specific contributions of emotional influence and changes in cardiac function to these fluctuations involves, after calculating the relative contribution ratios, further determining the specific numerical contributions of emotional influence and changes in cardiac function to the fluctuations of routine physiological indicators. For example, if a physiological indicator fluctuates by X units, it can be quantified that Y units are due to emotional influence and Z units are due to changes in cardiac function. The aim is to provide more operational assessment results to guide clinicians in making more precise interventions.

[0066] In some preferred embodiments, it is assumed that a breast cancer chemotherapy patient experiences a sudden fluctuation in their routine physiological indicators (such as heart rate) during the middle of chemotherapy.

[0067] First, the system will continuously monitor the instantaneous rate of change and duration of the patient's emotional energy index, as well as the amplitude of heart rate fluctuations.

[0068] Secondly, since the patient is in the middle stage of chemotherapy, the system will dynamically adjust the weight of the contribution of emotions to heart rate fluctuations based on preset rules or machine learning models (for example, increasing the weight of emotions, because patients' emotions may be more unstable in the middle stage of chemotherapy). At the same time, based on the patient's recent cardiac function test results (for example, a slight decrease in ejection fraction), the system will also dynamically adjust the weight of the contribution of changes in cardiac function to heart rate fluctuations (for example, appropriately increasing the weight of cardiac function).

[0069] Next, the system will comprehensively consider the instantaneous rate of change and duration of the emotional energy index, the amplitude of heart rate fluctuations, and the contribution weights of dynamically adjusted emotional influence and cardiac function changes to calculate the relative contribution ratio of emotional influence to heart rate fluctuations (e.g., 60%) and the relative contribution ratio of cardiac function changes to heart rate fluctuations (e.g., 40%).

[0070] Finally, based on these relative contribution ratios, the system performed multifactorial attribution of heart rate fluctuations and quantified that 60% of the amplitude of this heart rate fluctuation was due to emotional influences (e.g., anxiety) and 40% was due to changes in cardiac function (e.g., the slight effect of chemotherapy drugs on the myocardium).

[0071] In this way, doctors can clearly understand that although there is some impact on heart function, the main factor at present is emotional fluctuation. Therefore, they can prioritize psychological intervention measures while closely monitoring heart function to achieve more precise rehabilitation management.

[0072] The steps for attributing fluctuations in routine physiological indicators to multiple factors also include: Continuously monitor the instantaneous rate of change and duration of the patient's emotional energy index, as well as the fluctuation range of routine physiological indicators; Continuously monitor the patient's chemotherapy cycle or physiological recovery phase, as well as the dynamic changes in the patient's cardiac function. Based on the dynamic changes in the patient's cardiac function, the patient's current cardiac function status can be determined. Assess the patient’s current level of emotional sensitivity based on the stage of the chemotherapy cycle or the physiological recovery stage. Assess the patient's current cardiac workload level based on their current cardiac function status; Based on the emotional sensitivity level and cardiac function load level, the interaction coefficient between emotional influence and cardiac function changes is dynamically generated. The interaction coefficient reflects the amplification or inhibition effect of emotional fluctuations on cardiac function under the current physiological background. The instantaneous rate of change and duration of the emotional energy index, as well as the fluctuation range of conventional physiological indicators, the contribution weight of the dynamically adjusted emotional influence on the fluctuation of conventional physiological indicators, the contribution weight of the dynamically adjusted changes in cardiac function on the fluctuation of conventional physiological indicators, and the dynamically generated interaction coefficient are input into the multi-factor attribution logic. The calculation method adjusts the relative contribution ratio of emotional influence and cardiac function changes to the fluctuation of routine physiological indicators by using multi-factor attribution logic and based on the interaction coefficient. Based on the adjusted calculation method, the relative contribution ratios of emotional influence to fluctuations in routine physiological indicators and changes in cardiac function to fluctuations in routine physiological indicators were recalculated. Based on the recalculated relative contribution ratios, multifactorial attribution was performed on the fluctuations of routine physiological indicators, and the specific contributions of emotional influence and changes in cardiac function to physiological fluctuations were quantified.

[0073] Specifically, when attributing fluctuations in routine physiological indicators to multiple factors, it is first necessary to continuously monitor the instantaneous rate and duration of change in the patient's emotional energy index, as well as the amplitude of fluctuations in routine physiological indicators. Simultaneously, it is crucial to continuously monitor the patient's chemotherapy cycle stage or physiological recovery stage, and the dynamic changes in the patient's cardiac function. The dynamic changes in the patient's cardiac function can be used to determine their current cardiac function status, for example, through assessment using data from electrocardiograms, echocardiograms, or biomarkers. Furthermore, based on the patient's chemotherapy cycle stage or physiological recovery stage, their current level of emotional sensitivity can be assessed; for example, at specific stages of chemotherapy, patients may exhibit higher emotional vulnerability or stress responses. Simultaneously, based on the patient's current cardiac function status, their current level of cardiac workload can be assessed; for example, patients with impaired cardiac function will experience a higher level of cardiac workload when facing physiological or psychological stress.

[0074] Furthermore, based on the assessed emotional sensitivity level and cardiac function load level, an interaction coefficient between emotional influence and changes in cardiac function is dynamically generated. This interaction coefficient aims to reflect the amplification or inhibition effect of emotional fluctuations on cardiac function under the current specific physiological context. For example, when a patient has high emotional sensitivity and a high cardiac function load, even slight emotional fluctuations may be amplified, significantly affecting cardiac function; in this case, the interaction coefficient may be an amplification factor greater than 1. Conversely, if the patient's emotions are stable and cardiac function is good, the interaction coefficient may be an inhibition factor close to or less than 1.

[0075] Subsequently, the instantaneous rate of change and duration of the emotional energy index, the fluctuation amplitude of conventional physiological indicators, the dynamically adjusted contribution weight of emotional influence to the fluctuation of conventional physiological indicators, the dynamically adjusted contribution weight of changes in cardiac function to the fluctuation of conventional physiological indicators, and the dynamically generated interaction coefficient are all input into the multi-factor attribution logic. This multi-factor attribution logic dynamically adjusts the calculation method of the relative contribution ratio of emotional influence and changes in cardiac function to the fluctuation of conventional physiological indicators based on the interaction coefficient. For example, the interaction coefficient can be directly used as a multiplier factor to adjust the calculation of the contribution weight of emotion or cardiac function, or as part of a nonlinear function to more finely simulate their interaction. Thus, according to the adjusted calculation method, the relative contribution ratio of emotional influence to the fluctuation of conventional physiological indicators and the relative contribution ratio of changes in cardiac function to the fluctuation of conventional physiological indicators are recalculated. Finally, based on the recalculated relative contribution ratios, multi-factor attribution is performed on the fluctuation of conventional physiological indicators, and the specific contribution values ​​of emotional influence and changes in cardiac function to physiological fluctuations are quantified.

[0076] This application's solution addresses the issue of insufficient consideration of the complex interaction between emotion and cardiac function in the aforementioned multi-factor attribution process by introducing emotional sensitivity levels, cardiac function load levels, and dynamically generated interaction coefficients. This dynamic adjustment mechanism ensures the personalization and contextualization of attribution results, avoiding the biases that may arise from simple, static proportional allocation.

[0077] In some preferred embodiments, suppose a breast cancer chemotherapy patient is in the third cycle of chemotherapy. At this time, patients generally exhibit high levels of fatigue and low mood, and their emotional sensitivity level is assessed as "high." Simultaneously, due to the cumulative toxicity of chemotherapy drugs, the patient's cardiac function is slightly reduced, and their cardiac workload level is assessed as "moderate." In this context, if the patient experiences a sudden family conflict, leading to a significant increase in the instantaneous rate of change of the emotional energy index, and significant fluctuations in routine physiological indicators (such as heart rate and blood pressure), then, based on the "high" emotional sensitivity level and the "moderate" cardiac workload level, the system dynamically generates an interaction coefficient, which may be an amplification factor greater than 1 (e.g., 1.3). This means that under the current physiological context, the impact of emotional fluctuations on cardiac function will be amplified. In multifactor attribution logic, this interaction coefficient of 1.3 will be used to adjust the contribution weight of emotional influence on fluctuations in routine physiological indicators. For example, if in the original calculation, emotion and cardiac function each account for 50% of the contribution, after adjustment by the interaction coefficient, the relative contribution ratio of emotional influence may be recalculated to 70%, while the relative contribution ratio of changes in cardiac function may be 30%. In this way, this application can more accurately quantify the specific contribution of emotional influence to the current physiological fluctuations and distinguish it from the contribution of changes in cardiac function. This refined attribution result can indicate to healthcare professionals that the patient's current physiological fluctuations are mainly due to the amplified effect of emotional stress, rather than simply a deterioration in cardiac function. Therefore, psychological interventions or emotional management measures can be prioritized, while cardiac function is closely monitored to provide more precise rehabilitation guidance.

[0078] This application further proposes steps for dynamically generating the interaction coefficient between emotional influence and changes in cardiac function, including: Continuously monitor the instantaneous rate of change and duration of the patient's emotional energy index, as well as the fluctuation range of routine physiological indicators; Continuously monitor the patient's chemotherapy cycle or physiological recovery phase, as well as the dynamic changes in the patient's cardiac function. When there is uncertainty in the assessment of emotional sensitivity level or cardiac function load level, a multi-source information cross-validation mechanism is initiated based on the fluctuation range of routine physiological indicators, the instantaneous change rate and duration of emotional energy index, and the dynamic changes of cardiac function status to assess the confidence level of emotional sensitivity level and cardiac function load level. When the confidence level of the assessment is lower than the preset threshold, physiological response pattern data of similar patient groups in the past are retrieved to correct the emotional sensitivity level and cardiac function load level. Based on the revised emotional sensitivity level and the patient's current stage of chemotherapy cycle or physiological recovery, assess the patient's current emotional sensitivity level and weight it according to the confidence level of the emotional sensitivity level. The patient's current cardiac workload level is assessed based on the revised cardiac workload level and the patient's current cardiac function status, and weighted according to the confidence level of the cardiac workload level. Based on the weighted emotional sensitivity level and the weighted cardiac function load level, the generator function parameters of the interaction coefficient are dynamically adjusted to generate the interaction coefficient between emotional influence and changes in cardiac function.

[0079] Specifically, continuous monitoring of the instantaneous rate of change and duration of the patient's emotional energy index, as well as the fluctuation range of routine physiological indicators, aims to provide real-time and comprehensive physiological and emotional state data for subsequent assessment and validation. The instantaneous rate of change of the emotional energy index reflects the intensity of emotional fluctuations, while the duration indicates the length of the emotional event. The fluctuation range of routine physiological indicators provides direct evidence of changes in cardiac function or overall physiological state. Continuous monitoring of the patient's chemotherapy cycle or physiological recovery phase, as well as the dynamic changes in the patient's cardiac function, aims to provide important background information and individualized reference for assessing emotional sensitivity levels and cardiac workload levels.

[0080] When there is uncertainty in the assessment of emotional sensitivity level or cardiac function load level, such as when different data sources (e.g., heart rate variability data, skin conductance data, routine physiological indicator data, etc.) provide inconsistent indications for the same assessment indicator, or when the assessment results are within a critical range, a multi-source information cross-validation mechanism can be initiated based on the fluctuation amplitude of routine physiological indicators, the instantaneous rate of change and duration of the emotional energy index, and the dynamic changes in cardiac function status. This mechanism comprehensively analyzes information from different physiological signals and time dimensions to mutually verify or correct the assessment results, thereby assessing the confidence level of emotional sensitivity level and cardiac function load level. The confidence assessment aims to quantify the reliability of the current assessment results, providing a basis for subsequent correction and weighting.

[0081] When the confidence level of the assessment grade falls below a preset threshold, it indicates that the current assessment results may not be accurate or reliable enough. In this case, physiological response pattern data from historically similar patient groups can be used to correct the emotional sensitivity level and cardiac function load level. This data can be understood as a collection of physiological data and emotional response patterns from a large number of patients under similar chemotherapy stages, cardiac function states, and emotional backgrounds. By comparing and matching these historical data with patterns, the current patient's assessment grade can be calibrated and optimized, improving its accuracy.

[0082] Based on the revised emotional sensitivity level and the patient's stage of chemotherapy or physiological recovery, the patient's current emotional sensitivity level is assessed and weighted according to the confidence level of the emotional sensitivity level. Similarly, based on the revised cardiac workload level and the patient's current cardiac function status, the patient's current cardiac workload level is assessed and weighted according to the confidence level of the cardiac workload level. The purpose of weighting is to give greater influence to assessments with higher confidence levels when finally determining the emotional sensitivity level and cardiac workload level, while appropriately weakening the influence of assessments with lower confidence levels, thereby making the final level assessment more robust and reliable.

[0083] Based on weighted emotional sensitivity levels and weighted cardiac function load levels, the generator function parameters of the interaction coefficient are dynamically adjusted to generate the interaction coefficient between emotional influence and changes in cardiac function. Dynamic adjustment of the generator function parameters ensures that the interaction coefficient more accurately reflects the amplification or inhibition effect of emotional fluctuations on cardiac function under the current individual physiological context.

[0084] This application effectively addresses the uncertainties that may exist in the assessment of emotional sensitivity levels and cardiac function load levels by introducing a multi-source information cross-validation mechanism and confidence assessment. When the confidence of the assessment results is insufficient, correction can be made by calling physiological response pattern data from similar historical patient groups, which can significantly improve the accuracy and reliability of the assessment. Furthermore, by applying confidence weighting to the corrected assessment levels, it is ensured that the emotional sensitivity levels and cardiac function load levels used to generate the interaction coefficients are optimized and highly reliable. As a result, the generation of interaction coefficients will be more accurate and can more realistically reflect the complex interaction between emotions and cardiac function.

[0085] This application further proposes steps for generating the interaction coefficient between emotional influence and changes in cardiac function, including: At the start of a patient's chemotherapy cycle or during the physiological recovery phase, physiological response data of the patient under different emotional stimuli intensities were collected to construct an individualized emotional response curve. When a patient's cardiac function status changes, cardiac function index data are collected under different physiological loads to construct an individualized cardiac function load curve. Based on the weighted emotional sensitivity level and the weighted cardiac function load level, the critical point of interaction between emotion and cardiac function is identified at the intersection of the individualized emotional response curve and the individualized cardiac function load curve or in a specific region. Based on the critical point, the parameters of the generation function for the interaction coefficient are dynamically adjusted to generate the interaction coefficient between emotional influence and changes in cardiac function.

[0086] Specifically, at the start of a patient's chemotherapy cycle or during the physiological recovery phase, individualized emotional response curves can be constructed by collecting physiological response data under different intensities of emotional stimuli. The intensity of the emotional stimulus can be understood as quantifying the level of intensity of the emotional stimulus experienced by the patient through standardized emotion-inducing tasks (e.g., watching specific emotional videos, recalling personal experiences, etc.) or questionnaire assessments. Physiological response data includes heart rate variability data, skin conductance data, blood pressure, respiratory rate, etc., which reflect the immediate response of the patient's autonomic nervous system to emotional stimuli. The construction of individualized emotional response curves aims to depict the changing trends and thresholds of the patient's physiological response indicators under different intensities of emotional stimuli, thereby revealing the patient's unique sensitivity to emotional stimuli.

[0087] Furthermore, when a patient's cardiac function changes, individualized cardiac workload curves can be constructed by collecting cardiac function index data under different physiological loads. Physiological load can refer to the progressively increasing workload on the patient's heart through methods such as exercise stress testing (e.g., treadmill or stationary bike exercises) or pharmacological stress testing. Cardiac function index data can include cardiac output, ejection fraction, myocardial contractility, and heart rate, which reflect the heart's functional performance under different loads. The construction of individualized cardiac workload curves aims to depict the patient's cardiac functional reserves and compensatory capacity under different physiological loads, thereby revealing the patient's specific cardiac workload tolerance.

[0088] Based on this, and using weighted emotional sensitivity levels and weighted cardiac function load levels, critical points of interaction between emotion and cardiac function are identified at the intersections or specific regions of individualized emotional response curves and individualized cardiac function load curves. These critical points refer to the turning points or thresholds where cardiac function indicators show significant changes under specific emotional stimulus intensities, or where emotional response indicators show significant fluctuations under specific physiological loads. These critical points reflect key nodes in the interaction between emotion and cardiac function; for example, when the intensity of emotional stimulus reaches a certain level, the patient's cardiac function begins to show insufficient compensation; or when the cardiac load reaches a certain level, the patient's emotional fluctuations become abnormally intense.

[0089] Therefore, based on the identified critical point, the parameters of the generation function for the interaction coefficient are dynamically adjusted to generate the interaction coefficient between emotional influence and changes in cardiac function. The critical point provides a specific and individualized reference for parameter adjustment, enabling the generation function to more accurately reflect the amplification or inhibition effect of emotions on cardiac function under different emotional and physiological states.

[0090] This application proposes a method that, by constructing individualized emotional response curves and individualized cardiac function load curves, can provide a deeper understanding of the unique physiological response patterns of each breast cancer chemotherapy patient under different emotional stimuli and physiological loads.

[0091] The steps for confidence assessment of emotional sensitivity level and cardiac functional load level include: Continuously acquire patient data on heart rate variability, skin conductance, baseline fluctuation range of routine physiological indicators, instantaneous rate of change and duration of emotional energy index, and dynamic changes in cardiac function status; Feature extraction was performed on the heart rate variability data to obtain a heart rate variability feature set; Feature extraction is performed on the skin conductance data to obtain a skin conductance feature set; The baseline fluctuation range of routine physiological indicators was analyzed to obtain the characteristics of physiological baseline fluctuation. The instantaneous rate of change and duration of the emotional energy index are analyzed to obtain the dynamic characteristics of emotion; By analyzing the dynamic changes in cardiac function, the characteristics of cardiac function changes can be obtained. Based on the heart rate variability feature set, skin conductance feature set, physiological baseline fluctuation feature, emotional dynamic feature and cardiac function change feature, the degree of inconsistency between the features is identified and quantified. The weight of each feature in the confidence assessment is dynamically adjusted based on the degree of inconsistency among the features. Based on the dynamically adjusted weights of each feature in the confidence assessment, the fused physiological and emotional comprehensive features are obtained by integrating the heart rate variability feature set, skin conductance feature set, physiological baseline fluctuation feature, emotional dynamic feature and cardiac function change feature. Based on the integrated physiological and emotional characteristics, the confidence levels of emotional sensitivity level and cardiac functional load level were assessed.

[0092] The study continuously acquires patients' heart rate variability data, skin conductance data, baseline fluctuation ranges of routine physiological indicators, instantaneous rate of change and duration of emotional energy index, and dynamic changes in cardiac function status. This aims to provide comprehensive raw data input for subsequent feature extraction and confidence assessment. Heart rate variability and skin conductance data are key indicators reflecting autonomic nervous activity; baseline fluctuation ranges of routine physiological indicators provide a reference for the patient's physiological state; the instantaneous rate of change and duration of emotional energy index directly reflect the dynamic characteristics of emotional fluctuations; and dynamic changes in cardiac function status provide real-time feedback on cardiac health.

[0093] Furthermore, feature extraction is performed on the heart rate variability data to obtain a heart rate variability feature set; feature extraction is performed on the skin conductance data to obtain a skin conductance feature set; the baseline fluctuation range of conventional physiological indicators is analyzed to obtain physiological baseline fluctuation characteristics; the instantaneous rate of change and duration of the emotional energy index are analyzed to obtain emotional dynamic characteristics; and the dynamic changes in cardiac function status are analyzed to obtain cardiac function change characteristics. These features are a refinement and abstraction of the original data, enabling more effective capture of key information from various physiological and emotional dimensions. For example, the heart rate variability feature set can include time-domain, frequency-domain, and nonlinear indicators, and the skin conductance feature set can include the amplitude, latency, and recovery time of the skin conductance response.

[0094] Building upon this foundation, the degree of inconsistency among various features is identified and quantified based on sets of heart rate variability characteristics, skin conductance characteristics, physiological baseline fluctuations, emotional dynamics, and cardiac function changes. For example, if heart rate variability data indicates high stress, but skin conductance data indicates calmness, inconsistency exists. Quantifying this degree of inconsistency helps identify potential contradictions or sources of uncertainty in the assessment. Based on the quantified degree of inconsistency, the weights of each feature in the confidence assessment can be dynamically adjusted, giving higher weights to features that are more stable, consistent, or more relevant to the current situation, thereby improving the accuracy of the assessment.

[0095] Finally, based on the dynamically adjusted weights of each feature in the confidence assessment, the fused sets of heart rate variability features, skin conductance features, physiological baseline fluctuation features, emotional dynamics features, and cardiac function changes features are obtained to obtain the fused comprehensive physiological and emotional features. This comprehensive feature is a complete and weighted representation of the patient's overall physiological and emotional state. Based on this fused comprehensive physiological and emotional feature, the confidence levels of emotional sensitivity and cardiac functional load levels can be assessed, thus providing a more reliable basis for the subsequent generation of interaction coefficients.

[0096] Specifically, the system dataset is divided into six core modules: Raw physiological data (see Table 1, Raw Physiological Data Table) – Raw physiological signals collected by sensors, including heart rate variability, skin conductance, photoplethysmography waveforms, accelerometer data, etc. Emotional energy data (see Table 2, Emotional Energy Index Table) – Emotional energy index calculated based on autonomic nervous system activation state and related intermediate data. Physiological fluctuation attribution data (see Table 3, Physiological fluctuation attribution table) – Results of attribution analysis of physiological indicator fluctuations, distinguishing between emotional influence and changes in cardiac function. Patient information and stage data (see Table 4, Patient information and stage table) – Basic patient information, chemotherapy stage, physiological recovery stage, cardiac function status, etc. Assessment results and report data (see Table 5, Assessment Results Table) – Assessment results of cardiac rehabilitation efficacy and related analysis reports. System configuration and log data (see Table 6, System Configuration and Log Table) – System parameter configuration, model version, operation log, etc.

[0097] Table 1: Raw Physiological Data field name type describe Example id UUID Unique Identifier "p1q2r3s4-t5u6-..." patient_id String Patient ID "PAT202508001" timestamp DateTime Data collection timestamp "2025-12-09 09:23:40" data_type Enum Data types "hrv","gsr","ppg","acc" raw_signal Array[Float] Raw signal data [0.12,0.15,...] sampling_rate Float Sampling frequency (Hz) 100.0 sensor_status Enum Sensor status "normal", "loose", "offline" batch_id String Collection Batch ID "B20251209-001" anomaly_flag Boolean Abnormal data marking true Table 2: Emotional Energy Index Table field name type describe Example eei_id UUID Emotional Energy Index ID "e1f2g3h4-i5j6-..." patient_id String Patient ID "PAT202508001" timestamp DateTime Calculate timestamps "2025-12-09 10:22:17" instantaneous_intensity Float Instantaneous activation strength value 0.75 duration_seconds Float Duration (seconds) 30.5 emotional_energy_value Float Emotional Energy Index 22.8 baseline_deviation Float Degree of deviation from individualized baseline 0.45 stage_weight Float Stage Cumulative Weight 1.2 decay_factor Float Time decay factor 0.95 confidence_score Float Confidence score (0-1) 0.88 Table 3: Attribution Table of Physiological Fluctuations field name type describe Example attribution_id UUID Attribution Record ID "a1b2c3d4-e5f6-..." patient_id String Patient ID "PAT202508001" timestamp DateTime Attribution timestamp "2025-12-09 11:30:00" physiological_indicator Enum Physiological indicator types "heart_rate","blood_pressure" fluctuation_value Float Fluctuation range 12.5 emotion_contribution_ratio Float Emotional influence contribution ratio 0.65 cardiac_contribution_ratio Float Proportion of contribution from changes in cardiac function 0.35 interaction_coefficient Float Interaction coefficient 1.3 attribution_result Enum Attribution results "emotion","cardiac","mixed" confidence level Enum Confidence level "high","medium","low" Table 4: Patient Information and Stage Table field name type describe Example patient_id String Patient ID "PAT202508001" name String Patient Name "Zhang San" age Integer age 48 chemotherapy cycle Integer chemotherapy cycle 2 recovery_stage Enum Physiological recovery phase "acute","chronic" cardiac_function_level Enum Heart function level "normal","mild","moderate" emotional_sensitivity Enum Emotional sensitivity level "low","medium","high" baseline_hrv JSON Individualized baseline heart rate variability {"sdnn":45.2,"rmssd":32.1} baseline_gsr JSON Individualized skin conductance baseline {"mean":2.1,"std":0.3} last_assessment_date Date Last assessment date "2025-07-28" Table 5: Evaluation Results Table field name type describe Example assessment_id UUID Evaluation Record ID "r1s2t3u4-v5w6-..." patient_id String Patient ID "PAT202508001" assessment_date Date Evaluation Date "2025-12-09" overall_score Float Overall efficacy score (0-100) 78.5 emotion_impact_score Float Emotional Impact Score 65.0 cardiac_function_score Float Cardiac function score 85.0 recommendation Text Rehabilitation suggestions "Strengthen psychological intervention and monitor heart rate variability." physician_notes Text Doctor's Notes "Significant mood swings, but stable cardiac function." report_path String Assessment report storage path " / reports / 20251209_PAT001.pdf" Table 6: System Configuration and Log Table field name type describe Example log_id UUID Log ID "l1m2n3o4-p5q6-..." timestamp DateTime Log time "2025-12-09 12:00:00" module String Module Name "emotional_energy_calculator" action String Operation type "model_update","param_adjust" old_value JSON Adjusted previous value {"weight":1.0,"decay":0.9} new_value JSON Adjusted value {"weight":1.2,"decay":0.95} operator String Operator "system_auto" status Enum Execution status "success", "failed" Specifically, regarding the database selection for data storage, for time-series data: raw physiological data is stored using a time-series database (such as InfluxDB), supporting high-frequency data writing and querying. For relational data: emotional energy data, attribution data, patient information, assessment results, etc., are stored using PostgreSQL, supporting complex queries and transaction processing. For document-based data: individualized baselines, configuration parameters, and other JSON-structured data are stored using MongoDB for flexible expansion. Regarding data retention strategies: raw physiological data is retained for 30 days, compressed, and archived to object storage; emotional energy index data is retained for 2 years, stored by monthly partitions; physiological fluctuation attribution data is retained for 3 years, partitioned by quarter; patient information and stage data are retained permanently, with a main table and a historical version table; assessment result data is retained permanently, stored by patient ID partitions; system log data is retained for 1 year, archived by module and date.

[0098] Regarding data security and privacy, all patient data is anonymized, and the patient_id is de-identified; data transmission uses TLS / SSL encryption; data access permissions are based on role control (RBAC) to ensure that only authorized personnel can access the data; and the data protection regulations such as HIPAA / GDPR are in compliance with regulations.

[0099] Regarding dataset updates and maintenance: Daily automatic data quality checks are performed to mark abnormal data; weekly dataset health reports are generated, including indicators such as data integrity, consistency, and timeliness; manual data entry and correction processes are supported, and operation logs are recorded.

[0100] refer to Figure 2 This application discloses a cardiac rehabilitation efficacy assessment system for breast cancer chemotherapy patients, applied to a method for assessing the cardiac rehabilitation efficacy of breast cancer chemotherapy patients. The system includes: The acquisition module continuously acquires heart rate variability and skin conductance data from breast cancer chemotherapy patients; The judgment module, based on heart rate variability data and skin conductance data, determines the activation state of the patient's autonomic nervous system; The generation module quantifies the intensity and duration of the patient's internal emotional state based on the activation state of the patient's autonomic nervous system, and generates an emotional energy index. The attribution module, when a patient’s routine physiological indicators fluctuate, combines the emotional energy index to determine the attribution results of the physiological fluctuations, which is used to distinguish whether the physiological fluctuations are caused by changes in cardiac function or by emotional influences. The assessment module evaluates the effectiveness of cardiac rehabilitation for patients based on the attribution results of physiological fluctuations.

[0101] Specifically, the acquisition module is configured to continuously collect physiological data from breast cancer chemotherapy patients, such as heart rate variability data and skin conductance data. This data can be monitored and collected in real time through various sensors or wearable devices, with the aim of providing continuous and reliable raw input for subsequent autonomic nervous system state assessment and emotional energy index generation.

[0102] The judgment module is configured to receive heart rate variability data and skin conductance data from the acquisition module, and analyze and judge the activation state of the patient's autonomic nervous system based on this data. This module uses a specific algorithm model to identify the activation patterns of the sympathetic or parasympathetic nervous system, thus providing a basis for quantifying emotional states.

[0103] The generation module is configured to quantify the intensity and duration of the patient's internal emotional state based on the autonomic nervous system activation state output by the judgment module, and generate an emotional energy index. This index aims to objectively reflect the degree and persistence of the patient's emotional fluctuations, providing important emotional dimension information for attributing physiological fluctuations.

[0104] The attribution module is configured to determine the specific attribution of physiological fluctuations when they occur in a patient's routine physiological indicators, by combining the emotional energy index output by the generation module. This module comprehensively analyzes the correlation between physiological indicator fluctuations and the emotional energy index to distinguish whether the physiological fluctuations are primarily due to changes in cardiac function or influenced by emotions.

[0105] The assessment module is configured to receive physiological fluctuation attribution results from the attribution module and evaluate the patient's cardiac rehabilitation efficacy based on these results. This module can comprehensively assess the patient's cardiac function recovery, emotional management ability, and overall rehabilitation progress based on the attribution results, thereby providing a basis for medical decision-making.

[0106] Through this modular design and collaborative operation, the system overcomes the limitations of traditional methods, such as fragmented data processing and poor real-time performance, thereby automating and intelligentizing the evaluation process and significantly improving the efficiency and accuracy of the evaluation.

[0107] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A method for evaluating the therapeutic effect of cardiac rehabilitation for breast cancer chemotherapy patients, characterized by, The method includes the following steps: Continuously acquire heart rate variability and skin conductance data from breast cancer chemotherapy patients; Based on heart rate variability data and skin conductance data, the activation status of the patient's autonomic nervous system is determined; Based on the activation state of the patient's autonomic nervous system, the intensity and duration of the patient's internal emotional state are quantified to generate an emotional energy index. When a patient’s routine physiological indicators fluctuate, the emotional energy index is used to determine the attribution of physiological fluctuations, which can be used to distinguish whether the physiological fluctuations are caused by changes in cardiac function or by emotional influences. The efficacy of cardiac rehabilitation was evaluated based on the attribution results of physiological fluctuations.

2. The method for evaluating the therapeutic effect of cardiac rehabilitation of a breast cancer chemotherapy patient according to claim 1, wherein The steps for generating an emotional energy index, based on the activation state of the patient's autonomic nervous system, to quantify the intensity and duration of the patient's internal emotional state include: Analyze accelerometer data, assess equipment contact status, and obtain equipment contact status information; Continuously acquire raw waveforms of photoplethysmography, heart rate variability data, raw skin conductance data, and device contact status information; The original waveform of the photoplethysmography is filtered, and the waveform morphology parameters of the original waveform are analyzed. Calculate time-domain and frequency-domain indices for heart rate variability data; Filter the raw skin conductance data to identify skin conductance response characteristics; The waveform morphology parameters, time domain indicators, frequency domain indicators, skin conductance response characteristics, and device contact status information are input into the real-time discrimination mechanism. By using a real-time discrimination mechanism, the correlation and temporal consistency between waveform morphology parameters, time domain indicators, frequency domain indicators, skin conductance response characteristics, and device contact status information are analyzed to determine the authenticity of autonomic nerve activation. When autonomic nerve activation is judged to be real, the instantaneous activation intensity value is calculated; An emotional energy index is generated by accumulating the instantaneous activation intensity value and the duration of the instantaneous activation intensity value.

3. The method for evaluating the therapeutic effect of cardiac rehabilitation of a breast cancer chemotherapy patient according to claim 2, wherein The steps involved in accumulating and generating an emotional energy index include: At the start of a patient's chemotherapy cycle or during the physiological recovery phase, heart rate variability and skin conductance data were collected in the patient's calm state to construct an individualized autonomic nervous system response baseline. The cumulative weight and time decay factor of the emotional energy index are dynamically adjusted according to the patient's chemotherapy cycle stage or physiological recovery stage. Based on the degree of deviation between the instantaneous activation intensity value and the individualized autonomic nervous system response baseline, the contribution of the instantaneous activation intensity value to the emotional energy index is dynamically adjusted. Combined with the cumulative weight of the dynamically adjusted emotional energy index and the time decay factor, the duration of the instantaneous activation intensity value corresponding to the adjusted contribution is accumulated to generate the emotional energy index.

4. The method for evaluating the therapeutic effect of cardiac rehabilitation of a breast cancer chemotherapy patient according to claim 1, wherein The steps to determine the attribution of physiological fluctuations include: We collected routine physiological data, heart rate variability data, and skin conductance data from patients in a calm state to construct individualized physiological and emotional response baselines. The threshold for judging fluctuations in routine physiological indicators is dynamically adjusted based on the patient's chemotherapy cycle stage or physiological recovery stage, and in combination with individualized physiological baseline. The attribution threshold of the emotional energy index is dynamically adjusted based on the patient's chemotherapy cycle stage or physiological recovery stage, and in conjunction with the baseline of emotional response. When the fluctuation of routine physiological indicators exceeds the judgment threshold of the fluctuation of routine physiological indicators after dynamic adjustment, and the emotional energy index reaches or exceeds the attribution threshold of the emotional energy index after dynamic adjustment, the physiological fluctuation is judged to be attributed to the influence of emotions. When the fluctuation of routine physiological indicators exceeds the judgment threshold of the fluctuation of routine physiological indicators after dynamic adjustment, and the emotional energy index does not reach the attribution threshold of the emotional energy index after dynamic adjustment, the physiological fluctuation is judged to be attributed to changes in cardiac function.

5. The method for evaluating the therapeutic effect of cardiac rehabilitation of a breast cancer chemotherapy patient according to claim 1, wherein The steps to determine the attribution of physiological fluctuations also include: Continuously monitor the instantaneous rate of change and duration of the emotional energy index, as well as the fluctuation range of routine physiological indicators; The contribution weight of emotional influence to fluctuations in routine physiological indicators is dynamically adjusted according to the patient's chemotherapy cycle stage or physiological recovery stage. Based on the patient's current cardiac function status, dynamically adjust the weighting of the contribution of cardiac function changes to fluctuations in routine physiological indicators. Based on the instantaneous rate of change and duration of the emotional energy index, as well as the fluctuation range of conventional physiological indicators, the contribution weight of the dynamically adjusted emotional influence on the fluctuation of conventional physiological indicators, and the contribution weight of the dynamically adjusted changes in cardiac function on the fluctuation of conventional physiological indicators, the relative contribution ratio of emotional influence on the fluctuation of conventional physiological indicators and the relative contribution ratio of changes in cardiac function on the fluctuation of conventional physiological indicators are calculated. Based on the relative contribution of emotional influence to fluctuations in routine physiological indicators and the relative contribution of changes in cardiac function to fluctuations in routine physiological indicators, multifactorial attribution of fluctuations in routine physiological indicators was performed, and the specific contribution values ​​of emotional influence and changes in cardiac function to physiological fluctuations were quantified.

6. The method for evaluating the therapeutic effect of cardiac rehabilitation of a breast cancer chemotherapy patient according to claim 5, wherein The steps for multifactorial attribution of fluctuations in routine physiological indicators also include: Continuously monitor the instantaneous rate of change and duration of the patient's emotional energy index, as well as the fluctuation range of routine physiological indicators; Continuously monitor the patient's chemotherapy cycle or physiological recovery phase, as well as the dynamic changes in the patient's cardiac function. Based on the dynamic changes in the patient's cardiac function, the patient's current cardiac function status can be determined. Assess the patient’s current level of emotional sensitivity based on the stage of the chemotherapy cycle or the physiological recovery stage. Assess the patient's current cardiac workload level based on their current cardiac function status; Based on the emotional sensitivity level and cardiac function load level, the interaction coefficient between emotional influence and cardiac function changes is dynamically generated. The interaction coefficient reflects the amplification or inhibition effect of emotional fluctuations on cardiac function under the current physiological background. The instantaneous rate of change and duration of the emotional energy index, as well as the fluctuation range of conventional physiological indicators, the contribution weight of the dynamically adjusted emotional influence on the fluctuation of conventional physiological indicators, the contribution weight of the dynamically adjusted changes in cardiac function on the fluctuation of conventional physiological indicators, and the dynamically generated interaction coefficient are input into the multi-factor attribution logic. The calculation method adjusts the relative contribution ratio of emotional influence and cardiac function changes to the fluctuation of routine physiological indicators by using multi-factor attribution logic and based on the interaction coefficient. Based on the adjusted calculation method, the relative contribution ratios of emotional influence to fluctuations in routine physiological indicators and changes in cardiac function to fluctuations in routine physiological indicators were recalculated. Based on the recalculated relative contribution ratios, multifactorial attribution was performed on the fluctuations of routine physiological indicators, and the specific contributions of emotional influence and changes in cardiac function to physiological fluctuations were quantified.

7. The method for evaluating the therapeutic effect of cardiac rehabilitation of a breast cancer chemotherapy patient according to claim 6, wherein the cardiac rehabilitation is performed for 6 months or more. The steps for dynamically generating the interaction coefficient between emotional influence and changes in cardiac function include: Continuously monitor the instantaneous rate of change and duration of the patient's emotional energy index, as well as the fluctuation range of routine physiological indicators; Continuously monitor the patient's chemotherapy cycle or physiological recovery phase, as well as the dynamic changes in the patient's cardiac function. When there is uncertainty in the assessment of emotional sensitivity level or cardiac function load level, a multi-source information cross-validation mechanism is initiated based on the fluctuation range of routine physiological indicators, the instantaneous change rate and duration of emotional energy index, and the dynamic changes of cardiac function status to assess the confidence level of emotional sensitivity level and cardiac function load level. When the confidence level of the assessment is lower than the preset threshold, physiological response pattern data of similar patient groups in the past are retrieved to correct the emotional sensitivity level and cardiac function load level. Based on the revised emotional sensitivity level and the patient's current stage of chemotherapy cycle or physiological recovery, assess the patient's current emotional sensitivity level and weight it according to the confidence level of the emotional sensitivity level. The patient's current cardiac workload level is assessed based on the revised cardiac workload level and the patient's current cardiac function status, and weighted according to the confidence level of the cardiac workload level. Based on the weighted emotional sensitivity level and the weighted cardiac function load level, the generator function parameters of the interaction coefficient are dynamically adjusted to generate the interaction coefficient between emotional influence and changes in cardiac function.

8. The method for evaluating the therapeutic effect of cardiac rehabilitation of a breast cancer chemotherapy patient according to claim 7, characterized in that, The steps to generate the interaction coefficient between emotional influence and changes in cardiac function include: At the start of a patient's chemotherapy cycle or during the physiological recovery phase, physiological response data of the patient under different emotional stimuli intensities were collected to construct an individualized emotional response curve. When a patient's cardiac function status changes, cardiac function index data are collected under different physiological loads to construct an individualized cardiac function load curve. Based on the weighted emotional sensitivity level and the weighted cardiac function load level, the critical point of interaction between emotion and cardiac function is identified at the intersection of the individualized emotional response curve and the individualized cardiac function load curve or in a specific region. Based on the critical point, the parameters of the generation function for the interaction coefficient are dynamically adjusted to generate the interaction coefficient between emotional influence and changes in cardiac function.

9. The method for evaluating the therapeutic effect of cardiac rehabilitation of a breast cancer chemotherapy patient according to claim 7, wherein the cardiac rehabilitation is performed for 6 months or more. The steps for confidence assessment of emotional sensitivity level and cardiac functional load level include: Continuously acquire patient data on heart rate variability, skin conductance, baseline fluctuation range of routine physiological indicators, instantaneous rate of change and duration of emotional energy index, and dynamic changes in cardiac function status; Feature extraction was performed on the heart rate variability data to obtain a heart rate variability feature set; Feature extraction is performed on the skin conductance data to obtain a skin conductance feature set; The baseline fluctuation range of routine physiological indicators was analyzed to obtain the characteristics of physiological baseline fluctuation. The instantaneous rate of change and duration of the emotional energy index are analyzed to obtain the dynamic characteristics of emotion; By analyzing the dynamic changes in cardiac function, the characteristics of cardiac function changes can be obtained. Based on the heart rate variability feature set, skin conductance feature set, physiological baseline fluctuation feature, emotional dynamic feature and cardiac function change feature, the degree of inconsistency between the features is identified and quantified. The weight of each feature in the confidence assessment is dynamically adjusted based on the degree of inconsistency among the features. Based on the dynamically adjusted weights of each feature in the confidence assessment, the fused physiological and emotional comprehensive features are obtained by integrating the heart rate variability feature set, skin conductance feature set, physiological baseline fluctuation feature, emotional dynamic feature and cardiac function change feature. Based on the integrated physiological and emotional characteristics, the confidence levels of emotional sensitivity level and cardiac functional load level were assessed.

10. A breast cancer chemotherapy patient cardiac rehabilitation therapeutic effect evaluation system applied to the breast cancer chemotherapy patient cardiac rehabilitation therapeutic effect evaluation method of claim 1, characterized in that, The system includes: The acquisition module continuously acquires heart rate variability and skin conductance data from breast cancer chemotherapy patients; The judgment module, based on heart rate variability data and skin conductance data, determines the activation state of the patient's autonomic nervous system; The generation module quantifies the intensity and duration of the patient's internal emotional state based on the activation state of the patient's autonomic nervous system, and generates an emotional energy index. The attribution module, when a patient’s routine physiological indicators fluctuate, combines the emotional energy index to determine the attribution results of the physiological fluctuations, which is used to distinguish whether the physiological fluctuations are caused by changes in cardiac function or by emotional influences. The assessment module evaluates the effectiveness of cardiac rehabilitation for patients based on the attribution results of physiological fluctuations.

Images