WO2017016086A1 - 基于生理信息的抑郁症评估系统及其评估方法 - Google Patents
基于生理信息的抑郁症评估系统及其评估方法 Download PDFInfo
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Definitions
- the invention relates to a depression evaluation technology, in particular to a physiological information-based depression evaluation system and an evaluation method thereof.
- the pathogenesis of depression is mostly concentrated on neurotransmitters and their receptors, especially monoamine neurotransmitters and their receptors. It is believed that neuropeptides play an important role in the pathogenesis of depression. But so far, there is no unified conclusion about the pathogenesis of depression.
- physiological information such as ECG, pulse wave, EEG, skin electricity, stomach power, myoelectricity, ocular electricity, polysomnography, and temperature in patients with depression are different from those in normal people.
- the time domain, frequency domain, and time domain geometric parameters of the electrical signal are different. Therefore, according to the differences in the performance of various physiological information, the signal is processed, a large number of signal parameters are calculated, and the mathematical model of depression assessment is established to evaluate depression, which has research basis, feasibility and clinical applicability.
- the primary object of the present invention is to overcome the shortcomings and deficiencies of the existing depression evaluation techniques and to provide a A physiological information-based depression assessment system that collects human ECG information and one or more physiological information of pulse wave, EEG, galvanic, gastric, electromyographic, ocular, polysomn, and temperature. Calculate the time domain and frequency domain parameters of physiological information, extract feature parameter sets, establish a mathematical model of depression assessment, and then evaluate whether the subject has depression and depression level.
- Another object of the present invention is to overcome the shortcomings and deficiencies of the existing depression evaluation method, and to provide an evaluation method applied to a physiological information-based depression evaluation system, which can objectively and quantitatively assess whether a subject suffers from Depression and depression levels.
- a physiological information-based depression evaluation system comprising: an information acquisition module, a signal processing module, a parameter calculation module, a feature selection module, a machine learning module, and an output result module.
- the information collecting module is configured to collect an ECG signal and selectively acquire one of a pulse wave signal, an EEG signal, a skin electrical signal, an gastric electrical signal, an EMG signal, an EOG signal, a polysomnographic signal, and a temperature signal. More than one type of physiological information.
- the signal collected by the information acquisition module is transmitted to the signal processing module by means of USB serial cable transmission or Bluetooth wireless transmission.
- a signal processing module configured to perform signal processing on physiological information, including an electrocardiographic signal processing unit, a pulse wave signal processing unit, an electroencephalogram signal processing unit, a skin electrical signal processing unit, a gastric electrical signal processing unit, an electromyography signal processing unit, An ocular signal processing unit, a polysomnographic signal processing unit, and a temperature signal processing unit.
- the central electrical signal processing unit includes de-baseline processing, filter denoising processing, extraction of sinus beat interval (RR interval) processing, interpolation processing, Fourier transform processing, and spectral analysis and spectral estimation processing.
- the pulse wave signal processing unit includes de-baseline processing, filter denoising processing, extraction pulse interval (PP interval) processing, interpolation processing, Fourier transform processing, and spectral analysis and spectral estimation processing.
- the EEG signal processing unit includes de-baseline processing, threshold denoising processing, wavelet decomposition processing, and spectral analysis and spectral estimation processing.
- the electrical electrical signal processing unit includes a de-baseline processing and a wavelet filtering process.
- the gastric electrical signal processing unit includes de-baseline processing, Hilbert-Huang transform processing, wavelet analysis processing, multi-resolution analysis processing, and independent component analysis processing.
- the EMG signal processing unit includes de-baseline processing and wavelet packet adaptive threshold denoising.
- the EO signal processing unit includes de-baseline processing, weighted median filtering processing, and wavelet transform processing.
- the polysomnographic signal processing unit includes processing a sleep brain electrical signal, a sleep myoelectric signal, and a sleep ocular electrical signal, performing de-baseline processing, threshold denoising processing, wavelet decomposition processing, spectrum analysis, and spectral estimation processing on the sleep brain electrical signal.
- De-baseline processing, weighted median filtering processing, and wavelet transform processing are performed on the sleep electro-oculogram signal, and the sleep electromyogram signal is subjected to de-baseline processing, wavelet packet adaptive threshold denoising processing, and sleep staging processing.
- Temperature signal processing unit includes de-baseline processing, threshold filtering processing, and construction The relationship between the temperature value and the gray value of the image.
- the signal processing module outputs the processed signal to the parameter calculation module.
- the parameter calculation module is configured to calculate signal parameters of the processed signal, including an electrocardiogram parameter calculation unit, a pulse wave parameter calculation unit, an electroencephalogram parameter calculation unit, a skin electrical parameter calculation unit, a gastric electrical parameter calculation unit, and an electromyogram parameter calculation. Unit, electrooculogram parameter calculation unit, polysomnography parameter calculation unit, and temperature parameter calculation unit.
- the central electrical parameter calculation unit includes calculating the RR interval, the mean of all RR intervals (Mean), the standard deviation of the heartbeat interval (SDNN), the root mean square (RMSSD) of the difference between adjacent heartbeat intervals, and a 50-millisecond interval.
- the pulse wave parameter calculation unit includes calculating the PP interval, the mean of all PP intervals (Mean), the standard deviation of the pulse interval (SDNN), the root mean square (RMSSD) of the difference between adjacent pulse intervals, and the interval of 50 msec or more.
- Proportion of adjacent pulse interval differences PNN50
- standard deviation between adjacent pulse intervals SDSD
- VLF very low frequency components
- LF low frequency components
- HF high frequency components
- TP total spectrum power
- SD2 slope of short-term detrended fluctuation analysis
- a2 slope of long-term de-trend fluctuation analysis
- the EEG parameter calculation unit includes calculation of delta wave amplitude, ⁇ wave power, ⁇ wave mean, ⁇ wave variance, ⁇ wave hemiplegia, ⁇ wave kurtosis, ⁇ wave amplitude, ⁇ wave power, ⁇ wave mean, ⁇ wave Variance, ⁇ wave hemiplegia, ⁇ wave kurtosis, ⁇ wave amplitude, ⁇ wave power, ⁇ wave mean, ⁇ wave variance, ⁇ wave hemiplegia, ⁇ wave kurtosis, ⁇ wave amplitude, ⁇ wave power, ⁇ wave mean, ⁇ wave variance, ⁇ wave hemiplegia, ⁇ wave kurtosis and wavelet entropy.
- the skin electrical parameter calculation unit includes calculating a skin sympathetic response latency, a skin sympathetic response amplitude, and a skin resistance value.
- the gastric electrical parameter calculation unit includes calculating a normal gastric electrical rhythm, a slow wave, a hyperkinetic component, and a tachycardia component.
- the myoelectric parameter calculation unit includes a calculation base value, a minimum value, a maximum value, a myoelectric decline ability, and an electromyogram curve.
- the electrooculogram parameter calculation unit includes calculating an R wave component, an r wave component, an S wave component, and an s wave component.
- the polysomnographic signal parameter calculation unit includes calculating a sleep latency, a total sleep time, an arousal index, a sleep period (S1), a shallow sleep period (S2), a moderate sleep period (S3), a deep sleep period (S4), and rapid eye movement. Percentage, number of rapid eye movement sleep cycles, rapid eye movement sleep latency, rapid eye movement sleep intensity, rapid eye movement sleep density, and rapid eye movement sleep time.
- the temperature parameter calculation unit includes calculating a temperature distribution in the human body.
- the parameter calculation module outputs signal parameters to the feature selection module.
- a feature selection module is configured to obtain a feature parameter set related to a depression level among all signal parameters.
- the feature selection module outputs a feature parameter set to the machine learning module.
- the machine learning module is used to train the classifier of the depression level quantification, and the feature parameter set is used to establish a mathematical model of depression assessment to quantify the level of depression.
- the machine learning module outputs a depression level to the output result module.
- An output result module is used to display the level of depression output by the mathematical model of the depression assessment.
- an evaluation method applied to a physiological information-based depression evaluation system which may include the following steps:
- Step 1 Signal processing of the ECG signal and simultaneously signal one of a pulse wave signal, an EEG signal, a skin electrical signal, an gastric electrical signal, an EMG signal, an EEG signal, a polysomn signal, and a temperature signal or More than one signal is used for signal processing and the signal parameters of the processed signal are calculated. among them:
- Skin electrical signal processing and parameter calculation The skin sympathetic response latency, skin sympathetic response amplitude and skin resistance value were calculated by skin electrical signal to baseline processing and wavelet filtering;
- Gastric electrical signal processing and parameter calculations calculate normal gastric electrical rhythm, slow wave, gastric hyperactivity and gastric motility through gastric electrical signal de-baseline processing, Hilbert-Huang transform processing, wavelet analysis processing, multi-resolution analysis processing, and independent component analysis processing. Overspeed component
- EMG signal processing and parameter calculation The baseline value, minimum value, highest value, myoelectric decline ability and myoelectric curve were calculated by the EMG signal to baseline processing and the wavelet packet adaptive threshold denoising process;
- Electro-oculogram signal processing and parameter calculation The R wave component, the r wave component, the S wave component and the s wave component are calculated by the EOG de-baseline processing, the weighted median filtering process, and the wavelet transform process;
- Temperature signal processing and parameter calculation The temperature distribution in the human body is calculated by the temperature signal to baseline processing, the threshold filtering process, and the relationship between the temperature value and the gray value of the image.
- Step 2 normalize the signal parameters calculated in step 1, and perform feature selection on the parameter set composed of the normalized signal parameters to obtain a feature parameter set.
- the normalized processing method :
- X is the signal parameter of the parameter set
- X i is the i-th normalized signal parameter value
- X in is the i-th normalized value
- X imean is the normal mean of the i-th parameter.
- X istd represents the normal standard deviation of the ith parameter, and i is a positive integer.
- the feature selection is divided into two parts: feature search and evaluation criteria, wherein the search algorithm uses one or more combinations of the following algorithms: Complete Search, Sequential Search, Random Search Algorithm (Random) Search), Genetic Algorithm, Simulated Annealing, traceable greedy search expansion algorithm, evaluation criteria can optionally use Wapper model or CfsSubsetEval attribute evaluation method.
- the ECG and pulse wave signals are acquired during the evaluation process.
- the feature selection is combined with the full search algorithm and the Wapper model. During the evaluation process, ECG, EKG and polysomnography signals are acquired. The feature selection is combined with a random search algorithm. The way the CfsSubsetEval property evaluates methods. According to different types of acquired signals, select a combination of algorithms with appropriate and high accuracy.
- Step 3 Perform machine learning according to the feature parameter set obtained in step 2, and establish a mathematical model of depression assessment in the process of machine learning using the feature parameter set.
- the machine learning algorithm may selectively use one or more of the following algorithms: Bayesian, Decision Tree, AdaBoost, k-Nearest Neighbor ), Support Vector Machine (SVM).
- SVM Support Vector Machine
- Y is the output value of the mathematical model of depression assessment
- n is the number of machine learning algorithms selected for use
- y i is the output value of the i-th algorithm
- a i is the coefficient of the i-th algorithm
- i is a positive integer.
- the present invention has the following advantages and beneficial effects:
- the present invention relates to an electrocardiographic signal and a signal or more than one of a pulse wave signal, an EEG signal, a skin electrical signal, an gastric electrical signal, an EMG signal, an EOG signal, a polysomnographic signal, and a temperature signal.
- Signal combination, signal processing, parameter calculation, mathematical model establishment, multiple signal combinations can be selected for evaluation, flexibility and novelty;
- the present invention proposes a method for normalizing signal parameters, comparing parameters with mean and standard deviations in normal samples, eliminating differences in numerical values and deviations of parameters, and making parameter set feature selection more scientific and effective;
- the present invention proposes a combination of multiple feature selection and machine learning algorithms, and the mathematical model is more flexible in terms of signal types;
- Figure 1 is a schematic diagram of a depression assessment system based on physiological information.
- FIG. 2 is a structural diagram of a depression information evaluation system based on physiological information.
- a physiological information-based depression evaluation system includes: an information acquisition module, a signal processing module, a parameter calculation module, a feature selection module, a machine learning module, and an output result module; and the signal collected by the information acquisition module passes USB serial cable transmission or Bluetooth wireless transmission is transmitted to the signal processing module.
- the signal processing module outputs the processed signal to the parameter calculation module.
- the parameter calculation module outputs signal parameters to the feature selection module.
- the feature selection module outputs a feature parameter set to the machine learning module.
- the machine learning module outputs a depression level to the output result module.
- the structure of the physiological information-based depression evaluation system is as shown in FIG. 2, and the information collection module is configured to collect an electrocardiogram signal and collect a pulse wave signal, an EEG signal, a skin electrical signal, a gastric electrical signal, and a muscle.
- the signal processing module is configured to process physiological information, including de-baseline processing, filtering denoising processing, extracting heartbeat interval processing, time-frequency transform processing, and spectrum analysis and spectrum estimation processing.
- the parameter calculation module is configured to calculate a signal parameter of the processed signal, including a time domain parameter of a heart rate variability, a frequency domain parameter, and a time domain geometric parameter, and selectively calculate a pulse wave signal according to the collected physiological information, Time domain parameters, frequency domain parameters, histogram parameters, distribution maps of one or more signals in brain electrical signals, electrical signals, gastric electrical signals, myoelectric signals, ocular electrical signals, polysomnographic signals, and temperature signals parameter.
- the feature selection module is configured to obtain a feature parameter set related to a depression level among all signal parameters.
- the machine learning module is configured to train a classifier for quantifying the level of depression, and use the feature parameter set to establish a mathematical model of depression assessment to quantify the level of depression.
- the output result module is configured to display a depression level output by a mathematical model of depression assessment.
- Step 1 Obtain physiological information, including electrocardiogram, and one or more physiological information of pulse wave, brain electricity, skin electricity, stomach electricity, myoelectricity, ocular electricity, polysomnography, and temperature. among them:
- the ECG signal acquisition can be selected to measure the ECG signal in a resting state of five minutes, and the sampling rate of the ECG acquisition can be selected at 500 Hz or more;
- Pulse wave acquisition can selectively use the pulse sensor of the infrared light transmission tip to output the blood volume change of the end of the blood vessel to collect the pulse signal, or use the shock measurement method to collect the wrist pulse signal.
- Pulse wave acquisition sampling rate can be selected 500Hz or more;
- EEG acquisition can choose to use 10-20 system point excitation to collect spontaneous brain electrical activity in the cerebral cortex;
- the skin electrical collection was tested by skin sympathetic response, single pulse percutaneous electrical stimulation of the median nerve of the wrist, testing the initial latency and amplitude of the skin sympathetic response, and testing the skin resistance values of the right hand large fish muscle and forearm volar side;
- Gastric electricity collection uses gastric surface electrodes placed in the upper abdomen to measure gastric myoelectric activity
- the myoelectric collection is stimulated by a biofeedback device, and the myoelectric electrode connected to the forehead is used to measure the signal of the myoelectricity;
- Polysomnography measures sleep time and its parameters by simultaneously collecting ocular electricity, mandibular electromyography and EEG;
- Temperature acquisition can use the infrared temperature measurement principle to measure the temperature of the body.
- Signal acquisition is a common signal acquisition.
- Step 2 Perform signal processing on the physiological information acquired in step 1, and calculate signal parameters; the specific parameter list is shown in Table 1 below, and Table 1 is a list of electrical signals and their parameter descriptions:
- ECG signal processing and parameter calculation calculate RR interval, Mean, through ECG signal to baseline processing, filter denoising processing, extraction RR interval processing, interpolation processing, Fourier transform processing, and spectral analysis and spectral estimation processing.
- Skin electrical signal processing and parameter calculation The skin sympathetic response latency, skin sympathetic response amplitude and skin resistance value were calculated by skin electrical signal to baseline processing and wavelet filtering;
- Gastric electrical signal processing and parameter calculations calculate normal gastric electrical rhythm, slow wave, gastric hyperactivity and gastric motility through gastric electrical signal de-baseline processing, Hilbert-Huang transform processing, wavelet analysis processing, multi-resolution analysis processing, and independent component analysis processing. Overspeed component
- EMG signal processing and parameter calculation The baseline value, minimum value, highest value, myoelectric decline ability and myoelectric curve were calculated by the EMG signal to baseline processing and the wavelet packet adaptive threshold denoising process;
- Electro-oculogram signal processing and parameter calculation The R wave component, the r wave component, the S wave component and the s wave component are calculated by the EOG de-baseline processing, the weighted median filtering process, and the wavelet transform process;
- Temperature signal processing and parameter calculation The temperature distribution in the human body is calculated by the temperature signal to baseline processing, the threshold filtering process, and the relationship between the temperature value and the gray value of the image.
- Step 3 normalize the signal parameters calculated in step 2, perform feature selection on the parameter set composed of the normalized signal parameters, and obtain a feature parameter set, and the normalization processing method is as follows:
- X is the signal parameter of the parameter set
- X i is the i-th normalized signal parameter value
- X in is the i-th normalized value
- X imean is the normal mean of the i-th parameter.
- X istd represents the normal standard deviation of the ith parameter, and i is a positive integer.
- the feature selection is divided into two parts: feature search and evaluation criteria, wherein the search algorithm uses one or more combinations of the following algorithms: Complete Search, Sequential Search, Random Search Algorithm (Random) Search), Genetic Algorithm, Simulated Annealing, traceable greedy search expansion algorithm, evaluation criteria can optionally use Wapper model or CfsSubsetEval attribute evaluation method.
- the ECG and pulse wave signals are acquired during the evaluation process.
- the feature selection is combined with the full search algorithm and the Wapper model. During the evaluation process, ECG, EKG and polysomnography signals are acquired. The feature selection is combined with a random search algorithm. The way the CfsSubsetEval property evaluates methods. According to different types of acquired signals, select a combination of algorithms with appropriate and high accuracy.
- Step 4 Perform machine learning according to the feature parameter set obtained in step 3, and establish a mathematical model of depression assessment in the process of machine learning using the feature parameter set.
- the machine learning algorithm may selectively use one or more of the following algorithms: Bayesian, Decision Tree, AdaBoost, k-Nearest Neighbor ), Support Vector Machine (SVM).
- SVM Support Vector Machine
- Y is the output value of the mathematical model of depression assessment
- n is the number of machine learning algorithms selected for use
- y i is the output value of the i-th algorithm
- a i is the coefficient of the i-th algorithm
- i is a positive integer.
- the mathematical model of depression assessment establishes a mathematical model of depression assessment based on a variety of physiological information, and evaluates the level of depression using the output of the mathematical model of depression assessment, and classifies the depression into five levels: normal, general, mild depression, Moderate depression and major depression.
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Abstract
Description
Claims (9)
- 一种基于生理信息的抑郁症评估系统,其特征在于,包括:依次连接的信息采集模块、信号处理模块、参数计算模块、特征选择模块、机器学习模块和输出结果模块;信息采集模块,用于采集心电信号并采集脉搏波信号、脑电信号、皮电信号、胃电信号、肌电信号、眼电信号、多导睡眠信号和温度信号中的一种信号或一种以上的信号;信息采集模块采集的信号通过USB串口有线传输或蓝牙无线传输的方式传输到信号处理模块中;信号处理模块,用于处理生理信息,所述生理信息的处理包括去基线处理、滤波去噪处理、提取心搏间期处理、时频变换处理以及谱分析和谱估计处理,信号处理模块输出经过处理的信号到参数计算模块;参数计算模块,用于计算经过处理的信号的信号参数,所述信号参数包括心率变异性的时域参数、频域参数、时域几何参数以及根据采集的生理信息计算脉搏波信号、脑电信号、皮电信号、胃电信号、肌电信号、眼电信号、多导睡眠信号或温度信号中的一种或一种以上信号的时域参数、频域参数、直方图参数和分布图参数,参数计算模块输出信号参数到特征选择模块;特征选择模块,用于在全部信号参数中获取与抑郁等级相关的特征参数集,特征选择模块输出特征参数集到机器学习模块;机器学习模块,用于训练抑郁等级量化的分类器,利用特征参数集建立抑郁评估数学模型,量化抑郁等级,机器学习模块输出抑郁等级到输出结果模块;输出结果模块,用于显示抑郁评估数学模型输出的抑郁等级。
- 根据权利要求1所述的基于生理信息的抑郁症评估系统,其特征在于,所述的信息采集模块用于采集心电信号,所述的信息采集模块还用于采集心电信号采集脉搏波信号、脑电信号、皮电信号、胃电信号、肌电信号、眼电信号、多导睡眠信号和温度信号中的一种信号或一种以上的信号,所述的采集心电信号的采集方法采用三导联心电采集方法,在所述三导联心电采集方法中,采集到的心电信号经过放大、滤波和模数转换后,再通过数据传输将心电信号传输到电脑中,所述的数据传输采用USB串口有线传输或蓝牙无线传输。
- 根据权利要求1所述的基于生理信息的抑郁症评估系统,其特征在于,所述的信号处理模块包括:心电信号处理单元、脉搏波信号处理单元、脑电信 号处理单元、皮电信号处理单元、胃电信号处理单元、肌电信号处理单元、眼电信号处理单元、多导睡眠信号处理单元和温度信号处理单元;所述心电信号处理单元,用于去基线处理、滤波去噪处理、提取RR间期处理、插值处理、傅里叶变换处理以及谱分析和谱估计处理;所述脉搏波信号处理单元,用于去基线处理、滤波去噪处理、提取PP间期处理、插值处理、傅里叶变换处理以及谱分析和谱估计处理;所述脑电信号处理单元,用于去基线处理、阈值去噪处理、小波分解处理以及谱分析和谱估计处理;所述皮电信号处理单元,用于去基线处理和小波滤波处理;所述胃电信号处理单元,用于去基线处理、Hilbert-Huang变换处理、小波分析、多分辨率分析和独立成分分析;所述肌电信号处理单元,用于去基线处理和小波包自适应阈值去噪处理;所述眼电信号处理单元,用于去基线处理、加权中值滤波处理和小波变换处理;所述多导睡眠信号处理单元,用于处理睡眠脑电信号、睡眠眼电信号、睡眠肌电信号,对所述睡眠脑电信号进行去基线处理、阈值去噪处理、小波分解处理以及谱分析和谱估计处理,对所述睡眠眼电信号进行去基线处理、加权中值滤波处理和小波变换处理,对所述睡眠肌电信号进行去基线处理、小波包自适应阈值去噪处理和睡眠分期处理;所述温度信号处理单元,用于去基线处理、阈值滤波处理、建立温度值与图像灰度值的关系式和绘制人体热能分布图。
- 根据权利要求1所述的基于生理信息的抑郁症评估系统,其特征在于,所述的参数计算模块包括:心电参数计算单元、脉搏波参数计算单元、脑电参数计算单元、皮电参数计算单元、胃电参数计算单元、肌电参数计算单元、眼电参数计算单元、多导睡眠信号参数计算单元、温度参数计算单元;所述的心电参数计算单元包括:时域参数计算、频域参数计算和时域几何参数计算;所述心电参数计算单元,包括计算RR间期、时域参数、频域参数、和时域几何参数,所述时域参数包括:Mean、SDNN、RMSSD、PNN50和SDSD,所述频域参数包括:VLF、LF、HF、TP和LF/HF,所述时域几何参数包括:SD1、SD2、a1和a2;所述脉搏波参数计算单元,包括计算PP间期、时域参数、频域参数和时域 几何参数,所述时域参数包括Mean、SDNN、RMSSD、PNN50和SDSD,所述频域参数VLF、LF、HF、TP和LF/HF,所述时域几何参数包括SD1、SD2、a1和a2;所述脑电参数计算单元,用于计算δ波幅值、δ波功率、δ波均值、δ波方差、δ波偏歪度、δ波峭度、θ波幅值、θ波功率、θ波均值、θ波方差、θ波偏歪度、θ波峭度、α波幅值、α波功率、α波均值、α波方差、α波偏歪度、α波峭度、β波幅值、β波功率、β波均值、β波方差、β波偏歪度、β波峭度和小波熵;所述皮电参数计算单元,用于计算皮肤交感反应潜伏期、皮肤交感反应波幅和皮肤电阻值;所述胃电参数计算单元,用于计算正常胃电节律、慢波、胃动过缓成分和胃动过速成分;所述肌电参数计算单元,用于计算基础值、最小值、最高值、肌电下降能力和肌电曲线;所述眼电参数计算单元,用于计算R波成分、r波成分、S波成分和s波成分;所述多导睡眠信号参数计算单元,用于计算睡眠潜伏期、睡眠总时间、觉醒指数、S1、S2、S3、S4、快速眼动百分比、快速眼动睡眠周期数、快速眼动睡眠潜伏期、快速眼动睡眠强度、快速眼动睡眠密度和快速眼动睡眠时间;所述温度参数计算单元,用于计算人体体内温度分布和绘制人体热能图。
- 一种应用于权利要求1所述的基于生理信息的抑郁症评估系统的评估方法,其特征在于,包括以下步骤:步骤1:对心电信号进行信号处理并同时对脉搏波信号、脑电信号、皮电信号、胃电信号、肌电信号、眼电信号、多导睡眠信号和温度信号中的一种信号或一种以上的信号进行信号处理,再计算经过处理的信号的信号参数;步骤2:利用步骤1计算得到的信号参数进行归一化处理,对经过归一化处理后的信号参数组成的参数集进行特征选择,得到特征参数集;步骤3:利用步骤2得到的特征参数集进行机器学习,利用所述的特征参数集与抑郁等级的关系建立抑郁评估数学模型,利用所述的抑郁评估数学模型输出的抑郁等级评估结果,根据所述的抑郁等级的评估结果评估抑郁等级;所述的机器学习用于训练抑郁评估数学模型,使用特征参数集在机器学习的过程中建立抑郁评估数学模型,所述机器学习的算法使用以下算法中一种或一种以上的组合:贝叶斯分类器、决策树算法、AdaBoost算法、k-近邻法、支 持向量机,所述的抑郁评估数学模型的表达式为:其中,Y是抑郁评估数学模型输出值,n是选择使用的机器学习算法数,yi是第i种算法输出值,ai是第i种算法的系数,i是正整数。
- 根据权利要求5所述的评估方法,其特征在于,在步骤1中,所述信号处理包括心电信号处理,脉搏波信号处理,脑电信号处理,皮电信号处理,胃电信号处理,肌电信号处理,眼电信号处理,多导睡眠信号处理和温度信号处理,所述心电信号处理包括去基线处理、滤波去噪处理、提取RR间期、插值处理、傅里叶变换处理以及谱分析和谱估计处理,所述脉搏波信号处理包括去基线处理、滤波去噪处理、提取PP间期、插值处理、傅里叶变换处理以及谱分析和谱估计处理,所述脑电信号处理包括去基线处理、阈值去噪处理、小波分解处理以及谱分析和谱估计处理,所述皮电信号处理包括去基线处理以及小波滤波处理,所述胃电信号处理包括去基线处理、Hilbert-Huang变换处理、小波分析、多分辨率分析以及独立成分分析,所述肌电信号处理包括去基线处理和小波包自适应阈值去噪处理,所述眼电信号处理包括去基线处理、加权中值滤波处理和小波变换处理,所述多导睡眠信号处理包括处理睡眠脑电信号、睡眠肌电信号和睡眠眼电信号,对所述睡眠脑电信号进行去基线处理、阈值去噪处理、小波分解处理以及谱分析和谱估计处理,对所述睡眠眼电信号进行去基线处理、加权中值滤波处理和小波变换处理,对所述睡眠肌电信号进行去基线处理、小波包自适应阈值去噪处理和睡眠分期处理,所述温度信号处理包括去基线处理、阈值滤波处理和建立温度值与图像灰度值的关系式。
- 根据权利要求5所述的评估方法,其特征在于,在步骤1中,所述计算经过处理的信号的信号参数包括心电参数计算、脉搏波参数计算、脑电参数计 算、皮电参数计算、胃电参数计算、肌电参数计算、眼电参数计算、多导睡眠参数计算和温度参数计算,所述心电参数计算包括计算RR间期、时域参数、频域参数、和时域几何参数,所述时域参数包括Mean、SDNN、RMSSD、PNN50和SDSD,所述频域参数包括VLF、LF、HF、TP和LF/HF,所述时域几何参数包括SD1、SD2、a1和a2,所述脉搏波参数计算包括计算PP间期、时域参数、频域参数和时域几何参数,所述时域参数Mean、SDNN、RMSSD、PNN50、SDSD,所述频域参数包括VLF、LF、HF、TP和LF/HF,所述时域几何参数包括SD1、SD2、a1和a2,所述脑电参数计算包括计算δ波幅值、δ波功率、δ波均值、δ波方差、δ波偏歪度、δ波峭度、θ波幅值、θ波功率、θ波均值、θ波方差、θ波偏歪度、θ波峭度、α波幅值、α波功率、α波均值、α波方差、α波偏歪度、α波峭度、β波幅值、β波功率、β波均值、β波方差、β波偏歪度、β波峭度和小波熵,所述皮电参数计算包括计算皮肤交感反应潜伏期,皮肤交感反应波幅和皮肤电阻值,所述胃电参数计算包括计算正常胃电节律、慢波、胃动过缓和胃动过速成分,所述肌电参数计算包括计算基础值、最小值、最高值、肌电下降能力和肌电曲线,所述眼电参数计算包括计算R波、r波、S波和s波成分,所述多导睡眠信号参数计算包括计算睡眠潜伏期、睡眠总时间、觉醒指数、S1、S2、S3、S4、快速眼动百分比、快速眼动睡眠周期数、快速眼动睡眠潜伏期、快速眼动睡眠强度、快速眼动睡眠密度和快速眼动睡眠时间,所述温度参数计算包括计算体内温度分布。
- 根据权利要求5所述的评估方法,其特征在于,在步骤2中,所述特征选择根据参数计算模块输出的所有信号参数,训练数据集,每个样本用特征集表示,生成特征子集集合,根据评价准则搜索获取特征集中最好的特征子集,比较和评价当前的特征子集,当获取的特征子集是最好的特征子集,满足终止条件,输出与抑郁等级相关的特征参数集,所述搜索算法使用以下算法中一种或一种以上的组合:完全搜索算法、顺序搜索算法、随机搜索算法、遗传算法、模拟退火搜索算法和可回溯的贪婪搜索扩张算法;评价准则使用以下算法中一种或两种的组合:Wapper模型和CfsSubsetEval属性评估方法。
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WO2018227239A1 (en) * | 2017-06-12 | 2018-12-20 | Medibio Limited | Mental state indicator |
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