WO2018099120A1 - 一种心率变异性分析方法、装置及用途 - Google Patents
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02405—Determining heart rate variability
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4029—Detecting, measuring or recording for evaluating the nervous system for evaluating the peripheral nervous systems
- A61B5/4035—Evaluating the autonomic nervous system
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- A61B5/48—Other medical applications
- A61B5/4848—Monitoring or testing the effects of treatment, e.g. of medication
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
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- A—HUMAN NECESSITIES
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- A61B5/316—Modalities, i.e. specific diagnostic methods
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/339—Displays specially adapted therefor
- A61B5/341—Vectorcardiography [VCG]
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
Definitions
- the invention relates to a heart rate variability analysis method, device and use thereof, in particular to a heart rate variability analysis method and a device thereof for predicting the curative effect of the vagus nerve stimulation (VNS) in the treatment of drug-refractory epilepsy.
- VNS vagus nerve stimulation
- Epilepsy as a disease can affect a patient's life. Most patients can control the disease with one or more drugs, but some patients are still not sensitive to drug treatment. These patients are called drug refractory. Patients with epilepsy. Vagus Nerve Stimulation (VNS) as an adjuvant therapy can effectively control seizures in patients with drug-refractory epilepsy, but the individual differences in efficacy are very large and highly uncertain. Statistical analysis showed that only 5%-9% of patients with drug-refractory epilepsy who underwent VNS surgery had complete control of seizures, and about 10% of patients were completely ineffective, and the rest showed varying degrees of seizure frequency.
- VNS Vagus Nerve Stimulation
- FIG. 1 Patients with drug-refractory epilepsy need to have a systematic, comprehensive preoperative assessment (including demographics, medical history, seizure characteristics, MRI, EEG, etc.) before undergoing VNS surgery. ), then VNS implantation surgery, starting 2 weeks after surgery, according to the specific response of each patient to the acute stimulation of VNS according to the performance and technical characteristics of VNS products gradually adjust the stimulation parameters and regularly count all patients with seizure reduction. After the corresponding follow-up period (usually 1 year), the patients were classified according to the reduction of the patient's seizure, that is, the efficacy was analyzed. Finally, the preoperative evaluation data of the patients with different efficacy classifications were statistically analyzed, and there were Statistically different parameters as a VNS treatment for refractory epilepsy Sensitive factors for predicting efficacy.
- the existing EEG and MRI methods have the disadvantages of high cost, complicated operation and high requirements for the analyst's professional knowledge.
- the research based on the above methods has not been applied in clinical practice, mainly because of the study of the same problem.
- the conclusions are not consistent or even contradictory.
- the method based on the 24-hour dynamic electrocardiographic signal proposed by the invention only needs to wear a portable dynamic electrocardiogram recording box for the subject, and collects the ECG in the state where the subject is free to move, and the operation is simple.
- the general hospital outpatient service charges 240 yuan for 24-hour dynamic ECG, which is relatively low compared with the cost of long-term video EEG and MRI imaging of about 1,000 yuan.
- the most important thing is that 24-hour dynamic ECG acquisition is not restricted by activity. Compared with EEG and MRI, the acquisition is relatively simple and consistent.
- HRV Heart Rate Variability
- the present invention considers the use of a low-cost, portable, wearable signal acquisition device to obtain the preoperative 24-hour ECG signal of a patient with epilepsy, and calculates the time domain, frequency domain and nonlinear characteristic parameters of the ECG by a programmed HRV analysis method. And select the characteristic parameters that characterize vagal activity, and accurately and efficiently predict the efficacy of VNS treatment of refractory epilepsy.
- the HRV refers to the change of the adjacent heartbeat interval in the electrocardiography (ECG) signal over time. It originates from the autonomic nerve. The system's self-regulation of the sinus node makes the heartbeat interval have tens of milliseconds or even greater differences or fluctuations. HRV contains a large amount of information on the regulation of neurohumoral fluid. It is a quantitative, non-invasive and repeatable indicator for evaluating the autonomic nervous system activity and its regulatory functions in the cardiovascular system. It can be analyzed by HRV. Indirectly reflects the interaction between the sympathetic and parasympathetic nerves.
- the present invention uses a low-cost, portable, wearable signal acquisition device to obtain the preoperative 24-hour ECG signal of a patient with epilepsy, and calculates the time domain and frequency domain of the long-range and short-range ECG through a programmed HRV analysis method.
- Non-linear characteristic parameters and by characterizing the characteristic parameters of vagal activity in the autonomic nervous system, accurately and efficiently predict the efficacy of VNS treatment of refractory epilepsy, avoid unnecessary expenditures and avoid delays in optimal treatment timing,
- the vagus nerve activity parameters obtained by HRV analysis of ECG can be used to clearly select patients with VNS surgery indications, and the efficacy of VNS therapy can be improved as a whole.
- a heart rate variability analysis method includes the following steps:
- RR is the time interval or interval between two adjacent heartbeats
- N is the total number of RR intervals
- RR i is the i-th RR interval.
- Frequency domain index Power spectrum analysis shows that there are three separate spectral peaks on the typical HRV power spectrum, which are mainly concentrated in the frequency bands of 0.04 Hz, 0.1 Hz and greater than 0.15 Hz. The area under each frequency band can be used as the power spectrum energy measurement. The value is the frequency domain indicator of HRV.
- the frequency domain index reflects the modulation of the heart rate of the autonomic nerve in each frequency segment, and it is the appropriate separation of various physiological factors, so it can accurately reflect the true situation of HRV, and the clinical application value is large.
- Nonlinear indicators In recent years, great progress has been made in the study of nonlinear dynamics, and it has attracted great interest in the fields of physics, chemistry, and information science. Using nonlinear dynamics to describe and analyze the complex process of autonomic nervous system regulation of heart rate, more meaningful information can be obtained to improve the accuracy of HRV as a predictor.
- the HRV non-linear index used in the present invention is mainly the SD1 and SD2 indicators of the Poincare Plot (as shown in Fig. 4).
- the NN interval is used as the research object, and the characteristic scatter of the two NN intervals is displayed in the form of coordinates. The standard deviation between the long axis and the short axis at the center point of the scattergram is measured separately.
- the standard deviation measured by the short axis is called SD1
- the difference between the heartbeat and the heartbeat is instantaneous
- the long axis is the axis.
- the measured standard deviation is called SD2, which reflects the difference in NN interval over a long period of time.
- FIG. 1 Schematic diagram of 12 lead ECG acquisition
- FIG. 4 Schematic diagram of HRV nonlinear indicators SD1 and SD2
- the standard 12-lead ECG acquisition before surgery requires the sampling rate of the ECG acquisition device to be greater than or equal to 500 Hz, avoiding strenuous exercise, medication, and other activities that may affect cardiac function during ECG recording.
- the length of time is 24 hours.
- the recording environment and conditions of the subjects and individuals should be basically similar. It is also required to ensure that the data used for HRV analysis is a normal sinus NN interval.
- HRV analysis the 24-hour time domain, frequency domain and nonlinear indicators were extracted, and the time domain, frequency domain and nonlinear index of 5 min short-range ECG in awake and sleep state were extracted.
- the specific processing flow of the ECG signal is shown in Figure 3.
- ECG ECG was collected 24 hours before surgery.
- the collected 24-hour ECG data obtained the short-range normal sinus NN interval sequence of 24-hour long-range and 5-minute awake and sleep state according to the above treatment method.
- the long-range and short-range NN interval sequences were subjected to HRV time domain, frequency domain and nonlinear analysis according to the methods of Table 1, Table 2 and Figure 4, and the characteristic parameters RMSSD, pNN50, SD1 and HF which characterize vagal activity were selected. The comprehensive judgment selection is made through the corresponding threshold judgment, and then the effect of VNS treatment drug-refractory epilepsy is predicted and judged (as shown in Fig. 5).
- the preoperative ECG analysis result is greater than this value, and the improvement of postoperative VNS will be ⁇ 50%.
- the accuracy of prediction is 70.6%, and it can also be used as a basis for screening patients;
- the patient's preoperative ECG analysis results are greater than this value, and the improvement of postoperative VNS will be ⁇ 50%, and the accuracy of the prediction is 76.5%. It can also be used as the basis for screening patients.
- the invention can predict the curative effect of the patients with drug-refractory epilepsy before the VNS by using the ECG collection and the time domain, frequency domain and nonlinear analysis of the HRV in the patients with drug-refractory epilepsy.
- Patients with epilepsy choose VNS surgery correctly to avoid unnecessary expenditures and avoid delays in optimal treatment.
- the characteristics of vagus nerve activity obtained by ECG HRV analysis are used to predict the outcome of VNS and to clearly select patients with VNS indications.
- the efficacy of VNS therapy can be improved as a whole.
- VNS surgery at Tiantan Hospital in Beijing from August 13, 2014 to December 31, 2014 were selected for validation.
- Thirty-two patients with drug-refractory epilepsy underwent a complete assessment before VNS (including demographics, clinical history, anti-epileptic drug history, 24-hour video EEG, MRI, and 24-hour ambulatory electrocardiogram).
- the time domain, frequency domain and nonlinear analysis of HRV were performed by the above-mentioned ECG signal processing method according to the above-mentioned ECG signal processing method.
- the characteristic parameters RMSSD, pNN50, SD1 and HF of each patient's vagal activity were extracted.
- 17 patients had a 50% or more reduction in seizure frequency (of which 6 patients were completely controlled) as a response group Responder 50 , 15 patients with a reduction in the frequency of attacks less than 50% (four of them were completely ineffective) were considered non-responder group Non-responder 50 .
- the RMSSD, pNN50, SD1, and HF of the response group Responder 50 and the non-response group Non-responder 50 are shown in Figure 6.
- the predictive ability of RMSSD, pNN50, SD1 and HF in the long-term 24-hour and awake and sleep state was evaluated.
- the ROC curve analysis showed that the RMSSD prediction of 5 min in the above four parameters was the most.
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Abstract
一种心率变异性分析的方法、装置及用途,其中方法包括如下步骤:(1)体外采集心电数据;(2)对心电数据进行数字化、去噪处理;(3)将处理后的心电数据形成窦性NN间期序列;(4)分别选取被试20小时以上长程、清醒、睡眠状态下各2-10分钟短程的窦性NN间期数据;(5)对被试20小时以上长程、清醒、睡眠状态下各2-10分钟短程的窦性NN间期数据进行时域、频域、非线性指标至少之一的计算。通过程式化的HRV分析方法计算长程、短程的ECG的时域、频域和非线性指标,准确、高效地对迷走神经刺激手术的药物难治性癫痫患者进行疗效预测,避免不必要的经费支出和避免耽误最佳治疗时机。
Description
本发明涉及一种心率变异性分析方法、装置及用途,尤其涉及心率变异性分析方法及其装置在迷走神经刺激(Vagus Nerve Stimulation,VNS)治疗药物难治性癫痫领域的疗效预测。
癫痫作为一种疾病会影响患者的生活,大部分患者可以用一种或多种药物联合治疗对发病加以控制,但仍然会有部分患者对药物治疗并不敏感,这些患者被称为药物难治性癫痫患者。迷走神经刺激(Vagus Nerve Stimulation,VNS)作为一种辅助治疗手段可以有效控制药物难治性癫痫患者的癫痫发作,但其疗效的个体差异非常大,而且具有很高的不确定性。统计分析结果显示,仅有5%-9%接受VNS手术的药物难治性癫痫患者癫痫发作得到完全控制,另有约10%的患者完全无效,其余表现为不同程度的发作频率减少。总体而言,50%-60%接受VNS手术的药物难治性癫痫患者可以达到发作频率减少50%的治疗效果。针对VNS治疗药物难治性癫痫疗效的不确定性和个体差异大的问题,研究人员一直试图通过术前评估筛选出适合VNS手术的患者。
现在尚无明确的、量化的VNS治疗药物难治性癫痫的疗效预测方法应用于临床,而基于脑电(Electroencephalography,EEG)、核磁影像资料(Magnetic Resonance Imaging,MRI)、患者人口统计学特征(性别、年龄等)、临床病史(病程)、发作特征(包括发作类型、发作频率、病灶位置等)进行VNS疗效相关性因素的研究,结论也不相一致,甚至相互矛盾。
现有技术中的思路如附图1所示:药物难治性癫痫患者在接受VNS手术之前首先需进行***、全面的术前评估(包括人口统计学特征、病史、发作特征、MRI、EEG等),然后进行VNS植入手术,术后2周左右开机,之后根据每位患者对VNS急性刺激的具体反应情况按照VNS产品的性能、技术特点逐步调整刺激参数并定期统计所有患者发作减少情况。在相应的随访周期(一般为1年)结束后,根据患者发作减少情况即疗效对患者进行分类,最后对不同疗效分类患者的术前评估数据进行统计学分析,寻找不同疗效患者组之间有统计学差异的参数作为VNS治疗药物难治性癫痫
疗效预测的敏感因子。
现有的EEG和MRI的方法存在成本高、操作复杂和对分析人员专业知识水平要求高的缺点,总体而言,基于上述方法的研究都没有在临床得到应用,主要原因是对同一问题的研究结论不相一致,甚至相互矛盾。本发明提出的基于24小时动态心电信号的方法,仅需要给被试佩戴一个便携式动态心电记录盒,在被试自由活动的状态下采集ECG,操作简单。一般医院门诊对24小时动态心电检查的收费为240元,与长程视频脑电及核磁共振影像检查1000元左右的费用相比,成本相对较低。最为重要的是24小时动态心电采集不受活动限制,与EEG和MRI相比,采集相对简单,一致性较好。
心率变异性(Heart Rate Variability,HRV)是指心电(Electrocardiography,ECG)信号中的相邻心搏间期随时间的变化,它起源于自主神经***对窦房结自律性的调制,使心搏间期存在几十毫秒甚至更大的差异或波动。HRV蕴含着神经体液调节的大量信息,是目前评价心血管***自主神经***活性及其调节功能的定量、无创、可重复指标,通过HRV分析可间接反映交感神经及副交感神经之间的相互作用。
目前还没有利用心电信号HRV分析技术对VNS治疗药物难治性的疗效进行预测的研究和技术方案。由于癫痫疾病与心脏自主神经功能失调有着密切的联系。伴随癫痫的发病和进展,患者心脏自主神经***平衡被打破,一般表现为交感神经活性增强和迷走神经活性降低。基于这个结论,本发明考虑利用低成本、便携、可穿戴式信号采集设备获取癫痫患者的术前24小时ECG信号,通过程式化的HRV分析方法计算ECG的时域、频域和非线性特征参数,并选取表征迷走神经活性的特征参数,准确、高效地对VNS治疗药物难治性癫痫的疗效进行预测。
发明内容
申请人发现,癫痫疾病与心脏自主神经功能失调有着密切的联系。伴随癫痫的发病和进展,患者心脏自主神经***平衡被打破,一般表现为交感神经活性增强和迷走神经活性降低。而且,还发现其中心率变异性(Heart Rate Variability,HRV)这一指标非常重要,HRV是指心电(Electrocardiography,ECG)信号中的相邻心搏间期随时间的变化,它起源于自主神经***对窦房结自律性的调制,使心搏间期存在几十毫秒甚至更大的差异或波动。HRV蕴含着神经体液调节的大量信息,是目前评价心血管***自主神经***活性及其调节功能的定量、无创、可重复指标,通过HRV分析可
间接反映交感神经及副交感神经之间的相互作用。
基于这一研究发现,本发明利用低成本、便携、可穿戴式信号采集设备获取癫痫患者的术前24小时ECG信号,通过程式化的HRV分析方法计算长程、短程ECG的时域、频域及非线性的特征参数,并通过表征自主神经***中迷走神经活性的特征参数,准确、高效地预测VNS治疗药物难治性癫痫的疗效,避免不必要的经费支出和避免耽误最佳治疗时机,同时也可以利用ECG的HRV分析得到的迷走神经活性特征参数明确选择VNS手术适应症患者,可以从整体上提高VNS疗法的疗效。
一种心率变异性分析方法,包括如下步骤:
1)体外采集心电数据;
2)对心电数据进行数字化、去噪处理;
3)将处理后的心电数据形成窦性NN间期序列;
4)分别选取长程、清醒、睡眠状态下短程的窦性NN间期数据;
5)对长程、清醒、睡眠状态下各的窦性NN间期数据进行时域、频域、非线性指标的计算(各项指标的具体计算见下表);
表1时域指标
注:RR为相邻两次心跳之间的时间间隔或间期;N为RR间期的总数;RRi为第i个RR间期。
频域指标:功率谱分析得出,典型的HRV功率谱上有三个分离的谱峰,大致集中于0.04Hz、0.1Hz和大于0.15Hz频段,将各频段下的面积可作为功率谱能量的测定值,即为HRV的频域指标。频域指标反映了自主神经在各频率段对心率的调制,而且是将各种生理因素作适当的分离,因而能准确反映HRV的真实情况,临床应用价值较大。
表2频域指标
非线性指标:近年来非线性动力学的研究取得了很大的进展,而且在物理学、化学、信息科学领域中引起了人们很大的兴趣。用非线性动力学方法来描述和分析自主神经***对心率调控这一复杂过程,可以获得更多的有意义信息,以提高HRV作为预测指标的准确性。本发明采用的HRV非线性指标主要是庞卡莱散点图(Poincare Plot)的SD1和SD2指标(如图4所示)。以NN间期作为研究分析对象,并采用坐标形式显示前后两个NN间期的变化特征散点。分别测定散点图中心点处的长轴及短轴上的标准差,以短轴为轴心测定的标准差称为SD1,反应瞬时的心搏与心搏间差异,以长轴为轴心测得的标准差称为SD2,则反映长时间内的NN间期的差异性。
图1是现有技术筛选VNS适应症患者流程图
图2 12导联ECG采集示意图
图3 ECG信号具体处理流程图
图4 HRV非线性指标SD1和SD2计算示意图
图5判断流程图
实施例1
如图2所示,于术前的24小时标准12导联ECG采集:要求心电采集装置的采样率要大于或等于500Hz,ECG记录期间避免剧烈运动、服药等可能影响心脏功能的活动,记录时间长度为24小时。受试对象、个体的记录环境和条件应基本类似。并要求确保用于HRV分析的数据是正常窦性NN间期。在进行HRV分析时,提取24小时时域、频域和非线性指标的同时,提取清醒、睡眠状态下各5min短程心电的时域、频域和非线性指标。ECG信号具体处理流程如图3所示。
1)采集ECG信号,并对信号数字化处理;
2)对数字信号进行去噪声、去伪迹处理;
3)对其中的QRS波自动检测
4)对检测后的信号进行QRS波人工检视;
5)再对异位起搏QRS波信号进行剔除;
6)形成窦性NN间期序列;
7)分别选取被试24小时长程、清醒、睡眠状态下各5min短程的窦性NN间期数据;
8)按照表1、表2及图4的方法对被试24小时长程、清醒、睡眠状态下各5min短程的窦性NN间期数据进行时域、频域、非线性指标的计算;
9)选取表征迷走神经活性的特征参数RMSSD、pNN50、SD1和HF。
对药物难治性癫痫患者进行术前24小时心电采集,采集到的24小时心电数据按上述处理方法得到24小时长程和5min清醒、睡眠状态下的短程正常窦性NN间期序列。对上述长程、短程NN间期序列按表1、表2和图4的方法进行HRV时域、频域和非线性分析并选取表征迷走神经活性的特征参数RMSSD、pNN50、SD1和HF。通过相应的阈值判断进行综合判断选择,进而对VNS治疗药物难治性癫痫的疗效进行预测判断(如图5所示)。利用表征迷走神经活性的夜间5min的特征参数RMSSD、pNN50、SD1和HF分别单独进行VNS治疗药物难治性癫痫的疗效预测,其对应阈值的选择及其相应的预测准确率如下:
RMSSD=37ms时,患者术前ECG分析结果大于该值,其VNS术后发作改善将≥50%,其预测的准确率为88.2%,同时也可以作为筛选患者的依据;
pNN50=27%时,患者术前ECG分析结果大于该值,其VNS术后发作改善将≥50%,其预测的准确率为82.7%,同时也可以作为筛选患者的依据;
SD1=35ms时,患者术前ECG分析结果大于该值,其VNS术后发作改善将≥50%,其
预测的准确率为70.6%,同时也可以作为筛选患者的依据;
HF=798ms2时,患者术前ECG分析结果大于该值,其VNS术后发作改善将≥50%,其预测的准确率为76.5%,同时也可以作为筛选患者的依据;
实施例2
实施例1的HRV分析方法中其他可能表征迷走神经活性的标亦可用于上述VNS治疗药物难治性癫痫的疗效预测和患者筛选。
本发明通过药物难治性癫痫患者术前24小时ECG的采集和HRV的时域、频域及非线性分析,可以在VNS术前对药物难治性癫痫患者的疗效进行预测,指导药物难治性癫痫患者正确选择VNS手术以避免不必要的经费支出和避免耽误最佳治疗时机,同时通过ECG的HRV分析得到的表征迷走神经活性的特征参数进行VNS疗效的预测及明确选择VNS手术适应症患者,可以从整体上提高VNS疗法的疗效。
实施例3
根据上述筛选方法,选取于2014年8月13日至2014年12月31日期间在北京天坛医院完成VNS手术的32例药物难治性癫痫患者进行验证。32例药物难治性癫痫患者在VNS术前进行了完整的评估(包括人口统计学特征、临床病史、抗癫痫药物史、24小时视频脑电、MRI以及24小时动态心电等)。
通过术前24小时动态心电数据按上述ECG信号处理方法进行HRV的时域、频域和非线性分析,提取了每位患者表征迷走神经活性的特征参数RMSSD、pNN50、SD1和HF。术后1年随访结束时,32例接受VNS治疗的药物难治性癫痫患者中,有17例患者的发作频率减少50%或以上(其中6例患者发作完全得到了控制)视为响应组Responder50,15例患者的发作频率减少50%以下(其中4例患者完全无效)视为非响应组Non-responder50。响应组Responder50和非响应组Non-responder50的RMSSD、pNN50、SD1和HF如图6所示,两组患者的上述表征迷走神经活性的参数存在显著统计学差异,提示VNS疗效和患者术前迷走神经活性有关,同时也说明通过HRV分析方法可以进行VNS的疗效预测。进一步对24小时长程及清醒、睡眠状态下各5min短程的RMSSD、pNN50、SD1和HF的疗效预测能力进行评估,通过ROC曲线分析,结果表明上述4个参数中睡眠状态下5min的RMSSD预测的最为准确(如图7所示):将其阈值设定为37ms时,17例响应组的患者中仅有2例患者的RMSSD低于37ms,其余患者的均大于37ms,预测的准确率超过85%,从而证明上述HRV分析的获取的表征迷走神经活性的特征参数可以准确、有效的进行VNS疗效的预测及VNS适应症
的筛选。
以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本发明的保护范围。此外,尽管本说明书中使用了一些特定的术语,但这些术语仅仅是为了方便说明,并不对本发明构成任何限制。
Claims (10)
- 一种心率变异性分析方法,包括如下步骤:1)体外采集心电数据;2)对心电数据进行数字化、去噪处理;3)将处理后的心电数据形成窦性NN间期序列;4)分别选取被试20小时以上长程、清醒、睡眠状态下各2-10分钟短程的窦性NN间期数据;5)对被试清20小时以上长程、清醒、睡眠状态下各2-10分钟短程的窦性NN间期数据进行时域、频域、非线性指标至少之一的计算。
- 如权利要求1所述心率变异性分析方法,其特征在于:计算RMSSD、pNN50、SD1、HF参数之一。
- 如权利要求3所述心率变异性分析方法,其特征在于:对RMSSD、pNN50、SD1、HF参数之一进行阈值判断。
- 如权利要求1-4之一所述的心率变异性分析方法,其特征在于:长程时间为24小时,短程时间为5分钟。
- 如权利要求4所述心率变异性分析方法,其特征在于:RMSSD阈值为37ms、pNN50阈值为27%、SD1阈值为35ms、HF阈值为798ms。
- 一种心率变异性分析装置,其特征在于包括一计算模块,所述计算模块采用如权利要求1-6之一所述的方法进行计算。
- 如权利要求6所述心率变异性分析装置,其特征在于还包括数据采集模块、数字化处理模块、去噪模块、判断模块中的一个或多个。
- 一种可穿戴心电监测设备,其特征在于包含权利要求7或8所述的心率变异性分析装置。
- 如权利要求1-9之一所述的方法、装置及设备,其特征在于:用于VNS治疗药物难治性癫痫的分析。
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