WO2018036367A1 - 一种筛选手术患者的建模方法 - Google Patents

一种筛选手术患者的建模方法 Download PDF

Info

Publication number
WO2018036367A1
WO2018036367A1 PCT/CN2017/095951 CN2017095951W WO2018036367A1 WO 2018036367 A1 WO2018036367 A1 WO 2018036367A1 CN 2017095951 W CN2017095951 W CN 2017095951W WO 2018036367 A1 WO2018036367 A1 WO 2018036367A1
Authority
WO
WIPO (PCT)
Prior art keywords
modeling method
heart rate
vns
patients
mse
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2017/095951
Other languages
English (en)
French (fr)
Inventor
李路明
刘洪运
杨曌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tsinghua University
Beijing Pins Medical Co Ltd
Original Assignee
Tsinghua University
Beijing Pins Medical Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tsinghua University, Beijing Pins Medical Co Ltd filed Critical Tsinghua University
Priority to US16/086,428 priority Critical patent/US11504052B2/en
Publication of WO2018036367A1 publication Critical patent/WO2018036367A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • 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 for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • 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 for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/0245—Measuring pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/384—Recording apparatus or displays specially adapted therefor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7278—Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2505/00—Evaluating, monitoring or diagnosing in the context of a particular type of medical care
    • A61B2505/05—Surgical care
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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

Definitions

  • the invention relates to a modeling method for screening surgical patients, in particular to a modeling method for patients with indications of Vagus Nerve Stimulation (VNS).
  • 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
  • VNS surgical indications There is no clear screening method for VNS surgical indications in clinical practice, but based on electroencephalography (EEG), magnetic resonance imaging (MRI), demographic characteristics (gender, age, etc.), clinical The medical history (sickness), seizure characteristics (including type of seizure, frequency of episodes, location of lesions, etc.) were studied for the correlation factors of VNS efficacy, and the conclusions were not consistent or even contradictory.
  • EEG electroencephalography
  • MRI magnetic resonance imaging
  • demographic characteristics gender, age, etc.
  • seizure characteristics including type of seizure, frequency of episodes, location of lesions, etc.
  • 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 The statistically different parameters were used as sensitive factors for screening or efficacy prediction in patients with VNS surgery.
  • 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 contemplates obtaining a preoperative 24-hour ECG signal for a patient with epilepsy using a low-cost, portable, wearable signal acquisition device, and calculating a multiscale Entropy (MSE) of the ECG by a stylized HRV analysis method. Based on the MSE curve extraction and characterization of heart rate complexity parameters, the patients with drug-refractory epilepsy suitable for VNS surgery were screened accurately and efficiently.
  • MSE multiscale Entropy
  • 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 about neurohumoral regulation. It is a quantitative, non-invasive, and reproducible indicator for evaluating the autonomic nervous system activity and its regulatory functions in the cardiovascular system. It can indirectly reflect the interaction between sympathetic and parasympathetic nerves through HRV analysis.
  • the present invention utilizes a low-cost, portable, wearable signal acquisition device to obtain preoperative 24-hour ECG signals from patients with epilepsy, calculates the MSE of ECG by a programmed HRV analysis method, and extracts the heart rate based on the MSE curve.
  • Complexity characteristic parameters accurately and efficiently screen patients with drug-refractory epilepsy suitable for VNS surgery, avoid unnecessary expenditures and avoid delays in optimal treatment timing, and clearly select VNS surgical adaptation through ECG MSE heart rate complexity parameter Patients with symptoms can improve the efficacy of VNS therapy as a whole.
  • the present invention provides a modeling method for screening surgical patients characterized by modeling by heart rate variability.
  • step 2 coarsening the NN interval sequence ⁇ X 1 ,...,X i ,...,X N ⁇ obtained in step 2) to obtain reconstructed sequences of different scales ⁇ is a scale factor;
  • Iii Plot the sample entropy corresponding to different scale factors and obtain the heart rate complexity feature parameters.
  • the heart rate complexity feature parameters are obtained as follows:
  • step iii is linearly fitted to the point of the scale 1-n1 to obtain the slope Slope n1; the scale n2-n3 is segmented, and the area of each of the scale curve and the abscissa is calculated to obtain the parameter Areay.
  • n1 5
  • the slope parameter Slope is Slope 5
  • n2 6
  • n3 20
  • the Areal parameters are Areal1-5, Areal6-15, Areal6-20.
  • the selected sinus NN interval data is the NN interval data in the awake state.
  • the selected sinus NN interval data is the NN interval data of 4 hours in the awake state.
  • the above method is applicable to vagus nerve-related diseases such as epilepsy or depression.
  • FIG. 1 Schematic diagram of 12 lead ECG acquisition
  • 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. In the HRV analysis, the normal sinus NN interval in the awake state was selected from the 24-hour long-range ECG record for MSE analysis.
  • the specific processing flow of the ECG signal is shown in Figure 3.
  • the MSE calculation method in the HRV analysis to be used in the present invention extracts characteristic parameters of Slope5, Area1-5, Area6-15, and Area6-20 which characterize the heart rate complexity.
  • ECG ECG was collected 24 hours before surgery, and the collected 24-hour ECG data was obtained according to the above-mentioned treatment method.
  • the normal sinus NN interval sequence was obtained in the patient's awake state for 4 hours.
  • the VNS surgical patients in the training set were classified according to the efficacy of the follow-up period after surgery (effective group and ineffective group), and the heart rate complexity index of the effective group and the ineffective group was statistically analyzed and Slope5,
  • the Area1-5, Area6-15, and Area6-20 indicators respectively plot the Receiver Operating Characteristic (ROC) curve, and the threshold of each indicator is According to the point on each curve that is closest to the upper left corner, that is, the coordinate (1,1) (Udondon index), finally, according to the corresponding threshold, patients suitable for
  • the complexity index Area6-n index after the scale factor is expanded to n can also be used for the screening of the above VNS patients.
  • the invention can collect preoperative screening of patients with drug-refractory epilepsy by using ECG collection and HRV MSE analysis in patients with drug-refractory epilepsy, and guide patients who are not suitable for VNS therapy to receive other treatments. Avoid unnecessary expenditures and avoid delays in optimal treatment.
  • MSG curve of ECG to extract characteristic parameters that characterize heart rate complexity, clear selection of patients with VNS surgery indications can improve the efficacy of VNS therapy 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 MSG analysis was performed according to the ECG signal processing method described above by the 24-hour dynamic electrocardiogram data, and the corresponding Slope5, Area1-5, Area6-15, and Area6-20 characteristic parameters were extracted based on the MSE curve of each patient.
  • 32 of the 32 patients with refractory epilepsy who underwent VNS were not present.
  • the same degree of seizure reduction (including 6 patients with complete control of seizures) was considered as an effective group, and the remaining 4 patients had no change in the number of seizures compared with preoperative VNS as an ineffective group.
  • the MSE curve curves of the effective group and the ineffective group are very different, suggesting that the screening of patients with VNS surgery indications can be performed by the MSE method.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Physics & Mathematics (AREA)
  • Cardiology (AREA)
  • Veterinary Medicine (AREA)
  • Biophysics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Business, Economics & Management (AREA)
  • Primary Health Care (AREA)
  • Physiology (AREA)
  • Psychiatry (AREA)
  • Epidemiology (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Signal Processing (AREA)
  • General Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Databases & Information Systems (AREA)
  • Tourism & Hospitality (AREA)
  • Data Mining & Analysis (AREA)
  • Psychology (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • Neurology (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Neurosurgery (AREA)
  • Operations Research (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Quality & Reliability (AREA)

Abstract

一种筛选手术患者的建模方法，其对心率变异性(Heart Rate Variability，HRV)进行分析建模。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小时心电(Electrocardiography，ECG)信号，通过程式化的HRV分析方法计算ECG的多尺度熵(Multiscale Entropy，MSE)，并基于MSE曲线提取表征心率复杂度特征参数，准确、高效地筛选适合迷走神经刺激(Vagus Nerve Stimulation，VNS)手术的药物难治性癫痫患者，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MSE复杂度特征参数明确选择VNS手术适应症患者，可以从整体上提高VNS疗法的疗效。

Description

一种筛选手术患者的建模方法 技术领域
本发明涉及一种一种筛选手术患者的建模方法，尤其涉及在迷走神经刺激(Vagus Nerve Stimulation，VNS)适应症患者的建模方法。
背景技术
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VNS手术适应症患者筛选方法应用于临床，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系统、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手术患者筛选或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，它起源于自主神经系统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，是目前评价心血管系统自主神经系统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患者进行筛选的研究和技术方案。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，患者心脏自主神经系统平衡被打破，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的多尺度熵(Multiscale Entropy，MSE)，并基于MSE曲线提取表征心率复杂度特征参数，准确、高效地筛选适合VNS手术的药物难治性癫痫患者。
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，患者心脏自主神经系统平衡被打破，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，它起源于自主神经系统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，是目前评价心血管系统自主神经系统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ECG的MSE，并基于MSE曲线提取表征心率复杂度特征参数，准确、高效地筛选适合VNS手术的药物难治性癫痫患者，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MSE心率复杂度特征参数明确选择VNS手术适应症患者，可以从整体上提高VNS疗法的疗效。
本发明提供一种筛选手术患者的建模方法，其特征在于通过心率变异性进行建模。
进一步地，其包括如下步骤：
1)体外采集心电数据；
2)选取的窦性NN间期数据；
3)对选取的窦性NN间期序列进行MSE计算；
4)通过MSE曲线提取表征心率复杂度的参数。
第3)-4)步的具体方法如下：
i.对步骤2)中得到的NN间期序列{X1，...，Xi，...，XN}进行粗粒化处理得到不同尺度的重构序列
Figure PCTCN2017095951-appb-000001
τ为尺度因子；
ii.针对每个尺度的序列
Figure PCTCN2017095951-appb-000002
计算样本熵
Figure PCTCN2017095951-appb-000003
iii.将不同尺度因子对应的样本熵绘制曲线，并获取心率复杂度特征参数。
心率复杂度特征参数获取方法如下：
步骤iii中获取的曲线，对尺度1-n1的点进行线性拟合得到斜率Slope n1；对尺度n2-n3进行分段，计算每一段尺度曲线与横坐标构成区域的面积得到参数Areal。
进一步，n1＜n2＜n3，n3≤40；Areal参数的个数在1～7之间。
进一步，n1＝5，斜率参数Slope为Slope 5，n2＝6、n3＝20，Areal参数为Areal1-5、Areal6-15、Areal6-20。
进一步，选取的窦性NN间期数据为清醒状态下的NN间期数据。
进一步，选取的窦性NN间期数据为清醒状态下4小时的NN间期数据。
进一步，上述方法适用于癫痫病或抑郁症等迷走神经相关疾病。
附图说明：
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 MSE分析方法步骤流程图
图5 MSE复杂度指标提取图
图6阈值选择ROC曲线图
图7判断流程图
具体实施方式：
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，从24小时长程心电记录中选择4个小时被试清醒状态下的正常窦性NN间期用于MSE分析。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)选取被试清醒状态下4个小时窦性NN间期序列；
8)基于4个小时窦性NN间期序列计算MSE；
9)以尺度因子为横坐标、尺度因子对应的熵值为纵坐标描记MSE曲线；
10)根据MSE曲线提取表征心率复杂度的特征参数。
本发明拟采用的HRV分析中的MSE计算方法，提取表征心率复杂度的Slope5、Area1-5、Area6-15、Area6-20特征参数。
MSE方法步骤(见图4)：
(1)通过被试清醒状态下的4个小时正常窦性NN间期序列{X1，...，Xi，...，XN}进行粗粒化处理得到不同尺度的重构序列
Figure PCTCN2017095951-appb-000004
Figure PCTCN2017095951-appb-000005
τ为尺度因子；
(2)针对每个尺度的序列
Figure PCTCN2017095951-appb-000006
计算样本熵(Sample Entropy)
Figure PCTCN2017095951-appb-000007
(3)将不同尺度因子对应的样本熵绘制曲线，如图5，通过对尺度1-5的点进行线性拟合得到斜率Slope5、然后分别计算尺度1-5、尺度6-15、和尺度6-20曲线与横坐标构成区域的面积Area1-5、Area6-15、Area6-20，上述4个参数为MSE的心率复杂度特征参数。
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患者清醒状态下的4个小时正常窦性NN间期序列。对上述4个小时NN间期序列按上述方法进行MSE分析并提取Slope5、Area1-5、Area6-15、Area6-20等表征心率复杂度的特征参数，通过相应的阈值判断进行综合判断选择(如图6所示)，将训练集的VNS手术患者根据术后一段时间随访的疗效进行分类(有效组和无效组)，对有效组和无效组上述心率复杂度指标进行统计学分析并对Slope5、Area1-5、Area6-15、Area6-20指标分别画受试者工作特征(Receiver Operating Characteristic，ROC)曲线，每个指标的阈值是 根据每条曲线上与左上角即坐标(1，1)距离最近的点(尤登指数)，最后根据相应的阈值区分出适合VNS手术的患者和不适合VNS手术的患者(如图7所示)。利用表征心率复杂度的4个特征参数Slope5、Area1-5、Area6-15、Area6-20分别单独进行适合VNS手术和不适合VNS手术患者的区分时，其对应阈值的选择及其相应的筛选准确率如下：
Slope5＝0.071时，此时将大于该值的患者作为适合VNS手术的患者，其筛选的准确率为67.9％；
Area1-5＝4.32时，此时将大于该值的患者作为适合VNS手术的患者，其筛选的准确率为71.4％；
Area6-15＝10.57时，此时将大于该值的患者作为适合VNS手术的患者，其筛选的准确率为92.9％；
Area6-20＝15.85时，此时将大于该值的患者作为适合VNS手术的患者，其筛选的准确率为96.4％。
实施例2
实施例1的MSE分析方法中尺度因子扩大到n后的复杂度指标Area6-n指标亦可用于上述VNS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MSE分析，可以对药物难治性癫痫患者进行术前筛选，指导不适合VNS疗法的患者接受该手术而选择其他疗法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MSE曲线提取表征心率复杂度的特征参数明确选择VNS手术适应症患者，可以从整体上提高VNS疗法的疗效。
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MSE分析，基于每位患者的MSE曲线提取了相应的Slope5、Area1-5、Area6-15、Area6-20特征参数。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28例患者有不 同程度的发作减少(其中6例患者发作完全控制)视为有效组，其余4例患者发作次数和VNS术前相比没有变化视为无效组。有效组和无效组的MSE曲线曲线差别很大，提示通过MSE方法可以进行VNS手术适应症患者的筛选。进一步通过每位患者术前的Slope5、Area1-5、Area6-15、Area6-20特征参数对其疗效进行预测，结果表明上述4个参数中Area6-20预测的最为准确：将其阈值设定为15.85时，28例有效的患者中仅有1例患者的Area6-20＝15.09，如表1所示，其余患者的Area6-20均大于15.85，筛选的准备率超过96％，从而证明上述HRV分析的MSE方法可以准确、有效的进行VNS手术适应症患者的筛选。
Figure PCTCN2017095951-appb-000008
Figure PCTCN2017095951-appb-000009
表1
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)

  1. 一种建模方法，其特征在于通过心率变异性进行建模。
  2. 如权利要求1所述的建模方法，其特征在于，包括如下步骤：
    1)体外采集心电数据；
    2)选取的窦性NN间期数据；
    3)对选取的窦性NN间期序列进行MSE计算；
    4)通过MSE曲线提取表征心率复杂度的参数。
  3. 如权利要求2所述心率变异分析方法，其特征在于第3)-4)步的具体方法如下：
    i.对步骤2)中得到的NN间期序列{X1，...，Xi，...，XN}进行粗粒化处理得到不同尺度的重构序列
    Figure PCTCN2017095951-appb-100001
    1≤j≤N/τ，τ为尺度因子；
    ii.针对每个尺度的序列
    Figure PCTCN2017095951-appb-100002
    计算样本熵
    Figure PCTCN2017095951-appb-100003
    iii.将不同尺度因子对应的样本熵绘制曲线，并获取心率复杂度特征参数。
  4. 如权利要求3所述的建模方法，其特征在于心率复杂度特征参数获取方法如下：
    步骤iii中获取的曲线，对尺度1-n1的点进行线性拟合得到斜率Slope n1；对尺度n2-n3进行分段，计算每一段尺度曲线与横坐标构成区域的面积得到参数Areal。
  5. 如权利要求4所述的建模方法，其特征在于：n1＜n2＜n3，n3≤40；Areal参数的个数在1～7之间。
  6. 如权利要求4所述的建模方法，其特征在于：n1＝5，斜率参数Slope为Slope5，n2＝6、n3＝20，Areal参数为Areal1-5、Areal6-15、Areal6-20。
  7. 如权利要求2-6之一所述的建模方法，其特征在于：选取的窦性NN间期数据为清醒状态下的NN间期数据。
  8. 如权利要求7所述的建模方法，其特征在于：选取的窦性NN间期数据为清醒状态下4小时的NN间期数据。
  9. 如权利要求1-8之一所述的建模方法，其特征在于为迷走神经相关疾病病人进行建模。
  10. 如权利要求9所述的建模方法，其特征在于迷走神经相关疾病病人为癫痫病患者或者抑郁症患者。
PCT/CN2017/095951 2016-08-23 2017-08-04 一种筛选手术患者的建模方法 Ceased WO2018036367A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US16/086,428 US11504052B2 (en) 2016-08-23 2017-08-04 Modeling method for screening surgical patients

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610708479.3 2016-08-23
CN201610708479.3A CN106264510B (zh) 2016-08-23 2016-08-23 一种筛选手术患者的建模方法

Publications (1)

Publication Number Publication Date
WO2018036367A1 true WO2018036367A1 (zh) 2018-03-01

Family

ID=57614969

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/095951 Ceased WO2018036367A1 (zh) 2016-08-23 2017-08-04 一种筛选手术患者的建模方法

Country Status (3)

Country Link
US (1) US11504052B2 (zh)
CN (1) CN106264510B (zh)
WO (1) WO2018036367A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113499049A (zh) * 2021-07-05 2021-10-15 长治医学院 基于自适应多尺度熵分析心率变异性数据的方法
CN113950724A (zh) * 2019-05-27 2022-01-18 艾克斯-马赛大学 在癫痫患者的脑中标识可通过手术操作的目标区的方法

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106264510B (zh) 2016-08-23 2019-12-13 清华大学 一种筛选手术患者的建模方法
CN106343992B (zh) * 2016-08-23 2019-02-22 清华大学 心率变异性分析方法、装置及用途
CN106551691B (zh) * 2016-12-02 2020-01-21 清华大学 一种心率变异性分析方法、装置及用途
EP3624736A1 (en) * 2017-05-18 2020-03-25 Smith & Nephew, Inc. Systems and methods for determining the position and orientation of an implant for joint replacement surgery
CN109199362A (zh) * 2018-08-15 2019-01-15 哈尔滨理工大学 心脏早搏的ecg自动诊断系统及装置
CN113425312B (zh) * 2021-07-30 2023-03-21 清华大学 脑电数据处理方法及装置
CN115670478B (zh) * 2021-07-30 2025-06-24 清华大学 心电数据处理方法及装置
CN115644812B (zh) * 2022-10-31 2025-04-29 清华大学 疗效预测模型构建、预测方法、装置及电子设备
CN116230243B (zh) * 2023-05-04 2023-07-21 四川大学华西医院 难治性癫痫伴高分流量pfo患者的封堵手术诊疗系统
CN121549775A (zh) * 2026-01-23 2026-02-24 四川大学华西医院 基于心率变异性的癫痫辅助评估方法及装置、设备及介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140221780A1 (en) * 2011-07-22 2014-08-07 President And Fellows Of Harvard College Complexity based methods and systems for detecting depression
US20160113538A1 (en) * 2014-10-24 2016-04-28 National Tsing Hua University Relaxation state evaluation system and method and computer program product thereof
CN106264510A (zh) * 2016-08-23 2017-01-04 清华大学 一种筛选手术患者的建模方法
CN106343992A (zh) * 2016-08-23 2017-01-25 清华大学 心率变异性分析方法、装置及用途
CN106551691A (zh) * 2016-12-02 2017-04-05 清华大学 一种心率变异性分析方法、装置及用途

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009025817A2 (en) * 2007-08-20 2009-02-26 Medtronic, Inc. Evaluating therapeutic stimulation electrode configurations based on physiological responses
US20130060098A1 (en) * 2009-12-23 2013-03-07 Delta, Dansk Elektronik, Lys Og Akustik Monitoring device
WO2011115576A2 (en) * 2010-03-15 2011-09-22 Singapore Health Services Pte Ltd Method of predicting the survivability of a patient
US9427581B2 (en) * 2013-04-28 2016-08-30 ElectroCore, LLC Devices and methods for treating medical disorders with evoked potentials and vagus nerve stimulation
US9974962B2 (en) * 2014-05-01 2018-05-22 Regents Of The University Of Minnesota Alternans prevention and termination
CN104127193B (zh) * 2014-07-14 2017-08-18 华南理工大学 一种抑郁症程度量化的评估系统及其评估方法
DE102015105581A1 (de) * 2014-11-03 2016-05-04 Audi Ag System und Verfahren zur Überwachung des Gesundheitszustandes und/oder des Befindens eines Fahrzeuginsassen

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140221780A1 (en) * 2011-07-22 2014-08-07 President And Fellows Of Harvard College Complexity based methods and systems for detecting depression
US20160113538A1 (en) * 2014-10-24 2016-04-28 National Tsing Hua University Relaxation state evaluation system and method and computer program product thereof
CN106264510A (zh) * 2016-08-23 2017-01-04 清华大学 一种筛选手术患者的建模方法
CN106343992A (zh) * 2016-08-23 2017-01-25 清华大学 心率变异性分析方法、装置及用途
CN106551691A (zh) * 2016-12-02 2017-04-05 清华大学 一种心率变异性分析方法、装置及用途

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113950724A (zh) * 2019-05-27 2022-01-18 艾克斯-马赛大学 在癫痫患者的脑中标识可通过手术操作的目标区的方法
CN113499049A (zh) * 2021-07-05 2021-10-15 长治医学院 基于自适应多尺度熵分析心率变异性数据的方法
CN113499049B (zh) * 2021-07-05 2022-08-05 长治医学院 基于自适应多尺度熵分析心率变异性数据的方法

Also Published As

Publication number Publication date
US20190090803A1 (en) 2019-03-28
CN106264510A (zh) 2017-01-04
CN106264510B (zh) 2019-12-13
US11504052B2 (en) 2022-11-22

Similar Documents

Publication Publication Date Title
CN106264510B (zh) 一种筛选手术患者的建模方法
CN106551691B (zh) 一种心率变异性分析方法、装置及用途
Quintana et al. Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH): recommendations to advance research communication
Tripathi et al. Ensemble computational intelligent for insomnia sleep stage detection via the sleep ECG signal
Menshawy et al. An automatic mobile-health based approach for EEG epileptic seizures detection
US10820817B2 (en) Monitoring the effects of sleep deprivation using neuronal avalanches
WO2018036368A1 (zh) 心率变异性分析方法、装置及用途
US20240090817A1 (en) Cortical recording and signal processing methods and devices
Mohammadpour Touserkani et al. Photoplethysmographic evaluation of generalized tonic‐clonic seizures
Santana-Gomez et al. Harmonization of pipeline for detection of HFOs in a rat model of post-traumatic epilepsy in preclinical multicenter study on post-traumatic epileptogenesis
Podell et al. Leveraging continuous vital sign measurements for real-time assessment of autonomic nervous system dysfunction after brain injury: a narrative review of current and future applications
CN115486820B (zh) 一种针灸疗效及生理效应检测量化的方法、系统和装置
WO2019163836A1 (ja) てんかん診断を支援する脳波解析方法及び脳波解析装置
JP7098974B2 (ja) 気分障害測定装置および気分障害測定方法
Bacher et al. Validation of an EEG seizure detection paradigm optimized for clinical use in a chronically implanted subcutaneous device
Poh Continuous assessment of epileptic seizures with wrist-worn biosensors
CN115153562A (zh) 一种基于脑电图的预测方法、装置、电子设备及存储介质
CN120585277B (zh) 一种基于同步采集的脑电-胃电信号的认知障碍预测系统
Liu et al. Deceleration and acceleration capacities of heart rate in patients with drug-resistant epilepsy
Oliver et al. Rapid adjustments to autonomic control of cardiac rhythm at the onset of isometric exercise in healthy young adults
Dhar et al. Heart Failure Readmission Prediction Using Seismocardiogram Signal
Viana On the clinical potential of ultra long-term subcutaneous EEG monitoring in epilepsy
de Abreu Predicting and Monitoring Epilepsy through Peripheral Physiological Data
Mosca et al. Wearable-Enabled Screening of REM Sleep Behavior Disorder: Cross-Dataset Validation of a Heart Rate Variability-Based Machine Learning Approach
Shanks A compositional framework for quantifying neurophysiological motor recovery after stroke

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17842790

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17842790

Country of ref document: EP

Kind code of ref document: A1