WO2021042591A1 - Procédé d'analyse de signal de variabilité de fréquence cardiaque basé sur un procédé de décomposition d'énergie d'extrémum - Google Patents

Procédé d'analyse de signal de variabilité de fréquence cardiaque basé sur un procédé de décomposition d'énergie d'extrémum Download PDF

Info

Publication number
WO2021042591A1
WO2021042591A1 PCT/CN2019/121416 CN2019121416W WO2021042591A1 WO 2021042591 A1 WO2021042591 A1 WO 2021042591A1 CN 2019121416 W CN2019121416 W CN 2019121416W WO 2021042591 A1 WO2021042591 A1 WO 2021042591A1
Authority
WO
WIPO (PCT)
Prior art keywords
signal
extreme
energy
value
component
Prior art date
Application number
PCT/CN2019/121416
Other languages
English (en)
Chinese (zh)
Inventor
周作建
宁新宝
曾彭
姜晓东
王�华
王斌斌
刘红星
Original Assignee
江苏华康信息技术有限公司
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 江苏华康信息技术有限公司 filed Critical 江苏华康信息技术有限公司
Publication of WO2021042591A1 publication Critical patent/WO2021042591A1/fr

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
    • A61B5/02—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
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • 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
    • 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
    • A61B5/0245—Detecting, measuring or recording 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
    • 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
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • 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
    • A61B5/7235—Details of waveform analysis
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

Definitions

  • the invention relates to an electrocardiogram signal analysis, in particular to a heart rate variability signal analysis method based on an extreme value energy decomposition method.
  • Heart rate variability refers to the measurement of the time variability between consecutive cardiac cycles, to be precise, it should be the measurement of the variance of the difference between the normal P-P intervals that occur continuously.
  • HRV Heart rate variability
  • RRI R-R interval signal
  • HRV signal heart rhythm variability signal
  • HRV signal heart rhythm variability signal
  • RR interval Interbeat Intervals, RRI
  • PSD power spectrum analysis
  • the object of the present invention is to provide a heart rate variability signal analysis method based on extreme energy decomposition method that can use less data and can intuitively reflect the true law of the energy distribution and signal fluctuation of the electrocardiogram.
  • the present invention discloses a heart rate variability signal analysis method based on extreme energy decomposition method, which includes the following steps:
  • the first component p 1 represents the energy of the highest frequency band, which represents the proportion of the energy distribution of the signal in the highest frequency range, and the last component p n represents the proportion of the energy distribution of the signal in the lowest frequency range;
  • the normalized energy distribution vector Draw a normalized energy distribution map, where the abscissa represents the component level, the ordinate represents the normalized energy distribution vector value, the curve represents the average value, and the error bar represents the standard deviation;
  • the specific method of denoising preprocessing in the step (1) is: filtering the ECG signal through a 40Hz zero-phase FIR low-pass filter to eliminate high-frequency noise, and then through a median filter to remove baseline drift.
  • the conditions for determining the extreme value modal function in the step (3) are: (a). In the entire data sequence, the number of extreme points is equal to or different from the number of zero-crossing points; (b). At any moment, the upper and lower envelopes are symmetrical to the time axis.
  • ⁇ represents the screening threshold, which is between 0.2 and 0.3.
  • the first standard value in the step (7) is -0.15, and the second standard value is 0.08.
  • the present invention uses the extreme energy decomposition method (Extremum Energy Decomposition, EED) to analyze the RRI signal, decomposes the original signal into multiple components, that is, the extreme component function, calculates the energy of each component, and obtains its energy distribution;
  • EED Extrem Energy Decomposition
  • the signal can be decomposed into signals of different time levels from high frequency to low frequency according to the fluctuation law of the raw RRI signal, and the frequency band is divided more carefully; the data length obtained by extreme value decomposition at all levels is the same, so it will not lead to data length Reduced, so that it can be used for short-term data analysis, that is, a small amount of data is needed to analyze and get accurate results;
  • EED is not easy to be affected by noise for different levels of component energy analysis.
  • Figure 1 is a schematic diagram of the original signal in the present invention
  • Fig. 2 is a schematic diagram of obtaining an envelope of an original signal in the present invention
  • Fig. 3 is a schematic diagram of the original signal minus the envelope mean value signal in the present invention.
  • Fig. 4 is a schematic diagram of the first extremum modal function component obtained in the present invention.
  • Figure 5 is a schematic flow chart of the extreme value energy decomposition method in the present invention.
  • FIG. 6 is a schematic diagram of the EED decomposition of the RRI signal in Embodiment 1 of the present invention.
  • FIG. 7 is a schematic diagram of the normalized energy distribution in the RRI signal in Embodiment 1 of the present invention.
  • a heart rate variability signal analysis method based on extreme energy decomposition method of the present invention includes the following steps:
  • the envelope mean signal of the lower envelope m(t) (e max + e min )/2;
  • ⁇ represents the screening threshold, which is between 0.2 and 0.3; the extremum modal decomposition that meets the stopping criterion meets the following two conditions: (a) The final extremum modal function component c n (t) or margin r n (t) is less than the preset threshold; (b) the residual signal r n (t) becomes a monotonic signal, and the extreme modal function signal cannot be extracted from it;
  • the first component p 1 represents the energy of the highest frequency band, which represents the proportion of the energy distribution of the signal in the highest frequency range, and the last component p n represents the proportion of the energy distribution of the signal in the lowest frequency range;
  • the normalized energy distribution vector Draw a normalized energy distribution map, where the abscissa represents the component level, the ordinate represents the normalized energy distribution vector value, the curve represents the average value, and the error bar represents the standard deviation;
  • the extreme energy decomposition method (Extremum Energy Decomposition, EED) adopted by the present invention is a method based on the concept of extreme value modal function, which is a type of signal with a single frequency that satisfies the following two conditions at the same time , The two conditions are:
  • the upper envelope formed by the local maximum point and the lower envelope formed by the local minimum point have a mean value of zero, that is to say, the local upper and lower envelopes are locally symmetric with respect to the time axis;
  • condition (a) is similar to the requirement of Gaussian normal stationary process for traditional narrowband
  • condition (b) ensures that the instantaneous frequency calculated by the extreme value modal function has physical meaning.
  • the standard selection for the termination of the extreme value modal function decomposition of the present invention should be moderate. Too strict conditions will cause the last few extreme value modal function components to lose meaning; too loose conditions will cause useful components to be lost; in practical applications, It is also possible to set the number of extreme modal function component levels that need to be decomposed according to requirements, and the calculation is terminated when the decomposition level is satisfied.
  • the EED analysis method is used to analyze the energy distribution of ECG in healthy people and CHF patients at different levels.
  • a heart rate variability signal analysis method based on extreme energy decomposition method for healthy people includes the following steps:
  • the 8 extreme modal function components decomposed from the original signal x(t) represent the components of the original signal in different frequency bands, and then the energy of each component is calculated
  • the first component p 1 represents the energy of the highest frequency band, and represents the proportion of the energy distribution of the signal in the highest frequency range, and the last component p n represents the proportion of the energy distribution of the signal in the lowest frequency band.
  • a heart rate variability signal analysis method based on extreme energy decomposition method for CHF patients includes the following steps:
  • the 8 extreme modal function components decomposed from the original signal x(t) represent the components of the original signal in different frequency bands, and then the energy of each component is calculated
  • the first component p 1 represents the energy of the highest frequency band, which represents the proportion of the energy distribution of the signal in the highest frequency range, and the last component p n represents the proportion of the energy distribution of the signal in the lowest frequency range;
  • a normalized energy distribution map is drawn with the unified energy distribution vector, where the abscissa represents the component level, the ordinate represents the normalized energy distribution vector value, the curve represents the average value, and the error bar represents the standard deviation;
  • the present invention obtains the results in Table 1.
  • Table 1 Average center frequencies of HRV signals of healthy people and CHF patients at different component levels
  • the typical methods of traditional Power Spectral Density (PSD) method for frequency domain segmentation are: HF (0.15 ⁇ 0.4 Hz), LF (0.04 ⁇ 0.15 Hz), VLF (0.0033 ⁇ 0.04 Hz).
  • the frequency of the EED method of the present invention is higher than HF; level 2 is in the frequency range of HF; levels 3 and 4 are in the range of LF; levels 5-7 are in the frequency range of VLF; and level 8 is lower than VLF.
  • the frequency of CHF patients at the same level is slightly higher than that of healthy people, which reflects the influence of heart disease on HRV fluctuation rhythm. At the same level, HRV fluctuates faster in CHF patients.
  • the extreme value modal function components C i (t) obtained by decomposing the HRV signals of the two groups are calculated to obtain the normalized energy distribution vector, and the EED curve is drawn, as shown in FIG. 7.
  • Figure 7 is a schematic diagram of EED analysis results of RR interval signals in healthy people and CHF patients.
  • the data length is 10000 points
  • the curve represents the mean value
  • the error bar represents the standard deviation.
  • the symbol * above the curve indicates that the energy T test p ⁇ 0.01 of the two groups of people. In the selection of levels, levels 1 and higher than 7, including margins, are removed.
  • Level 1 is easily affected by noise, leading to large fluctuations in energy, causing excessive standard deviation of the results, and its frequency can be as high as several kHz, so it has no clear physiological significance; levels higher than 7 reflect the long-term signal Rhythm is very susceptible to the influence of the external environment, and its frequency is very low, and its physiological significance is unknown.
  • Level 7 at the low-weight level (levels 2, 3), the normalized energy value of CHF patients is significantly higher than that of healthy people, and at the high-weight level (level>5), the opposite changes occur, and the energy of healthy people is higher than that of healthy people. Energy of CHF patients; healthy people's energy is relatively stable at levels 2 to 5.
  • the present invention adds the EED analysis results of surrogate data (Healthy Surrogate, CHF Surrogate). As shown in Figure 7, the surrogate data is generated by randomizing the original data. The energy distribution of the surrogate data is monotonous as the scale increases. Decrease, compared with CHF patients, the energy is higher on a small time scale, and energy is lower on a long time scale.
  • the energy difference value EDV of each group is calculated.
  • a high EDV value indicates a higher component low-level energy distribution and a lower component high-level energy distribution of the RRI signal.
  • the EDV values of healthy people, CHF patients and their replacement data are calculated, as shown in Table 2.
  • Heart rate variability refers to the measurement of the time variability between consecutive cardiac cycles, to be precise, it should be the measurement of the variance of the difference between the normal P-P intervals that occur continuously.
  • HRV Heart rate variability
  • RRI R-R interval signal

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Surgery (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Physics & Mathematics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Pathology (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Cardiology (AREA)
  • Physiology (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Psychiatry (AREA)
  • Pulmonology (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention concerne un procédé d'analyse de signal de variabilité de fréquence cardiaque basé sur un procédé de décomposition d'énergie d'extrémum, comprenant les étapes consistant à obtenir un signal ECG dans un état inconnu à un instant donné et pour une fréquence d'échantillonnage donnée et débruiter le signal ECG pour obtenir un signal x(t) d'intervalle R-R ; utiliser le signal x(t) d'intervalle R-R en tant que signal d'origine et décomposer le signal d'origine x(t) en n composantes de fonction de mode d'extrémum et une marge, les n composantes de fonction de mode d'extrémum obtenues par décomposition du signal d'origine x(t) représentant des composantes du signal d'origine dans différentes bandes de fréquence ; et déterminer, en fonction des n composantes de fonction de mode d'extrémum, si le signal d'intervalle R-R est un signal de variabilité de fréquence cardiaque anormal. Selon la présente invention, le signal d'intervalle R-R est analysé à l'aide d'un procédé de décomposition d'énergie d'extrémum, le signal d'origine est décomposé en une pluralité de composantes, par exemple une fonction de composante d'extrémum, et l'énergie de chaque composante est calculée pour obtenir une distribution d'énergie correspondante.
PCT/CN2019/121416 2019-09-06 2019-11-28 Procédé d'analyse de signal de variabilité de fréquence cardiaque basé sur un procédé de décomposition d'énergie d'extrémum WO2021042591A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910841605.6 2019-09-06
CN201910841605.6A CN110464337B (zh) 2019-09-06 2019-09-06 一种基于极值能量分解法的心率变异性信号分析方法

Publications (1)

Publication Number Publication Date
WO2021042591A1 true WO2021042591A1 (fr) 2021-03-11

Family

ID=68515060

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/121416 WO2021042591A1 (fr) 2019-09-06 2019-11-28 Procédé d'analyse de signal de variabilité de fréquence cardiaque basé sur un procédé de décomposition d'énergie d'extrémum

Country Status (3)

Country Link
CN (1) CN110464337B (fr)
AU (1) AU2019101756A4 (fr)
WO (1) WO2021042591A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116738153A (zh) * 2023-08-15 2023-09-12 中国科学院东北地理与农业生态研究所 基于光谱分析的有机肥利用效果评价方法
CN117503153A (zh) * 2024-01-05 2024-02-06 北华大学 基于人工智能的患者术后康复评价方法

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110464337B (zh) * 2019-09-06 2020-09-01 江苏华康信息技术有限公司 一种基于极值能量分解法的心率变异性信号分析方法
CN116548928B (zh) * 2023-07-11 2023-09-08 西安浩阳志德医疗科技有限公司 一种基于互联网的护理服务系统
CN117390380B (zh) * 2023-12-12 2024-02-13 泰安金冠宏食品科技有限公司 一种油渣分离系统中的数据分析方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080269628A1 (en) * 2007-04-25 2008-10-30 Siemens Medical Solutions Usa, Inc. Denoising and Artifact Rejection for Cardiac Signal in a Sensis System
CN101496716A (zh) * 2009-02-26 2009-08-05 周洪建 利用ecg信号检测睡眠呼吸暂停的测量方法
CN101642369A (zh) * 2008-08-04 2010-02-10 南京大学 自主神经功能生物反馈方法和系统
CN110464337A (zh) * 2019-09-06 2019-11-19 江苏华康信息技术有限公司 一种基于极值能量分解法的心率变异性信号分析方法

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108814579B (zh) * 2018-04-16 2020-05-22 西安交通大学 一种基于emd分解的心电、呼吸联合计算心率变异性的方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080269628A1 (en) * 2007-04-25 2008-10-30 Siemens Medical Solutions Usa, Inc. Denoising and Artifact Rejection for Cardiac Signal in a Sensis System
CN101642369A (zh) * 2008-08-04 2010-02-10 南京大学 自主神经功能生物反馈方法和系统
CN101496716A (zh) * 2009-02-26 2009-08-05 周洪建 利用ecg信号检测睡眠呼吸暂停的测量方法
CN110464337A (zh) * 2019-09-06 2019-11-19 江苏华康信息技术有限公司 一种基于极值能量分解法的心率变异性信号分析方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
DONG HONGSHENG, ZHANG AIHUA, QIU TIANSHUANG, HAO XIAOCHONG: "Time-frequency analysis method of heart rate variability signal based on the Hilbert spectrum", CHINESE JOURNAL OF SCIENTIFIC INSTRUMENT, vol. 32, no. 2, 1 February 2011 (2011-02-01), pages 271 - 278, XP055787881 *
PANG CHUN-YING, XIAO-TIAN WANG, SUN XIAO-LIN: "A New Method of Epileptic EEG Identification Based on Improved EMD", CHINESE JOURNAL OF BIOMEDICAL ENGINEERING VOL．, vol. 32, no. 6, 1 June 2013 (2013-06-01), pages 663 - 669, XP055787878 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116738153A (zh) * 2023-08-15 2023-09-12 中国科学院东北地理与农业生态研究所 基于光谱分析的有机肥利用效果评价方法
CN116738153B (zh) * 2023-08-15 2023-10-17 中国科学院东北地理与农业生态研究所 基于光谱分析的有机肥利用效果评价方法
CN117503153A (zh) * 2024-01-05 2024-02-06 北华大学 基于人工智能的患者术后康复评价方法
CN117503153B (zh) * 2024-01-05 2024-03-15 北华大学 基于人工智能的患者术后康复评价方法

Also Published As

Publication number Publication date
AU2019101756A4 (en) 2020-11-12
CN110464337B (zh) 2020-09-01
CN110464337A (zh) 2019-11-19

Similar Documents

Publication Publication Date Title
WO2021042591A1 (fr) Procédé d'analyse de signal de variabilité de fréquence cardiaque basé sur un procédé de décomposition d'énergie d'extrémum
CN110558973B (zh) 一种执行基于极值能量分解法的心电图信号量化分析方法的计算机设备
Rajendra Acharya et al. Heart rate variability: a review
WO2021042592A1 (fr) Procédé d'analyse de signal de vfc d'apprentissage de méditation basé sur un procédé de décomposition d'énergie d'extremum
Long et al. Time-frequency analysis of heart rate variability for sleep and wake classification
Pan et al. Multi-frequency components entropy as novel heart rate variability indices in congestive heart failure assessment
CN108814579A (zh) 一种基于emd分解的心电、呼吸联合计算心率变异性的方法
WO2021042589A1 (fr) Procédé d'analyse de signal d'électrocardiogramme fondé sur la décomposition d'énergie de l'extremum
Ghiasi et al. A new sympathovagal balance index from electrodermal activity and instantaneous vagal dynamics: a preliminary cold pressor study
Yeh et al. The critical role of respiratory sinus arrhythmia on temporal cardiac dynamics
Ramteke et al. Stress detection of students at academic level from heart rate variability
Raab et al. Large-scale dimension densities for heart rate variability analysis
Aliani et al. Cardiovascular dynamics in COVID-19: a heart rate variability investigation
Abdul-Kadir et al. Atrial fibrillation classification and association between the natural frequency and the autonomic nervous system
Valenza et al. Validation of sympathetic activity index from heart rate variability series: a preliminary muscle sympathetic nerve activity study
Saraiva et al. Classification of cardiovascular signals
Patil et al. Discrimination between atrial fibrillation (AF) & normal sinus rhythm (NSR) using linear parameters
Torbey et al. Time-Scale Analysis of SignalsWithout Basis Functions: Application to Sudden Cardiac Arrest Prediction.
Kim et al. Pre-processing of photoplethysmographic waveform for amplitude regularization
Goyal et al. Study of HRV dynamics and comparison using wavelet analysis and Pan Tompkins algorithm
Tobón et al. Improved heart rate variability measurement based on modulation spectral processing of noisy electrocardiogram signals
Sunkaria et al. Ageing effects on HRV dynamics: a comparative study with FFT and AR models
Kokosinska et al. The analysis of human heart rate for healthy and ill patients using the recently published method Multiscale Multifractal Analysis
Nardelli et al. Characterization of autonomic states by complex sympathetic and parasympathetic dynamics
Vallverdú et al. Measuring heart rate variability by means of information entropies based on Choi-Williams distribution

Legal Events

Date Code Title Description
NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19944487

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 19944487

Country of ref document: EP

Kind code of ref document: A1