WO2024007152A1 - Procédé de diagnostic de maladies cardiovasculaires pédiatriques basé sur des signaux électrocardiographiques et phonocardiographiques - Google Patents

Procédé de diagnostic de maladies cardiovasculaires pédiatriques basé sur des signaux électrocardiographiques et phonocardiographiques Download PDF

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WO2024007152A1
WO2024007152A1 PCT/CN2022/103931 CN2022103931W WO2024007152A1 WO 2024007152 A1 WO2024007152 A1 WO 2024007152A1 CN 2022103931 W CN2022103931 W CN 2022103931W WO 2024007152 A1 WO2024007152 A1 WO 2024007152A1
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heart sound
heart
signal
cardiovascular diseases
features
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PCT/CN2022/103931
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Chinese (zh)
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张福伟
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张福伟
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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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1095—Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes

Definitions

  • the present invention relates to the technical field of cardiovascular disease diagnosis, and in particular to a method for diagnosing cardiovascular disease in children based on electrocardiogram and heart sounds.
  • VSD ventricular Septal Defect
  • the present invention provides a method for diagnosing cardiovascular diseases in children based on electrocardiogram and heart sounds; the method includes the following steps:
  • S2 Process the electrocardiogram signal and heart sound signal to obtain multi-dimensional heart sound characteristics
  • S3 Input the multi-dimensional heart sound features into the diagnostic model to obtain a diagnostic result, where the diagnostic result is the type of cardiovascular disease.
  • the ECG signal and the heart sound signal are processed to obtain the ECG and heart sound characteristics.
  • Methods include:
  • the multidimensional heart sound features include time domain heart sound features, frequency domain heart sound features, and energy domain heart sound features.
  • the method for preprocessing the ECG signal and the heart sound signal includes:
  • S212 Perform filtering and noise reduction processing on the sampled ECG signals and heart sound signals
  • S213 Normalize the ECG signal and heart sound signal after filtering and noise reduction.
  • the method for obtaining initial heart sound features based on the preprocessed ECG signal and heart sound signal includes:
  • S222 According to the Q wave of the QRS wave, identify the duration and amplitude information of the first heart sound from the preprocessed heart sound signal, and according to the T wave of the QRS wave, identify the second heart sound signal from the preprocessed heart sound signal. Duration and amplitude information of heart sounds;
  • S223 According to the duration and amplitude information of the first heart sound and the second heart sound, obtain the duration and amplitude information of the systolic phase and the duration and amplitude information of the diastolic phase;
  • S224 Use the duration and amplitude information of the first heart sound, the second heart sound, systole and diastole as initial heart sound features.
  • the initial heart sound characteristics are analyzed through a time domain analysis method.
  • the method of obtaining the time domain heart sound characteristics includes: respectively calculating the first heart sound, systole, second heart sound and diastole. Amplitude ratio; calculate the time difference between adjacent first heart sound, systole, second heart sound and diastole respectively; calculate EMAT%; calculate the average of each amplitude ratio, each time difference and EMAT% in the entire cardiac cycle, and get 11 time domain heart sound features.
  • the initial heart sound features are analyzed through a frequency domain analysis method, and the method for obtaining the time domain heart sound features includes: performing Fourier transform on the initial heart sound features to obtain a corresponding spectrum sequence, wherein:
  • the spectrum sequence includes a first heart rate spectrum sequence, a second heart rate spectrum sequence, a systolic spectrum sequence, and a diastolic spectrum sequence; the first heart rate spectrum sequence, the second heart rate spectrum sequence, the systolic spectrum sequence, and the diastolic spectrum are extracted respectively.
  • the frequencies in the sequence are multiple values of specified frequencies, and the average value of each value in all cardiac cycles is calculated and a new spectrum sequence is formed; 4 frequency domain heart sound characteristics are obtained.
  • the plurality of designated frequencies corresponding to the first heartbeat spectrum sequence, the second heartbeat spectrum sequence, the systolic phase, and the diastolic phase are 60HZ, 70HZ, 80HZ, 90HZ, 100HZ, 110HZ, 120HZ, 130HZ, 80HZ, and 150HZ.
  • the initial heart sound characteristics are analyzed through a frequency domain analysis method, and the method for obtaining the time domain heart sound characteristics includes: separately calculating the first heart sound, systolic period, second heart sound and diastolic period. Energy ratio, and find the average value of each energy ratio in all cardiac cycles to obtain 6 energy domain heart sound characteristics.
  • the diagnostic model includes an input layer, a hidden layer, an output layer and a softmax function, wherein the input layer consists of 21 neurons, and the 21 neurons correspond to multi-dimensional heart sound features; the hidden The layers have 7 and 8 neurons respectively; the output layer consists of 8 neurons, corresponding to the types of cardiovascular diseases; finally, a softmax function is used to create a multi-classification problem.
  • embodiments of the present invention provide a method for diagnosing cardiovascular diseases in children based on electrocardiogram and heart sounds, which combines heart sound signals and electrocardiogram signals and fully considers the manifestation of cardiovascular diseases in heart sound signals.
  • Multi-dimensional heart sound features including time domain, frequency domain and energy domain heart sound features are extracted, and accurate prediction of cardiovascular diseases is achieved by building a diagnostic model and multi-dimensional heart sound features.
  • the present invention also provides a method for extracting multi-dimensional heart sound features, which provides basic support for the diagnostic model to accurately predict cardiovascular diseases.
  • the invention has the advantages of practicality, high efficiency and the like.
  • Figure 1 is a flow chart of a method for diagnosing cardiovascular diseases in children based on electrocardiogram and heart sounds provided by an embodiment of the present invention
  • Figure 2 is a construction diagram of a diagnostic model provided by an embodiment of the present invention.
  • an embodiment of the present invention provides a method for diagnosing cardiovascular disease in children based on electrocardiogram and heart sounds; the method includes the following steps:
  • Step 1 Obtain the ECG signal and heart sound signal of the child to be diagnosed
  • the method of obtaining the ECG signal and heart sound signal of the child to be diagnosed is to place the twelve leads of the ECG according to the conventional ECG operation method, and on this basis, simultaneously place the high-precision heart sound probe and simultaneously record the characteristics of the heart sound. Obtain the ECG signal and heart sound signal of the child to be diagnosed.
  • Step 2 Process the electrocardiogram signal and heart sound signal to obtain multi-dimensional heart sound characteristics
  • the method of processing the ECG signal and the heart sound signal to obtain the ECG and heart sound characteristics includes:
  • Preprocess the ECG signal and heart sound signal includes: intercepting the ECG signal and heart sound signal; performing sampled ECG signal and heart sound signal. Filtering and noise reduction processing; normalizing the ECG signal and heart sound signal after filtering and noise reduction;
  • the filtering and noise reduction process uses an infinite impulse corresponding digital band group filter for filtering. This filter is an existing technology, and its working process will not be described in detail here.
  • the method is specifically to identify the QRS wave of the preprocessed ECG signal; based on the Q wave of the QRS wave, identify the preprocessed heart sound signal
  • the duration and amplitude information of the first heart sound is obtained, and the duration and amplitude information of the second heart sound are identified from the preprocessed heart sound signal according to the T wave of the QRS wave; according to the first heart sound and the second heart sound
  • the duration and amplitude information of the heart sound is obtained, and the duration and amplitude information of the systolic phase and the duration and amplitude information of the diastolic phase are obtained; the duration of the first heart sound, the second heart sound, the systolic phase and the diastolic phase are and amplitude information as initial heart sound features;
  • the multidimensional heart sound features include time domain heart sound features, frequency domain heart sound features, and energy domain heart sound features.
  • the initial heart sound characteristics are analyzed through the time domain analysis method, and the method of obtaining the time domain heart sound characteristics includes: separately calculating the amplitudes between the first heart sound, systole, second heart sound and diastole. Ratio; calculate the time difference between adjacent first heart sound, systole, second heart sound and diastole respectively; calculate EMAT% of each cardiac cycle respectively; calculate each amplitude ratio, each time difference and each EMAT%; get 11 time domain heart sound characteristics.
  • each amplitude ratio, each time difference and each EMAT% mentioned above in each cardiac cycle are calculated to construct 6 amplitude ratio sequences, 4 time difference sequences, and 1 EMAT% sequence, and 11 time domain heart sound features are obtained. .
  • EMAT is the time from the onset of the QRS wave in the electrocardiogram to the onset of the first heart sound (mitral valve closure), including the electro-mechanical delay time and the left ventricular systolic period before mitral valve closure. It is the presystolic period of the left ventricle.
  • EMAT% refers to the proportion of EMAT in the RR interval. Studies have shown that EMAT% has a significant impact on cardiovascular disease. Therefore, the embodiment of the present invention uses the EMAT% sequence as a time-domain heart sound feature.
  • the sequence length is a multiple of the preset sequence length
  • the sequence will be segmented according to the multiple, and the average value of each value in each segment will be calculated as the new sequence.
  • the sequence length is not the preset sequence length
  • Multiples of the sequence delete the first value of the sequence until the sequence length is a multiple of the preset sequence length.
  • the sequence length provided by the embodiment of the present invention is 10, and the cardiac cycle intercepted in the previous step is 12, then the length of the constructed time difference sequence is 11.
  • the first value of the time length sequence is deleted to obtain a new time difference sequence.
  • the new time difference sequence is 1 times the length of the preset sequence, then use 1 as the segmentation parameter to divide the time difference sequence into 10 segments, calculate the average of the 10 segments, and obtain a time difference sequence with a length of 10.
  • the method of analyzing the initial heart sound features through the frequency domain analysis method to obtain the time domain heart sound features includes: performing Fourier transform on the initial heart sound features to obtain the corresponding spectrum sequence, wherein the spectrum
  • the sequence includes the first heart rate spectrum sequence, the second heart rate spectrum sequence, the systolic spectrum sequence and the diastolic spectrum sequence; respectively extract the first heart rate spectrum sequence, the second heart rate spectrum sequence, the systolic spectrum sequence and the diastolic spectrum sequence.
  • the frequency is a number of values at specified frequencies. The average value of each value in all cardiac cycles is calculated and a new spectrum sequence is formed; 4 frequency domain heart sound features are obtained.
  • the spectrum distribution of the first heart sound and the second heart sound is 50-100HZ, if it exceeds 100HZ, it is determined that there is a murmur, and the preset sequence length is 10. Therefore, the five designated frequencies selected in the embodiment of the present invention
  • the normal group and the abnormal group of 5 specified frequencies construct spectral heart sound characteristics.
  • the multiple specified frequencies corresponding to the first heart sound spectrum sequence, the second heart sound spectrum sequence, the systolic period, and the diastolic period are 60HZ, 70HZ, 80HZ, and 90HZ. , 100HZ, 110HZ, 120HZ, 130HZ, 80HZ, 150HZ, and thus obtain 4 frequency domain heart sound features with a length of 10.
  • the method of analyzing the initial heart sound characteristics through the frequency domain analysis method and obtaining the time domain heart sound characteristics includes: separately calculating the energy ratio between the first heart sound, systole, second heart sound and diastole. , and calculate the average value of each energy ratio in all cardiac cycles to obtain 6 energy domain heart sound characteristics.
  • the method for processing the sequence length is the same as the above-mentioned method for obtaining the sequence length for transmitting the time domain heart sound feature, and will not be described again here.
  • Step 3 Input the multi-dimensional heart sound features into the diagnostic model to obtain a diagnostic result, where the diagnostic result is the type of cardiovascular disease.
  • the diagnostic model includes an input layer, a hidden layer, an output layer and a softmax function, wherein the input layer is composed of 21 neurons, and the 21 neurons correspond to Multi-dimensional heart sound features; the hidden layer has 7 and 8 neurons respectively, and the hidden layer is a 2-layer bidirectional long short-term memory network model; the output layer is composed of 8 neurons, corresponding to the type of cardiovascular disease; Finally, a softmax function is used, which is created to solve multi-classification problems.
  • the output result is the probability of multiple cardiovascular diseases. The probability is a value between 0-1, and the sum of the obtained probabilities is 1.
  • the number of neurons in the output layer can be 8, 50, or 100, and can be set according to existing common cardiovascular diseases. If the output layer neurons are added, diagnostic model training needs to be added. time samples to ensure the accuracy of diagnosis by the diagnostic model.
  • the diagnostic model may be a fully connected neural network model, with two fully connected layers as hidden layers of the fully connected neural network model, and an S-shaped growth curve layer as the output layer of the fully connected neural network model; when the The diagnostic model is a fully connected neural network model, and its output results are the probabilities of multiple types of cardiovascular diseases.
  • the probabilities are 0-1 values, and the sum of the output probabilities is not necessarily 1.
  • embodiments of the present invention provide a method for diagnosing cardiovascular diseases in children based on ECG heart sounds, which combines heart sound signals and ECG signals, fully considers the manifestation of cardiovascular diseases in heart sound signals, and extracts time domain , multi-dimensional heart sound features of frequency domain and energy domain heart sound features, and by building diagnostic models and multi-dimensional heart sound features, accurate prediction of cardiovascular diseases is achieved.
  • the present invention also provides a method for extracting multi-dimensional heart sound features, which provides basic support for the diagnostic model to accurately predict cardiovascular diseases.
  • the invention has the advantages of practicality, high efficiency and the like.

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Abstract

La présente invention concerne un procédé de diagnostic de maladies cardiovasculaires pédiatriques basé sur des signaux électrocardiographiques et phonocardiographiques. Le procédé se rapporte au domaine technique du diagnostic de maladies cardiovasculaires et consiste : à acquérir des signaux électrocardiographiques et phonocardiographiques (S1) à partir d'un patient pédiatrique à diagnostiquer ; à traiter les signaux électrocardiographiques et phonocardiographiques pour obtenir des caractéristiques de bruits cardiaques multidimensionnelles (S2) ; et à entrer les caractéristiques de bruits cardiaques multidimensionnelles dans un modèle de diagnostic pour obtenir un résultat de diagnostic (S3). Le procédé de diagnostic de maladies cardiovasculaires pédiatriques basé sur des signaux électrocardiographiques et phonocardiographiques combine des signaux phonocardiographiques et électrocardiographiques, en tenant pleinement compte des manifestations des maladies cardiovasculaires dans les signaux phonocardiographiques. Le procédé extrait des caractéristiques de bruits cardiaques multidimensionnelles incluant des caractéristiques de bruits cardiaques dans le domaine temporel, dans le domaine fréquentiel et dans le domaine énergétique. En construisant un modèle de diagnostic et en utilisant les caractéristiques de bruits cardiaques multidimensionnelles, une prédiction précise des maladies cardiovasculaires est obtenue. En outre, l'invention concerne un procédé d'extraction de caractéristiques de bruits cardiaques multidimensionnelles, offrant un support fondamental pour une prédiction précise des maladies cardiovasculaires par des modèles de diagnostic. Le procédé présente les avantages d'être pratique et efficace, et analogue.
PCT/CN2022/103931 2022-07-05 2022-07-05 Procédé de diagnostic de maladies cardiovasculaires pédiatriques basé sur des signaux électrocardiographiques et phonocardiographiques WO2024007152A1 (fr)

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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050222515A1 (en) * 2004-02-23 2005-10-06 Biosignetics Corporation Cardiovascular sound signature: method, process and format
US20090192401A1 (en) * 2008-01-25 2009-07-30 Sourabh Ravindran Method and system for heart sound identification
US20130226019A1 (en) * 2010-08-25 2013-08-29 Diacoustic Medical Devices (Pty) Ltd System and method for classifying a heart sound
US20140276132A1 (en) * 2013-03-15 2014-09-18 Andreas J. Schriefl Automated Diagnosis-Assisting Medical Devices Utilizing Rate/Frequency Estimation
CN111329508A (zh) * 2020-03-02 2020-06-26 浙江大学 用于先心病筛查的心脏杂音智能分析方法
CN112971839A (zh) * 2021-02-05 2021-06-18 云南大学 一种基于前馈卷积神经网络的心音分类方法
CN115040135A (zh) * 2022-06-09 2022-09-13 张福伟 一种基于心电心音的儿童心血管疾病诊断方法

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050222515A1 (en) * 2004-02-23 2005-10-06 Biosignetics Corporation Cardiovascular sound signature: method, process and format
US20090192401A1 (en) * 2008-01-25 2009-07-30 Sourabh Ravindran Method and system for heart sound identification
US20130226019A1 (en) * 2010-08-25 2013-08-29 Diacoustic Medical Devices (Pty) Ltd System and method for classifying a heart sound
US20140276132A1 (en) * 2013-03-15 2014-09-18 Andreas J. Schriefl Automated Diagnosis-Assisting Medical Devices Utilizing Rate/Frequency Estimation
CN111329508A (zh) * 2020-03-02 2020-06-26 浙江大学 用于先心病筛查的心脏杂音智能分析方法
CN112971839A (zh) * 2021-02-05 2021-06-18 云南大学 一种基于前馈卷积神经网络的心音分类方法
CN115040135A (zh) * 2022-06-09 2022-09-13 张福伟 一种基于心电心音的儿童心血管疾病诊断方法

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