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CN109009073B - Atrial fibrillation detection apparatus and storage medium - Google Patents

Atrial fibrillation detection apparatus and storage medium Download PDF

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CN109009073B
CN109009073B CN201810800619.9A CN201810800619A CN109009073B CN 109009073 B CN109009073 B CN 109009073B CN 201810800619 A CN201810800619 A CN 201810800619A CN 109009073 B CN109009073 B CN 109009073B
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胡静
赵巍
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Guangzhou Shiyuan Electronics Thecnology Co Ltd
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Abstract

The invention provides an atrial fibrillation detection device and a storage medium, wherein the atrial fibrillation detection device comprises: the extraction module is used for extracting the P wave waveform information and the QRS wave waveform information in the electrocardiosignals; the first determining module is connected with the extracting module and used for determining the P wave change characteristics according to the P wave waveform information; the second determining module is connected with the extracting module and used for determining PR interval change characteristics according to the QRS wave waveform information; the third determining module is connected with the extracting module and used for determining RR interval change characteristics according to the QRS wave waveform information; the calculating module is connected with the third determining module and used for calculating the information entropy of the RR interval change characteristics by adopting an entropy estimation method; and the fourth determining module is connected with the first determining module, the second determining module and the calculating module and is used for determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and the preset classification model. The method can improve the robustness of atrial fibrillation detection.

Description

Atrial fibrillation detection apparatus and storage medium
Technical Field
Embodiments of the present invention relate to signal processing technologies, and in particular, to an atrial fibrillation detection apparatus and a storage medium.
Background
Atrial Fibrillation (AF) is a common clinical arrhythmia disease and is characterized by disordered Atrial activity and subsequent complications such as cerebral apoplexy and myocardial infarction, which lead to high disability rate and death rate and seriously harm human health and life. In order to find and treat as early as possible and reduce the morbidity and mortality of atrial fibrillation, the research on the detection of atrial fibrillation has important clinical significance and social significance.
However, the existing research on atrial fibrillation detection focuses on researching one clinical manifestation of atrial fibrillation attack, so that the robustness is poor, and the clinical requirement is difficult to meet.
Disclosure of Invention
The embodiment of the invention provides an atrial fibrillation detection device and a storage medium, which are used for improving the robustness of atrial fibrillation detection and meeting clinical requirements.
In a first aspect, an embodiment of the present invention provides an atrial fibrillation detection apparatus, including:
the extraction module is used for extracting the P wave waveform information and the QRS wave waveform information in the electrocardiosignals;
the first determining module is connected with the extracting module and used for determining P wave change characteristics according to the P wave waveform information, and the P wave change characteristics are used for expressing the relation between the maximum value and the minimum value in the P wave waveform information;
the second determination module is connected with the extraction module and used for determining PR interval change characteristics according to the QRS wave waveform information;
a third determining module, connected to the extracting module, for determining RR interval variation characteristics according to the QRS wave waveform information;
the calculating module is connected with the third determining module and used for calculating the information entropy of the RR interval change characteristics by adopting an entropy estimation method;
and the fourth determining module is connected with the first determining module, the second determining module and the calculating module and is used for determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model.
In a possible implementation manner, the first determining module is specifically configured to:
dividing the P wave sequence corresponding to the P wave waveform information into a plurality of subsequences;
determining the difference between the maximum value and the minimum value in each subsequence;
determining a maximum difference value among the difference values of the plurality of subsequences;
and dividing the maximum difference value by the maximum value in the P wave sequence to obtain the P wave change characteristic.
In one possible implementation, the second determining module includes:
a first determining submodule for determining a PR interval according to the QRS wave waveform information;
and the second determining submodule is used for determining the change characteristics of the PR interval according to the probability density function of the corresponding phase space of the PR interval.
In a possible implementation, the second determining submodule is specifically configured to:
determining the PR interval variation characteristic according to the following formula:
Figure BDA0001737006740000021
wherein PRIV represents the PR interval variation characteristic; the PR interval is denoted as x (n), n ═ 1.. m, m being the PR interval; the probability density function of the PR interval corresponding to the phase space is expressed as y (n) (x (n), x (n +1),.., x (n + (m-1) t)); Σ represents a summation symbol, | | | | represents an euclidean distance, h represents a step function, t represents delay time, C represents a combination operation, r represents a preset parameter, and N represents the number of samples.
In one possible implementation, the third determining module includes:
a third determining submodule, configured to determine an RR interval according to the QRS wave waveform information;
a fourth determining submodule connected to the third determining submodule and configured to determine an interval difference sequence of the RR intervals and histogram features of the interval difference sequence, where the histogram features include a median, a mean, and a standard deviation of a histogram of the interval difference sequence;
correspondingly, the calculation module is specifically configured to: and calculating the information entropy of the interval difference sequence and the information entropy of the histogram corresponding to the interval difference sequence.
In one possible embodiment, the fourth determination submodule, when used to determine histogram features of the sequence of interval differences, is in particular to:
determining a histogram corresponding to the sequence of interval differences according to the following formula:
Figure BDA0001737006740000031
wherein H.DELTA.RR (i)1) A histogram representing the interval difference sequence; delta RRmax、△RRminA maximum constraint value and a minimum constraint value representing the sequence of interval differences, respectively; i.e. i1Represents the abscissa of the histogram, with a maximum value of M1，M1Representing the width of the histogram, M1The adjustment mode is as follows:
Figure BDA0001737006740000032
N1is the length of the interval difference sequence; j is a function of1J representing the interval difference sequence1An element; sgn () is a sign function; []To round the symbol down.
In one possible embodiment, the calculation module, before calculating the information entropy of the sequence of interval differences and the information entropy of the histogram corresponding to the sequence of interval differences, is further configured to:
selecting an interval difference sequence with a preset length as a first sequence;
and removing the most value of the preset number in the first sequence, wherein the most value at least comprises any one of a maximum value and a minimum value.
In a possible embodiment, the atrial fibrillation detection apparatus may further include: and the output module is connected with the fourth determination module and used for outputting the result whether the electrocardiosignal is atrial fibrillation or not.
In a second aspect, an embodiment of the present invention provides an atrial fibrillation detection apparatus, including a memory, a processor, and a computer program stored in the memory and executable by the processor; the processor executes the computer program to realize the following operations:
extracting P wave waveform information and QRS wave waveform information in the electrocardiosignals;
determining P-wave change characteristics according to the P-wave waveform information, wherein the P-wave change characteristics are used for expressing the relation between the maximum value and the minimum value in the P-wave waveform information;
determining PR interval change characteristics and RR interval change characteristics according to the QRS wave waveform information;
calculating the information entropy of the RR interval change characteristics by adopting an entropy estimation method;
and determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes computer-readable instructions, and when the computer-readable instructions are read and executed by a processor, the processor is caused to perform the following operations:
extracting P wave waveform information and QRS wave waveform information in the electrocardiosignals;
determining P-wave change characteristics according to the P-wave waveform information, wherein the P-wave change characteristics are used for expressing the relation between the maximum value and the minimum value in the P-wave waveform information;
determining PR interval change characteristics and RR interval change characteristics according to the QRS wave waveform information;
calculating the information entropy of the RR interval change characteristics by adopting an entropy estimation method;
and determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model.
In any of the above designs, the preset classification model is a classification model in which the accuracy of a detection result obtained according to the training data is higher than a preset value.
According to the atrial fibrillation detection device and the storage medium provided by the embodiment of the invention, P wave waveform information and QRS wave waveform information in electrocardiosignals are extracted firstly; then, determining a P wave change characteristic according to the P wave waveform information, wherein the P wave change characteristic is used for expressing the relation between the maximum value and the minimum value in the P wave waveform information, and determining a PR interval change characteristic and an RR interval change characteristic according to the QRS wave waveform information; then, calculating the information entropy of RR interval change characteristics by adopting an entropy estimation method; and finally, determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model. According to the embodiment of the invention, whether the electrocardiosignal is atrial fibrillation is determined by integrating the P wave change characteristic, the PR interval change characteristic and the information entropy, so that compared with the conventional implementation mode of researching whether the atrial fibrillation is attack through one clinical expression, the robustness of atrial fibrillation detection can be improved, and the clinical requirement is met.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an atrial fibrillation detection apparatus according to an embodiment of the present invention;
FIG. 2 is an exemplary diagram of an actual acquisition of an acquired cardiac electrical signal;
FIG. 3 is an exemplary diagram of P-wave waveform information and QRS-wave waveform information;
fig. 4 is a schematic structural diagram of an atrial fibrillation detection apparatus according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of an atrial fibrillation detection apparatus according to another embodiment of the present invention;
fig. 6 is a schematic structural diagram of an atrial fibrillation detection apparatus according to another embodiment of the present invention;
fig. 7 is a schematic structural diagram of an atrial fibrillation detection apparatus according to another embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first" and "second" and the like in the description and in the claims, and in the accompanying drawings of embodiments of the invention, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The inventor finds that: two important clinical manifestations at the onset of atrial fibrillation are: 1) the P wave disappears and continuous unequal f waves appear; 2) the RR interval is absolutely irregular. In addition, the difficulty of atrial fibrillation detection is: on one hand, P wave and f wave signals are weak and difficult to detect; on the other hand, RR interval irregularities are also one of the characteristics of other arrhythmias. At present, the research of atrial fibrillation detection mainly focuses on researching a single clinical manifestation of the attack of atrial fibrillation, the robustness is poor, and the clinical practical requirement is difficult to meet.
Based on the above, embodiments of the present invention provide an atrial fibrillation detection apparatus and a storage medium that integrate P-wave variation characteristics, PR interval variation characteristics, and information entropy to improve robustness of atrial fibrillation detection, and are suitable for practical application scenarios.
Fig. 1 is a schematic structural diagram of an atrial fibrillation detection apparatus according to an embodiment of the present invention. This embodiment provides an atrial fibrillation detection apparatus, which may be implemented in software and/or hardware. Illustratively, the atrial fibrillation detection apparatus may include, but is not limited to, a portable electrocardiograph, a wearable device, and electronic devices such as a computer and a server. The server may be a server, a server cluster composed of a plurality of servers, or a cloud computing service center.
As shown in fig. 1, the atrial fibrillation detection apparatus 10 includes: an extraction module 11, a first determination module 12, a second determination module 13, a third determination module 14, a calculation module 15 and a fourth determination module 16.
The extraction module 11 is configured to extract P-wave waveform information and QRS-wave waveform information in the electrocardiographic signal.
Specifically, the electrocardiographic signal may be an acquired original electrocardiographic signal, or may be a preprocessed electrocardiographic signal. The preprocessing may include impedance matching, filtering, amplifying, filtering, and the like. It can be understood that the electrocardiographic signals obtained by actual acquisition, as shown in fig. 2, contain various noises, and the waveforms are rough and not smooth, so that the useful information contained in the QRS waves is difficult to extract. Therefore, noise reduction and the like can be performed by preprocessing.
Illustratively, in practical applications, the multichannel synchronous data acquisition can be used to acquire the human heart signal to be processed and the background noise, i.e. the original electrocardio signal. Firstly, acquiring an original electrocardiosignal through an electrocardio lead and a sensor; then, the acquired original electrocardiosignals are subjected to impedance matching, filtering, amplification and other processing through an analog circuit to obtain analog signals; then, the analog-to-digital converter converts the analog signal into a digital signal, and the digital signal is stored by a memory; then, a low-pass digital filter (e.g., a butterworth filter) is used to perform low-pass filtering on the digital signal, so as to filter high-frequency noise (above 300 Hz) and obtain the filtered electrocardiosignal.
Wherein, the P wave is atrial depolarization wave and represents the activation of the left atrium and the right atrium. Since the sinoatrial node is located under the right atrial subintium, activation passes first to the right atrium and later to the left atrium. The depolarization in the right atrium is thus also completed slightly earlier than in the left atrium. Clinically for practical purposes, the anterior portion of the P-wave represents the right atrial activation and the posterior portion represents the left atrial activation. The analysis of P wave has important significance for the diagnosis and differential diagnosis of arrhythmia.
QRS wave shape information reflects changes in left and right ventricular depolarization potentials and time, with the first downward wave being the Q wave, the upward wave being the R wave, and the next downward wave being the S wave. The time from the starting point of the QRS wave to the end point of the QRS wave is the QRS time limit. Referring to fig. 3, an example of P-wave waveform information and QRS-wave waveform information is shown.
In some embodiments, wavelet transform techniques may be used to extract the P-wave waveform information and QRS-wave waveform information from the electrocardiographic signal.
The first determining module 12 is connected to the extracting module 11, and is configured to determine a P-wave variation characteristic according to the P-wave waveform information. The P-wave variation characteristic is a characteristic indicating a variation of the P-wave waveform information, and is used to indicate a relationship between a maximum value and a minimum value in the P-wave waveform information, for example.
The second determining module 13 is connected to the extracting module 11, and is configured to determine a PR interval change characteristic according to the QRS wave waveform information. Wherein the PR interval change characteristic is a characteristic used for representing the change of the PR interval. The specific values of the PR interval may be different in different cardiac signal cycles. The PR interval is the period of time from the beginning of depolarization of the atria to the beginning of depolarization of the ventricles. When the heart rate of an adult is in a normal range, the PR interval is 0.12-0.20 seconds. The PR interval varies with heart rate and age, with the general rule that the faster the heart rate or the smaller the age, the shorter the PR interval; conversely, the longer the pulse, the slower the heart rate of the elderly, and the longer the PR interval may be 0.21-0.22 seconds.
The third determining module 14 is connected to the extracting module 11, and is configured to determine an RR interval variation characteristic according to the QRS wave waveform information. Wherein the RR interval variation characteristic is a characteristic used for representing the variation of the RR interval. The specific values of the RR intervals may be different in different cardiac signal periods. Illustratively, the RR interval is calculated by: the interval PP time is 0.6-1.0 s because the heart rate is divided by 60 (normal sinus rhythm is 60-100 times/min).
Specifically, the waveform information includes a variation trend of the waveform, the waveform corresponds to time and amplitude, and the amplitude is in a fluctuation state. Therefore, the P-wave change characteristic can be determined according to the P-wave waveform information, and the PR interval change characteristic and the RR interval change characteristic can be determined according to the QRS wave waveform information.
Still taking fig. 3 as an example, the reference point of the electrocardiographic signal can be obtained through the TP baseline and the PQ baseline, and the RR interval, the PR interval and the P-wave sequence are obtained through calculation, so as to determine the RR interval change characteristic, the PR interval change characteristic and the P-wave change characteristic.
The calculating module 15 is connected to the third determining module 14, and is configured to calculate the information entropy of the RR interval change feature by using an entropy estimation method. In atrial fibrillation, the uncertainty of RR interval generation is enhanced due to high-frequency stimulation signals in an atrium, so that the corresponding information entropy is increased, and the basic principle that the entropy estimation method can be applied to atrial fibrillation detection is provided.
The fourth determining module 16 is connected to the first determining module 12, the second determining module 13 and the calculating module 15, and is configured to determine whether the electrocardiographic signal is atrial fibrillation according to the P-wave variation characteristic, the PR interval variation characteristic, the information entropy and the preset classification model.
Specifically, the P-wave change characteristic, the PR interval change characteristic and the information entropy are used as input characteristics of a preset classification model, and atrial fibrillation and non-atrial fibrillation can be distinguished through classification of the preset classification model. The preset classification model is a classification model with the detection result accuracy higher than a preset value, wherein the detection result accuracy is obtained according to a large amount of training data. Optionally, the value of the preset value may be set according to actual requirements, for example, the value is 99.9%.
In the process of obtaining the preset classification model through training, the extracted P-wave change features, PR interval change features and information entropy of the training data are used as input samples X of the preset classification model, and the labels of atrial fibrillation and non-atrial fibrillation are used as output samples Y of the preset classification model, wherein the (X and Y) jointly form a training sample pair of the preset classification model, and the preset classification model training is carried out. And obtaining the trained preset classification model based on the training sample pair and the optimal parameters of the preset classification model obtained by training. The method comprises the following steps of utilizing a preset classification model obtained by training, inputting a P wave change characteristic, a PR interval change characteristic and an information entropy of an electrocardiosignal to be detected into the preset classification model as an input sample X, carrying out atrial fibrillation recognition, and obtaining an output Y: "atrial fibrillation" or "non-atrial fibrillation".
Optionally, the preset classification model may be a Support Vector Machine (SVM) regression model, but the embodiment of the present invention is not limited thereto.
In summary, first, the P wave waveform information and the QRS wave waveform information in the electrocardiographic signal are extracted; then, determining a P wave change characteristic according to the P wave waveform information, wherein the P wave change characteristic is used for expressing the relation between the maximum value and the minimum value in the P wave waveform information, and determining a PR interval change characteristic and an RR interval change characteristic according to the QRS wave waveform information; then, calculating the information entropy of RR interval change characteristics by adopting an entropy estimation method; and finally, determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model. According to the embodiment of the invention, whether the electrocardiosignal is atrial fibrillation is determined by integrating the P wave change characteristic, the PR interval change characteristic and the information entropy, so that compared with the conventional implementation mode of researching whether the atrial fibrillation is attack through one clinical expression, the robustness of atrial fibrillation detection can be improved, and the clinical requirement is met.
On the basis of the foregoing embodiment, in an implementation manner, the first determining module 12 may specifically be configured to: dividing a P wave sequence corresponding to the P wave waveform information into a plurality of subsequences; determining the difference between the maximum value and the minimum value in each subsequence; determining a maximum difference value of the difference values of the plurality of subsequences; and dividing the maximum difference value by the maximum value in the P wave sequence to obtain the P wave change characteristic.
For example, let P (i, j) represent a P-wave sequence corresponding to P-wave waveform information, where i represents the number of samples of the sub-sequence, and i is less than or equal to the number of samples of the P-wave sequence; j denotes the jth sample of the P-wave sequence. Next, the difference between the maximum value and the minimum value in the P-wave sequence is calculated, which can be expressed as pd (i):
Figure BDA0001737006740000081
the P wave change characteristic is expressed by adopting PDI, and the calculation formula is as follows:
Figure BDA0001737006740000082
wherein,
Figure BDA0001737006740000083
it is indicated that the maximum difference value is,
Figure BDA0001737006740000084
represents the maximum value in the P-wave sequence.
In another implementation, the first determining module 12 may specifically be configured to: determining the difference value between the maximum value and the minimum value in a P wave sequence corresponding to the P wave waveform information; and dividing the difference value by the maximum value in the P wave sequence to obtain the P wave change characteristic. It will be appreciated that in this implementation, i is equal to the number of samples of the P-wave sequence.
Fig. 4 is a schematic structural diagram of an atrial fibrillation detection apparatus according to another embodiment of the present invention. As shown in fig. 4, in the atrial fibrillation detection apparatus 40, based on the structure shown in fig. 1, the second determination module 13 may include: a first determination submodule 131 and a second determination submodule 132. Wherein,
a first determination submodule 131 may be used to determine the PR interval based on the QRS wave waveform information.
The second determination submodule 132 may be configured to determine PR interval variation characteristics based on a probability density function of the phase space corresponding to the PR intervals.
Further, the second determining submodule 132 is specifically configured to:
the PR interval change characteristic is determined according to the following formula:
Figure BDA0001737006740000091
wherein PRIV represents the PR interval variation characteristic; the PR interval is denoted as x (n), n ═ 1.. m, m being the PR interval; the probability density function of the PR interval corresponding to the phase space is expressed as y (n) (x (n), x (n +1),.., x (n + (m-1) t)); Σ represents a summation symbol, | | | | represents an euclidean distance, h represents a step function, t represents delay time, C represents a combination operation, r represents a preset parameter, and N represents the number of samples.
Fig. 5 is a schematic structural diagram of an atrial fibrillation detection apparatus according to another embodiment of the present invention. As shown in fig. 5, in the atrial fibrillation detection apparatus 50, based on the structure shown in fig. 1, the third determination module 14 may include: a third determination submodule 141 and a fourth determination submodule 142. Wherein,
the third determining submodule 141 is configured to determine an RR interval according to the QRS wave waveform information.
The fourth determining submodule 142 is connected 141 to the third determining submodule and is configured to determine the sequence of interval differences of the RR intervals and histogram features of the sequence of interval differences. The histogram features may include, among others, the median, mean, and standard deviation of the histogram of the interval difference sequence.
Specifically, for QRS wave waveform information, an interval difference sequence Δ RR of RR intervals is calculated:
ΔRR(n1)＝abs(R(n1+1)-RR(n1)),n1＝1,...,N1-1
wherein, Δ RR (n)1) Denotes from the n-th1A period of the cardiac signal and n1RR interval, N, of +1 cardiac signal cycles1Is the total number of RR intervals.
In some embodiments, the fourth determining submodule 142, when used for determining the histogram feature of the interval difference sequence, is specifically:
determining a corresponding histogram of the interval difference sequence according to the following formula:
Figure BDA0001737006740000101
wherein H.DELTA.RR (i)1) A histogram representing the interval difference sequence; delta RRmax、△RRminA maximum constraint value and a minimum constraint value representing the sequence of interval differences, respectively; i.e. i1Represents the abscissa of the histogram, with a maximum value of M1，M1Representing the width of the histogram, M1The adjustment mode is as follows:
Figure BDA0001737006740000102
N1is the length of the interval difference sequence; j is a function of1J representing the interval difference sequence1An element; sgn () is a sign function, and outputs 1 when the value in the bracket is greater than 0, outputs-1 when the value in the bracket is less than 0, and outputs 0 when the value in the bracket is equal to 0; []To round the symbol down.
Illustratively, Δ RRmaxAnd Δ RRminIs a fixed value set in advance. E.g. Δ RRmaxThe value is 1500ms, Δ RRminThe value is-1500 ms. Alternatively, the histogram width may be adaptively adjusted to meet actual requirements. It is understood that when N is1When the size is larger, the processing speed can be increased by carrying out segmentation processing on the small-size-array-type array.
Finally, the fourth determination submodule 142 calculates histogram features from the histogram of the sequence of interval differences as features for atrial fibrillation recognition.
Optionally, the RR interval variation feature includes an interval difference sequence and a histogram corresponding to the interval difference sequence and a histogram feature. Accordingly, the calculation module 15 may be specifically configured to: and calculating the information entropy of the interval difference sequence and the information entropy of the histogram corresponding to the interval difference sequence.
In some embodiments, a shannon entropy algorithm is designed to extract information entropy of the interval difference sequence and a histogram corresponding to the interval difference sequence, and the information entropy is used as a characteristic of atrial fibrillation identification. A specific information entropy calculation method is explained below.
In one implementation, the information entropy calculation method of the interval difference sequence may be as follows:
1. solving the maximum and minimum RR intervals of the interval difference sequence to obtain an RR interval range;
2. dividing the interval difference sequence into N within the RR interval range2Segment, solving the number M (n) of RR intervals corresponding to each segment2) Then the probability of each RR interval existing is
Figure BDA0001737006740000103
n2＝1,2,...,N2。
3. Calculating the information entropy SE of the interval difference sequence:
Figure BDA0001737006740000104
in another implementation, the information entropy calculation method of the interval difference sequence may be as follows:
1. selecting an interval difference sequence with a preset length as a first sequence;
2. and removing the maximum value of the preset number in the first sequence to obtain a second sequence. The maximum value may include at least any one of a maximum value and a minimum value. By removing the maximum value of the preset number, the interference of the ectopic heart beat can be reduced. The preset number can be set according to actual conditions.
3. Solving the maximum and minimum RR intervals of the second sequence to obtain an RR interval range;
4. dividing the second sequence into N in the RR interval range2Segment, solving the number M (n) of RR intervals corresponding to each segment2) Then the probability of each RR interval existing is
Figure BDA0001737006740000111
n2＝1,2,...,N2；
5. Calculating the information entropy SE of the second sequence as the information entropy of the interval difference sequence:
Figure BDA0001737006740000112
the calculation method of the information entropy of the histogram corresponding to the interval difference sequence is the same as above, and is not described here again.
In summary, the following characteristic parameters were determined:
p-wave variation characteristic PDI;
PR interval variation characteristic PRIV;
median, mean and standard deviation of the histogram of the interval difference sequence;
information entropy of the interval difference sequence;
and information entropy of a histogram corresponding to the interval difference sequence.
And taking the characteristic parameters as input atrial fibrillation characteristic parameters, establishing a preset classification model through a training sample, and acting on a test sample to output a detection result so as to realize atrial fibrillation identification.
Still further, the calculation module 15 may also be connected to the first determination module 12 and the second determination module 13 for calculating the relative changes of the P-wave change characteristic and the PR interval change characteristic. The ratio of the P-wave change characteristic to the PR interval change characteristic is calculated, and the ratio is the relative change of the two. For example, if PDI is used to characterize the change in P-wave, PRIV is used to characterize the change in PR interval, and PPR is used to characterize the relative change, then
Figure BDA0001737006740000113
And the PPR with relative change is also used as an input atrial fibrillation characteristic parameter, a preset classification model is established through a training sample, and the preset classification model acts on a test sample to output a detection result, so that atrial fibrillation identification is realized.
The above processing is described for a single lead ecg signal. Optionally, the cardiac electrical signal may also be a multi-lead cardiac electrical signal. In this case, calculating the relative change of the P-wave variation characteristic and the PR interval variation characteristic may specifically include: calculating a first relative change of a P wave change characteristic and a PR interval change characteristic corresponding to each lead electrocardiosignal; and determining the mean value of the first relative change corresponding to each lead electrocardiosignal as the relative change.
For multi-lead electrocardiosignals, assuming that the number of leads is M, M first relative changes PPR are obtained, and an average value PPRM thereof is calculated as shown in the following formula:
Figure BDA0001737006740000121
wherein PPRMq represents the calculated PPRM, PPR of the qth in the continuous monitoring processs,qThe PPR of the s-th lead in the q-th calculation is shown.
In the above embodiment, the accuracy of the detection result can be improved by determining the mean value of the first relative change corresponding to each lead electrocardiograph signal as the relative change.
Fig. 6 is a schematic structural diagram of an atrial fibrillation detection apparatus according to another embodiment of the present invention. Referring to fig. 6, further to the structure shown in fig. 1, atrial fibrillation detection apparatus 60 may further include: and an output module 61.
The output module 61 is connected to the fourth determining module 16, and is configured to output a result of whether the electrocardiographic signal is atrial fibrillation after the fourth determining module 16 determines whether the electrocardiographic signal is atrial fibrillation according to the P-wave change feature, the PR interval change feature, the information entropy, and the preset classification model.
In the embodiment, after the obtained atrial fibrillation classification result is identified, the atrial fibrillation classification result is displayed on electronic equipment such as a single lead electrocardiogram plaster, a multi-sign device and a monitor device which comprise an electrocardiogram module and is used as a basis for detection and diagnosis of individuals or doctors. Or, the output of whether the electrocardiographic signal is the result of atrial fibrillation may also be performed in an audio form, and the embodiment of the present invention is not limited in specific form.
Fig. 7 is a schematic structural diagram of an atrial fibrillation detection apparatus according to another embodiment of the present invention. As shown in fig. 7, the atrial fibrillation detection apparatus 70 includes a memory 71 and a processor 72, and a computer program stored on the memory 71 for execution by the processor 72. Processor 72 executes a computer program that causes atrial fibrillation detection apparatus 70 to:
extracting P wave waveform information and QRS wave waveform information in the electrocardiosignals;
determining P-wave change characteristics according to the P-wave waveform information, wherein the P-wave change characteristics are used for expressing the relation between the maximum value and the minimum value in the P-wave waveform information;
determining PR interval change characteristics according to the QRS wave waveform information;
determining RR interval change characteristics according to the QRS wave waveform information;
calculating the information entropy of the RR interval change characteristics by adopting an entropy estimation method;
and determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model.
It should be noted that, regarding the number of the memories 71 and the processors 72, the number of the memories 71 and the number of the processors 72 are not limited in the embodiments of the present invention, and may be one or more, and fig. 7 illustrates one example; the memory 71 and the processor 72 may be connected by various means, such as wire or wireless.
In one implementation, the determining, by the atrial fibrillation detection apparatus 70, a P-wave variation characteristic according to the P-wave waveform information includes:
dividing the P wave sequence corresponding to the P wave waveform information into a plurality of subsequences;
determining the difference between the maximum value and the minimum value in each subsequence;
determining a maximum difference value among the difference values of the plurality of subsequences;
and dividing the maximum difference value by the maximum value in the P wave sequence to obtain the P wave change.
In some embodiments, determining PR interval change characteristics from QRS wave waveform information by atrial fibrillation detection apparatus 70 may include:
determining a PR interval according to the QRS wave waveform information;
and determining the PR interval change characteristics according to the probability density function of the corresponding phase space of the PR intervals.
Optionally, atrial fibrillation detection device 70 determines the PR interval variation characteristics according to the probability density function of the corresponding phase space of the PR intervals, including:
determining the PR interval variation according to the following formula:
Figure BDA0001737006740000131
wherein PRIV represents the PR interval variation; the PR interval is denoted as x (n), n ═ 1.. m, m being the PR interval; the probability density function of the PR interval corresponding to the phase space is expressed as y (n) (x (n), x (n +1),.., x (n + (m-1) t)); Σ represents a summation symbol, | | | | represents an euclidean distance, h represents a step function, t represents delay time, C represents a combination operation, r represents a preset parameter, and N represents the number of samples.
In some embodiments, atrial fibrillation detection apparatus 70 determines RR interval variation characteristics from the QRS wave waveform information, including:
determining RR intervals according to the QRS wave waveform information;
determining a sequence of interval differences of the RR intervals and histogram features of the sequence of interval differences, the histogram features including median, mean, and standard deviation of a histogram of the sequence of interval differences.
Accordingly, the atrial fibrillation detection device 70 calculates the information entropy of the RR interval variation feature by using an entropy estimation method, which may include: and calculating the information entropy of the interval difference sequence and the information entropy of the histogram corresponding to the interval difference sequence.
Further, atrial fibrillation detection means 70 determines histogram features of said sequence of interval differences, which may include:
determining a histogram corresponding to the sequence of interval differences according to the following formula:
Figure BDA0001737006740000141
wherein H.DELTA.RR (i)1) A histogram representing the interval difference sequence; delta RRmax、△RRminA maximum constraint value and a minimum constraint value representing the sequence of interval differences, respectively; i.e. i1Represents the abscissa of the histogram, with a maximum value of M1，M1Representing the width of the histogram, M1The adjustment mode is as follows:
Figure BDA0001737006740000142
N1is the length of the interval difference sequence; j is a function of1J representing the interval difference sequence1An element; sgn () is a sign function; []To round the symbol down.
In some embodiments, the computer program when executed by the processor 72 further causes the atrial fibrillation detection apparatus 70 to: selecting an interval difference sequence with a preset length as a first sequence before calculating the information entropy of the interval difference sequence and the information entropy of a histogram corresponding to the interval difference sequence; and removing the most value of the preset number in the first sequence, wherein the most value at least comprises any one of a maximum value and a minimum value.
In some embodiments, the computer program when executed by the processor 72 further causes the atrial fibrillation detection apparatus 70 to: and after determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model, outputting a result whether the electrocardiosignal is atrial fibrillation or not.
Accordingly, the atrial fibrillation detection apparatus 70 may also include a display screen 73. The display screen 73 can be used to output the result of whether the ecg signal is atrial fibrillation.
The display screen 73 may be a capacitive screen, an electromagnetic screen, or an infrared screen. In general, the display screen 73 is used for displaying data according to the instructions of the processor 72, and is also used for receiving a touch operation applied to the display screen 73 and sending a corresponding signal to the processor 72 or other components of the atrial fibrillation detection apparatus 70. Optionally, when the display screen 73 is an infrared screen, it further includes an infrared touch frame, which is disposed around the display screen 73, and which can also be used to receive an infrared signal and send the infrared signal to the processor 72 or other components of the atrial fibrillation detection apparatus 70.
Embodiments of the present invention further provide a computer-readable storage medium, which includes computer-readable instructions, and when the computer-readable instructions are read and executed by a processor, the processor is caused to perform the steps in any of the above embodiments.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media capable of storing program codes, such as Read-Only Memory (ROM), Random Access Memory (RAM), magnetic disk, or optical disk.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (9)

1. An atrial fibrillation detection apparatus, comprising:
the extraction module is used for extracting the P wave waveform information and the QRS wave waveform information in the electrocardiosignals;
the first determining module is connected with the extracting module and used for determining P wave change characteristics according to the P wave waveform information, and the P wave change characteristics are used for expressing the relation between the maximum value and the minimum value in the P wave waveform information;
the second determination module is connected with the extraction module and used for determining PR interval change characteristics according to the QRS wave waveform information;
a third determining module, connected to the extracting module, for determining RR interval variation characteristics according to the QRS wave waveform information;
the calculating module is connected with the third determining module and used for calculating the information entropy of the RR interval change characteristics by adopting an entropy estimation method;
a fourth determining module, connected to the first determining module, the second determining module and the calculating module, for determining whether the electrocardiographic signal is atrial fibrillation according to the P-wave variation characteristic, the PR interval variation characteristic, the information entropy and a preset classification model;
the first determining module is specifically configured to:
dividing the P wave sequence corresponding to the P wave waveform information into a plurality of subsequences;
determining the difference between the maximum value and the minimum value in each subsequence;
determining a maximum difference value among the difference values of the plurality of subsequences;
and dividing the maximum difference value by the maximum value in the P wave sequence to obtain the P wave change characteristic.
2. The apparatus of claim 1, wherein the second determining module comprises:
a first determining submodule for determining a PR interval according to the QRS wave waveform information;
and the second determining submodule is used for determining the change characteristics of the PR interval according to the probability density function of the corresponding phase space of the PR interval.
3. The apparatus of claim 2, wherein the second determination submodule is specifically configured to:
determining the PR interval variation characteristic according to the following formula:
Figure FDA0002927047420000011
wherein PRIV represents the PR interval variation characteristic; the PR interval is denoted as x (n), n ═ 1.. m, m being the PR interval; the probability density function of the PR interval corresponding to the phase space is expressed as y (n) (x (n), x (n +1),.., x (n + (m-1) t)); Σ represents a summation symbol, | | | | represents an euclidean distance, h represents a step function, t represents delay time, C represents a combination operation, r represents a preset parameter, and N represents the number of samples.
4. The apparatus of claim 1, wherein the third determining module comprises:
a third determining submodule, configured to determine an RR interval according to the QRS wave waveform information;
a fourth determining submodule connected to the third determining submodule and configured to determine an interval difference sequence of the RR intervals and histogram features of the interval difference sequence, where the histogram features include a median, a mean, and a standard deviation of a histogram of the interval difference sequence;
correspondingly, the calculation module is specifically configured to: and calculating the information entropy of the interval difference sequence and the information entropy of the histogram corresponding to the interval difference sequence.
5. The apparatus according to claim 4, wherein the fourth determination submodule, when being configured to determine histogram features of the sequence of interval differences, is specifically configured to:
determining a histogram corresponding to the sequence of interval differences according to the following formula:
Figure FDA0002927047420000021
wherein H.DELTA.RR (i)1) A histogram representing the interval difference sequence; delta RRmax、△RRminA maximum constraint value and a minimum constraint value representing the sequence of interval differences, respectively; i.e. i1Represents the abscissa of the histogram, with a maximum value of M1，M1Representing the width of the histogram, M1The adjustment mode is as follows:
Figure FDA0002927047420000022
N1is the length of the interval difference sequence; j is a function of1J representing the interval difference sequence1An element; sgn () is a sign function; []To round the symbol down.
6. The apparatus of claim 4, wherein the calculation module, prior to calculating the information entropy of the sequence of interval differences and the information entropy of the histogram to which the sequence of interval differences corresponds, is further configured to:
selecting an interval difference sequence with a preset length as a first sequence;
and removing the most value of the preset number in the first sequence, wherein the most value at least comprises any one of a maximum value and a minimum value.
7. The apparatus of any one of claims 1 to 6, further comprising:
and the output module is connected with the fourth determination module and used for outputting the result whether the electrocardiosignal is atrial fibrillation or not.
8. An atrial fibrillation detection apparatus comprising a memory and a processor, and a computer program stored on the memory for execution by the processor;
the processor executes the computer program to realize the following operations:
extracting P wave waveform information and QRS wave waveform information in the electrocardiosignals;
determining P-wave change characteristics according to the P-wave waveform information, wherein the P-wave change characteristics are used for expressing the relation between the maximum value and the minimum value in the P-wave waveform information;
determining PR interval change characteristics and RR interval change characteristics according to the QRS wave waveform information;
calculating the information entropy of the RR interval change characteristics by adopting an entropy estimation method;
determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model;
the processor executing the computer program specifically implements the following operations:
dividing the P wave sequence corresponding to the P wave waveform information into a plurality of subsequences;
determining the difference between the maximum value and the minimum value in each subsequence;
determining a maximum difference value among the difference values of the plurality of subsequences;
and dividing the maximum difference value by the maximum value in the P wave sequence to obtain the P wave change characteristic.
9. A computer-readable storage medium comprising computer-readable instructions that, when read and executed by a processor, cause the processor to:
extracting P wave waveform information and QRS wave waveform information in the electrocardiosignals;
determining P-wave change characteristics according to the P-wave waveform information, wherein the P-wave change characteristics are used for expressing the relation between the maximum value and the minimum value in the P-wave waveform information;
determining PR interval change characteristics and RR interval change characteristics according to the QRS wave waveform information;
calculating the information entropy of the RR interval change characteristics by adopting an entropy estimation method;
determining whether the electrocardiosignal is atrial fibrillation or not according to the P wave change characteristics, the PR interval change characteristics, the information entropy and a preset classification model;
the processor is specifically configured to perform the following operations:
dividing the P wave sequence corresponding to the P wave waveform information into a plurality of subsequences;
determining the difference between the maximum value and the minimum value in each subsequence;
determining a maximum difference value among the difference values of the plurality of subsequences;
and dividing the maximum difference value by the maximum value in the P wave sequence to obtain the P wave change characteristic.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110226921B (en) * 2019-06-27 2022-07-29 广州视源电子科技股份有限公司 Electrocardiosignal detection and classification method and device, electronic equipment and storage medium
CN110403596A (en) * 2019-07-23 2019-11-05 北京大学深圳医院 IoT-based ECG monitoring system and method
CN111038104B (en) * 2019-09-28 2021-04-27 森大（深圳）技术有限公司 Method, device and equipment for detecting abnormal nozzle and storage medium
CN112826514B (en) * 2019-11-22 2022-07-22 华为技术有限公司 Atrial fibrillation signal classification method, device, terminal and storage medium
CN112971790A (en) * 2019-12-18 2021-06-18 华为技术有限公司 Electrocardiosignal detection method, device, terminal and storage medium
CN112244867B (en) * 2020-10-10 2023-10-20 深圳大学 Electrocardiosignal analysis method and system for atrial fibrillation detection
CN113499079B (en) * 2021-06-18 2023-12-08 南京信息工程大学 Atrial fibrillation detection method in electrocardiogram

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106073784A (en) * 2016-08-17 2016-11-09 广州视源电子科技股份有限公司 Respiration rate extraction method and device
CN108143407A (en) * 2017-12-25 2018-06-12 四川大学 A kind of heart sound segmentation method for automatically extracting heart sound envelope characteristic

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FI113835B (en) * 2003-04-10 2004-06-30 Pentti Korhonen Cardiac analysis method for monitoring atrial arrhythmias, involves focusing cardiac analysis to dynamic changes of configuration of P-wave of ECG signal, and comparing every detected P-wave to reference P-wave in defined time period
US8560058B2 (en) * 2008-01-14 2013-10-15 Koninklijke Philips N.V. Real time atrial fibrillation monitoring
US8755876B2 (en) * 2011-12-02 2014-06-17 Worcester Polytechnic Institute Methods and systems for atrial fibrillation detection
US20150141859A1 (en) * 2012-11-19 2015-05-21 Alex Sandler Systems and methods for cardiac rhythm variability analysis
CN103445770B (en) * 2013-09-02 2015-04-22 中山大学 Urination sensing detection method and device
US9901276B2 (en) * 2015-02-18 2018-02-27 Medtronic, Inc. Method and apparatus for identifying sick sinus syndrome in an implantable cardiac monitoring device
CN105748066B (en) * 2016-03-03 2018-03-16 深圳竹信科技有限公司 A kind of extracting method and device of electro-cardiologic signal waveforms characteristic point
CN107303177B (en) * 2016-04-15 2020-04-14 中国科学院微电子研究所 A method and system for detecting T waves and P waves of an electrocardiogram
CN106214123B (en) * 2016-07-20 2019-01-25 杨一平 A kind of electrocardiogram compressive classification method based on deep learning algorithm
CN107296604A (en) * 2017-08-29 2017-10-27 心云（北京）医疗器械有限公司 A kind of atrial fibrillation determination methods

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106073784A (en) * 2016-08-17 2016-11-09 广州视源电子科技股份有限公司 Respiration rate extraction method and device
CN108143407A (en) * 2017-12-25 2018-06-12 四川大学 A kind of heart sound segmentation method for automatically extracting heart sound envelope characteristic

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
A novel nonsense mutation in LMNA gene identified by Exome Sequencing in an atrial fibrillation family;Zhao, Jinzhao; Yao, Hong; Li, Zongzhe; 等.;《EUROPEAN JOURNAL OF MEDICAL GENETICS》;20160831;第59卷(第8期);396-400 *
QT dispersion in patients with acromegaly;Unubol, Mustafa; Eryilmaz, Ufuk; Guney, Engin; 等.;《ENDOCRINE》;20130430;第43卷(第2期);133-137 *
体表心电图PmaxPd对阵发性房颤的预测价值;沈钧乐等;《重庆医学》;20041230(第12期);1817-1818 *

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