CN115770054A - Electrocardiosignal processing method, portable electrocardiosignal acquisition equipment and storage medium - Google Patents
Electrocardiosignal processing method, portable electrocardiosignal acquisition equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure provides a portable electrocardiosignal acquisition device, an electrocardiosignal processing method used in the portable electrocardiosignal acquisition device and a computer-readable information storage medium. The electrocardiosignal processing method comprises the following steps: acquiring continuously acquired electrocardiosignals; sampling the electrocardiosignals in a target time period to obtain electrocardio sampling data; dividing the electrocardio sampling data into a plurality of sub-period electrocardio sampling data sets; determining the data variation average value in each sub-period; obtaining a first data set of data change values between sub-periods by calculating the data change values between the sub-periods adjacent to each other; calculating a difference root mean square value, RMSSD, for the items of the first data set; determining whether an event meeting a particular abnormal heart rhythm characteristic occurs according to the first data set and the RMSSD; if it is determined that an event has occurred that is characteristic of a particular heart rhythm abnormality, information of the event is recorded and information of the recorded event is transmitted.
Description
Technical Field
The disclosed embodiments relate to electrocardiographic data processing, and more particularly, to a portable electrocardiographic acquisition device, an electrocardiographic signal processing method used therein, and a computer-readable information storage medium.
Background
The membrane of human cardiac muscle cell is a semi-permeable membrane, when it is in resting state, a certain number of positive ions are arranged outside the membrane, the same number of negative ions are arranged inside the membrane, and the potential outside the membrane is higher than that inside the membrane, so that it is called polarization state. Under the resting state, because the myocardial cells of all parts of the heart are in the polarization state and have no potential difference, the potential curve traced by the current recorder is straight and straight, namely the equipotential line of the electrocardiogram on the body surface. When the myocardial cells are stimulated by a certain intensity, the permeability of cell membranes is changed, a large number of cations flow into the membranes in a short time, so that the potential in the membranes is changed from negative to positive, and the process is called as depolarization.
For the whole heart, the potential change of the myocardial cells in the sequential depolarization process from the endocardium to the epicardium is called depolarization waves, namely P waves of atria and QRS waves of ventricles on a surface electrocardiogram, by a potential curve traced by a current recorder. After the cell depolarization is completed, the cell membrane discharges a large amount of cations, so that the potential in the membrane changes from positive to negative and returns to the original polarization state, and the process is carried out from epicardium to endocardium, which is called repolarization. Similarly, the potential change during repolarization of cardiomyocytes is recorded by the current recorder as a repolarization wave. Because the repolarization process is relatively slow, the repolarization wave is lower than the depolarization wave. The repolarization wave of the atria is low and is embedded in the depolarization wave of the ventricles, so that the body surface electrocardiogram is not easy to identify. Repolarization of the ventricles is manifested as T-waves on the surface electrocardiogram. After the whole myocardial cells are fully repolarized, the polarization state is restored again, no potential difference exists among the myocardial cells of all the parts, and the equipotential lines are recorded by the body surface electrocardiogram.
Fig. 1 is a schematic diagram showing the variation of cardiac electrical activity over different time periods in an electrocardiographic waveform. Referring to fig. 1, an electrocardiographic waveform of a human body generally includes the following several bands.
1. P wave: representing atrial electrical activity, with a normal time period of 0.08-0.11 seconds, with a smaller upward dome shape on the electrocardiogram. Typically, when the heart begins to contract, a discharge signal is first conducted from the sinoatrial node to the atrium, causing depolarization of the atrium, at which point activation of the atrium occurs, followed by atrial contraction.
When the atria are enlarged and abnormal conduction occurs in the two rooms, the P wave can be expressed as a high-tip or double-peak P wave.
2. PR section: activation is conducted along the antero-medial posterior internode bundle to the atrioventricular node. Due to the slow conduction velocity of the atrioventricular node, a PR segment on the electrocardiogram, also known as the PR interval, is formed. The normal PR interval is 0.12-0.20 second.
When the atrial to ventricular conduction is blocked, it manifests as a prolongation of the PR interval or a disappearance of the ventricular wave after the P-wave.
3. QRS complex: the electrical activity generated during the depolarization and the excitation of the ventricles is represented, the normal time limit is 0.06-0.10 seconds, and the amplitude is larger on an electrocardiogram and is obviously larger than a P wave. After the atrium is excited, the electric signal is continuously conducted downwards and conducted to the ventricle through the atrioventricular node to cause depolarization and excitation of ventricular muscles, and when the ventricle is excited, the ventricle contracts and simultaneously generates electric activity. At this time, the electrical activity is conducted to the body surface and recorded by the electrocardiograph, the waveform of the QRS wave on the electrocardiogram is large, and may have more than 1 main peak and may have upper and lower 2 peaks, and the waveform is called QRS wave.
4. And ST stage: all depolarization of ventricular muscles is complete and repolarization has not yet begun for a period of time. The ST segment should normally be on the equipotential line.
When the myocardial ischemia or necrosis occurs in a certain part, the potential difference still exists in the ventricles after the depolarization is finished, and the ST segment on the electrocardiogram is deviated.
5. T wave: representing the process of cardiac repolarization, i.e. the process of activation of the ventricles from depolarization to restitution, usually the process of ventricular relaxation, with a normal time period of 0.05-0.25 seconds, with a T wave slightly higher than the P wave but lower than the peak of the QRS wave on the electrocardiogram.
6. U wave: u-waves are visible after T-waves on some leads, and are presently believed to be closely related to repolarization of the ventricles.
Furthermore, since the QT interval is affected by heart rate, the concept of a corrected QT interval (QTC) was introduced. The QT interval is the period of time from depolarization (beginning of QRS segment) to repolarization (end of T-wave) of the ventricle. The normal QT interval is 0.44 seconds long. Prolongation of the QT interval is often associated with the development of malignant arrhythmias.
When the heart of a human body is abnormal due to an inherent structural defect or during life, various kinds of arrhythmia may occur, as shown in fig. 2. Each arrhythmia has specific electrocardiographic waveform characteristics.
Among them, atrial Fibrillation (AF) is the most common arrhythmia. The incidence of atrial fibrillation increases with age, reaching 10% for people over 75 years of age. The exciting frequency of the atria during atrial fibrillation reaches 300-600 times per minute, the heartbeat frequency is often fast and irregular and sometimes reaches 100-160 times per minute, the heartbeat is much faster than that of a normal person and is absolutely irregular, and the atria lose effective contraction function. The incidence of atrial fibrillation is also closely related to coronary heart disease, hypertension, heart failure and other diseases.
Depending on the source of the message, up to 50% of stroke accidents are caused by persistent or paroxysmal atrial fibrillation. Anticoagulant therapy such as warfarin can reduce the risk of stroke and death. However, continuous anticoagulation increases the risk of bleeding. Novel anticoagulants (NOACs), such as Pradaxa, xarelo or Elliquist, have a more rapid effect than, for example, warfarin. NOACs reduce the risk of bleeding by replacing successive anticoagulants with anticoagulants when needed (also known as the "self-medication" method). The criteria for taking the drug are determined by the threshold level of Paroxysmal AF (PAF) burden. AF burden is defined as the total duration of all AF events within a monitoring period (typically 24 hours or more).
Self-administration of anticoagulant therapy presents a number of challenges. AF events, especially transient PAF events, are difficult to detect. Patients with symptoms of AF may be asymptomatic in most cases. Patients with atrial fibrillation need to be monitored 24/7 continuously for many years to automatically detect atrial fibrillation events. Most AF detection devices are currently designed to diagnose AF, rather than to assess PAF burden. They may be well suited for detecting at least one AF event during long-term monitoring, but short PAF events cannot be detected with the required accuracy to assess PAF burden, or they are too prominent in long-term continuous monitoring.
To address this problem, medtronic developed and marketed an Insertable Cardiac Monitor (ICM) with AF detection capability. The ICM device isInserted into a miniature ECG (electrocardiograph) monitor under the skin of a patient. Latest version of such monitors, reveal LINQ TM Continuous monitoring for up to 3 years can be provided. LINQ showed good results in detection of PAF events lasting 2 minutes or more, but was unable to detect transient PAFs. Because transient PAF events are observed in many AF patients and can significantly increase the overall PAF burden, the use of ICM devices in anticoagulant therapy based on a "self-administered" AF burden is limited.
Disclosure of Invention
The embodiment of the invention provides portable electrocardio-acquisition equipment and an electrocardio-signal processing scheme used therein, which are used for accurately monitoring specific abnormal events of heart rhythm by simple operation and limited computing resources.
According to an aspect of the embodiments of the present invention, there is provided an electrocardiographic signal processing method for a small electrocardiographic acquisition device, including: acquiring electrocardiosignals continuously acquired from a human body; sampling the electrocardiosignals in a target time period to obtain electrocardio sampling data; dividing the electrocardio sampling data in the target time period into sub-period electrocardio sampling data sets corresponding to a plurality of sub-periods respectively; determining the data variation average value in the sub-period of the electrocardio sampling data corresponding to each sub-period; obtaining a first data set of data variation values between sub-periods by calculating data variation values between data variation average values in each adjacent sub-period; calculating a difference root mean square value, RMSSD, for the items of the first set of data; determining whether an event meeting the specific abnormal heart rhythm characteristic occurs in the target time period according to the first data set and a difference root mean square value (RMSSD); if the event meeting the specific abnormal heart rhythm characteristic is determined to occur, recording the information of the event and/or sending the information of the event meeting the specific abnormal heart rhythm characteristic.
Optionally, the method further comprises: determining a fraction of the number of entries in the first data set for which an inter-sub-period data variance value is greater than the difference root mean square value (RMSSD) among the first data set; and if the ratio is between a preset ratio upper limit value and a preset ratio lower limit value, stopping the processing of the electrocardiosignals in the target time period.
Optionally, the determining whether an event meeting a specific abnormal heart rhythm characteristic occurs in the target time period according to the first data set and the difference root mean square value RMSSD includes: acquiring a second data set containing the data change values among the sub-periods and corresponding sub-periods by selecting the data change values among the first data set, which satisfy the following conditions: the inter-sub-period data variation value is not less than a difference root mean square value RMSSD, and the inter-sub-period data variation value corresponding to a next adjacent sub-period is less than the difference root mean square value RMSSD; according to the time interval between adjacent sub-time periods in the second data set, acquiring a third data set of the time interval meeting the following conditions: the time interval between adjacent sub-time periods exceeds a preset time threshold; and determining whether an event conforming to atrial fibrillation characteristics occurs in the target time period according to the values of the items in the third data set.
Optionally, the determining whether an event conforming to atrial fibrillation characteristics occurs within the target time period according to the values of the items in the third data set includes: acquiring the total number m of items in the third data set and the number p of item values in the third data set; calculating a complexity index C of the electrocardiosignals in the target time period according to the total number m and the number p of the item values; if the calculated complexity index C > the preset PAF threshold, it may be determined that an event matching the atrial fibrillation characteristics has occurred within the target time period.
Optionally, the method further comprises: storing data of electrocardiosignals continuously acquired from a human body, and/or sending the stored data of the electrocardiosignals and information of recorded events conforming to the abnormal characteristics of the specific heart rhythm when detecting that the small electrocardiosignal acquisition equipment establishes network connection.
According to another aspect of the embodiments of the present invention, there is provided a portable electrocardiograph acquisition apparatus, including: the ECG acquisition electrode is used for continuously acquiring electrocardiosignals of a human body, and the electrocardiosignals are electrocardio voltage signals; and, a microcontroller unit comprising ECG processing firmware, wherein the ECG processing firmware comprises:
the preprocessing module is used for sampling the electrocardiosignals of a target time period acquired by the ECG acquisition electrode to obtain electrocardio sampling data, and dividing the electrocardio sampling data in the target time period into sub-period electrocardio sampling data sets corresponding to a plurality of sub-periods respectively;
the averaging module is used for determining data variation average values in sub-periods of the electrocardio sampling data corresponding to the sub-periods obtained by the processing of the preprocessing module, obtaining a first data set of the data variation values in the sub-periods by calculating data variation values in the sub-periods between the data variation average values in the adjacent sub-periods, and calculating a difference root mean square value RMSSD for items of the first data set;
an abnormal event detection module, configured to determine whether an event meeting a specific abnormal heart rhythm characteristic occurs within the target time period according to the first data set and the difference root mean square value RMSSD;
an exception event handling module comprising: a memory unit for recording information of the event according with the specific abnormal heart rhythm characteristic if the event is determined to occur; and/or a communication unit for transmitting information of the recorded events which are in accordance with the specific abnormal heart rhythm characteristics.
Optionally, the ECG processing firmware further comprises: a noise filtering unit for: determining that, among the first data set, a sub-interval data change value is larger than a percentage value of the number of items of the RMSSD in the first data set; if the ratio is between a preset ratio upper limit value and a preset ratio lower limit value, stopping the processing of the electrocardiosignals of the target time period; and if the ratio is not between the preset ratio upper limit value and the preset ratio lower limit value, informing the abnormal event detection module to detect the first data set.
Optionally, the abnormal event detecting module includes:
the first processing unit is used for selecting a data change value between sub-periods meeting the following conditions from the first data set obtained by the processing of the averaging module, and acquiring a second data set containing the data change value between sub-periods and a corresponding sub-time period: the inter-sub-period data variation value is not less than the difference root mean square value RMSSD and the inter-sub-period data variation value corresponding to the next adjacent sub-period is less than the difference root mean square value RMSSD;
a second processing unit, configured to obtain, according to the time interval between adjacent sub-time periods in the second data set obtained by the first processing unit, a third data set of a time interval that satisfies the following condition: the time interval between adjacent sub-time periods exceeds a preset time length threshold value;
and the detection unit is used for determining whether an event meeting the atrial fibrillation characteristics occurs in the target time period according to the values of the items in the third data set.
Optionally, the detecting unit is configured to obtain a total number m of items in the third data set and a number p of item values in the third data set, calculate a complexity index C of the electrocardiographic signal of the target time period according to the total number m and the number p of item values, and determine that an event meeting atrial fibrillation characteristics occurs in the target time period if the calculated complexity index C > a preset PAF threshold.
Optionally, the memory unit is further configured to store electrocardiographic signals continuously acquired by the ECG acquisition electrodes from a human body, and/or the communication unit is further configured to send data of the electrocardiographic signals stored in the memory and information of recorded events conforming to specific abnormal heart rhythm characteristics when it is detected that the small electrocardiographic acquisition device establishes a network connection.
According to a further aspect of the embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions, wherein the program instructions, when executed by a processor, implement any of the aforementioned steps of the cardiac signal processing method for a small cardiac electrical acquisition device.
According to the portable electrocardio-acquisition device, the electrocardio-signal processing method for the small electrocardio-acquisition device and the computer-readable storage medium, electrocardiosignals continuously acquired from a human body are divided by a time window (target time period) to be used as a detection unit, the divided electrocardiosignals are sampled, electrocardio sampling data obtained by sampling are divided into sub-period electrocardio sampling data sets respectively corresponding to a plurality of sub-time periods, the average value of data change in each sub-time period is calculated, the data change value between adjacent sub-periods is calculated, a first data set reflecting the change of the electrocardio data between the sub-periods is obtained, and the root mean square value RMSSD of the difference of items of the first data set is calculated. Then, for a specific abnormal event of the heart rhythm to be detected, the first data set and the difference root mean square value RMSSD are analyzed, so that whether an event meeting the specific abnormal feature of the heart rhythm occurs in the target time period can be determined, and information of the recorded event meeting the specific abnormal feature of the heart rhythm can be recorded and sent. The foregoing process enables detection of specific abnormal heart rhythm events with minimal CPU usage and sufficient accuracy by simple arithmetic operations suitable for small devices of limited computational power by exploiting the electrocardiographic waveform characteristics of specific abnormal heart rhythms, so that the device can provide long-term monitoring for years without the need to replace batteries; moreover, by continuously executing the electrocardiographic signal processing method on the continuously acquired electrocardiographic signals, detection can be continuously performed for a long time, and abnormal rule information of the electrocardiographic signals can be obtained.
Drawings
FIG. 1 is a schematic diagram showing the variation of cardiac electrical activity over different time periods in an electrocardiographic waveform;
fig. 2 shows classification of various arrhythmias;
FIG. 3 shows a schematic diagram of an ECG waveform of a premature heart beat;
FIG. 4 shows a schematic of an ECG waveform for a normal sinus rhythm with a regular heart beat and an ECG waveform for an atrial fibrillation event with an irregular heart beat;
FIG. 5 shows a flow chart of a method of processing an ECG signal for a miniature ECG acquisition device, according to an embodiment of the invention;
FIG. 6 illustrates a flow diagram of a process for noise detection and filtering of a cardiac electrical signal according to an embodiment of the present invention;
FIG. 7 shows a flowchart of an exemplary process of step S270 in FIG. 5;
FIG. 8 illustrates a portion 410 of the original acquired cardiac signal and a portion 420 of derived data obtained from processing according to an embodiment of the present invention within a sliding window of 33 seconds;
FIG. 9 illustrates a histogram of complexity index C of processing an electrocardiographic waveform signal within a 33-second sliding window in accordance with an embodiment of the present invention;
fig. 10 shows a block diagram of a portable electrocardiogram acquisition apparatus according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the disclosure is provided in conjunction with the following drawings (where like numerals represent like elements throughout the several figures) and examples. The following examples are intended to illustrate the invention, but are not intended to limit the scope of the invention.
It will be understood by those within the art that the terms "first", "second", etc. in the embodiments of the present disclosure are used only for distinguishing between different steps, devices or modules, etc., and do not denote any particular technical meaning or necessary logical order therebetween.
As previously mentioned, both normal heart rhythms and arrhythmias each have specific electrocardiographic waveform characteristics.
For example, the premature beat of the heart is called a premature beat, and the normal pacing point of the heart is at the sinus node, which is a regular, 60-100 beats per minute rhythm of delivery. However, if ectopic pacing sites, such as the atria, ventricles or other sites, have advanced the heart's impulses, a premature beat or premature beat is called. Thus, the ECG performance of premature beats may vary depending on the location of delivery, and premature beats may occur in the atria, in the ventricles, or at the atrioventricular junction of the atria. Fig. 3 shows a schematic diagram of an ECG waveform of a premature heart beat.
In atrial premature beats, the P wave representing atrial beats appears early on the electrocardiogram, and the QS waveform of the following sinus rhythm is the same as the sinus rhythm and is the manifestation of atrial premature beats. The premature beat characteristic of the atrioventricular junction region is that a ventricular beat occurs in advance, and retrograde P-waves can be seen before, during or after the premature beat. The ventricular premature beat features more unique features, and the wide and malformed QRS wave group, the widest wave group in electrocardiogram, has no fluctuation of atrial premature beat.
In the case of atrial fibrillation again, atrial Fibrillation (AF) is defined as an arrhythmia lasting 30 seconds or more. Its main electrocardiogram representation includes the following aspects: first, the P-wave disappears and instead becomes small and irregular baseline fluctuations, with variable morphology and amplitude, called small F-waves, with a frequency of about 350-600 cycles/min. Second, the ventricular rate is extremely irregular, atrial fibrillation is untreated, atrioventricular conduction is normal, and the ventricular rate is usually between 100 and 160 beats/minute. Drugs, exercise, fever, hyperthyroidism, etc. can shorten the refractory period of the atrioventricular node, further accelerating the ventricular rate, and conversely, digitalis prolongs the refractory period of the atrioventricular node and slows down the ventricular rate. Thirdly, the QRS complex is normally in a normal shape, when the ventricular rate is too fast, indoor differential conduction occurs, and the QRS complex can be widened and deformed, which is the main manifestation of the atrial fibrillation electrocardiogram. Fig. 4 shows an ECG waveform for a normal sinus rhythm with a regular heart beat and an ECG waveform for an atrial fibrillation event with an irregular heart beat. The ECG waveform plots the electrical activity of the heart, typically in microvolts or millivolts.
The embodiment of the invention provides an electrocardiosignal processing method suitable for small-sized electrocardio acquisition equipment (such as portable electrocardio acquisition equipment), which adopts relatively simple arithmetic operation and utilizes waveform characteristics presented by an electrocardio waveform to detect an event conforming to specific abnormal heart rhythm.
The cardiac signal processing method is based on the measurement of beat irregularities by quantitatively evaluating the complexity of a time series of selected data points of a sliding window of ECG data.
Fig. 6 shows a flow chart of an electrocardiographic signal processing method for a small electrocardiographic acquisition device according to an embodiment of the present invention. Of course, the cardiac signal processing method may also be implemented by software or firmware in a general purpose computer or a dedicated computing device.
According to an exemplary embodiment of the present invention, the electrocardiographic signals continuously acquired by the ECG acquisition electrodes are divided and processed in a sliding window corresponding to a time length, which is defined as a target time period. According to the electrocardiosignal processing method provided by the embodiment of the invention, the electrocardiosignal corresponding to the target time period is processed. No textual distinction is made between the target time period and the sliding window in the context of the present application.
Referring to fig. 6, in step S210, electrocardiographic signals continuously acquired from the human body are acquired.
For example, a small electrocardiographic acquisition device may continuously acquire electrocardiographic signals of a human body through ECG acquisition electrodes or a dedicated computing device may receive continuously acquired electrocardiographic signals from the electrocardiographic acquisition device. The continuously collected electrocardiosignals are electrocardio-voltage signals collected by the collecting electrode. It will be appreciated that an electrocardiographic current signal or impedance signal, etc. may be used as desired for processing.
In step S220, the electrocardiographic signals of the target time period are sampled to obtain electrocardiographic sample data.
The duration of the target time period of the electrocardiosignal can be determined according to the waveform characteristics, waveform distribution, duration and the like of the arrhythmia to be detected. In the case of atrial fibrillation, since atrial fibrillation is generally characterized as an arrhythmia lasting 30 seconds or more, the target time period may be determined to be a duration exceeding 30 seconds, such as 33 seconds, 36 seconds, etc. Further, the value of the target period (sliding window) may be appropriately adjusted according to the sampling frequency.
For example, the target time period (sliding window) may be determined a priori to be 33 seconds or so. Assuming that the cardiac electrical signal is sampled at a sampling frequency of 250 samples per second, 8250 cardiac electrical sample data can be obtained by the processing of step S210. For convenience of calculation processing, the target time period may be adjusted to a time period corresponding to 8192 (an exponential multiple of 2) pieces of electrocardiographic sample data, i.e., 8192/250=32.768 seconds, and each electrocardiographic sample data contains electrocardiographic signal data of 4ms =1,000/250 time period.
It is noted that the sampling frequency, the size of the sliding window and the number of average samples may vary depending on the parameters of the ECG signal, the device specifications and the application.
In step S230, the electrocardiographic sample data in the target time period is divided into Sub-period electrocardiographic sample data sets corresponding to a plurality of Sub-periods (corresponding to Sub windows), and the Sub-time periods corresponding to the plurality of Sub-period electrocardiographic sample data sets represent the time sequence in the target time period.
That is, in step S210, the sliding window (corresponding to the target time segment) is divided into equal-length sub-windows (corresponding to equal-length sub-time segments), and the sampled electrocardiographic data corresponding to the sliding window W is divided into a plurality of equal-length sub-period sampled electrocardiographic data sets, each sub-period sampled electrocardiographic data set having the same number of sampled electrocardiographic data.
For example, the aforementioned 32.768 second sliding window (target time period) may be divided into 16 sub-windows (sub-time periods), so that 8192 cardiac electrical sample data are divided into 16 sub-period cardiac electrical sample data sets, each of which contains 512 cardiac electrical sample data.
In step S240, for a plurality of subinterval electrocardiographic sample data sets, an average value of data variation in subintervals of electrocardiographic sample data corresponding to the respective subintervals is determined.
In particular, it is assumed that the sliding window W is divided into equally long sub-windows W i Each sub-window w i There are Save cardiac electrical sampling data. For each w i The data variation mean value Vi in the sub-period between the values of the continuous electrocardiographic sample data is calculated by equation 1 for the sub-period electrocardiographic sample data set of the sub-window (sub-period).
Wherein i represents the ith sub-time period, and j is the electrocardio-acquisition of the ith sub-time periodSample data index, save is the number of ECG sample data in each sub-period, j = {1,2,3, \8230;, save }, v i,j Is the value of the jth electrocardio sampling data in the electrocardio sampling data of the ith sub-time period, v i,j-1 And Vi is the data variation average value in the sub-period among Save continuous electrocardio sampling data in the sub-period electrocardio sampling data set corresponding to the ith sub-period.
Through the foregoing processing, the data variation average V = { V) over N sub-periods is obtained 1 ，V 2 ，V 3 ，…V n }。
In step S250, a first data set of the inter-sub-period data variation values is obtained by calculating the inter-sub-period data variation values between the average values of the data variations in each adjacent sub-period, and the inter-sub-period data variation values in the first data set also correspond to the sub-periods in the target time period, which also represents the time sequence in the target time period.
Specifically, a set V = { V ] of data variation averages in a subinterval 1 ，V 2 ，V 3 ，…V n Converting to a first set of data D = { D } for inter-subinterval data change values 1 ,d 2 ,d 3 ,…d i ,…d N-1 In which d is i ＝|V i+1 –V i L. the method is used for the preparation of the medicament. Furthermore, each d i With time stamp Td i ，Td i Is the average value V of the data variation in the sub-period i The time of the corresponding sub-period i, such as the start time, the end time or the intermediate time point of the sub-period i.
In step S260, D = { D ] for the first set of data 1 ,d 2 ,d 3 ,…d i ,…d N-1 Item of (h), calculate difference root mean square value RMSSD.
Specifically, the root mean square RMSSD of the successive differences is calculated by equation (2).
Wherein d is i ＝|V i+1 –V i |。
The difference root mean square value RMSSD is the root mean square of the difference of adjacent normal cardiac cycles, the normal value range is (27 +/-12) ms, and the index is the heart rate variability.
Thereafter, in step S270, it is determined whether an event meeting the specific characteristics of the cardiac rhythm abnormality has occurred within the target time period, based on the first data set obtained in step S250 and the difference root mean square value RMSSD calculated in step S260.
The variation value of the data among the sub-periods in the first data set represents the variation of the electrocardiographic data among the sub-periods, and the difference root mean square value RMSSD represents the sample standard deviation of the variation value of the data among the sub-periods in the first data set. For a specific arrhythmia to be detected, the detection process may be performed according to the two data (the first data set and the difference rms ssd) obtained by the arithmetic operation, and accordingly, an event that meets the characteristics of the specific arrhythmia is detected.
If it is determined in step S270 that an event matching the specific abnormal heart rate characteristic does not occur, the process returns to steps S220 to S260, and continues to sample and detect the electrocardiographic signal of the next target time period, so that the abnormal heart rate event is continuously detected.
If, at step S270, it is determined that an event has occurred that meets the characteristics of a particular heart rhythm abnormality, step S280 is performed. In step S280, information of the event complying with the specific abnormal heart rate characteristic is recorded and/or transmitted, such as, but not limited to, the type of the event (e.g., premature beat, atrial fibrillation, etc.) and the time information of the event occurring.
The recorded information of the abnormal event can be provided for the user or uploaded to the control equipment or the server according to the time or the operation mode suitable for the small electrocardiogram collecting equipment. For example, in order to save power consumption of the miniature electrocardiograph acquisition device, the miniature electrocardiograph acquisition device is generally configured/designed to establish a network connection only when service is necessary or for a predetermined period of time. To this end, according to an alternative embodiment of the invention, when the small ecg device establishes a network connection, it sends information on the events that have been recorded that are characteristic of the particular arrhythmia.
Furthermore, according to an exemplary embodiment of the present invention, it is also possible to store data of the cardiac electric signals continuously collected from the human body and/or to transmit the stored data of the cardiac electric signals and information of the recorded events conforming to the specific abnormal heart rhythm characteristics when the small cardiac electric collecting apparatus establishes a network connection.
According to the electrocardiosignal processing method for the small-sized electrocardiosignal acquisition equipment, electrocardiosignals continuously acquired from a human body are divided by a time window (target time period) to be used as a detection unit, the divided electrocardiosignals are sampled, then, the sampled electrocardio sampling data are divided into sub-period electrocardio sampling data sets corresponding to a plurality of sub-time periods respectively, the average value of data change in each sub-time period is calculated, the data change value between adjacent sub-time periods is calculated, a first data set reflecting the change of the electrocardio data between the sub-time periods is obtained, and the root mean square value RMSSD of the difference value of the items of the first data set is obtained through calculation. Then, for a specific abnormal event of the heart rhythm to be detected, the first data set and the difference root mean square value RMSSD are analyzed, so as to determine whether an event meeting the specific abnormal feature of the heart rhythm occurs in the target time period, and information of the recorded event meeting the specific abnormal feature of the heart rhythm can be recorded and sent. The foregoing process enables detection of specific heart rhythm abnormality events with minimal CPU usage and sufficient accuracy by simple arithmetic operations suitable for small devices of limited computing power by utilizing the electrocardiographic waveform characteristics of specific heart rhythm abnormalities so that the device can provide long-term monitoring for years without battery replacement; moreover, by continuously executing the electrocardiosignal processing method on the continuously acquired electrocardiosignals, the electrocardiosignal processing method can continuously detect for a long time and obtain abnormal rule information of the electrocardiosignals.
It should be noted that the information of the event corresponding to the specific abnormal heart rhythm feature obtained by detection cannot be used as the final diagnosis result, but is only used for medical reference, and the method for processing the electrocardiosignals is used for performing preliminary detection corresponding to the specific signal feature on the electrocardiosignals collected from a human body, so that a user (or medical staff) can monitor and prompt the state of the user (or a monitored object) in real time, and is not used for diagnosing diseases. Medical personnel can carry out comprehensive inspection and evaluation to the monitoring object according to the received prompt, and then carry out necessary treatment. The electrocardiosignal processing method can realize long-time continuous detection, is used for timely prompting a user or medical staff, saves the monitoring time of the user and improves the monitoring efficiency.
In daily life, because a monitored object may be in a state of large-scale motion or due to signal interference, the acquired electrocardiosignals may have large noises, and the noises will affect the accuracy of processing the electrocardiosignals, so that noise detection is required and the electrocardiosignals containing the noises are skipped.
Accordingly, according to an alternative embodiment of the present invention, after step S260 is performed and before step S270 is performed, the noise detection and filtering process of operations S262 and S265 below is performed.
Referring to fig. 6, in operation S262, it is determined that D = { D } in the first data set 1 ,d 2 ,d 3 ,…d i ,…d N-1 Among the sub-periods, the occupation value P (RMSSD) of the number of items of which the data variation value is larger than RMSSD in the first data set.
The probability P (x) is calculated by equation (3):
P(x)＝Pr(X>x) (3)
wherein, pr (X)>X) represents the probability that the variable X takes a value greater than X. In the method of the embodiment of the present invention, the random variable X in formula (7) is from the first data set D = { D = { (D) } 1 ,d 2 ,d 3 ,…d i ,…d N-1 And x is the root mean square RMSSD. In this case, equation (3) is converted to
P(RMSSD)＝Pr(X>RMSSD) (4)
In operation S265, it is determined whether the duty value is between predetermined duty upper and lower limits.
Specifically, it is determined whether the following equation holds: f min ≤P(RMSSD)<F max . Wherein, F max To account for the upper limit, F min Is the lower limit of the occupation ratio. F can be determined empirically max And F min A value of, e.g. F min ＝25％～30％，F max And =60% -65%. It will be apparent to those skilled in the art that F depends on the ECG signal characteristics, window of analysis, application, and other factors min And F max May have different values.
If it is determined in operation S265 that the ratio value is at the predetermined ratio upper limit value F max Lower limit of sum ratio F min And if so, judging that the electrocardiosignals in the target time period have larger noise, and stopping processing the electrocardiosignals in the target time period. That is, if P (RMSSD) is not in F as described above max And F min If the first data set D is within the range of (b), it is determined that the first data set D stops further analysis of the electrocardiographic signals of the target time zone due to the presence of excessive noise, and the process returns to step S220 to process and analyze the electrocardiographic data of the next target time zone.
On the other hand, if it is determined that the duty value is not at the predetermined duty upper limit value F in operation S265 max Lower limit of sum ratio F min Otherwise, it is determined that the electrocardiographic signal of the target time period meets the processing requirement, and the process of step S270 is continuously executed.
The process of detecting an event conforming to the atrial fibrillation feature at step S270 will be described in detail below with reference to fig. 7. The complexity analysis is carried out on the sequence of data change values among the sub-periods in the first data set, a complexity index indicating existence of atrial fibrillation events is calculated, and whether events conforming to atrial fibrillation characteristics occur is determined according to the calculated complexity index.
Referring to fig. 7, in step S271, a second data set including the inter-sub-period data variation value and the corresponding sub-period is obtained by selecting the inter-sub-period data variation value satisfying the following condition among the first data sets: the sub-period data variation value is not less than the RMSSD and the sub-period data variation value corresponding to the next adjacent sub-period is less than the RMSSD.
Specifically, first of all, from the first data set D = { D = 1 ,d 2 ,d 3 ,…d i ,…d N-1 In the above, an item d satisfying the following condition (5) is selected i ：
d i+1 <RMSSD≤d i (5)
That is, the inter-sub-period data variation value d is selected such that the inter-sub-period data variation value is not less than the RMSSD value, but the inter-sub-period data variation value is less than the RMSSD value i 。
Thereafter, the item d selected according to the condition (5) i Time stamp Td of the corresponding sub-period i Constitute a second data set Td = { Td = { Td 1 ,Td 2 ,…Td i ,…Td k In which Td 1 ,Td 2 ,…Td i ,…Td k Respectively with the selected sub-interval data variation value d 1 ,d 2 ,…d k And (7) corresponding. The second data set contains entries for sub-periods of time during which the cardiac electrical signal transitions from above (and equal to) the RMSSD to below the RMSSD.
By screening out a second data set containing the data change values between the sub-periods and the corresponding sub-periods, the data change values between the adjacent sub-periods meet the screening conditions, the data of the sub-periods corresponding to the overlarge data change values (such as QRS waves) within a short time and the data of the sub-periods corresponding to the undersized data change values (without wave deformation change rules) within the sub-periods can be filtered out. Thus, the items in the second data set Td (timestamps Td of the sub-periods of time) i ) Not all corresponding to adjacent sub-periods but only to sub-periods of the waveform portion of the baseline fluctuation.
In step S272, according to the time interval between adjacent sub-periods in the second data set, a third data set of the time interval satisfying the following condition is obtained: the time interval between adjacent sub-periods exceeds a preset time threshold.
Specifically, it is obtained by equation (6) firstObtaining the time interval TT between each adjacent sub-period i ：
TT i ＝|Td i+1 –Td i | (6)
Thereby, a set of time intervals between adjacent sub-periods is obtained.
Then, a third data set of time intervals satisfying condition (7) is selected from the set of time intervals:
|Td i+1 –Td i |>B (7)
where B is a threshold determined for a blanking period after a heartbeat. The threshold is set to 256 milliseconds. However, it is apparent to those skilled in the art that the threshold B may have a different value based on the application.
Thus, a third data set TT = { TT =isobtained 1 ,TT 2 ,TT 3 ,…,TT i ,…,TT m }. The third data set TT comprises items of which the time interval between the sub-periods of the electrocardiosignal which is higher than the RMSSD and is lower than the RMSSD is larger than the blank period threshold of the heartbeat, and the items correspond to the time points (such as TT) i Td of i Or the timestamp of Tdi + 1) constitutes a timing characteristic point of the time interval satisfying the condition (7).
Figure 8 illustrates a portion 410 of the cardiac signal originally acquired and a portion 420 of derived data obtained by processing in accordance with an embodiment of the present invention within a sliding window of 33 seconds. Where line 430 shows the RMSSD value calculated by equation (2). The small black dot 440 points to the signal value V from the average i D of a set D of consecutive difference values i The value is obtained. The large black dot 450 points to the space Td selected according to the condition (7) from the time point 470 1 ,Td 2 ,…Td k Related difference d 1 ,d 2 ,…d k }. The large black dot 460 points to the same point as the large black dot 450, but is disposed on the raw ECG signal 410. All differences are greater than B, so n = k-1.
In step S273, it is determined whether an event conforming to the atrial fibrillation feature has occurred in the target time period according to the values of the items in the third data set.
Firstly, acquiring the total number m of items in the third data set and the number p of item values in the third data set. In the third data set TT = { TT = { (L }) 1 ,TT 2 ,TT 3 ,…,TT i ,…,TT m In, m time interval entries are contained, which can have p different values, where 1 ≦ p ≦ m. As an example of two extreme cases, p =1 if all time intervals are the same, and p = m if all time intervals are different. More likely, p is between 1 and m. The number p of items of different time intervals characterizes the variation characteristics of the electrocardiosignals with specific heart rhythm characteristics.
Secondly, a complexity index of the electrocardiographic signal of the target time period is calculated from the aforementioned values of p and m of the third data set TT. For example, the complexity index C may be calculated by equation (8):
C＝p*m (8)
if the calculated complexity index C is larger than a preset PAF threshold value, determining that an event meeting the atrial fibrillation characteristics occurs in the target time period; if C ≦ a preset PAF threshold, it may be determined that no event consistent with atrial fibrillation characteristics occurred within the target time period. The value may be determined empirically, for example by analyzing Receiver Operating Characteristics (ROC) of a database of electrocardiograms of hundreds of patients with and without PAF events, and empirically determining the PAF threshold. For example, in the method of an embodiment of the present invention, the PAF threshold is set to 356. It will be apparent to those skilled in the art that the PAF threshold may have different values based on the parameters of the cardiac electrical signal.
Fig. 9 shows a histogram of the complexity index C of the electrocardiographic waveform signals collected from 150 subjects within a 33-second sliding window, where 510 represents the complexity index portion of the atrial fibrillation-free electrocardiographic waveform signal, 520 represents the complexity index portion of the atrial fibrillation-free electrocardiographic waveform signal, and 530 represents the preset PAF threshold.
According to the processing of the foregoing steps S210 to S280, short and long events conforming to the characteristics of Paroxysmal Atrial Fibrillation (PAF) can be accurately detected from among the continuously collected electrocardiographic signals of the human body by a relatively simple arithmetic operation in a sliding window manner. The electrocardiosignal processing method occupies less calculation and storage resources, and can be well embedded into small electrocardiosignal acquisition equipment with limited calculation capacity so as to monitor continuously acquired electrocardiosignals in real time for a long time. The electrocardiosignal processing method provided by the embodiment of the application can be used for monitoring the electrocardiosignals for a long time, is also beneficial to monitoring the persistent atrial fibrillation with the duration of more than 7 days and the persistent atrial fibrillation with the duration of more than 1 year, and is beneficial to monitoring the burden of atrial fibrillation events.
The embodiment of the present invention further provides a computer-readable storage medium storing steps for executing any one of the foregoing electrocardiographic signal processing methods, and the computer-readable storage medium has the same beneficial effects as the foregoing electrocardiographic signal processing method.
The embodiment of the invention also provides portable electrocardio acquisition equipment, which is shown in fig. 10.
Referring to fig. 10, the portable electrocardiograph acquisition device 300 includes an ECG acquisition electrode 310, a microcontroller unit 320 electrically connected to the ECG acquisition electrode 310, and ECG processing firmware 330 contained in the microcontroller unit 320.
The ECG collecting electrode 310 is used for continuously collecting the electrocardiosignals of the human body, which are electrocardio-voltage signals.
The micro controller unit 320 may be, for example, a Micro Control Unit (MCU), an ARM microprocessor, an ASIC (application specific integrated circuit) chip, an FPGA (field programmable gate array) chip, etc., but is not limited thereto.
The microcontroller unit 320 has ECG processing firmware 330 for processing and analyzing the cardiac electrical signal. Among other things, the ECG processing firmware 330 includes:
the preprocessing module 331 is configured to sample an electrocardiographic signal acquired by the ECG acquisition electrode 310 in a target time period (corresponding to Window) to obtain electrocardiographic sample data, and divide the electrocardiographic sample data in the target time period into Sub-time period electrocardiographic sample data sets corresponding to a plurality of Sub-time periods (corresponding to Sub Window);
an averaging module 332, configured to determine an average value of data variation in a sub-period of the electrocardiographic sample data corresponding to each sub-period obtained through processing by the preprocessing module 331, obtain a first data set of data variation values in the sub-period by calculating a data variation value in the sub-period between the average values of data variation in each adjacent sub-period, and calculate a difference root mean square value RMSSD for an item of the first data set;
an abnormal event detecting module 333, configured to determine whether an event meeting a specific abnormal heart rhythm characteristic occurs within the target time period according to the first data set and the difference root mean square value RMSSD;
an exception event handling module 334, comprising: a memory unit for recording information of the event according with the specific abnormal heart rhythm characteristic if the event is determined to occur; and/or a communication unit for sending information of the event that is characteristic of the particular heart rhythm abnormality.
Optionally, the ECG processing firmware 330 further comprises: a noise filtering unit to: determining that, among the first set of data, a sub-period data variation value is greater than a fraction of the number of entries of the RMSSD in the first set of data; if the ratio is between a preset ratio upper limit value and a preset ratio lower limit value, stopping the processing of the electrocardiosignals of the target time period; and if the ratio is not between the preset ratio upper limit value and the predetermined ratio lower limit value, notifying the abnormal event detection module 333 of performing detection processing on the first data set.
Optionally, the abnormal event detecting module 333 specifically includes:
a first processing unit (not shown) configured to obtain a second data set including inter-sub-period data variation values and corresponding sub-periods by selecting inter-sub-period data variation values satisfying the following conditions from among the first data sets processed by the averaging module: the inter-sub-period data variation value is not less than a difference root mean square value RMSSD, and the inter-sub-period data variation value corresponding to a next adjacent sub-period is less than the difference root mean square value RMSSD;
a second processing unit (not shown) configured to obtain a third data set of a time interval satisfying the following condition according to a time interval between adjacent sub-periods in the second data set obtained by the first processing unit: the time interval between adjacent sub-time periods exceeds a preset time length threshold value;
a detecting unit (not shown) for determining whether an event complying with an atrial fibrillation feature has occurred within the target time period according to the values of the items in the third data set.
Further optionally, the detecting unit is configured to obtain a total number m of items in the third data set and a number p of item values in the third data set, calculate a complexity index C of the electrocardiographic signal in the target time period according to the total number m and the number p of item values, and determine that an event meeting the atrial fibrillation feature occurs in the target time period if the calculated complexity index C > a preset PAF threshold.
Optionally, the memory unit is further configured to store the electrocardiographic signals continuously acquired by the ECG acquisition electrodes from the human body, and/or the communication unit is further configured to send the data of the electrocardiographic signals stored in the memory and the recorded information of the events conforming to the specific abnormal heart rhythm characteristics when it is detected that the network connection is established by the small electrocardiographic acquisition device.
The portable electrocardio-acquisition equipment provided by the embodiment of the invention has the beneficial effects similar to those of the electrocardio-signal processing method.
It should be noted that, according to the implementation requirement, each component/step described in the present application may be divided into more components/steps, and two or more components/steps or partial operations of the components/steps may also be combined into a new component/step to achieve the purpose of the embodiment of the present invention.
The method and apparatus, electronic device, and storage medium of the present disclosure may be implemented in many ways. For example, the method and apparatus, the electronic device, and the storage medium of the embodiments of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the embodiments of the present invention are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing methods according to embodiments of the present invention. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the embodiment of the present invention.
The description of the present embodiments has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed, and many modifications and variations will be apparent to those skilled in the art. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (11)
1. An electrocardiosignal processing method for a small electrocardiosignal acquisition device comprises the following steps:
acquiring electrocardiosignals continuously acquired from a human body;
sampling the electrocardiosignals in a target time period to obtain electrocardio sampling data;
dividing the electrocardio sampling data in the target time period into sub-period electrocardio sampling data sets corresponding to a plurality of sub-periods respectively;
determining the data variation average value in the sub-period of the electrocardio sampling data corresponding to each sub-period;
obtaining a first data set of data variation values between sub-periods by calculating data variation values between data variation average values in each adjacent sub-period;
calculating a difference root mean square value, RMSSD, for the items of the first set of data;
determining whether an event meeting the specific abnormal heart rhythm characteristic occurs in the target time period according to the first data set and a difference root mean square value (RMSSD);
if the event meeting the specific abnormal heart rhythm characteristic is determined to occur, recording the information of the event and/or sending the information of the event meeting the specific abnormal heart rhythm characteristic.
2. The method of claim 1, further comprising:
determining a fraction of the number of entries in the first data set for which an inter-sub-period data variance value is greater than the difference root mean square value (RMSSD) among the first data set;
and if the ratio is between a preset ratio upper limit value and a preset ratio lower limit value, stopping the processing of the electrocardiosignals in the target time period.
3. The method of claim 1 or 2, wherein said determining from the first set of data and a difference root mean square value (RMSSD) whether an event has occurred within the target time period that meets a particular cardiac rhythm anomaly characteristic comprises:
acquiring a second data set containing the data change values among the sub-periods and corresponding sub-periods by selecting the data change values among the first data set, which satisfy the following conditions: the inter-sub-period data variation value is not less than the difference root mean square value RMSSD and the inter-sub-period data variation value corresponding to the next adjacent sub-period is less than the difference root mean square value RMSSD;
according to the time interval between adjacent sub-time periods in the second data set, acquiring a third data set of the time interval meeting the following conditions: the time interval between adjacent sub-time periods exceeds a preset time threshold;
and determining whether an event conforming to atrial fibrillation characteristics occurs in the target time period according to the values of the items in the third data set.
4. The method of claim 3, wherein said determining whether an event meeting atrial fibrillation characteristics occurred within the target time period based on values of items in the third data set comprises:
acquiring the total number m of items in the third data set and the number p of item values in the third data set;
calculating a complexity index C of the electrocardiosignals of the target time period according to the total number m and the number p of the item values;
if the calculated complexity index C > the preset PAF threshold, it may be determined that an event matching the atrial fibrillation characteristics has occurred within the target time period.
5. The method of claim 4, further comprising:
storing data of the cardiac electrical signal continuously acquired from the human body, and/or,
and when the small electrocardio acquisition equipment is detected to establish network connection, sending the stored electrocardiosignal data and the recorded information of the event which accords with the abnormal characteristic of the specific heart rhythm.
6. A portable electrocardiographic acquisition device comprising:
the ECG acquisition electrode is used for continuously acquiring electrocardiosignals of a human body, and the electrocardiosignals are electrocardio voltage signals; and
a micro-controller unit comprising ECG processing firmware,
wherein the ECG processing firmware comprises:
the preprocessing module is used for sampling the electrocardiosignals of a target time period acquired by the ECG acquisition electrode to obtain electrocardio sampling data, and dividing the electrocardio sampling data in the target time period into sub-period electrocardio sampling data sets corresponding to a plurality of sub-periods respectively;
the averaging module is used for determining data variation average values in sub-periods of the electrocardio sampling data corresponding to the sub-periods obtained by the processing of the preprocessing module, obtaining a first data set of the data variation values in the sub-periods by calculating data variation values in the sub-periods between the data variation average values in the adjacent sub-periods, and calculating a difference root mean square value RMSSD for items of the first data set;
an abnormal event detection module, configured to determine whether an event meeting a specific abnormal heart rhythm characteristic occurs in the target time period according to the first data set and the difference root mean square value RMSSD;
an exception event handling module comprising:
a memory unit for recording information of the event according to the specific abnormal heart rhythm characteristic if the event is determined to occur; and/or the like, and/or,
a communication unit for sending information of the recorded events that are characteristic of the particular heart rhythm abnormality.
7. The portable electrocardiograph acquisition device of claim 6, the ECG processing firmware further comprising: a noise filtering unit to:
determining that, among the first data set, a sub-interval data change value is larger than a percentage value of the number of items of the RMSSD in the first data set;
if the ratio is between a preset ratio upper limit value and a preset ratio lower limit value, stopping processing the electrocardiosignals in the target time period;
and if the ratio is not between the preset ratio upper limit value and the preset ratio lower limit value, informing the abnormal event detection module to detect the first data set.
8. The portable electrocardiograph acquisition device according to claim 7 wherein said abnormal event detection module comprises:
the first processing unit is used for selecting a data change value between sub-periods meeting the following conditions from the first data set obtained by the processing of the averaging module, and acquiring a second data set containing the data change value between sub-periods and a corresponding sub-time period: the inter-sub-period data variation value is not less than the difference root mean square value RMSSD and the inter-sub-period data variation value corresponding to the next adjacent sub-period is less than the difference root mean square value RMSSD;
a second processing unit, configured to obtain, according to a time interval between adjacent sub-time periods in the second data set obtained by the first processing unit, a third data set of a time interval that satisfies the following condition: the time interval between adjacent sub-time periods exceeds a preset time length threshold value;
and the detection unit is used for determining whether an event conforming to the atrial fibrillation characteristics occurs in the target time period according to the values of the items in the third data set.
9. The portable electrocardiograph acquisition apparatus according to claim 8, wherein the detection unit is configured to obtain a total number m of items in the third data set and a number p of item values in the third data set, calculate a complexity index C of the electrocardiograph signal in the target time period according to the total number m and the number p of item values, and determine that an event that meets atrial fibrillation characteristics occurs in the target time period if the calculated complexity index C > a preset PAF threshold.
10. The portable electrocardiograph acquisition device of claim 4 wherein,
the memory unit is also used for storing electrocardiosignals continuously acquired by the ECG acquisition electrode from a human body, and/or,
the communication unit is also used for sending the electrocardiosignal data stored in the memory and the recorded information of the event conforming to the abnormal characteristic of the specific heart rhythm when the small electrocardiosignal acquisition equipment is detected to be connected with the network.
11. A computer-readable storage medium, on which computer program instructions are stored, wherein said program instructions, when executed by a processor, implement the steps of the electrocardiosignal processing method for a small electrocardiosignal acquisition device according to any one of claims 1 to 5.
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