KR102475521B1 - PHYSIOLOGICAL ABNORMAL SIGNAL ANALYSIS APPARATUS and METHOD - Google Patents

Abstract

The present invention provides an abnormal physiological signal analysis device and method, comprising the steps of removing noise from a biosignal generated from a sensor; estimating the signal quality of bio-signals for a preset unit time; Classifying the biosignal during the unit time into an abnormal signal, such as atrial fibrillation (AF) or a normal signal, such as normal sinus rhythm (NSR), which is an abnormal signal; Determining whether an AF episode, which is an abnormal state episode, occurs and a duration thereof based on a signal quality of a preset number of consecutive unit times and a result of AF classification, which is an abnormal signal; and calculating an atrial fibrillation load, which is an abnormal signal, based on occurrence and duration of the AF episode, which is the abnormal state episode.

Description

Physiological abnormal state analysis device and analysis method {PHYSIOLOGICAL ABNORMAL SIGNAL ANALYSIS APPARATUS and METHOD}

The present invention relates to a method and apparatus for analyzing a physiological abnormal state such as atrial fibrillation, and more particularly, to analyze a biosignal obtained from a sensor interfaced with a subject to determine a physiological abnormal state episode such as atrial fibrillation. It relates to methods and devices.

A condition in which the heart beat is too slow, too fast, or irregular, that is, a condition in which the heart beat is not normal is called an arrhythmia. Atrial fibrillation (AF) is one of these arrhythmias, in which the atria do not beat normally, and each part trembles disorderly and thinly, resulting in a fast and irregular heartbeat.

Atrial fibrillation is one of the relatively frequent arrhythmias, and it is the most common among arrhythmias that require treatment, and about 33% of all arrhythmia-related inpatients are patients with atrial fibrillation. Early and accurate prediction of frequent atrial fibrillation like this is of great medical significance.

If atrial fibrillation occurs, a state in which the atria do not contract normally and tremble thinly, it can itself cause symptoms such as shortness of breath or chest pain. However, in addition to the symptoms of atrial fibrillation itself, the probability of more serious and dangerous arrhythmia increases, and the risk of various serious problems derived from the inability to effectively pump blood out of the heart due to atrial fibrillation also increases.

Patients with atrial fibrillation have a five-fold increased risk of stroke compared to normal people, and a two-fold increase in mortality due to this. In addition, due to various complications related to atrial fibrillation, the mortality rate due to heart disease is about twice as high as that of normal people.

When atrial fibrillation occurs suddenly and the pulse rate becomes too fast, there is not enough time to fill the heart with blood, and cardiac output (the amount of blood pumped out when the heart contracts) rapidly decreases. Since it accounts for about 30% of the heart rate, the heart rate continuously speeds up to compensate for the insufficient cardiac output.

Accordingly, it is known that a burden on the heart causes a decrease in the function of the heart, causing a structural change in the heart, and thereby causing heart failure (Heart Failure) to occur or worsen. In addition, when the heart is unable to contract normally due to atrial fibrillation, the risk of blood coagulation in the heart increases as blood is congested in the heart.

Thus, the blood clot formed in the heart travels through the artery and blocks the cerebrovascular and other blood vessels, and accordingly, the risk of stroke or thromboembolism in patients with atrial fibrillation greatly increases.

Accurate prediction of atrial fibrillation can help reduce hospitalization costs and patient pain by increasing the possibility of preventing dangerous symptoms such as stroke or embolism by considering various atrial pacing methods.

In this way, a method of accurately determining and analyzing not only the atrial fibrillation described as an example but also various abnormal physiological abnormalities by analyzing many bio-signals to prevent dangerous symptoms is required.

An object of the present invention is to provide a method and apparatus for accurately estimating an abnormal state, eg, an atrial fibrillation load, by analyzing a biosignal obtained from a sensor interfaced with a subject.

As a method for analyzing abnormal physiological conditions according to the present invention for achieving the above object,

removing noise from the biosignal generated from the sensor; estimating signal quality of a biosignal of a predetermined unit time; Classifying a biosignal of a preset unit time into an abnormal signal or a normal signal; Determining whether an abnormal state episode occurs and a duration thereof based on signal quality and abnormal signal classification results for a plurality of unit time biosignals; and calculating an abnormal state load based on whether the abnormal state episode has occurred and a duration thereof.

In one embodiment according to the present invention, removing noise of the biosignal generated from the sensor; estimating signal quality of a biosignal of a predetermined unit time; AF classifying a biosignal of a preset unit time into atrial fibrillation (AF) or normal sinus rhythm (NSR); Determining whether an AF episode occurs and a duration thereof based on signal quality of a plurality of unit time biosignals and atrial fibrillation classification results; and calculating an atrial fibrillation load based on whether the AF episode has occurred and the duration of the AF episode.

In one embodiment, the calculating of the atrial fibrillation load may include calculating an analysis time by summing durations of unit signals that can be analyzed among actually collected biosignals; calculating a sum of durations of atrial fibrillation episodes occurring at the analysis time; and calculating a ratio of the total duration of the atrial fibrillation episode to the analysis time.

In one embodiment, the unit signal that can be analyzed is a unit signal included in a minimum unit in which the signal quality of each biosignal of a preset minimum number of consecutive unit times is determined to be good, and a preset minimum number of consecutive units. At least one of the biosignals of time has good signal quality and a unit signal included in the minimum unit determined to be normal sinus rhythm, and each of the biosignals of a preset minimum number or more consecutive unit times is classified as atrial fibrillation and has good signal quality It is characterized in that it includes the biosignals from after the AF episode is determined to be the end of the AF episode when each of the biosignals of the minimum number of consecutive unit times is determined to be non-atrial fibrillation.

In one embodiment, in the step of determining whether an AF episode has occurred and its duration, it is determined that an AF episode has occurred when each biosignal of at least a preset minimum number of consecutive unit times is classified as atrial fibrillation and it is determined that the signal quality is good. determining; determining that the AF episode has ended when each of the consecutive biosignals of the minimum number of consecutive unit times is determined to be non-atrial fibrillation after the occurrence of the AF episode; and calculating, as a duration time, a time from a start point of occurrence of the AF episode to an end point of the AF episode.

In one embodiment, the non-atrial fibrillation is characterized by including a case where the signal quality of the unit signal is good and normal sinus rhythm and a case where the signal quality is poor.

In one embodiment, if the analysis time is less than a preset minimum analysis time, it is characterized in that it is treated as an overestimation error of the AF load.

Preferably, an atrial fibrillation analyzer according to the present invention for achieving the above object includes a biosignal collection unit for collecting biosignals generated from a sensor; a pre-processing unit that removes noise from the collected bio-signals; a signal quality estimator for estimating signal quality of a biosignal of a predetermined unit time; a signal analyzer for AF classifying biosignals of a preset unit time into atrial fibrillation (AF) or normal sinus rhythm (NSR); an episode determination unit for determining whether an AF episode has occurred and a duration based on signal quality of a plurality of unit time biosignals and AF classification results; and an AF load estimator calculating an atrial fibrillation load based on whether the AF episode has occurred and its duration.

In one embodiment, the AF load estimator calculates the analysis time by summing the durations of the unit signals that can be analyzed among the actually collected biosignals, and calculates the sum of the durations of atrial fibrillation episodes occurring at the analysis time , characterized in that for calculating the ratio of the sum of the duration of the atrial fibrillation episode to the analysis time.

In one embodiment, the unit signal that can be analyzed is a unit signal included in a minimum unit in which the signal quality of each biosignal of a preset minimum number of consecutive unit times is determined to be good, and a preset minimum number of consecutive units. At least one of the biosignals of time has good signal quality and a unit signal included in the minimum unit determined to be normal sinus rhythm, and each of the biosignals of a preset minimum number or more consecutive unit times is classified as atrial fibrillation and has good signal quality It is characterized in that it includes all unit signals from when it is determined that AF episode occurs to when it is determined that the AF episode is terminated when each of the biosignals of the minimum number of consecutive unit times is determined to be non-atrial fibrillation. .

In one embodiment, the episode determining unit determines that an AF episode has occurred when each of the biosignals of a predetermined minimum number or more consecutive unit times is classified as atrial fibrillation and the signal quality is good, and after the occurrence of the AF episode, the continuous When each of the biosignals of the preset minimum number of unit times is determined to be non-atrial fibrillation, it is determined that the AF episode has ended, and the time from the start point of the AF episode to the end point of the AF episode is calculated as the duration time. do.

In one embodiment, the non-atrial fibrillation is characterized by including a case where the signal quality of the unit signal is good and normal sinus rhythm and a case where the signal quality is poor.

In one embodiment, the AF load estimator is characterized in that if the analysis time is less than a preset minimum analysis time, the possibility of overestimating the AF load as an error.

In one embodiment, the atrial fibrillation analyzer includes hourly/daily/weekly/monthly statistics of AF load, hourly/daily/weekly/monthly statistics of total duration of AF episodes, minimum/maximum duration of atrial fibrillation episodes, atrial fibrillation Hourly/daily/weekly/monthly statistics of quartiles of episodes, hourly/daily/weekly/monthly statistics of median/average atrial fibrillation episodes, hourly/daily/weekly/monthly statistics of analysis time for AF load, It is characterized in that it further comprises an output unit for outputting any one or more of hourly/daily/weekly/monthly statistics of usage time (operation time).

In addition, although cardiac fibrillation is described as an example of the abnormal signal described in the specification of the present invention, it is not limited thereto, and the abnormal signal may be various physiological abnormal signals such as a high blood pressure signal and a hypoxia signal.

According to the present invention, it is possible to accurately estimate the abnormal state load by analyzing the biosignal obtained from the sensor interfaced with the subject.

1 is a block diagram illustrating an atrial fibrillation analyzer according to an embodiment of the present invention.
2 is a flowchart illustrating an analysis method of an atrial fibrillation analyzer according to an embodiment of the present invention.
3 is a vertical line for explaining a case defined as a minimum unit AF episode and a case not defined according to an embodiment of the present invention.
4 is a vertical line for explaining the occurrence time of an AF episode according to time according to an embodiment of the present invention.
5 is a vertical line for additional description of an AF episode over time according to an embodiment of the present invention.
6 is a vertical line for explaining a combination of unit signals capable of defining an atrial fibrillation episode according to an embodiment of the present invention.
7 is a vertical line for explaining a minimum analysis time according to an embodiment of the present invention.
8 is a block diagram showing the configuration of an atrial fibrillation analyzer according to another embodiment of the present invention.

Hereinafter, the invention will be described in detail so that those skilled in the art can easily understand and reproduce the invention through embodiments described with reference to the accompanying drawings.

In describing the invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the embodiments, the detailed description will be omitted.

Prior to the description of the present invention, the unit signal in this specification refers to a signal collected for a predetermined unit time (eg, 5 seconds, 10 seconds, or 20 seconds).

An atrial fibrillation episode (AF episode), which is an example of an abnormal signal among physiological abnormalities, is a clinically significant 'event in which atrial fibrillation occurred' based on a unit signal classified as AF (Atrial Fibrillation), as described below. means That is, an atrial fibrillation episode is one event in which AF occurs and ends.

Here, the physiological abnormality is not limited to cardiac fibrillation, and may include various things such as hypertension, hypoxia, and high fever.

In addition, an abnormal state episode lasting for a predetermined minimum time (eg, 15 seconds, 20 seconds, or 30 seconds, etc.) is referred to as a minimum unit abnormal state episode.

In the present specification, 'capable of defining an episode of an abnormal state (for example, an AF) in a minimum unit' means that it is possible to determine whether or not a corresponding minimum unit is an abnormal state episode.

In this specification, 'defined as a minimum unit abnormal state episode' means that the minimum unit is an abnormal state episode.

In the present specification, 'cannot be defined as an abnormal state episode of the minimum unit' means that the corresponding minimum unit is not an abnormal state episode.

In this specification, 'unable to define minimum unit abnormal state episode': means that the minimum unit 'cannot determine whether or not it is an abnormal state episode'.

Hereinafter, an embodiment will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating an atrial fibrillation analyzer according to an embodiment of the present invention, and FIG. 2 is a flowchart illustrating an analysis method of the atrial fibrillation analyzer according to an embodiment of the present invention.

As shown in FIG. 1, the atrial fibrillation analyzer 100 includes a biosignal collector 110 and a processor P1. The processor P1 may include a preprocessor 120, a signal quality estimation unit 130, a signal analysis unit 140, a unit signal determination unit 150, an episode determination unit 160, and a load estimation unit 170. can

The atrial fibrillation analyzer 100 of FIG. 1 may perform steps S100 to S170 included in the atrial fibrillation analysis method of FIG. 2 .

In step S110, the bio-signal collection unit 110 may include a sensor for measuring a bio-signal, such as PPG (PhotoPlethysmoGraphy), and obtain a bio-signal such as a pulse wave signal from a sensor interfaced with the subject. have. In this case, the sensor may include a light source for radiating light to the object under test, and a detector for detecting light emitted from the light radiated by the light source and scattered or reflected from a surface of the object or a biological tissue such as a blood vessel. For example, the PPG sensor is a ring type and can continuously collect and store biosignals from blood vessels located in the finger.

The light source may include one or more light emitting diodes (LEDs), laser diodes (LDs), or phosphors. In this case, when one or more light sources are configured, each light source may emit light having a different wavelength.

The detector may include one or more photo diodes, photo transistors (PTr), or image sensors (eg, CMOS image sensors). Can be placed on the street. Also, here, the pulse wave signal may mean a PPG signal, but is not limited thereto.

Meanwhile, in another embodiment, the bio-signal collection unit 110 may receive a user's pulse wave signal from the external device by communicating with the external device. For example, the bio-signal collection unit 110 uses Bluetooth communication, Bluetooth Low Energy (BLE) communication, Near Field Communication (NFC), WLAN communication, Zigbee communication, infrared data Association, IrDA) communication, WFD (Wi-Fi Direct) communication, UWB (ultra-wideband) communication, Ant+ communication, WIFI communication, RFID (Radio Frequency Identification) communication, etc. can receive

In step S120, the signal pre-processing unit 120 may include a filter, amplifies the collected bio-signals, and eliminates noise. Since the biosignal collected through the sensor may include elements that make measurement inaccurate due to noise or the like, the signal preprocessor 120 removes elements that make measurement inaccurate.

In one embodiment, the signal pre-processor 120 may remove noise by processing signals exceeding a predetermined threshold based on the magnitude of the unit signal as an exception.

In one embodiment, the signal pre-processing unit 120 may extract a waveform of the signal and extract a feature from the extracted waveform.

In step S130, the signal quality estimation unit 130 estimates the signal quality (SQ) level of the preprocessed unit signal.

In one embodiment, the signal quality estimation unit 130 operates based on a machine learning-based algorithm for quality estimation. For example, signal quality may be estimated based on characteristics of a biosignal from which noise has been removed. It can be estimated that the signal quality is good as it is similar to the reference signal provided based on the machine learning-based algorithm generated based on the correlation between the characteristics of the biosignal and the signal quality to be estimated. The range of the signal quality level is between 0 and 1, and the closer the signal quality level is to 1, the higher the signal quality. The machine learning-based algorithm may receive and use a pre-generated machine learning algorithm from an external device through wired/wireless communication.

The signal quality estimator 130 determines that the signal quality is good if the estimated signal quality level is greater than or equal to a preset threshold, and determines that the signal quality is poor if the estimated signal quality level is less than the preset threshold. judge by

In step S140, the signal analyzer 140 estimates whether an abnormal signal, for example, atrial fibrillation (AF) has occurred from the preprocessed biosignal. That is, the signal analyzer 140 determines whether the preprocessed biosignal corresponds to atrial fibrillation, which is an abnormal signal, or normal sinus rhythm (NSR), which is a normal signal.

In one embodiment, the signal analyzer 140 operates based on a machine learning algorithm for classifying an abnormal signal (eg, AF).

In step S150, the unit signal determination unit 150 determines whether or not an abnormal state has occurred with respect to the unit signal based on the results of the signal quality estimation unit 130 and the signal analysis unit 140. For example, the unit signal determination unit 150 determines the reliability of the signal classification result of the signal analysis unit 140 based on the result of the signal quality estimation unit 130 . That is, the analysis result of the signal analyzer 140 is trusted only when the signal quality as a result of the signal quality estimation unit 130 is good, and the reliability of the signal itself is reliable when the signal quality as a result of the signal quality estimation unit 130 is poor. Since is low, the result of the signal analyzer 140 analyzing the signal with low reliability is also unreliable, so the corresponding unit signal is determined to be 'unknown'.

In summary, the unit signal determination unit 150 treats the analysis result of the biological signal as 'unknown' when the signal quality is low (signal bad) and the reliability is low, and when the signal quality is high (signal good), the signal analysis unit 140 ), it is finally determined whether the biological signal is an abnormal signal such as atrial fibrillation (AF) or a normal signal such as normal sinus rhythm (NSR).

In step S160, the episode analyzer 160 determines whether an abnormal state episode has occurred during a certain period and its duration, based on the unit signals analyzed by the unit signal determiner 150.

In one embodiment, an episode in which the unit times determined as atrial fibrillation by the unit signal determination unit 150 lasts for a preset minimum time (eg, 15 seconds, 20 seconds, or 30 seconds) is defined as a minimum unit AF episode. .

In step S170, the load estimator 170 estimates an abnormal state load, for example, an atrial fibrillation load (AF burden).

The atrial fibrillation load (AF burden) is calculated by Equation 1 below.

[Equation 1]

Here, the analysis time is the total duration of unit signals that can be analyzed among the actually collected biosignals. Here, 'analyzable' means that it is possible to determine whether or not a corresponding unit signal is included in an AF episode. In other words, the analysis time means the total time of unit signals capable of determining whether or not they are included in an AF episode. If it is possible to determine whether a specific unit signal is included in an AF episode, the corresponding unit signal is classified as 'Analyzable', and if it cannot be determined whether or not it is included in an AF episode, the unit signal is classified as 'Non-Analyzable'. -Analyzable)'. For example, let's assume that signals are collected for 5 minutes and all collected unit signals are 'SQ Poor'. In this case, since it is not known whether AF has occurred for all the collected unit signals, it is also impossible to determine whether each unit signal includes an AF episode. Therefore, the analysis time for the collected 5 minutes becomes zero. That is, the analysis time is the total duration of each unit signal capable of determining 'whether included in an AF episode' or not.

That is, the load estimator 170 includes the unit signals included in the minimum unit for which it is determined that the signal quality of each of the bio signals of the preset minimum number of consecutive unit times is good, and the bio signals of the preset minimum number of consecutive unit times. At least one of the unit signals included in the minimum unit that has good signal quality and is determined to be a normal signal such as normal sinus rhythm, and each of the biosignals of a preset minimum number or more consecutive unit times is classified as an abnormal signal such as atrial fibrillation, After the signal quality is determined to be good, after the occurrence of an abnormal state episode, for example, an AF episode, when each of the biosignals of a consecutive preset minimum number of unit times is determined to be non-atrial fibrillation, all of the biosignals until it is determined that the AF episode has ended The unit signal is judged as a biosignal that can be analyzed.

As described above, an AF episode has a minimum unit. Therefore, 'the AF episode can be defined' ultimately leads to 'the smallest unit AF episode can be defined', which is the smallest unit of the AF episode.

Hereinafter, some steps of the atrial fibrillation analysis method of FIG. 2 will be described in detail with reference to FIGS. 3 to 7, but atrial fibrillation analysis will be described as an example for convenience of explanation, but the present invention is not limited thereto. Atrial fibrillation described in the following embodiments becomes an abnormal signal in another modification, and normal sinus rhythm becomes a normal signal in another modification.

3 is a vertical line for explaining a case defined as a minimum unit AF episode and a case not defined according to an embodiment of the present invention.

(a) of FIG. 3 is a vertical line for explaining a case defined as a minimum AF episode, and (b) of FIG. 3 is a vertical line for explaining a case that is not defined as a minimum unit AF episode.

For example, assuming that the duration of the unit signal is 5 seconds and the duration of the minimum unit AF episode is 15 seconds, the duration of the minimum unit AF episode is satisfied if the unit signal is three or more times. Also, assuming that the duration of the unit signal is 20 seconds and the duration is 100 seconds, the minimum unit AF episode duration is satisfied if the unit signal is 5 times or more. In this way, the consecutive number of unit signals satisfying the duration of the minimum unit AF episode is referred to as the minimum number of times.

As shown in (a) of FIG. 3 , the episode analyzer 160 determines an event in which the unit signal determined to be atrial fibrillation lasts three consecutive times and satisfies the minimum number of times or minimum time as an AF episode.

As shown in (b) of FIG. 3, when the unit signal determined as atrial fibrillation does not satisfy the minimum number of times or the minimum time, for example, after continuing for 5 seconds, that is, once, the unit signal determined as NSR of 10 seconds is repeated twice, that is, 10 seconds. seconds, the event is not judged as an AF episode.

As such, the episode analyzer (160 in FIG. 1 ) calculates the occurrence time of the AF episode, ignoring the non-continuous time when the non-continuous time is less than a preset minimum time, even if the AF episode is sensed as non-continuous.

4 is a vertical line for explaining the occurrence time of an AF episode according to time according to an embodiment of the present invention.

As shown in (a) of FIG. 4 , NSR occurs at the point T1 where the atrial fibrillation AF signal AF1 lasted for 1 minute, and at the point T2 where the NSR lasted for 5 seconds, the atrial fibrillation AF signal AF signal lasted 40 seconds. Let's look at the case where (AF2) persists.

The episode analyzer 160 determines that the AF signal AF1 and the AF signal AF2 are discontinuous when the time between discontinuous points T1 and T2 is less than a preset minimum time (for example, 20 seconds) for defining an episode. Ignore the time. In (a) of FIG. 4, since the time between T1 and T2 is 5 seconds, which is less than the preset minimum time of 20 seconds, the discontinuous time is ignored. Accordingly, the AF episode occurrence time is 1 minute and 45 seconds, which is the sum of the duration of the AF signal AF1, the duration of the NSR, and the duration of the AF signal AF2.

Here, the reason why the episode analyzer 160 ignores the NSR time less than the minimum time is to correct errors in sensing and analysis.

Specifically, when the signal analyzer 140 determines that an arbitrary biosignal is NSR, the signal analyzer 140 may correctly determine the actual NSR as the NSR, or may misjudge the actual AF as the NSR.

However, based on clinical information, the probability of an AF episode occurring again after an AF episode has been restored to normal rhythm (NSR occurrence) for a very short time is extremely low, so NSR is judged as NSR for a very short time between AF episodes. Vital signs are ignored. Here, the very short time may be less than about 20 seconds as a meaningful time for clinical judgment. Accordingly, in (a) of FIG. 4 , the biosignal determined as NSR for a very short time between AF1 and AF2, for example, 5 seconds, is determined to be an erroneous estimate of the actual AF generation signal as NSR and is ignored. Of course, in the present specification, a significant time or a very short time is not limited to the above 20 seconds and 5 seconds.

Here, if the probability that the signal analyzer 140 correctly determines the actual NSR as NSR is P1, and the probability of misdiagnosing the actual AF as NSR is P2, the probability that the signal analyzer 140 is the real AF signal Х real AF It can be determined by comparing the calculated probability P2 of misdiagnosing a signal as an NSR signal with the probability P1 of correctly determining the actual NSR as an NSR signal and the probability that the signal is an actual NSR signal. Here, the probability that the signal is an actual AF signal and the probability that the signal is an actual NSR signal may be set in advance according to clinical statistics.

Looking at another example, as shown in (b) of FIG. 4, after the atrial fibrillation AF signal AF1 lasted for 50 seconds and the NSR generated for 5 seconds lasted 4 consecutive times, the atrial fibrillation AF signal AF1 lasted for 30 seconds. Let's look at the case where the signal AF2 is sustained. Since the NSR time is 20 seconds or more, which is the preset minimum time, the episode analyzer 160 analyzes the case of FIG. 2 Episodes A total of 2 episodes are analyzed. Of course, the preset minimum time is not limited to 20 seconds, and may be set to other times such as 10 seconds or 30 seconds.

As described above, if the NSR signal in unit time occurs 4 times in a row, the probability that the 4 consecutive NSR signals are real AF is (probability that it is real AF signal Х probability that the real AF signal is mistakenly determined as NSR signal (P2)) ^{is 4} is the calculated value. (P2) Since ⁴ is a value close to 0 (probability that it is an actual AF signal Х probability that an actual AF signal is mistakenly determined to be an NSR signal (P2)), the calculated value of ⁴ is also a small value close to 0. In other words, it is interpreted that the probability of incorrectly judging the actual AF signal as NSR in succession is almost slim. Therefore, the probability of actual NSR occurring in at least one of the four NSR signals is higher than the probability of incorrectly determining four signals as NSR in succession. Accordingly, when a predetermined number of consecutive NSR signals occur, for example, 4 or more times, it may be determined that there is a reliable NSR.

5 is a vertical line for additional description of an AF episode over time according to an embodiment of the present invention. Hereinafter, the AF episode described with reference to FIG. 5 is not limited thereto as an example, and the AF episode may be various physiologically abnormal state episodes.

3 and 4 assume a case in which signal quality is good, but in FIG. 5 a case in which poor signal quality is included will be described.

As shown in (a) of FIG. 5, after the atrial fibrillation AF signal lasts for 1 minute and the unit signal (signal quality defect) determined to be poor signal quality continues for 5 seconds, atrial fibrillation occurs for 40 seconds. Let's take a look at the case where the AF signal persists.

Since the episode analysis unit 160 occurs between the AF signal AF1 and the AF signal AF2 for 5 seconds, which is a very short time of less than 20 seconds, the signal quality is poor, so the signal quality is poor. It is not unreasonable to judge the signal as an AF signal. That is, a signal generated in a very short period of time (for example, 5 seconds) in which the signal quality is poor is determined according to the result of the classification of the front and back signals.

In the case of (a) of FIG. 5 , the AF episode occurrence time is 1 minute and 45 seconds, which is the sum of the duration of the AF signal (AF1), the duration of the poor signal quality, and the duration of the AF signal (AF2). More specifically, since an AF episode occurs and the probability of an AF episode occurring again after an NSR occurs for a very short time is extremely low, even if it is impossible to determine whether it is AF due to a signal failure between AF episodes, It is okay to consider a very short time as AF.

Looking at another example, as shown in (b) of FIG. 5, after the occurrence of the atrial fibrillation AF signal (AF1), it lasts for 50 seconds, and after that, after the 5-second signal with poor signal quality lasts 4 consecutive times, 30 seconds Let's take a look at a case where the atrial fibrillation AF signal (AF2) continues.

Since the time of poor signal quality is 20 seconds or more, which is the preset minimum time, the episode analysis unit 160 analyzes the 20 seconds of poor signal quality in a meaningful way, and the case of FIG. The first episode and the second episode with an occurrence time of 30 seconds after 20 seconds are analyzed as a total of two episodes.

Looking at another example, as shown in (c) of FIG. 5, after the atrial fibrillation AF signal (AF1) occurs, it lasts for 50 seconds and then a signal for 5 seconds with poor signal quality, followed by a normal sinus rhythm with good signal quality ( NSR) lasts for 10 seconds, a signal with poor signal quality lasts for 5 seconds, and then the atrial fibrillation AF signal AF2 lasts for 30 seconds.

Signals with poor signal quality and normal sinus rhythm signals are referred to as non-atrial fibrillation signals, and non-atrial fibrillation signals are processed similarly to signal processing with poor signal quality. Therefore, since the time at which non-atrial fibrillation signals are continuously generated is 20 seconds or more, which is the preset minimum time, the episode analyzer 160 interprets the 20-second period in which non-atrial fibrillation signals are continuously generated in a meaningful way, and FIG. 5 ( In the case of c), a total of two episodes are analyzed: the first episode with an occurrence time of 50 seconds and the second episode with an occurrence time of 30 seconds that occurred after 20 seconds.

In summary, the episode analysis unit 160 determines that each unit signal is 'signal quality good (SQ Good) & NSR' or 'signal quality good (SQ Good) & NSR' or ' If signal quality is poor (SQ Poor), the atrial fibrillation episode ends.

6 is a vertical line for explaining a combination of unit signals capable of defining an atrial fibrillation episode according to an embodiment of the present invention.

6 (a) and (b) illustrate combinations of various unit signals included in the minimum unit of an AF episode having a duration of 15 seconds.

As shown in (a) of FIG. 6, when all unit signals included in the minimum unit have good signal quality, it is analyzed as 'a minimum unit AF episode can be defined'. Here, 'capable of defining a minimum unit AF episode' means 'capable of determining whether or not it is an AF episode'. Conversely, 'the minimum unit AF episode cannot be defined' means that the minimum unit 'cannot determine whether or not it is an AF episode'.

As shown in (b) of FIG. 6, if at least one of the unit signals included in the minimum unit is 'good signal quality & normal sinus rhythm (NSR)', regardless of the signal quality level of the remaining unit signals or the occurrence of atrial fibrillation, 'the corresponding minimum The unit is not an AF episode'.

In short,

1. 'A minimum unit AF episode can be defined' means that the minimum unit 'can determine whether or not it is an AF episode'.

2. 'Defined as a minimum unit AF episode' means that the minimum unit is 'an AF episode'.

3. 'Cannot be defined as a minimum unit AF episode' means that the minimum unit is 'not an AF episode'.

4. 'A minimum unit AF episode cannot be defined': It means that the minimum unit 'cannot determine whether or not it is an AF episode'.

For example, 1) If the signal quality of all unit signals included in the minimum unit is good and all unit signals are determined as AF, the minimum unit is determined as an AF episode, and 2) At least one of the unit signals included in the minimum unit If is 'good signal quality & normal sinus rhythm', it is determined that the minimum unit is not an AF episode.

7 is a vertical line for explaining a minimum analysis time according to an embodiment of the present invention.

As described above, the atrial fibrillation load is a numerical value expressed as a relative ratio of the total time of an atrial fibrillation episode to the analysis time. Thus, the atrial fibrillation load is affected by the analysis time as well as the total duration of the atrial fibrillation load. In particular, if the analysis time for the atrial fibrillation load is very small, distortion of the atrial fibrillation load may occur.

7 (a), (b), and (c) show an example in which the atrial fibrillation episode duration is the same as 5 minutes, but the analysis time is different from each other.

In (a) of FIG. 7, since the duration of an atrial fibrillation episode is 5 minutes and the analysis time is 8 hours and 5 minutes, the atrial fibrillation load is 1.0%.

In (b) of FIG. 7, since the duration of an atrial fibrillation episode is 5 minutes and the analysis time is 2 hours and 5 minutes, the atrial fibrillation load is 4.0%.

In (a) of FIG. 7, since the duration of an atrial fibrillation episode is 5 minutes and the analysis time is 15 minutes, the atrial fibrillation load is 33.3%.

As can be seen by comparing (a), (b) and (c) of FIG. 7 , even if the atrial fibrillation episode duration is the same, the atrial fibrillation load is different according to the analysis time. In particular, in the case of (c) of FIG. 7 , it can be confirmed that the atrial fibrillation load is overestimated at 33.3% because the analysis time is 15 minutes, which is very short to measure a clinically significant load.

In order to solve the problem of such overestimation, the AF load estimator 170 defines a minimum analysis time that satisfies the following two conditions to be clinically meaningful.

First, the AF load value of the smallest unit AF episode compared to the minimum analysis time is less than 1%

Second, an analysis time of more than a preset time that can be sufficiently collected during the day, for example, 3 hours (here, the analysis time of more than a preset time can be adjusted according to the purpose of atrial fibrillation load analysis and sensor sensitivity, etc.).

That is, the AF load estimator 170 calculates the atrial fibrillation load when the two minimum analysis time conditions are satisfied. If the AF load estimator 170 does not satisfy the minimum analysis time, it may be treated as a load overestimation error.

8 is a block diagram showing the configuration of an atrial fibrillation analyzer according to another embodiment of the present invention.

Referring to FIG. 8 , the atrial fibrillation analyzer 200 includes a biosignal collection unit 210, a process P2, an input unit 220, a storage unit 230, a communication unit 240, and an output unit 250. can do. Since the bio-signal collection unit 210 and the process P2 generally perform the same functions as the bio-signal collection unit 110 and the process P1 described above with reference to FIG. Explain.

The input unit 220 converts the user's input operation into an input signal and transmits it to the processor P2. The input unit 220 may include, for example, a keyboard, a dome switch, a jog wheel, a touch sensor on a touch screen, a touch pad, a keypad, voice input, and other inputs that are currently available, in the past, or will be available in the future. It can be implemented as processing devices. For example, the input unit 220 may receive an atrial fibrillation load information provision request input and transmit it to the processor P2.

The storage unit 230 may store information received by the communication unit 240, information necessary for the operation of the atrial fibrillation analyzer 200, and program codes.

The communication unit 240 exchanges data with an external device. For example, the communication unit 240 may transmit data analyzed by the processor P to an external device. The communication technology used by the communication unit 240 may vary depending on the type of communication network or other circumstances.

The output unit 250 may be implemented as a liquid crystal display (LCD), a light emitting diode (LED), an organic light emitting diode (OLED), a projector, and other display devices currently available in the past or to be available in the future. For example, the output unit 250 may output information such as a biosignal, occurrence and duration of an atrial fibrillation episode, an alarm according to the occurrence of atrial fibrillation, and atrial fibrillation load. In addition, the output unit 250 may output a result in response to the atrial fibrillation load information request input of the input unit 210 according to the instruction of the processor P2. Also, the output unit 250 may display an interface page for providing information or a result page for providing information. Depending on the embodiment, other information such as voice output or vibration may be delivered to the user instead of screen output.

In an embodiment, the processor P2 may calculate an atrial fibrillation load in response to an atrial fibrillation load information provision request input, and output the calculated result through the output unit 250 . At this time, the processor P2 may output a warning message together with the AF load when the analysis time of the atrial fibrillation episode load is less than the total minimum analysis time.

A warning message may be displayed, for example, 'There is a possibility that the atrial fibrillation load value was measured higher than the actual one because the analysis time was not sufficient. Please increase the usage time to collect sufficient analysis time.'

The processor P2 may calculate statistical values related to loads other than the atrial fibrillation load, and output the calculated statistical values through the output unit 250 .

For example, processor P2 may include hourly/daily/weekly/monthly statistics of atrial fibrillation load, hourly/daily/weekly/monthly statistics of total duration of AF episodes, minimum/maximum duration of atrial fibrillation episodes, atrial fibrillation Hourly/daily/weekly/monthly statistics of quartiles of episodes, hourly/daily/weekly/monthly statistics of median/mean AF episodes, hourly/daily/weekly/monthly statistics of analysis time of atrial fibrillation episodes, Hourly/daily/weekly/monthly statistics of usage time (operation time) can be analyzed and provided.

The devices described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. can be embodied in Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler.

As described above, although the embodiments have been described with limited examples and drawings, those skilled in the art can make various modifications and variations from the above description. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

100: abnormal state analysis device
110: biosignal collection unit
120: pre-processing unit
130: signal quality estimation unit
140: signal analysis unit
150: unit signal determination unit
160: episode judgment unit
170: load estimation unit

Claims (14)

a bio-signal collection unit that collects bio-signals generated by the sensor;
a pre-processing unit that removes noise from the collected bio-signals;
a signal quality estimator for estimating signal quality of a biosignal of a predetermined unit time;
a signal analysis unit that classifies the biosignal of a preset unit time into an abnormal signal or a normal signal;
an episode determining unit for determining whether an abnormal state episode has occurred and a duration based on signal quality of a plurality of unit time bio signals and abnormal signal classification results; and
An abnormal signal load estimator calculating an abnormal state load based on whether the abnormal state episode occurs and a duration thereof
including,
The abnormal signal load estimation unit,
The analysis time is calculated by summing the durations of the unit signals that can be analyzed among the actually collected biosignals,
By calculating the duration of the abnormal state episode occurring at the analysis time,
Calculate a ratio of the sum of the durations of the abnormal state episodes to the analysis time;
The episode determination unit,
If each biosignal of a predetermined minimum number or more consecutive unit times is classified as an abnormal signal and the signal quality is determined to be good, it is determined that an abnormal state episode has occurred;
After the occurrence of the abnormal state episode, if each of the bio signals of the minimum number of consecutive unit times is determined to be a non-abnormal signal, it is determined that the abnormal state episode has ended.
Physiological abnormal signal analysis device for calculating the time from the start of occurrence of the abnormal state episode to the end of the abnormal state episode as a duration. delete According to claim 1,
The abnormal state load estimation unit
A unit signal included in the minimum unit in which the signal quality of each biosignal of a predetermined minimum number of consecutive unit times is determined to be good,
A unit signal included in the minimum unit in which at least one of the preset minimum number of biosignals of consecutive unit time is determined to have good signal quality and to be a normal signal, and
After each biosignal of a preset minimum number or more consecutive unit times is classified as an abnormal signal and the signal quality is determined to be good, after an abnormal state episode occurs, each of the consecutive biosignals of a preset minimum number of unit time is non-defective. If it is judged as an abnormal signal, all unit signals until it is determined that the abnormal state episode is over
is judged as a unit signal that can be analyzed
Physiological abnormal signal analysis device, characterized in that. delete The abnormal physiological signal analyzer according to claim 3, wherein the non-abnormal signal includes a case where the signal quality of the unit signal is good and normal, and a case where the signal quality is poor. According to claim 1,
Wherein the abnormal signal load estimator processes the abnormal signal load as an error of overestimation if the analysis time is less than a preset minimum analysis time. According to claim 1,
Hourly/daily/weekly/monthly statistics of abnormal load, hourly/daily/weekly/monthly statistics of total duration of adverse episodes, minimum/maximum duration of adverse episodes, quartiles of adverse episodes Hourly/daily/weekly/monthly statistics, hourly/daily/weekly/monthly statistics of the median/average of abnormal episodes, hourly/daily/weekly/monthly statistics of analysis time of abnormal episodes, hourly usage time (operation time) /Output unit that outputs one or more of daily/weekly/monthly statistics
Physiological abnormal signal analyzer further comprising a. As an analysis method of a physiological abnormal signal analyzer,
removing noise from the biosignal generated from the sensor;
estimating signal quality of a biosignal of a predetermined unit time;
Classifying a biosignal of a preset unit time into an abnormal signal or a normal signal;
Determining whether an abnormal state episode occurs and a duration thereof based on signal quality and abnormal signal classification results for a plurality of unit time biosignals; and
Calculating an abnormal state load based on whether the abnormal state episode occurs and a duration thereof
Including,
Determining whether the abnormal state episode occurs and the duration thereof,
determining that an abnormal state episode has occurred when each of the biosignals of consecutive unit time equal to or greater than a predetermined minimum number is classified as an abnormal signal and the signal quality is determined to be good;
determining that the abnormal state episode is over when each of the consecutive biosignals of a predetermined minimum number of unit times is determined to be a non-abnormal state after the occurrence of the abnormal state episode; and
Calculating the time from the start of occurrence of the abnormal state episode to the end of the abnormal state episode as a duration time
including,
The step of calculating the abnormal state load,
Calculating an analysis time by summing the durations of unit signals that can be analyzed among the actually collected biosignals;
calculating a total sum of durations of abnormal state episodes occurring during the analysis time; and
Calculating a ratio of the total sum of durations of the abnormal state episodes to the analysis time
Physiological abnormal signal analysis method comprising a. delete According to claim 8,
The unit signal that can be analyzed is
A unit signal included in the minimum unit in which the signal quality of each biosignal of a predetermined minimum number of consecutive unit times is determined to be good,
A unit signal included in the minimum unit in which at least one of the preset minimum number of biosignals of consecutive unit time is determined to have good signal quality and to be a normal signal, and
After each biosignal of a preset minimum number or more consecutive unit times is classified as an abnormal signal and the signal quality is determined to be good, after an abnormal state episode occurs, each of the consecutive biosignals of a preset minimum number of unit time is non-defective. If it is judged as an abnormal signal, all unit signals until it is determined that the abnormal state episode has ended
Physiological abnormal signal analysis method comprising a. delete According to claim 10,
The abnormal physiological signal analysis method, characterized in that the non-abnormal signal includes a signal that is a normal signal with good signal quality of the unit signal and a signal with poor signal quality. According to claim 8,
The step of calculating the analysis time is,
If the analysis time is less than the preset minimum analysis time, the physiological abnormal signal analysis method characterized in that it is treated as an overestimation error of the load of the abnormal state. According to claim 8,
The physiological abnormal signal analysis method, characterized in that the abnormal signal is atrial fibrillation (AF), the normal signal is normal sinus rhythm (NSR), and the abnormal state episode is an AF episode.

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