WO2016204396A1 - Method for detecting arrhythmia and device for supporting same - Google Patents

Abstract

A method for detecting arrhythmia, according to one embodiment of the present invention, can comprise the steps of: acquiring an electrocardiogram (ECG) signal; calculating a signal-to-noise ratio (SNR) of the acquired ECG signal; and determining whether to use another biological signal, according to the calculated SNR.

Description

Method for detecting arrhythmia and apparatus supporting same

The present invention relates to, for example, a method for detecting arrhythmia and a device supporting the same.

As the standard of living improves and health concerns rise, 'living longer and longer' is one of the biggest concerns for people living in recent years. In addition, due to changes in diet, lack of exercise, as well as stress, people who were healthy yesterday suddenly are injured or died due to adult diseases, heart-related diseases, or brain diseases.

In particular, arrhythmia (arrhythmia) is an abnormally fast heartbeat or irregular symptoms, severe arrhythmia, heart function is paralyzed and may immediately die from a heart attack.

In the prior art, arrhythmia is mainly done through electrocardiogram measurements. For example, the heart rate is measured through a plurality of electrodes and leads, and an electrocardiogram measuring device coupled thereto, and whether arrhythmia is determined based on the measured heart rate.

However, in the electrocardiogram measurement in the prior art, when the electrode is not properly attached, the electrode is not properly adhered due to long-term attachment, or if noise caused by the user's movement occurs, the problem of inferior accuracy have.

Various embodiments of the present invention relate to a method for detecting arrhythmia that improves the accuracy of arrhythmia detection and an apparatus for supporting the same by using an electrocardiogram signal or a heart sound signal in addition to an electrocardiogram signal.

Method for detecting arrhythmia according to an embodiment of the present invention comprises the steps of obtaining an electrocardiogram signal; Calculating a signal-to-noise ratio of the obtained electrocardiogram signal; And determining whether to use another biosignal according to the calculated signal-to-noise ratio.

In one embodiment, the other bio-signal may include at least one of a heart ballistic signal and a heart sound signal.

In one embodiment, according to the calculated signal-to-noise ratio, determining whether to use the other bio-signals, comparing the calculated signal-to-noise ratio, and the specified first threshold value, the calculated When a signal-to-noise ratio is less than or equal to the specified first threshold value, detecting a feature point of the ECG signal using the other biosignal, and determining whether arrhythmia is generated based on the feature point of the detected ECG signal. It may include.

In one embodiment, the detecting of the feature point of the ECG signal by using the other biosignal may include detecting the feature point of the other biosignal and using the detected feature point of the other biosignal. Detecting the R peak point of the signal, wherein detecting the R peak point of the ECG signal comprises: specifying a predetermined time range from the detected feature point of the other biosignal, and The method may include determining a voltage peak point of the ECG signal within the predetermined time range from the detected feature point as the R peak point.

In one embodiment, the detected feature point of the other biosignal may include at least one of a J peak point of the cardiac ballistic signal and a peak point corresponding to a ventricular systolic period of the heart sound signal.

In one embodiment, according to the calculated signal-to-noise ratio, determining whether to use the other bio-signal, comparing the calculated signal-to-noise ratio, and the second specified threshold value, and the calculation When the signal-to-noise ratio is less than or equal to the specified second threshold value, it may be determined whether arrhythmia is generated based on the other biosignal instead of the ECG signal.

In another embodiment, the other biosignal is a cardiogram signal, and based on at least one of the ECG signal and the ECG signal, determining arrhythmia generation, the ECG signal, and the ECG signal. The process of calculating blood pressure, the risk of arrhythmia based on the occurrence of the arrhythmia and the calculated blood pressure may be determined, and the notification information may be transmitted through the communication unit.

In an embodiment, the method may further include determining an item out of a predetermined range of the user's biometric information or external environment information as a factor causing arrhythmia and storing the factor causing the arrhythmia upon detecting arrhythmia. Can be.

In one embodiment, the item may include at least one of blood pressure, stress, temperature, caffeine intake, drinking amount, smoking amount, sleep state.

In one embodiment, the step of determining the frequency or cumulative amount of occurrence of the item out of the predetermined range of the user's biometric information or external environmental information as a factor causing arrhythmias by comparing with a threshold value, and the determined The method may further include storing a factor causing arrhythmia.

In one embodiment, the biometric information or external environmental information of the current user is measured, and if the measured biometric information or external environmental information of the current user corresponds to a factor causing the arrhythmias, the risk of occurrence of arrhythmia The method may further include outputting a notification for the.

An apparatus for detecting arrhythmia according to an embodiment of the present invention includes a sensor unit for obtaining an electrocardiogram signal, and a controller, wherein the controller calculates a signal-to-noise ratio of the obtained electrocardiogram signal. According to the calculated signal to noise ratio, it is possible to determine whether to use the other bio-signals.

The method for detecting arrhythmia according to an embodiment of the present invention includes the steps of: obtaining an electrocardiogram signal, acquiring at least one of a cardiogram signal or a heart sound signal, and the obtained electrocardiogram signal and the cardiogram signal and And / or detecting arrhythmia using the heartbeat signal or the heartbeat signal and / or the heartbeat signal.

A method for detecting arrhythmia and an apparatus supporting the same according to various embodiments of the present disclosure may support accurate measurement of arrhythmias occurring to a user.

1 illustrates an example in which an arrhythmia detection device according to an embodiment of the present invention is attached to a body of a user.

2A to 2E are diagrams illustrating waveforms of an electrocardiogram signal and a ballistic ball signal measured by an arrhythmia detection apparatus according to various embodiments of the present disclosure.

3 is a block diagram of an apparatus for detecting arrhythmia according to an embodiment of the present invention.

4 is a flowchart illustrating a method for detecting arrhythmia according to an embodiment of the present invention.

FIG. 5 is a diagram illustrating a signal to noise ratio (SNR) calculation of an electrocardiogram signal according to an embodiment of the present invention.

6 is a flowchart illustrating a method of detecting arrhythmia using a heart ballistic signal according to an embodiment of the present invention.

7 is a view for explaining a threshold value designation of an ECG signal according to an embodiment of the present invention.

FIG. 8 is a diagram for describing a method of detecting an R peak point of an ECG signal by using a J peak point of a ECG signal according to an exemplary embodiment of the present invention.

9 is a flowchart illustrating a method of detecting arrhythmia using a heart ballistic signal according to another exemplary embodiment of the present invention.

FIG. 10 illustrates a threshold value designation of an ECG signal according to an exemplary embodiment of the present invention.

11 is a flowchart illustrating a method for detecting arrhythmia using a heart sound signal according to another embodiment of the present invention.

12 is a diagram for describing a method of detecting an R peak point of an ECG signal by using feature points of a heart sound signal according to an exemplary embodiment of the present invention.

13 is a flowchart illustrating a method of detecting arrhythmia using a heart sound signal according to another embodiment of the present invention.

14 is a flowchart illustrating a method of outputting an arrhythmia risk notification according to an embodiment of the present invention. FIG. 15 is an exemplary diagram of an arrhythmia risk output from an external electronic device and a notification for reducing an arrhythmia risk according to an embodiment of the present disclosure.

16 is a flowchart illustrating a method of storing an individual arrhythmia causing factor according to an embodiment of the present invention.

17 is a flowchart illustrating a method of notifying arrhythmia risk and arrhythmia reduction according to an embodiment of the present invention.

18 is an exemplary diagram for arrhythmia risk and arrhythmia reduction notification according to an embodiment of the present invention.

19 is a flowchart illustrating a method of outputting a reattachment notification when there is an error in attachment of an arrhythmia detection apparatus according to an embodiment of the present invention.

20 is an exemplary view of an ECG signal measured when there is an error in the attachment of the arrhythmia detection apparatus according to an embodiment of the present invention.

21 is an exemplary diagram for describing a method of outputting a reattachment notification when there is an error in attachment of an arrhythmia detection apparatus according to an embodiment of the present invention.

The threshold and the second threshold may be compared. Regarding the threshold value designation, the following description will be made with reference to FIG. 10.

FIG. 10 illustrates a threshold value designation of an ECG signal according to an exemplary embodiment of the present invention.

Referring to FIG. 10, 1001 is a graph illustrating an ECG signal having a relatively high signal-to-noise ratio (ie, an ECG signal having a signal-to-noise ratio exceeding a threshold value) in the time domain and the frequency domain. 1003 is a graph showing an ECG signal having a relatively low signal-to-noise ratio in the time domain and frequency domain. 1005 is a graph illustrating an ECG signal having a very low signal-to-noise ratio of an ECG signal in the time domain and the frequency domain. In one embodiment, 1001 and 1003 may be the same as 701 and 703 of FIG. 7. In another embodiment, comparing 1003 and 1005 may correspond to the case where the signal to noise ratio of the ECG signal shown in 1005 is lower than the signal to noise ratio of the ECG signal shown in 1003. For example, in the frequency domain of 1005, a frequency range of about 7 Hz to 9 Hz of the ECG signal may correspond to a signal range, while a frequency range of 0 Hz to 7 Hz and a range of 9 Hz or more may correspond to a noise range. In another example, comparing 1001 and 1005, the R peak points in the time domain at 1001 are clearly measured without distortion, while the peak points of voltage in the time domain at 1005 are measured in an irregular, distorted form. have.

In an embodiment, the controller 140 may designate a first threshold value and a second threshold value to distinguish the ECG signal waveform of 1001, the ECG signal waveform of 1003, and the ECG signal waveform of 1005.

For example, the controller 140 may measure the first threshold value in the ECG signal in which the magnitude of the voltage peak corresponding to the R peak point of the ECG signal of 1003 is greater than a specified magnitude greater than the magnitude of the voltage peak point corresponding to the noise. It can be specified as a signal-to-noise ratio.

In another example, the controller 140 may designate a first threshold by analyzing a spectrum of an ECG signal measured in the frequency domain. For example, comparing 1001 to 1005, the number of harmonic (harmonic or harmonic) components of an electrocardiogram signal may be measured as 8 at 1001, 3 at 1003, and 1 at 1005. The controller 140 may designate a first threshold value according to the number of harmonic components of the ECG signal. For example, the controller 140 sets the signal-to-noise ratio of the electrocardiogram signal, in which the number of harmonic components of the electrocardiogram signal is 6, to distinguish the electrocardiogram signal waveform of 1001 and the electrocardiogram signal waveform of 1003 as a first threshold. Can be specified.

In another example, the controller 140 may designate a second threshold by analyzing the number of voltage peaks in a specified time interval in the time domain. For example, the number of R peak points of an ECG signal in a time interval of 1001 may be measured as three. During the same time interval as the time interval of 1001, the number of voltage peak points at 1003 may be measured as six, and the number of voltage peak points at 1005 may be measured as nine. The controller 140 may designate a signal-to-noise ratio of the ECG signal corresponding to seven voltage peak points during the same time interval as the time interval of 1001 as the second threshold value.

In another example, the controller 140 may designate a second threshold by analyzing a spectrum of an ECG signal measured in the frequency domain. For example, comparing 1001 to 1005, the number of harmonic (harmonic or harmonic) components of an electrocardiogram signal may be measured as 8 at 1001, 3 at 1003, and 1 at 1005. The controller 140 may designate a second threshold value according to the number of harmonic components of the ECG signal. For example, the controller 140 sets the signal-to-noise ratio of the ECG signal in which the number of harmonic components of the ECG signal is measured as two to distinguish the ECG signal waveform of 1003 and the ECG signal waveform of 1005 as a second threshold. Can be specified.

In one embodiment, the controller 140 may designate the first threshold value and the second threshold value differently according to the hardware configuration of the arrhythmia detection apparatus 100. For example, the controller 140 may designate the first threshold value and the second threshold value differently according to the type of the electrode of the ECG sensor 121 included in the arrhythmia detection apparatus 100. In another example, the controller 140 may designate the first threshold value and the second threshold value differently in consideration of the position where the arrhythmia detection apparatus 100 is attached to the user's body. In another embodiment, the controller 140 may specify the first threshold value and the second threshold value differently according to a measurement target, for example, a user. However, the threshold value designation is not limited to the above example.

Referring to FIG. 9, in step 911, when the controller 140 determines that the signal-to-noise ratio of the ECG signal is greater than the first threshold value, in step 909, the controller 140 may detect arrhythmia based on the ECG signal. Can be. Process 911 overlaps with 411 of FIG. 4 and 617 of FIG. 6, and thus a detailed description thereof will be omitted.

In step 915, when the controller 140 determines that the signal-to-noise ratio of the ECG signal is less than or equal to the first threshold and greater than the second threshold, in step 913, the controller 140 uses the J peak point of the ECG signal. The R peak point of the ECG signal can be detected. In operation 917, the controller 140 may extract a feature of the ECG signal based on the detected R peak point. In operation 919, the controller 140 may detect arrhythmia using the extracted feature. Processes 915 to 919 overlap with process 611 to process 615 of FIG. 6, and thus a detailed description thereof will be omitted.

In operation 921, when the controller 140 determines that the signal-to-noise ratio of the electrocardiogram signal is less than or equal to the second threshold in operation 913, the controller 140 may extract a feature of the ECG signal. The controller 140 may determine that the waveform of the ECG signal is distorted to the extent that the ECG signal cannot be extracted, and extract a feature of the ECG signal, for example, a feature point (or a feature section). For example, the controller 140 may extract a time interval, that is, a J-J interval, between the J peaks of the ECG signal. However, the feature extracted from the ballistics signal is not limited thereto.

In operation 923, the controller 140 may detect arrhythmia using a feature extracted from the cardiograph signal. For example, the controller 140 may calculate the number of heartbeats by calculating the number of J peaks of the cardiograph signal for a predetermined time (eg, 1 minute). The controller 140 may detect the presence or absence of arrhythmia, the type of arrhythmia, and the like based on the calculated heart rate. In another example, the controller 140 may calculate the J-J interval of the cardiac ballistic signal and detect the presence or absence of arrhythmia, the type of arrhythmia, and the like based on the calculated J-J interval. In one embodiment, various algorithms may be used to recognize the presence of arrhythmia and the type of arrhythmia. For example, a Hidden Markov Model based algorithm, a Heart best matrix based algorithm, or the like may be used to recognize arrhythmia and arrhythmia. However, the algorithm used is not limited thereto.

11 is a flowchart illustrating a method for detecting arrhythmia using a heart sound signal according to another embodiment of the present invention.

Referring to FIG. 11, in step 1101, the controller 140 may acquire an electrocardiogram signal and a heart sound signal. In operation 1103, the controller 140 may calculate a signal-to-noise ratio (SNR) of the ECG signal obtained in operation 1101. Processes 1101 and 1103 are overlapped with processes 401 and 403, respectively, and thus detailed descriptions thereof will be omitted.

In operation 1105, the controller 140 may calculate a signal-to-noise ratio of the heart sound signal. For example, the controller 140 may analyze the waveform (or pattern of the heart sound signal) of the heart sound signal in the frequency domain of the heart sound signal. The controller 140 may calculate a signal-to-noise ratio of the heart sound signal by calculating a ratio of the area corresponding to the harmonic components to the harmonic components in the pattern of the heart sound signal.

In an embodiment, although not shown in FIG. 11, when the signal-to-noise ratio of the heart sound signal exceeds a specified threshold, the process may proceed to step 1107. In another embodiment, when the signal-to-noise ratio of the heartbeat signal is equal to or less than a predetermined threshold value, the controller 140 may detect arrhythmias or re-acquire a heartbeat signal based only on the ECG signal.

In one embodiment, in FIG. 11, the control unit 140 calculates the signal-to-noise ratio of the ECG signal and the signal-to-noise ratio of the heart sound signal in step 1103, but is not limited thereto. For example, the controller 140 may not calculate the signal-to-noise ratio of the heart sound signal in step 1105, but may calculate the signal-to-noise ratio of the heart sound signal when the signal-to-noise ratio of the ECG signal is less than or equal to a specified threshold in step 1109. have.

In an embodiment, between steps 1109 and 1111, a process of calculating a signal-to-noise ratio of the heart sound signal is performed, and when the signal-to-noise ratio of the calculated heart sound signal is equal to or less than a specified threshold value, the controller 140 performs an ECG signal. And the sound signal can be obtained again. For example, if the signal-to-noise ratio of the ECG signal is less than the threshold value and the calculated signal-to-noise ratio of the calculated heart sound signal is also less than the threshold value, the ECG signal and the heart sound signal contain a lot of noise. Can be. In this case, the controller 140 may obtain the ECG signal and the heart sound signal again from the ECG sensor 121 and the heart sound sensor 125. In another embodiment, a process of calculating a signal-to-noise ratio of the heart sound signal is performed between the process 1109 and the process 1111, and if the signal-to-noise ratio of the calculated heart sound signal exceeds a specified threshold, the process may proceed to process 1111. have.

In operation 1107, the controller 140 may compare the signal-to-noise ratio and the specified threshold value of the ECG signal. Process 1107 is overlapped with process 607 of FIG. 6, so a detailed description thereof will be omitted.

In operation 1111, when the controller 140 determines that the signal-to-noise ratio of the ECG signal is less than or equal to the specified threshold value, in operation 1109, the controller 140 may detect the R peak point of the ECG signal by using the designated point of the heart sound signal. have. Hereinafter, referring to FIG. 12, a method of detecting the R peak point of the ECG signal using the feature point of the heart sound signal will be described in detail.

12 is a diagram for describing a method of detecting an R peak point of an ECG signal by using feature points of a heart sound signal according to an exemplary embodiment of the present invention.

Referring to FIG. 12, FIG. 12 illustrates an ECG signal waveform 1201 and a heart sound signal waveform 1203 measured based on the same time axis. In one embodiment, the time interval of the heart sound signal waveform 1203 may include four time intervals in which the heart sound is measured. The S1 time interval may correspond to a time interval in which the atrioventricular valve and the tricuspid valve close at the beginning of the systole of the ventricles. In the S1 time interval the heart sounds are low and dull, and the duration of sound can be long. The frequency during the S1 time interval may be 57 Hz to 70 Hz per second. The S2 time interval is a time interval in which the aortic valve and the pulmonary valve are closed at the beginning of the dilatation of the ventricles. The heartbeat may be high, sharp, and short in sound duration. The frequency may be 90 Hz to 100 Hz per second during the S2 time interval, and may coincide with the end point of the T wave of the ECG signal. The time interval of S3 is a time interval in which the ventricular wall vibrates due to the blood flowing from the atrium at the beginning of the ventricular diastolic, and the heartbeat is generated by the oscillation of the mitral valve. The time interval of S4 may be a time interval during which heart sounds are generated due to the driving force of blood flowing into the ventricles during the atrial systolic period.

In an embodiment, the controller 140 may detect a first peak point of the S1 time interval as a feature point of the heart sound signal. For example, the controller 140 may measure the frequency of the heart sound signal and distinguish between the S1 time interval and the S4 time interval according to the measured frequency. The controller 140 may detect the S1 time interval, and may detect the first peak point 1211 that is measured first among the peak points of which the magnitude of the pressure of the heart sound signal is greater than or equal to a predetermined magnitude within the S1 time interval.

In one embodiment, the controller 140 may designate a predetermined time range (or a windowing range) to detect the R peak point of the ECG signal from the first peak point 1211 of the heart sound signal. For example, the controller 140 may designate a time interval l1 between the point 1213 at which the S4 time interval starts from the first peak point as a predetermined time range. However, the predetermined time range for detecting the R peak point of the ECG signal is not limited thereto. For example, the controller 140 may determine the R peak point of the ECG signal as a time interval corresponding to a multiple of the time interval between the point 1213 at which the S4 time interval starts from the first peak point 1211. Can be specified as a range. In another example, a time range can be specified as a statistical average, a time range that reflects demographic information (e.g. gender, age, race, etc.), or tailored to individual characteristics based on user (or wearer's) measurement information. A time range, for example, an electrocardiogram and a heart sound signal, may be analyzed to specify a time range differently for each person by reflecting an average value and a minimum / maximum value of the R peak and the first peak interval measurements. However, the present invention is not limited thereto.

In an embodiment, the controller 140 may detect the R peak point of the ECG signal within a predetermined time range from the first peak point 1211 of the heart sound signal. For example, referring to FIG. 7, in the ECG signal measured in the time domain of 703 of FIG. 7, the first peak point 1211 of the planting signal is detected at a time of 100 ms and 500 ms, and a predetermined time range (eg, It may be assumed that the time interval l1 between the first peak point 1211 and the point 1213 at which the S4 time interval starts is designated as a time range of 100 ms. The controller 140 detects the voltage peak point 714 and the voltage peak point 716 of the ECG signal measured within the 100 ms time range from the first peak point of the 100 ms and 500 ms time of the heart sound signal as the R peak point of the ECG signal. can do. In another embodiment, the controller 140 may detect the voltage peak point 715 measured between the voltage peak point 714 and the voltage peak point 716 as the voltage peak point generated by the noise.

Referring to FIG. 11, in step 1113, the controller 140 may extract a feature of the ECG signal based on the R peak point of the ECG signal. For example, the controller 140 may extract the distance between the R peak points of the ECG signal, that is, the R-R interval. However, the characteristic of the ECG signal is not limited to the R-R interval. For example, the controller 140 may extract the PR section, the QT section, the length of the ST segment, the TP segment, and the like from the ECG signal.

In operation 1115, the controller 140 may detect arrhythmia using the extracted feature. For example, the controller 140 may generate information about the heart rate based on the extracted feature. In one embodiment, the controller 140 may recognize the presence and absence of arrhythmia based on the generated information. In one embodiment, various algorithms may be used to recognize the presence of arrhythmia and the type of arrhythmia. For example, a Hidden Markov Model based algorithm, a Heart best matrix based algorithm, or the like may be used to recognize arrhythmia and arrhythmia. However, the algorithm used to recognize the presence of arrhythmia and the type of arrhythmia is not limited thereto.

In operation 1117, when the signal-to-noise ratio of the ECG signal exceeds a predetermined threshold value in operation 1109, the controller 140 may detect arrhythmias based only on the ECG signal without using the heart sound signal. For example, the controller 140 may extract a feature such as a distance between R peak points, that is, an R-R interval, of the ECG signal, and generate information about a heart rate based on the extracted feature. The controller 140 may recognize the presence or absence of arrhythmia based on the generated information.

13 is a flowchart illustrating a method of detecting arrhythmia using a heart sound signal according to another embodiment of the present invention.

Referring to FIG. 13, in step 1301, the controller 140 may acquire an electrocardiogram signal and a heart sound signal. In operation 1303, the controller 140 may calculate a signal-to-noise ratio of the ECG signal. In operation 1305, the controller 140 may calculate a signal-to-noise ratio of the heart sound signal. Processes 1301 to 1305 overlap with process 1101 to process 1105 of FIG. 11, and thus a detailed description thereof will be omitted.

In operation 1307, the controller 140 may compare the signal-to-noise ratio of the ECG signal with a specified first threshold value and a second threshold value. Process 1307 overlaps with process 907 of FIG. 9, and thus a detailed description thereof will be omitted.

In operation 1311, when the controller 140 determines that the signal-to-noise ratio of the ECG signal is greater than the first threshold value in operation 1309, the controller 140 may detect arrhythmia based on the ECG signal. Process 1311 overlaps with 1117 of FIG. 11, and thus a detailed description thereof will be omitted.

In step 1315, when the controller 140 determines that the signal-to-noise ratio of the ECG signal is less than or equal to the first threshold value and is greater than the second threshold value, in step 1313, the controller 140 uses an ECG signal using a feature point of the heart sound signal. The R peak point of can be detected. In operation 1317, the controller 140 may extract a feature of the ECG signal based on the detected R peak point. In operation 1319, the controller 140 may detect arrhythmia using the extracted feature. Processes 1315 to 1319 overlap with process 1111 to process 1115 of FIG. 11, and thus detailed description thereof will be omitted.

In operation 1321, when the controller 140 determines that the signal-to-noise ratio of the ECG signal is less than or equal to the second threshold value, the controller 140 may extract a feature of the heart sound signal. The characteristic of the heart sound signal may be a time distance between the first peak point of one period and the first peak point of the next period. The first peak may be a point at which the magnitude of the pressure of the heart sound signal is measured first of the peak points within the S1 time interval. However, the feature extracted from the heart sound signal is not limited thereto. For example, the controller 140 may measure a point at which the vibration ends in any one of S1 to S4 time intervals in which the vibration of the heart sound is measured. The controller 140 may calculate a time distance between the point where the measured vibration ends and the point where the vibration ends in the next cycle.

In operation 1323, the controller 140 may detect arrhythmia using a feature extracted from the heart sound signal. For example, the controller 140 calculates the number of first peak points, or a time interval between the first peak points, of the heart sound signal measured for a predetermined time (for example, one minute), and based on the heart rate and Information including whether the heartbeat interval is abnormal may be calculated.

In one embodiment, the controller 140 may recognize the presence and absence of arrhythmia based on the calculated information. In one embodiment, various algorithms may be used to recognize the presence of arrhythmia and the type of arrhythmia. For example, a Hidden Markov Model based algorithm, a Heart best matrix based algorithm, or the like may be used to recognize arrhythmia and arrhythmia. However, the algorithm used is not limited thereto.

In an embodiment, the process described with reference to FIGS. 4 to 13 may be performed through at least one or more signal processing units other than the controller 140. In another embodiment, the process described with reference to FIGS. 4 to 13 may be implemented through a separate programming module (eg, an application) other than the controller. In one embodiment, the programming module may be stored in the storage 130.

In an embodiment, the controller 140 may store at least one of an electrocardiogram signal, a cardiogram signal, and a heart sound signal in the storage 130. For example, the controller 140 may measure and store at least one of an electrocardiogram signal, an electrocardiogram signal, and a heart sound signal in all time sections of the electrocardiogram signal, in the storage 130. In another example, the controller 140 measures the ECG signal, the ECG signal, and the heart sound signal in a section in which an ECG signal is abnormal (for example, a section in which the signal-to-noise ratio is low) and stores the storage unit 130. Can be stored in In another embodiment, the controller 140 measures at least one of the ECG signal, the ECG signal and the heart sound signal in all time intervals of the ECG signal, and stores the ECG signal in the storage 130 and stores an abnormal time in the ECG signal. You can mark events for a section (for example, mark the point or show section time information). In an exemplary embodiment, arrhythmia may be detected by analyzing the ECG signal stored in the storage 130 and at least one of the ECG signal and the heart sound signal, for example, in a hospital.

In an embodiment, the controller 140 may control the communication unit 110 to transmit a measurement signal to a server (eg, a cloud server) or an external device (eg, a smartphone). For example, the controller 140 may control the communicator 110 to transmit at least one of an electrocardiogram signal, an electrocardiogram signal, and a heart sound signal measured in all time sections of the electrocardiogram signal. In another example, the controller 140 may transmit the ECG signal measured in the section in which the ECG signal is abnormal (for example, a section in which the signal to noise ratio is low) and the at least one of the ECG signal and the heart sound signal. ) Can be controlled. In another embodiment, the control unit 140 transmits at least one of an ECG signal (for example, displaying a corresponding point or displaying section time information, etc.), a measured ECG signal, and a sound sound signal for an abnormal time period. The communication unit 110 may be controlled to perform the control. In one embodiment, the arrhythmia may be detected by analyzing an ECG signal received from the arrhythmia detection apparatus 100, and at least one of a heart rate signal and a heart sound signal at the server or an external device.

According to an embodiment, the controller 140 may analyze the arrhythmias in the arrhythmia measurement apparatus 100 and then mark an event in a section in which arrhythmias are detected. In an embodiment, when there is an abnormality in the ECG signal, the controller 140 may store data about at least one of the ECG signal and the heart sound signal. In another embodiment, the controller 140 may store the ECG signal, the ECG signal, and the data of the heart sound signal in the storage 130 at all time intervals.

14 is a flowchart illustrating a method of outputting an arrhythmia risk notification according to an embodiment of the present invention.

In operation 1401, the controller 140 may obtain biometric information or the like from the sensor unit 120.

For example, the controller 140 may obtain an electrocardiogram signal. In another example, the controller may acquire an electrocardiogram signal and a cardiogram signal or a heart sound signal. In another example, the controller may acquire a heart rate signal or a heart sound signal.

According to an embodiment, the controller 140 may further acquire an ECG signal or a heart ball signal according to the signal-to-noise ratio of the acquired ECG signal.

In operation 1403, the controller 140 may detect arrhythmia based on the obtained biometric information.

In an embodiment, the controller 140 may extract a feature point (or feature section) from at least one of an electrocardiogram signal, a ballistic trajectory signal, and a heart sound signal to detect arrhythmia based on the obtained biometric information.

In one embodiment, the controller 140 may calculate the blood pressure, etc. based on the obtained biometric information. For example, the controller 140 may extract the R peak point from the ECG signal and the J peak point from the ECG signal. The controller 140 may extract a time interval between the R peak point and the J peak point, that is, the R-J interval, based on the R peak point and the J peak point. The controller 140 may calculate the blood pressure by using the calculated R-J interval and linear regression analysis. However, the method of calculating the blood pressure is not limited thereto.

In one embodiment, the controller 140 may detect arrhythmia using a feature extracted from an ECG signal or the like. For example, the controller 140 may generate information about the heart rate based on the extracted feature. In one embodiment, the controller 140 may recognize the presence and absence of arrhythmia based on the generated information. In one embodiment, various algorithms may be used to recognize the presence of arrhythmia and the type of arrhythmia. For example, a Hidden Markov Model based algorithm, a Heart best matrix based algorithm, or the like may be used to recognize arrhythmia and arrhythmia. However, the algorithm used to recognize the presence of arrhythmia and the type of arrhythmia is not limited thereto.

In operation 1405, the controller 140 may output an arrhythmia risk. For example, when the arrhythmia is detected, the controller 140 may output a notification regarding the risk of arrhythmia.

In one embodiment, the arrhythmia risk may be defined as the degree of risk, for example LOW / HIGH.

In one embodiment, when the arrhythmias are detected and the calculated blood pressure corresponds to hypertension, the controller 140 may output a high risk of arrhythmia. For example, atrial fibrillation (a type of arrhythmia) may induce a blood clot to increase the risk of cerebral infarction, and hypertension may be the cause of atrial fibrillation. The control unit may output a high risk when arrhythmias and hypertension symptoms are expressed together.

In one embodiment, the blood pressure may be calculated (or measured) in the arrhythmia measurement apparatus 100, as described in process 1403. In another embodiment, the blood pressure may be calculated at an external device, such as a wearable device (eg, a smart watch) or a blood pressure monitor. In another example, blood pressure may be received from outside. For example, information about blood pressure may be received from the cloud. For example, information about blood pressure measured externally, such as a hospital, may be received by the user's mobile terminal or arrhythmia detection apparatus 100 through a personal health record (PHR) server connected to the user's portable terminal. Can be. When receiving the externally measured blood pressure, the controller may compare the blood pressure calculated in the arrhythmia detection apparatus 100 and correct the weight in the calculation formula to be close to the actual blood pressure.

In one embodiment, the control unit 140 may have a high stress level other than blood pressure, severe exercise, high tissue hydration (e.g., edema in the body due to abnormal heart function), or low temperature (e.g., low temperature). Risk notification may be output using information such as the generation of blood clots).

In one embodiment, the controller 140 may control the communication unit to transmit information on the risk of arrhythmia to an external device. For example, the controller 140 may control the communication unit 110 to transmit information about the calculated blood pressure, the presence or absence of arrhythmia, or the type of arrhythmia to an external device (eg, a smartphone, a tablet, etc.). In one embodiment, the external device receiving the information on the arrhythmia risk may output the arrhythmia risk.

In another embodiment, the controller 140 may control the configuration to output the arrhythmia risk through at least one of voice notification, LED blink / color, display, and vibration in the arrhythmia detection device 100 itself. For example, the arrhythmia detection apparatus 100 may further include an audio output unit, an optical output unit (eg, an LED, etc.), a display, or a haptic module to output an arrhythmia risk.

FIG. 15 is an exemplary diagram of an arrhythmia risk output from an external electronic device and a notification for reducing an arrhythmia risk according to an embodiment of the present disclosure.

Referring to FIG. 15, in an embodiment, the external electronic device 1501 may receive data on arrhythmia detection from the arrhythmia detection apparatus 100. In another embodiment, the external electronic device 1501 may receive data from the arrhythmia detection apparatus 100 on the risk of arrhythmia and a notification for reducing the arrhythmia risk.

In an embodiment, when the external electronic device 1501 confirms the arrhythmia detection and blood pressure abnormality based on the data on the received arrhythmia detection, as shown in FIG. Arrhythmia detected. Your blood pressure is above the normal threshold. I recommend dieting and exercising. " In another embodiment, if the external electronic device 1501 detects that it is exercising through an acceleration sensor or if it is determined that the stress level is high, "arrhythmia is detected. Blood pressure is above the normal reference value. Take a deep breath and rest. Coaching can be provided to help lower blood pressure and reduce arrhythmias as appropriate.

16 is a flowchart illustrating a method of storing an individual arrhythmia causing factor according to an embodiment of the present invention.

Referring to FIG. 16, in FIG. 1601, the controller 140 may acquire biometric information and external environment information when detecting arrhythmia. Alternatively, the controller 140 may analyze the acquired biological information and external environment information when detecting arrhythmia. For example, the controller 140 may detect arrhythmia based on biometric information (eg, an electrocardiogram signal, a cardiograph signal, a heart sound signal, etc.). When the controller 140 detects arrhythmia, the controller 140 may acquire biometric information (eg, stress level, sleep state, etc.), external environment information (eg, temperature, humidity, etc.).

For example, the controller 140 may obtain information about the amount of caffeine in the body from a spectrophotometer. In another example, the controller 140 may obtain information about alcohol or blood concentration in the breath from the breathalyzer. In another example, the controller 140 may obtain information about the amount of carbon monoxide in the breath from the smoking measurement sensor. In another example, the controller 140 may obtain information on the stress level based on the ECG signal obtained from the ECG sensor 121. In another example, the controller 140 may obtain information about the sleep state of the user from the acceleration sensor 127, the electrocardiogram sensor 121, the photoplethysmography sensor, and the like. For example, the controller 140 obtains information on the sleep posture from the acceleration sensor 127, obtains information about sleep quality from the electrocardiogram sensor 121, and sleep efficiency from the photoblood flow measurement sensor. Obtain information about.

In one embodiment, the controller 140 may calculate the blood pressure using the obtained electrocardiogram signal and the ballistic ballistic signal. For example, the controller 140 may extract the R peak point from the ECG signal and the J peak point from the ECG signal. The controller 140 may extract a time interval between the R peak point and the J peak point, that is, the R-J interval, based on the R peak point and the J peak point. The controller 140 may calculate the blood pressure by using the calculated R-J interval and linear regression analysis. However, the method of calculating the blood pressure is not limited thereto.

In one embodiment, the controller 140 may obtain information on the external environment information through the sensor unit 120. For example, the controller 140 may obtain information about a user's movement, posture, etc. from the acceleration sensor 127. In another example, the controller 140 may acquire information about a temperature of an external environment and a temperature of a user's body through a temperature sensor. In another example, the controller 140 may obtain information about the humidity of the external environment and the hydration of the user's body tissue.

However, the biometric information and the external environment information obtained by the arrhythmia detection apparatus 100 are not limited to the examples listed above.

In one embodiment, the controller 140 may receive biometric information and external environment information measured from the outside. For example, the controller 140 may receive biometric information and external environment information from an external device or a cloud server.

In an embodiment, the external device (or server) may measure (or sense) biometric information and external environmental information in various ways. For example, an external device (eg, a mobile terminal) may store and manage the collected user's life log information in the external device, or may be integratedly managed by a server such as S-health.

In the case of caffeine, for example, the external device may receive information about caffeine from an IoT device such as a smart cup that analyzes the components of the internal beverage.

In another example, the external device may extract payment details related to caffeine-containing foods such as coffee, tea, chocolate, etc., for example, at a credit card payment character. When the caffeine-containing food payment details are extracted, the external device may receive information (eg, caffeine amount) of caffeine of the caffeine-containing food from which the payment details are extracted from a server operated by the food company. In another example, when the caffeine-containing food payment details are extracted, the external device outputs a questionnaire on whether the user consumes the caffeine-containing food, for example, in the form of a pop-up window or a push notification and induces the user's input. Collect information

In another example, the external device outputs a questionnaire regarding the intake of caffeine-containing foods such as coffee / tea when receiving information from the beacons in the coffee shop or when there is a visit to the coffee shop by GPS information. By deriving input, we can collect information about caffeine.

In another example, the external device may collect information about caffeine by receiving an input from a user about whether a caffeine-containing food such as coffee / tea is ingested.

In the case of drinking, for example, the external device may receive information about drinking from an IoT device such as a smart cup that analyzes the components of the internal beverage.

In another example, the external device may extract payment details related to alcohol-containing goods, such as alcohol in a credit card payment character, for example, a restaurant, a convenience store, and the like. When the alcohol-containing product payment details are extracted, the external device may receive information on the alcohol of the alcohol-containing product from which the product details have been extracted from a server operated by the corresponding product company. In another example, when the alcohol-containing product payment history is extracted, the external device outputs a questionnaire on whether the alcohol-containing product is ingested, for example, in the form of a pop-up window or a push notification and induces the user's input. Information about (or drinking) can be collected.

In another example, the external device outputs a questionnaire regarding the intake of alcohol-containing alcohol such as alcohol when receiving information from a beacon in a restaurant or a liquor store such as a pub, or when there is a visit to the liquor store by GPS information. And by inducing the user input can collect information about alcohol (or drinking).

In another example, the external device may collect information about the alcohol by receiving an input from the user about whether the alcohol-containing food such as alcohol is ingested.

In another embodiment, the external device may receive information on alcohol (or alcohol) from an external device-linked alcohol measurement device (or an alcohol measurement sensor in the external device).

In the case of blood pressure, the external device may receive information about blood pressure from another external device when the blood pressure is measured by another external device such as a blood pressure monitor. For example, the external device may receive information about the blood pressure from a personal blood pressure monitor or a publicly available blood pressure monitor device (eg, a blood pressure monitor device provided in an institution / hospital or the like). In another example, the external device may download information about blood pressure from a personal health record (PHR) server associated with the hospital's medical record. In an embodiment, when the external device receives the information on the blood pressure from another external device or the server, the information on the received blood pressure and the calculated blood pressure to increase the accuracy of the blood pressure estimate calculation calculated from the arrhythmia measurement apparatus 100 By comparing the estimated blood pressure estimates and adjusting the factors such as scale factors, the accuracy of the calculated blood pressure estimates can be increased.

In the case of stress, in one embodiment, the external device may determine the stress level by measuring / analyzing heart variability using a heart rate sensor or the like.

In the sleep state, for example, the sleep state may be tracked (or measured) by an external device (eg, a wearable device). In another example, the external device may receive information about the sleep state measured using a pressure sensor, an RF sensor, or the like of another external device (eg, a bed). In one embodiment, the sleep may be reversed in relation to the two autonomic nerves in the parasympathetic nerve that was activated during sleep, the body wakes up and the sympathetic nerve is advanced when waking from sleep. Thus, arrhythmia may occur frequently in the early morning when the function of the sympathetic nerve is activated most abruptly.

In the case of the external temperature, it may be measured by an external device (eg, a mobile terminal) or measured by a server and received by the external device. In one embodiment, cold weather may increase the risk of arrhythmia, blood vessels are constricted, blood vessels may be blocked by blood clots or the like, and blood pressure may be elevated.

In FIG. 1603, the controller 140 may determine an arrhythmia causing factor based on the obtained biometric information and external environmental information. For example, the controller 140 may check information about caffeine intake (or intake), alcohol concentration (or drinking), carbon monoxide, stress level, user's sleep state, external temperature, and blood pressure size. have. The controller 140 may determine the specific factor measured (or detected) before or after a predetermined time from the arrhythmia generation time as the arrhythmia causing factor among the checked information. For example, the controller 140 may determine caffeine as an arrhythmia inducing factor when the amount of caffeine intake is measured to be higher than a reference value. In another example, when the measured alcohol concentration is measured to be greater than or equal to the reference value, the controller 140 may determine the alcohol (or drinking) as the arrhythmia causing factor.

In one embodiment, the process of determining the arrhythmia inducing factor may be performed in an external device. For example, the external device may receive biometric information obtained from the arrhythmia detection apparatus 100 from the arrhythmia detection apparatus 100. For example, the external device may receive from the arrhythmia detection apparatus 100 biometric information such as an electrocardiogram signal, a ballistics signal and / or a heart sound signal, information on the presence or absence of the detected arrhythmia or the type of arrhythmia, and external environmental information. Can be. The external device may determine the arrhythmia causing factor based on the received biometric information.

In one embodiment, an external device may determine (or estimate) the arrhythmia causing factor. When the external device determines the arrhythmia causing factor, the external device may transmit information on the determined cause to the arrhythmia detection apparatus 100. For example, when an external device detects arrhythmia, the external device may indicate that the user is currently taking (e.g., caffeine ingestion, or vigorous exercise), or that the user's current biological state is above a threshold (e.g., high stress levels, high blood pressure, Arrhythmia triggers can be determined either by waking up from sleep, or by outside environmental conditions (such as low temperatures). In another embodiment, the external device detects arrhythmia when the user's behavior accumulated over a period of time (eg, after a day or after waking up) is above a threshold (eg, the amount of caffeine, alcohol, or smoking consumed during the day). Etc.), one or more of the above-mentioned thresholds (eg, the number of stress occurrences per day, the number of hypertensions per day, etc.) may be determined as an arrhythmia inducing factor.

In one embodiment, the control unit 140 may determine a factor determined to be highly correlated with arrhythmia detection for a predetermined period (eg, one month) as an arrhythmia inducing factor. For example, when arrhythmias are detected for a certain period of time, when the stress level is abnormally measured many times, the controller 140 may determine the stress as an arrhythmia inducing factor.

In operation 1605, the controller 140 may store the arrhythmia causing factor in the storage 130. For example, the controller 140 may store the arrhythmia causing factor in the storage 130 for each user.

In one embodiment, the controller 140 may transmit the arrhythmia causing factor to an external device. When the external device receives the arrhythmia inducing factor, the external device may store the arrhythmia inducing factor.

In another embodiment, when an arrhythmia trigger is determined at the external device, the external device may store the determined arrhythmia trigger. The external device may transmit information on the determined arrhythmia inducing factor to the arrhythmia detection apparatus 100 or the server.

In another embodiment, when the arrhythmia inducing factor is determined in the server or the like, the server or the like may store the determined arrhythmia inducing factor. The server or the like may transmit information on the determined arrhythmia inducing factor to the arrhythmia detection apparatus 100 or an external device.

17 is a flowchart illustrating a method of notifying arrhythmia risk and arrhythmia reduction according to an embodiment of the present invention. The method described in FIG. 17 may be executed in the arrhythmia detection device 100 or an external electronic device (eg, a wearable device). However, for convenience of explanation, it is assumed that it is executed in the external electronic device.

In operation 1701, the external electronic device may acquire biometric information or external environment information. For example, the external electronic device uses the sensor unit 120 to detect biometric information of the user (for example, the amount of caffeine, alcohol concentration, carbon monoxide, stress level, sleep state of the user, and blood pressure measured in the body). Information) can be obtained. In another example, the external electronic device may acquire movement information of the user through the acceleration sensor 127, external temperature information through the temperature sensor, and external humidity information through the humidity sensor.

In operation 1703, the external electronic device may determine whether the arrhythmia causing factor stored in the storage 130 is abnormally measured based on the obtained biometric information or external environment information. For example, the external electronic device may check whether the acquired biometric information or the external environment information is an arrhythmia causing factor stored in the storage 130. In an embodiment, as described above with reference to FIG. 16, the arrhythmia inducing factor may correspond to a factor highly correlated with the occurrence of arrhythmia for the user. When the external electronic device determines that the acquired biometric information or the external environmental information corresponds to an arrhythmia causing factor, the external electronic device may determine whether the biological information or the external environmental information is abnormally measured. For example, when the external electronic device determines that caffeine corresponds to an arrhythmia-inducing factor, the external electronic device may check whether the amount of caffeine is measured above a reference value.

In operation 1705, the external electronic device may output a risk notification of arrhythmia. For example, as the amount of caffeine having a high correlation with the occurrence of arrhythmia is measured, the external electronic device may output a high possibility of occurrence of arrhythmia.

In one embodiment, the external electronic device may output light, voice, or vibration to inform the risk of arrhythmia.

In operation 1707, the external electronic device may output a notification of the arrhythmia reduction method. For example, when the arrhythmia inducing factor is caffeine, the external electronic device may output a phrase indicating that the intake of caffeine is reduced in order to reduce arrhythmia.

In one embodiment, in one embodiment, the external electronic device may output light, voice, or vibration to inform the arrhythmia reduction method.

18 is an exemplary diagram for arrhythmia risk and arrhythmia reduction notification according to an embodiment of the present invention.

Referring to FIG. 18, when the arrhythmia inducing factor is caffeine, the external electronic device 1801 may output a phrase indicating a risk of arrhythmia, such as, "When caffeine is ingested, arrhythmia is high." The external electronic device 1801 may output a notification 1803 such as "Please consume less caffeine" to reduce the risk of arrhythmia. In another example, the external electronic device 1801 ate two cups of coffee today. OOO recommends taking up to two cups of coffee per day because caffeine is more likely to cause arrhythmias. ”You can provide notifications based on your life log cumulative information.

19 is a flowchart illustrating a method of outputting a reattachment notification when there is an error in attachment of the arrhythmia detection apparatus 100 according to an embodiment of the present invention.

Referring to FIG. 19, in operation 1901, the controller 140 may acquire an ECG signal from the ECG sensor 121.

In step 1903, the controller 140 may check whether there is an error in the attachment of the arrhythmia detection apparatus 100. In one embodiment, the control unit 140 may determine whether the arrhythmia detection apparatus 100 has an error by measuring the waveform of the acquired ECG signal and analyzing the measured waveform. In another embodiment, the controller 140 may detect an attachment angle of the arrhythmia detection apparatus 100 based on the obtained acceleration signal, and the controller 140 may patch when the detected attachment angle is out of a preset range. It can be determined that there is an error in the state of attachment of. In another embodiment, the controller 140 may acquire an electrocardiogram signal and analyze the signal to noise ratio. The controller 140 may determine that there is an error in the patch attachment state, such as when some electrodes are not in close contact with each other when the signal-to-noise ratio is continuously below a predetermined threshold value (that is, when there is a lot of noise) for a predetermined time. When there is an error in the attachment state of the arrhythmia detection apparatus 100, the controller 140 may output a notification so that the patch attachment state may be confirmed or transmit the notification to an external device through the communication unit. In another embodiment, the controller 140 may output a notification for guiding the arrhythmia detection apparatus 100 to be attached at an angle at which the correct waveform comes out, or transmit the notification to an external device through the communication unit. For details, see FIG. 20 below.

20 is an exemplary diagram of an ECG signal measured when there is an error in attachment of the arrhythmia detection apparatus 100 according to an embodiment of the present invention.

Referring to FIG. 20, in one embodiment, 2001 may correspond to a case where the arrhythmia detection apparatus 100 is normally attached to a user without an attachment error. For example, as shown in 2001, when the arrhythmia detection apparatus 100 is attached to the user's body in a direction parallel to the gravity acceleration direction, the ECG signal may be measured normally. For example, the controller 140 may set a direction parallel to the gravity acceleration direction as the reference direction. When the arrhythmia detection apparatus 100 is attached to the body in the reference direction, the ECG signal waveform without distortion may be measured.

2003 illustrates the arrhythmia detection apparatus 100 in a state rotated to the left by a roll angle Ø from the direction of the arrhythmia detection apparatus 100 of 2001, and the measured ECG signal. In one embodiment, the roll angle at which the arrhythmia detection device 100 is rotated may be measured by the acceleration sensor 127.

2005 illustrates the arrhythmia detection apparatus 100 in a state rotated to the right by the roll angle Ø from the direction of the arrhythmia detection apparatus 100 of 2001, and the measured ECG signal.

Comparing 2001 to 2005, it can be seen that the ECG signal waveforms in 2003 and 2005 are distorted compared to the ECG signal waveforms of 2001. For example, compared to 2001 and 2003, the size of the R peak point is smaller in 2003 than in 2001, and in the case of the T wave, the sign is inverted and measured. In another example, comparing 2001 and 2005, the size of the Q point can be measured smaller in 2005 than in 2001.

In one embodiment, the control unit 140 may designate the ECG signal waveform measured without distortion as a reference ECG signal, and confirm the attachment error of the arrhythmia detection apparatus 100 by comparing with the obtained ECG signal. For example, the controller 140 may designate the ECG signal waveform shown in 2001 as the reference ECG signal waveform. The controller 140 may compare the signal waveforms between the ECG signal waveform and the reference ECG signal waveform obtained in 2003 to confirm the attachment error of the arrhythmia detection apparatus 100.

In step 1905, when the controller 140 confirms an attachment error of the arrhythmia detection apparatus 100, the controller 140 controls the display unit 140 to output a notification for reattachment of the arrhythmia detection apparatus 100. can do.

In an embodiment, the controller 140 may detect an angle (eg, a roll angle Ø) and use the ECG process. For example, the controller 140 may store information on the ECG waveform pattern at a corresponding angle. For example, as shown in 2005, the controller 140 may store information about a state in which the R peak is smaller and a state in which the R peak is rotated to the right by the roll angle Ø from the direction of the arrhythmia detection apparatus 100. The controller 140 may adjust the peak detection threshold lower when using the R-peak detection algorithm. In another example, when detecting that the electrode is inverted and attached through the acceleration signal, the controller 140 may change the signal channel for each electrode and process the signal. This prevents the ECG waveform from being inverted and distorted.

21 is an exemplary diagram for describing a method of outputting a reattachment notification when there is an error in attachment of the arrhythmia detection apparatus 100 according to an embodiment of the present invention.

Referring to FIG. 21, when there is an error in attachment of the arrhythmia detection apparatus 100 in 2101, the arrhythmia detection apparatus 100 may transmit information about an attachment error to the external electronic device 2111. In an embodiment, when the information on the attachment error of the arrhythmia detection apparatus 100 is received, the external electronic device may output a reattachment notification such as “Please reattach it!”. In another embodiment, when the information on the attachment error of the arrhythmia detection apparatus 100 is received, the external electronic device may output a notification such as "Please turn the device 3 degrees!".

In 2103, when the arrhythmia detection apparatus 100 is normally attached without an error, the arrhythmia detection apparatus 100 may transmit information about the normal attachment to the external electronic device. In one embodiment, when the information on the normal attachment of the arrhythmia detection device 100 is received, the external electronic device may output a notification indicating that the arrhythmia detection device 100 is normally attached, such as "during normal measurement!" .

As such, the method for detecting arrhythmia and the arrhythmia occurring to a user who supports the same may be accurately measured according to various embodiments of the present disclosure.

Meanwhile, the above-described embodiments of the present invention can be written as a program that can be executed in a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium. In addition, the structure of the data used in the above-described embodiment of the present invention can be recorded on the computer-readable recording medium through various means. The computer readable recording medium may include a storage medium such as a magnetic storage medium (eg, a ROM, a floppy disk, a hard disk, etc.), an optical reading medium (eg, a CD-ROM, a DVD, etc.).

So far I looked at the center of the preferred embodiment for the present invention. Those skilled in the art will appreciate that the present invention can be implemented in a modified form without departing from the essential features of the present invention. Therefore, the disclosed embodiments should be considered in descriptive sense only and not for purposes of limitation. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the scope will be construed as being included in the present invention.

* Description of the sign *

100: arrhythmia detection device

110: communication unit

120: sensor

130: storage unit

140: control unit

Claims (15)

1. In the method for detecting arrhythmia,

   Obtaining an electrocardiogram signal;

   Calculating a signal-to-noise ratio of the obtained electrocardiogram signal; And

   Determining whether to use another biosignal according to the calculated signal-to-noise ratio.
2. The method of claim 1,

   Wherein said other biometric signal comprises at least one of a ballistics signal and a heart sound signal.
3. The method of claim 2,

   According to the calculated signal-to-noise ratio, determining whether to use the other biosignal includes: comparing the calculated signal-to-noise ratio and a first predetermined threshold value;

   Detecting a feature point of the ECG signal using the other biosignal when the calculated signal to noise ratio is less than or equal to the specified first threshold value; And

   And determining whether arrhythmia is generated based on the feature point of the detected ECG signal.
4. The method of claim 3, wherein

   Detecting a feature point of the ECG signal using the other biosignal,

   Detecting feature points of the other biosignal; And

   Detecting an R peak point of the ECG signal by using the detected feature point of the other biosignal;

   The process of detecting the R peak point of the ECG signal,

   Designating a predetermined time range from the detected feature points of the other biosignals; And

   Detecting the R peak point using the ECG signal within the predetermined time range from the detected feature point of the other biosignal.
5. The method of claim 4, wherein

   The detected feature point of the other biosignal is

   And at least one of a peak point corresponding to a J peak point of the heart ball signal and a ventricular systolic point of the heart sound signal.
6. The method of claim 2,

   According to the calculated signal to noise ratio, determining whether to use the other bio-signal,

   Comparing the calculated signal to noise ratio and a designated second threshold value; And

   If the calculated signal-to-noise ratio is less than or equal to the specified second threshold, determining whether arrhythmia is generated based on the other biosignal instead of the electrocardiogram signal.
7. The method of claim 1,

   The other biosignal is a cardiogram signal,

   Determining arrhythmia generation based on at least one of the ECG signal and the ECG signal;

   Calculating blood pressure based on the ECG signal and the ECG signal;

   Determining an arrhythmia risk based on the occurrence of the arrhythmia and the calculated blood pressure, and transmitting notification information to an external device according to the determined arrhythmia risk.
8. The method of claim 1,

   Determining an item that causes arrhythmia from a user's biometric information or external environment information out of a predetermined range when detecting arrhythmia; And

   And storing the factor causing the determined arrhythmia.
9. The method of claim 8,

   The item includes at least one of blood pressure, stress, temperature, caffeine intake, alcohol consumption, smoking amount, sleep state.
10. The method of claim 1,

    Determining an incidence of arrhythmia as a factor that causes arrhythmia by comparing the occurrence frequency or cumulative amount of the item out of a predetermined range of the user's biometric information or external environment information with a threshold value when detecting arrhythmia; And

    And storing the factor causing the determined arrhythmia.
11. The method of claim 10,

    Measuring biometric information or external environment information of the current user; And

    If at least some of the items included in the measured biometric information or external environment information of the current user corresponds to a factor causing the arrhythmia and is out of a predetermined range, transmitting the notification information on the risk of arrhythmia to the external device How to include more.
12. In the arrhythmia detection device,

    A sensor unit obtaining an electrocardiogram signal; And

    Including a control unit,

    The control unit,

    Calculating a signal-to-noise ratio of the obtained ECG signal,

    And an arrhythmia detection device according to the calculated signal to noise ratio.
13. The method of claim 12,

    The other bio-signal is at least one of a cardiac ballistic signal and a heart sound signal.
14. The method of claim 12,

    Further comprising an acceleration sensor for sensing the angle to which the arrhythmia detection device is attached,

    The control unit,

    An arrhythmia detection device that detects an attachment error of the arrhythmia detection device based on the information on the sensed angle from the acceleration sensor, and transmits a reattachment notification of the arrhythmia detection device to an external device when the attachment error is detected. .
15. In the method for detecting the arrhythmia,

    Obtaining an electrocardiogram signal;

    Obtaining at least one of a heart rate signal or a heart sound signal; And

    Using the obtained electrocardiogram signal and the cardiogram signal and / or the heart sound signal, or detecting arrhythmia with the cardiogram signal and / or the heart sound signal.

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