Patent Application
20230240543
The present application claims priority from Japanese Patent Application No. 2022-011855, filed on Jan. 28, 2022, the entire content of which is incorporated herein by reference.
The present disclosure relates to a physiological information processing method and a physiological information processing apparatus configured to evaluate a respiratory muscle activity amount of a subject. Further, the present disclosure relates to a computer-readable storage medium that stores a program for causing a computer to execute the information processing method.
JPH06-30908A discloses a physiological information processing method for acquiring an electromyogram signal indicating a respiratory muscle activity amount based on an electrocardiogram signal using a band-pass filter functioning as a low-cut filter. In general, a signal indicating an electrocardiogram waveform is obtained at a low frequency component of an electrocardiogram signal obtained from an electrocardiogram sensor, and an electromyogram signal is obtained as a noise signal at a high frequency component of the electrocardiogram signal.
Since a frequency band of the electromyogram signal actually extends from a low frequency band to a high frequency band, the frequency band of the electromyogram signal and a frequency band of the electrocardiogram signal partially overlap each other. Therefore, when the electromyogram signal is acquired based on the electrocardiogram signal using the band-pass filter, the low frequency component of the electromyogram signal is removed. As a result, a respiratory muscle activity amount of a subject is evaluated based on only the high frequency component of the electromyogram signal, and thus there is a limit to accuracy of the evaluation of the respiratory muscle activity amount. From the above viewpoint, there is room for examination on a method capable of evaluating the respiratory muscle activity amount of a subject with higher accuracy.
An object of the present disclosure is to provide a physiological information processing method and a physiological information processing apparatus that are capable of evaluating a respiratory muscle activity amount of a subject with higher accuracy. Another object of the present disclosure is to provide a computer-readable storage medium that stores a program for causing a computer to execute the information processing method.
According to an aspect of the present disclosure, there is provided a physiological information processing method executed by a computer. The physiological information processing method includes a step of acquiring, from a plurality of electrodes attached to a body surface of a subject, a plurality of electrocardiogram signals indicating an electrocardiogram waveform of the subject, a step of separating, through independent component analysis, each of the plurality of electrocardiogram signals into one or more first waveform components indicating the electrocardiogram waveform and one or more second waveform components indicating a waveform other than the electrocardiogram waveform, a step of acquiring an activity evaluation index indicating a respiratory muscle activity amount of the subject based on an electrocardiogram signal that is obtained from a predetermined electrode among the plurality of electrodes attached to the body surface of the subject and from which the one or more first waveform components have been removed, and a step of outputting information on the activity evaluation index.
According to another aspect of the present disclosure, there is provided a physiological information processing apparatus including one or more processors, and one or more memories configured to store a computer readable command. When the computer readable command is executed by the processor, the physiological information processing apparatus executes a step of acquiring, from a plurality of electrodes attached to a body surface of a subject, a plurality of electrocardiogram signals indicating an electrocardiogram waveform of the subject, a step of separating, through independent component analysis, each of the plurality of electrocardiogram signals into one or more first waveform components indicating the electrocardiogram waveform and one or more second waveform components indicating a waveform other than the electrocardiogram waveform, a step of acquiring an activity evaluation index indicating a respiratory muscle activity amount of the subject based on an electrocardiogram signal that is obtained from a predetermined electrode among the plurality of electrodes attached to the body surface of the subject and from which the one or more first waveform components have been removed, and a step of outputting information on the activity evaluation index.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium that stores a program. The program causes a computer to execute a step of acquiring, from a plurality of electrodes attached to a body surface of a subject, a plurality of electrocardiogram signals indicating an electrocardiogram waveform of the subject, a step of separating, through independent component analysis, each of the plurality of electrocardiogram signals into one or more first waveform components indicating the electrocardiogram waveform and one or more second waveform components indicating a waveform other than the electrocardiogram waveform, a step of acquiring an activity evaluation index indicating a respiratory muscle activity amount of the subject based on an electrocardiogram signal that is obtained from a predetermined electrode among the plurality of electrodes attached to the body surface of the subject and from which the one or more first waveform components have been removed, and a step of outputting information on the activity evaluation index.
Hereinafter, the present embodiment will be described with reference to the drawings.
The processing apparatus 1 may be a medical instrument (for example, a patient monitor) that displays physiological information on a subject P, a personal computer, a workstation, a smartphone, a tablet, or a wearable device (for example, an AR glass) worn on a body (for example, an arm or a head) of a medical worker. The processing apparatus 1 is configured to output information on an activity evaluation index indicating a respiratory muscle activity amount of the subject P.
The controller 2 includes one or more processors and one or more memories. The memory is configured to store a computer readable command (a program). The memory can include, for example, a read only memory (ROM) that stores various programs and the like, and a random access memory (RAM) having a plurality of work areas in which various programs and the like to be executed by the processor are stored. The processor includes, for example, at least one of a central processing unit (CPU), a micro processing unit (MPU), and a graphics processing unit (GPU). The CPU may include a plurality of CPU cores. The GPU may include a plurality of GPU cores. The processor may load a designated program from various programs provided in the storage device 3 or the ROM onto the RAM and execute various types of processing in cooperation with the RAM. In particular, the processor loads a physiological information processing program for executing a series of pieces of processing illustrated in
The storage device 3 is, for example, a storage device (a storage) such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory, and is configured to store a program and various types of data. The physiological information processing program may be provided in the storage device 3. Further, physiological information data (electrocardiogram data or the like) indicating physiological information on the subject P may be stored in the storage device 3. For example, the electrocardiogram data acquired from an electrocardiogram sensor 10 may be stored in the storage device 3 via the sensor interface 8.
The communication unit 5 is configured to connect the processing apparatus 1 to an in-hospital network. Specifically, the communication unit 5 may include various wired connection terminals that communicate with a central monitor or a server provided in the in-hospital network. The communication unit 5 may further include a wireless communication module that performs wireless communication with the central monitor or the server. The communication unit 5 may include, for example, a wireless communication module corresponding to a medical telemeter system. The communication unit 5 may include a wireless communication module corresponding to a wireless communication standard such as Wi-Fi (registered trademark) or Bluetooth (registered trademark) and/or a wireless communication module corresponding to a mobile communication system using a SIM. The in-hospital network may be, for example, a local area network (LAN) or a wide area network (WAN). The processing apparatus 1 may be connected to the Internet via the in-hospital network.
The display 4 is configured to display physiological information (for example, information related to an activity evaluation index) of the subject P, and is, for example, a liquid crystal panel or an organic EL panel. The input operation unit 6 is, for example, a touch panel overlapping the display 4, a mouse, and/or a keyboard. The input operation unit 6 is configured to receive an input operation performed by the medical worker and to generate an operation signal corresponding to the input operation performed by the medical worker. After the operation signal generated by the input operation unit 6 has been transmitted to the controller 2 via the bus 14, the controller 2 executes a predetermined operation according to the operation signal. The audio output unit 7 includes one or more speakers.
The sensor interface 8 is an interface configured to connect the electrocardiogram sensor 10 to the processing apparatus 1. The sensor interface 8 may include an input terminal to which an electrocardiogram signal output from the electrocardiogram sensor 10 is input. The electrocardiogram sensor 10 is configured to acquire an electrocardiogram signal indicating an electrical activity (an electrocardiogram waveform) of a heart of the subject P, and can include a plurality of electrodes attached to a body surface of the subject P. In this example, the electrocardiogram sensor 10 can be applied to a 12-lead electrocardiogram examination. Therefore, the electrocardiogram sensor 10 can include four electrodes (an example of a second electrode) associated with extremity leads and six electrodes (an example of a first electrode) associated with a chest lead.
As illustrated in
As illustrated in
The sensor interface 8 can include at least a plurality of differential amplifier circuits and an AD converter. Each of the plurality of differential amplifier circuits is configured to amplify an electrocardiogram signal output from a corresponding lead electrode. The AD converter is configured to convert an electrocardiogram signal from an analog signal to a digital signal. The electrocardiogram signal converted into the digital signal is transmitted from the sensor interface 8 to the controller 2.
In the present embodiment, the processing apparatus 1 can acquire 12 types of electrocardiogram signals through the 12-lead electrocardiogram examination. In particular, the processing apparatus 1 can acquire three electrocardiogram signals associated with standard limb leads, three electrocardiogram signals associated with unipolar limb leads, and six electrocardiogram signals associated with chest leads. As illustrated in
Next, a physiological information processing method according to the present embodiment will be described below with reference to
As illustrated in
In step S2, the controller 2 separates, by independent component analysis (ICA), a waveform component of each electrocardiogram signal into a waveform component indicating an electrocardiogram waveform and a waveform component indicating a waveform other than the electrocardiogram waveform. The independent component analysis is a calculation method for separating multivariate signals into a plurality of additive components. In the present embodiment, since the 12 types of electrocardiogram signals are simultaneously acquired by the 12-lead electrocardiogram examination, each of the 12 types of electrocardiogram signals can be separated into 12 types of waveform components by the independent component analysis.
When an electrocardiogram signal vector x(t) including the 12 types of electrocardiogram signals is x(t) = (x1(t), ... s12(t))T, and a waveform component vector s(t) including the 12 types of waveform components is (s1(t), ... s12(t))T, relation between x(t) and s(t) in the independent component analysis is expressed as x(t) = As(t). Here, A is a coefficient matrix formed by 12 rows x 12 columns.
Next, the controller 2 classifies the 12 types of waveform components into an electrocardiogram waveform component and a noise waveform component in each electrocardiogram signal. In this regard, the controller 2 can classify the 12 types of waveform components into the electrocardiogram waveform component and the noise waveform component by comparing an electrocardiogram waveform (hereinafter referred to as a reference electrocardiogram waveform) serving as a reference with each waveform component. For example, when a correlation coefficient between a predetermined waveform component and the reference electrocardiogram waveform is large, the predetermined waveform component may be classified as the electrocardiogram waveform component. On the other hand, when the correlation coefficient between the predetermined waveform component and the reference electrocardiogram waveform is small, the predetermined waveform component may be classified as the noise waveform component.
Next, in step S3, the controller 2 generates, based on only the noise waveform component indicating waveforms other than the electrocardiogram waveform, 12 types of electrocardiogram signals from which electrocardiogram waveform components have been removed. As described above, each of the 12 types of electrocardiogram signals includes a plurality of electrocardiogram waveform components and a plurality of noise waveform components. Therefore, the controller 2 generates 12 types of electrocardiogram signals each including only a plurality of noise waveform components such that the electrocardiogram waveform components are removed in each electrocardiogram signal.
For example, when the electrocardiogram signal x1 associated with the V1 lead is expressed by x1 = a11s1 + a12s2 + ... a112s12, and s1, S2, and S3 are electrocardiogram waveform components, the electrocardiogram signal x1′ from which the electrocardiogram waveform components have been removed is expressed as x1′ = a14S4 + a15S5 + ... a112S12. In this way, the electrocardiogram waveform component can be removed from each electrocardiogram signal through the independent component analysis. The electrocardiogram signal from which the electrocardiogram waveform components have been removed is treated as an electromyogram signal indicating the respiratory muscle activity amount of the subject P. In this regard, in the related-art electromyogram signal, the low frequency component has been removed, whereas the electromyogram signal acquired in the present embodiment includes both the low frequency component and the high frequency component.
Next, in step S4, the controller 2 executes quantification processing on the electrocardiogram signal from which the electrocardiogram waveform components have been removed to acquire the activity evaluation index indicating the respiratory muscle activity amount of the subject P. Hereinafter, for convenience of description, the electrocardiogram signal from which the electrocardiogram waveform components have been removed is referred to as an electromyogram signal. In this regard, the controller 2 may execute the quantification processing on all of the 12 types of electromyogram signals, or may execute the quantification processing on some of the 12 types of electromyogram signals. In the quantification processing, the controller 2 executes averaging processing on the electromyogram signal, and then determines a maximum amplitude, an average amplitude, or an integrated value of the electromyogram signal subjected to the averaging processing as the activity evaluation index.
As the averaging processing, root mean square (RMS) processing, interval averaging processing in which absolute value processing is set as preprocessing, integration processing, and the like may be used. A section set in the RMS processing may be, for example, within a range of 100 ms to 300 ms.
Further, the controller 2 may acquire, as the activity evaluation index indicating the respiratory muscle activity amount, a first activity evaluation index indicating an activity amount of a diaphragm (an example of a first respiratory muscle) of the subject P and a second activity evaluation index indicating an activity amount of an intercostal muscle (an example of a second respiratory muscle) of the subject P.
The controller 2 may acquire the first activity evaluation index indicating the activity amount of the diaphragm by executing the quantification processing on at least one of the six types of electromyogram signals associated with the chest leads. For example, the controller 2 calculates a difference between the electromyogram signal associated with the V4 lead and the electromyogram signal associated with the V5 lead, and then executes the averaging processing on a waveform of the calculated difference.
The controller 2 may acquire the second activity evaluation index indicating the activity amount of the intercostal muscle by executing the quantification processing on the electromyogram signal associated with the aVR lead. For example, the controller 2 executes the averaging processing on the electromyogram signal associated with the aVR lead.
The inventor of the present disclosure classifies the 12 types of electromyogram signals into four factors through factor analysis, which is a type of the multivariate analysis. As a result of the factor analysis, it has been found that the electromyogram signals associated with the V4 lead, the V5 lead, and V6 lead particularly strongly indicate the activity amount of the diaphragm. It has been found that the electromyogram signals associated with the aVR lead and the aVL lead particularly strongly indicate the activity amount of the intercostal muscle. In this way, in the first activity evaluation index indicating the activity amount of the diaphragm, the electromyogram signals associated with the V4 lead, the V5 lead, and V6 lead are used, whereas in the second activity evaluation index indicating the activity amount of the intercostal muscle, the electromyogram signals associated with the aVR lead and the aVL lead are used.
Next, in step S5, the controller 2 outputs information on the activity evaluation index. In this regard, the controller 2 may display information on the activity evaluation index on a display screen of the display 4. Examples of the information on the activity evaluation index include a current numerical value of the activity evaluation index, a trend graph indicating a temporal change in the activity evaluation index, and an increase or decrease amount or an increase or decrease rate of the activity evaluation index with respect to a predetermined reference value. Here, the predetermined reference value may be determined based on a plurality of acquired activity evaluation indices. The current numerical value of the activity evaluation index, the trend graph of the index, and the increase or decrease amount of the index may be simultaneously displayed on the display screen of the display 4. In particular, the controller 2 may simultaneously display, on the display screen of the display 4, the information on the first activity evaluation index indicating the activity amount of the diaphragm and the information on the second activity evaluation index indicating the activity amount of the intercostal muscle. More specifically, the trend graph of the first activity evaluation index and the trend graph of the second activity evaluation index may be simultaneously displayed on the display screen. In this way, a medical worker can grasp an activity state of the diaphragm of the subject P and an activity state of the intercostal muscle of the subject P by viewing the two trend graphs displayed on the display screen.
The controller 2 may store information on the activity evaluation index in the storage device 3, or may transmit the information to a central monitor or a server provided in the in-hospital network.
According to the present embodiment, the electrocardiogram signal is separated into the electrocardiogram waveform component and the noise waveform component other than the electrocardiogram waveform component through the independent component analysis. Thereafter, the first activity evaluation index indicating the activity amount of the diaphragm and the second activity evaluation index indicating the activity amount of the intercostal muscle are acquired based on the electrocardiogram signal (the electromyogram signal) from which the electrocardiogram waveform component has been removed. Thereafter, the information on the first activity evaluation index and the second activity evaluation index is output. In this way, in the present embodiment, the respiratory muscle activity amount of the subject P is evaluated based on both the low frequency component and the high frequency component of the electromyogram signal, unlike the related-art method for evaluating the respiratory muscle activity amount using a band-pass filter. That is, in the present embodiment, it is possible to acquire the electromyogram signal without removing a specific frequency component. Therefore, it is possible to evaluate the respiratory muscle activity amount of the subject with high accuracy as compared with the related-art method in which the respiratory muscle activity amount of the subject is evaluated based on the electromyogram signal from which the low frequency component has been removed.
The medical worker can grasp the temporal change in the respiratory muscle activity amount of the subject P by viewing the trend graph or the like of the activity evaluation index of the subject P output from the processing apparatus 1. The medical worker can grasp a state (for example, exhaustion or weakening of the respiratory muscle of the diaphragm or the like associated with resting, weighted side lung injury due to a decrease in the activity amount of the diaphragm, an effect of respiratory muscle training, and an activity of a sternocleidomastoid muscle and an accessory respiratory muscle) of the respiratory muscle of the subject P based on the temporal change in the respiratory muscle activity amount of the subject P.
In the present embodiment, the activity evaluation index indicating the respiratory muscle activity amount of the subject P can be acquired using the electrodes used in the 12-lead electrocardiogram examination. In this way, it is possible to evaluate the respiratory muscle activity amount of the subject P with high accuracy while measuring the electrocardiogram of the subject P.
In the present embodiment, the activity amount of the two respiratory muscles which are the diaphragm and the intercostal muscle is evaluated based on the quantified electromyogram signal. However, the present embodiment is not limited thereto. In this regard, the controller 2 may evaluate the activity amount of the accessory respiratory muscle by adding the activity amount of the accessory respiratory muscle to the two activity amounts of the diaphragm and the intercostal muscle. That is, the controller 2 may acquire a third activity evaluation index indicating the activity amount of the accessory respiratory muscle based on the quantified electromyogram signal, and then add the third activity evaluation index to the information on the first activity evaluation index and the second activity evaluation index and output information on the third activity evaluation index.
In order to implement the processing apparatus 1 according to the present embodiment by software, a physiological information processing program may be assembled into the storage device 3 or the ROM in advance. Alternatively, the physiological information processing program may be stored in a computer-readable storage medium such as a magnetic disk (for example, a HDD or a floppy disk), an optical disk (for example, a CD-ROM, a DVD-ROM, or a Blu-ray (registered trademark) disk), a magneto-optical disk (for example, a MO), or a flash memory (for example, a SD card, a USB memory, or a SSD). In this case, the physiological information processing program stored in the storage medium may be assembled into the storage device 3. Further, the program assembled in the storage device 3 may be loaded onto the RAM, and then the processor may execute the program loaded onto the RAM. In this way, the physiological information processing method according to the present embodiment is executed by the processing apparatus 1.
The physiological information processing program may be downloaded from a computer on a communication network via the communication unit 5. In this case, same or similarly, the downloaded program may be assembled into the storage device 3.
According to the present disclosure, it is possible to provide a physiological information processing method and a physiological information processing apparatus that are capable of evaluating a respiratory muscle activity amount of a subject with higher accuracy. It is also possible to provide a program for causing a computer to execute the information processing method and a computer-readable storage medium that stores the program.
According to the physiological information processing method in the present disclosure, through the independent component analysis, the electrocardiogram signal is separated into one or more first waveform components indicating an electrocardiogram waveform and one or more second waveform components indicating a waveform other than the electrocardiogram waveform, and then the respiratory muscle activity evaluation index indicating the activity amount of the respiratory muscle is acquired based on the electrocardiogram signal from which the first waveform component has been removed. In this way, in the above-described method, the respiratory muscle activity amount of the subject is evaluated based on both the low frequency component and the high frequency component of the electromyogram signal (that is, the electrocardiogram signal from which the first waveform component has been removed), unlike the related-art method for evaluating the respiratory muscle activity amount using the band-pass filter. That is, it is possible to evaluate the respiratory muscle activity amount of the subject with high accuracy as compared with the related-art method in which the respiratory muscle activity amount is evaluated based on the electromyogram signal from which the low frequency component has been removed.
According to the physiological information processing apparatus in the present disclosure, it is possible to provide the physiological information processing apparatus capable of evaluating the respiratory muscle activity amount of the subject with high accuracy as compared with the related-art method in which the respiratory muscle activity amount is evaluated based on the electromyogram signal from which the low frequency component has been removed.
The embodiment of the presently disclosed subject matter is described above. However, the technical scope of the presently disclosed subject matter should not be construed as being limited to the description of the embodiment. It is understood by those skilled in the art that the present embodiment is an example and various modifications can be made within the scope of the inventions described in the claims. The technical scope of the presently disclosed subject matter should be determined based on the scope of the invention described in the claims and the scope of equivalents thereof.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2022-011855 | Jan 2022 | JP | national |
