Patent Application
20200008691
The present invention relates to a technique for processing blood pressure data.
It is known that in a patient affected by sleep apnea syndrome (SAS), when breathing is resumed after apnea, the blood pressure suddenly rises and thereafter falls. Hereinafter, this sudden blood pressure fluctuation will be called a “blood pressure surge”. It is thought that indices relating to blood pressure surges that occur in a patient (e.g., number of instances in which blood pressure surges occur per unit time) are useful in diagnosing and treating a disease that increases the risk of onset of brain disease or vascular disease, such as SAS or high blood pressure.
In order to observe a blood pressure surge, a blood pressure measurement apparatus that can continuously measure blood pressure, such as a blood pressure measurement apparatus that can obtain the blood pressure for each heartbeat, for example, is needed. The amount of blood pressure data obtained through continuous blood pressure measurement is large, and thus it is difficult for an expert such as a doctor or researcher to analyze the blood pressure data to extract the blood pressure surge. For this reason, development of a technique for automatically extracting a blood pressure surge from blood pressure data has been progressing.
Incidentally, blood pressure measurement apparatuses are used in various scenes, such as health management, and treatment or diagnosis of illness. JP 2001-299707A discloses a blood pressure measurement apparatus that monitors fluctuations in the heart rate of a patient and measures the blood pressure in response to a fluctuation in the heart rate being detected. This blood pressure measurement apparatus predicts that a blood pressure fluctuation in which the blood pressure drops or rises to a dangerous level will occur in the patient based on fluctuations in the heart rate.
A blood pressure surge occurs also due to causal factors other than apnea. For example, apnea, REM (rapid eye movement) sleep, and an arousal response are examples of main causal factors that cause blood pressure surges during sleep. It is possible to judge which causal factor caused the blood pressure surge by measuring the sleep state and the blood pressure through PSG (polysomnography). However, PSG is a high-cost and large-scale device, and thus measurement using PSG cannot be performed readily in a home. Also, with the blood pressure measurement apparatus disclosed in JP 2001-299707A, it is possible to predict that a blood pressure fluctuation will occur, but the bodily cause contributing to the blood pressure fluctuation cannot be specified. There has been demand to be able to specify causal factors of a blood pressure surge without using a high-cost and large-scale device such as PSG.
The present invention was made with attention given to the above-described circumstances, and it is an object thereof to provide a blood pressure data processing apparatus, a blood pressure data processing method, and a program, according to which it is possible to identify causal factors of a blood pressure surge based on blood pressure data.
In a first aspect of the present invention, a blood pressure data processing apparatus includes: a blood pressure data acquisition unit configured to acquire blood pressure data; a blood pressure surge detection unit configured to detect a blood pressure surge based on the blood pressure data; a blood pressure waveform extraction unit configured to extract a blood pressure waveform of one or more heartbeats from the blood pressure surge; a waveform feature amount calculation unit configured to calculate a waveform feature amount for each blood pressure waveform of one heartbeat isolated from the blood pressure waveform of one or more heartbeats, or for an average blood pressure waveform obtained by averaging the blood pressure waveforms of one heartbeat isolated from the blood pressure waveform of one or more heartbeats; and a causal factor identification unit configured to identify a causal factor of the blood pressure surge from among predetermined causal factors, based on the waveform feature amount.
In a second aspect of the present invention, the causal factor identification unit identifies the causal factor of the blood pressure surge using a learning result obtained by learning waveform feature amounts corresponding to the predetermined causal factors.
In a third aspect of the present invention, the waveform feature amount includes a plurality of types of waveform feature amounts, and the causal factor identification unit identifies a causal factor of the blood pressure surge based on the plurality of types of waveform feature amounts, and boundaries relating to the respective predetermined causal factors, the boundaries being set in a feature space.
In a fourth aspect of the present invention, the blood pressure surge includes a rising portion and a falling portion that is subsequent to the rising portion, and the blood pressure waveform extraction unit extracts the blood pressure waveform of one or more heartbeats from the rising portion of the blood pressure surge.
In a fifth aspect of the present invention, the waveform feature amount is based on at least one of a time interval from a time of a diastolic peak to a time of a systolic peak, a time interval from the time of the diastolic peak to a time of a dicrotic peak, a time width of the systolic peak, a total pulse time, an amplitude of the systolic peak, and an amplitude of the dicrotic peak.
In a sixth aspect of the present invention, the waveform feature amount includes a waveform feature amount obtained based on a ratio between the time width of the systolic peak and the total pulse time.
In a seventh aspect of the present invention, the waveform feature amount calculation unit performs pre-processing including a first derivative or a second derivative on the blood pressure waveform of one or more heartbeats, and specifies the diastolic peak, the systolic peak, and the dicrotic peak based on the waveform obtained through the pre-processing.
In an eighth aspect of the present invention, the blood pressure data processing apparatus further includes an output unit configured to output information relating to the causal factor of the blood pressure surge identified by the causal factor identification unit.
According to the first aspect, a blood pressure surge is detected based on the blood pressure data, a blood pressure waveform of one or more heartbeats is extracted from the blood pressure surge, a waveform feature amount is calculated for each blood pressure waveform of one heartbeat isolated from the blood pressure waveform of one or more heartbeats, or for an average blood pressure waveform obtained by averaging the blood pressure waveforms of one heartbeat isolated from the blood pressure waveform of one or more heartbeats, and a causal factor of the blood pressure surge is identified from among predetermined causal factors based on the waveform feature amount. Accordingly, it is possible to identify a causal factor of a blood pressure surge based on blood pressure data, without using a high-cost and large-scale device such as PSG.
According to the second aspect, a learning result obtained by learning the waveform feature amounts corresponding to the predetermined causal factors is used to identify a causal factor of the blood pressure surge. Accordingly, it is possible to easily generate data that is needed for identifying a causal factor of the blood pressure surge.
According to the third aspect, boundaries relating to the respective predetermined causal factors are determined in advance in a feature space. Accordingly, it is possible to identify a causal factor of a blood pressure surge with a small amount of processing.
According to the fourth aspect, a waveform feature amount is calculated for each blood pressure waveform of one heartbeat included in a rising portion of a blood pressure surge, or for an average blood pressure waveform. Accordingly, a causal factor of the blood pressure surge can be accurately identified.
According to the fifth aspect, a waveform feature amount obtained based on at least one of the time interval from the time of the diastolic peak to the time of the systolic peak, the time interval from the time of the diastolic peak to the dicrotic peak, the time width of the systolic peak, total pulse time, the amplitude of the systolic peak, and the amplitude of the diastolic peak is used. Accordingly, a causal factor of the blood pressure surge can be accurately identified.
According to the sixth aspect, the waveform feature amount obtained based on the ratio between the time span of the systolic peak and the total pulse time is used. Accordingly, a causal factor of the blood pressure surge can be accurately identified.
According to the seventh aspect, pre-processing including a first derivative or a second derivative is performed on a blood pressure waveform of one heartbeat or more. Accordingly, it is easier to perform processing for specifying a feature point such as a diastolic peak, a systolic peak, and a dicrotic peak.
According to the eighth aspect, information relating to a causal factor of a blood surge identified by the causal factor identification unit is output. With this information, a doctor can verify how to deal with the symptoms of the patient.
That is, according to the present invention, it is possible to provide a blood pressure data processing apparatus, a blood pressure data processing method, and a program, according to which a causal factor of a blood pressure surge can be identified based on blood pressure data.
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
First, the blood pressure measurement apparatus 20 will be described. The blood pressure measurement apparatus 20 generates blood pressure data by continuously measuring the blood pressure of the measurement subject. Specifically, the blood pressure measurement apparatus 20 measures a pulse wave of an artery of the measurement subject and generates the blood pressure data by converting the measured pulse wave into blood pressure. The blood pressure data includes data on the blood pressure waveform corresponding to the waveform of the measured pulse wave. The blood pressure data may also further include chronological data of a blood pressure feature amount (blood pressure value). Although the blood pressure feature amounts include the systolic blood pressure (SBP) and the diastolic blood pressure (DBP) for example, there is no limitation to this. The maximum value of the pulse wave waveform of one heartbeat corresponds to the systolic blood pressure, and the minimum value of the pulse wave waveform of one heartbeat corresponds to the diastolic blood pressure.
In the first embodiment, the blood pressure measurement apparatus 20 measures the pressure pulse wave serving as the pulse wave through tonometry. Here, “tonometry” refers to a method in which a flat portion is formed in the artery by pressing the artery with an appropriate pressure from above the skin, and a pressure pulse wave is non-invasively measured using the pressure sensor in a state in which the interior and exterior of the artery are balanced. According to tonometry, it is possible to obtain a blood pressure value for each heartbeat.
The blood pressure measurement apparatus 20 may also be a wearable apparatus to be attached to the measurement subject, or may be a stationary apparatus that performs blood pressure measurement in a state in which the upper arm of the measurement subject is placed on a fixed platform. In an example that will be described below with reference to
The blood pressure measurement unit 21 measures the pressure pulse wave of the radial artery.
The pressure sensors 222 generate pressure data by measuring the pressure. Piezoelectric elements that convert pressure into electric signals can be used as the pressure sensors. The output signals of the piezoelectric elements are converted into digital signals at a predetermined (e.g., 125-Hz) sampling frequency, whereby the pressure data is obtained. The pressure pulse wave data corresponding to the above-described pulse wave data is generated based on the pressure data output from one pressure sensor (active channel) 222 selected adaptively from among the pressure sensors 222.
For example, the pressing mechanism 23 includes an air bag and a pump for adjusting the internal pressure of the air bag. When the pump is driven by the control unit 28 so as to increase the internal pressure of the air bag, the pressure sensor 222 is pressed to the wrist W due to the inflation of the air bag. Note that the pressing mechanism 23 is not limited to a structure using an air bag, and may also be realized by any structure in which the force pressing the pressuring sensor 222 to the wrist W can be adjusted.
The acceleration sensor 24 detects the acceleration acting on the blood pressure measurement apparatus 20 and generates acceleration data. For example, a triple-axial acceleration sensor can be used as the acceleration sensor 24. The detection of the acceleration is carried out in parallel with the blood pressure measurement.
The storage unit 25 includes a computer-readable storage medium. For example, the storage unit 25 includes a RAM (random access memory) and an auxiliary storage apparatus. The ROM stores the above-described control program. The RAM is used as a work memory by the CPU. The auxiliary storage apparatus stores various types of data including the blood pressure data generated by the blood pressure measurement unit 21, and the acceleration data generated by the acceleration sensor 24. The auxiliary storage apparatus includes a flash memory, for example. The auxiliary storage apparatus includes one or both of a storage medium built into the blood pressure measurement apparatus 20 and a removable medium such as a memory card.
The input unit 26 receives an instruction from the measurement subject. For example, the input unit 26 includes an operation button, a touch panel, and the like. The output portion 27 outputs information such as a pressure measurement result. For example, the output unit 27 includes a display apparatus such as a liquid crystal display apparatus.
According to the blood pressure measurement apparatus 20 having the above-described configuration, the blood pressure data and the acceleration data are obtained. For example, measurement is performed over an entire period during which the measurement subject is asleep (for example, one night), and the blood pressure data and acceleration data obtained through measurement are input to the blood pressure data processing apparatus 10.
Note that the blood pressure measurement apparatus 20 is not limited to a blood pressure measurement apparatus using tonometry, and may also be a blood pressure measurement apparatus of any type that can continuously measure blood pressure. For example, a blood pressure measurement apparatus that measures a volume pulse wave serving as a pulse wave may also be used. For example, the blood pressure measurement apparatus can measure a volume pulse wave of an artery using a photoelectric sensor or an ultrasonic probe, and can estimate the blood pressure based on the measured volume pulse wave. A blood pressure measurement apparatus may also be used which measures a pulse transit time (PTT), which is the transit time of a pulse wave being transmitted through an artery, and estimates the blood pressure based on the estimated pulse transit time.
Next, the blood pressure data processing apparatus 10 will be described. As shown in
The blood pressure data acquisition unit 11 acquires the blood pressure data from the blood pressure measurement apparatus 20 and stores the acquired blood pressure data in the blood pressure data storage unit 12. The blood pressure data may also be provided from the blood pressure measurement apparatus 20 to the blood pressure data processing apparatus 10 through a removable medium such as a medium card. Alternatively, the blood pressure data may also be provided from the blood pressure measurement apparatus 20 to the blood pressure data processing apparatus 10 through communication (wired communication or wireless communication). Furthermore, the blood pressure data acquisition unit 11 may also further acquire acceleration data output from the acceleration sensor provided in the blood pressure measurement apparatus 20, or the like.
The pre-processing unit 13 receives the blood pressure data from the blood pressure data storage unit 12 and performs pre-processing on the blood pressure data. For example, the pre-processing unit 13 performs pre-processing such as smoothing, spike noise removal, and high-frequency component removal on the chronological data of the systolic blood pressure that is included in the blood pressure data or was generated based on the blood pressure data. The pre-processing may also include processing for detecting bodily movement of the measurement subject using the acceleration data and correcting the blood pressure data of a time segment in which bodily movement has been detected.
The blood pressure surge detection unit 14 detects the blood pressure surge based on the pre-processed blood pressure data. Any method may be used to detect the blood pressure surge. For example, the processing for detecting a blood pressure surge may also be executed using the chronological data of the systolic blood pressure or the diastolic blood pressure. In the first embodiment, there is no limitation regarding what kind of blood pressure waveform is detected as the blood pressure surge.
The causal factor determination unit 15 determines which of the predetermined causal factors caused the blood pressure surge detected by the blood pressure surge detection unit 14. As an example, the predetermined causal factors include apnea, REM sleep, and an arousal response. Note that the predetermined causal factors may also include other causal factors (specifically, causal factors other than apnea, REM sleep, and an arousal response). There need only be two or more causal factors. Apnea, REM sleep, and an arousal response are examples of causal factors, and there is no limitation thereto. Causal factors can be selected from elements relating to some disease, as with apnea. The processing of the causal factor determination unit 15 will be described in more detail later.
The information generation unit 16 generates the measurement blood pressure information. The information generation unit 16 can generate indices relating to blood pressure surges based on the blood pressure waveforms determined to be blood pressure surges by the causal factor determination unit 15. For example, the indices relating to the blood pressure surges include: the number of instances of blood pressure surges per unit time, the average value of the maximum blood pressure values of the blood pressure surges, and the maximum value of the maximum blood pressure values of the blood pressure surges. Accordingly, it is possible to provide indices relating to blood pressure surges that occur in the measurement subject. Furthermore, the information generation unit 16 can generate various indices relating to the blood pressure, such as the average blood pressure value, based on the blood pressure data stored in the blood pressure data storage unit 12.
The information output unit 17 outputs the measurement blood pressure information generated by the information generation unit 16. For example, the information output unit 17 generates image data including the measurement blood pressure information, and an image corresponding to the image data is displayed on the display apparatus.
The causal factor determination unit 15 will be described in detail later.
The target segment setting unit 151 sets a target segment for extracting a blood pressure waveform of one or more heartbeats from the blood pressure surge. For example, the rising period of the blood pressure surge is set as the target segment. The rising period of the blood pressure surge indicates the time segment from the start time t1 to the peak time t2. A portion of the rising segment may also be set as the target segment. Also, a portion or the entirety of the falling period may also be set as the target segment. The falling period indicates the time segment from the peak time t2 to the end time t3. The inventors confirmed that it is possible to accurately perform determination of which causal factor caused the blood pressure surge by using the rising period of the blood pressure surge as the target segment. Accordingly, a portion or the entirety of the rising period of the blood pressure surge is preferably set as the target segment.
The blood pressure waveform extraction unit 152 extracts the blood pressure waveform of one or more heartbeats from the blood pressure surge in the target segment. The rising period of the blood pressure surge is typically about 5 to 25 seconds, and thus the blood pressure waveform over multiple heartbeats is extracted. It should be noted that if the target segment is short, as in the case where a portion of the rising period of the blood pressure surge is used as the target segment, a blood pressure waveform corresponding to less than two heartbeats is extracted in some cases.
The waveform feature amount calculation unit 153 extracts the waveform feature amount from the blood pressure waveform of one or more heartbeats extracted by the blood pressure waveform extraction unit 152. For example, the waveform feature amount calculation unit 153 isolates or extracts one or more blood pressure waveforms of one heartbeat from the blood pressure waveform of one or more heartbeats extracted by the blood pressure waveform extraction unit 152, and calculates the waveform feature amount for each isolated blood pressure waveform of one heartbeat. Also, the waveform feature amount calculation unit 153 may also generate the average blood pressure waveform obtained by averaging the isolated or extracted blood pressure waveforms of one heartbeat, and may calculate the waveform feature amount for the average blood pressure waveform. The waveform feature amount is calculated based on the shape of the blood pressure waveform of one heartbeat. The waveform feature amount includes one or more types of waveform feature amounts. In the first embodiment, multiple types of waveform feature amounts are used. The waveform feature amount can be expressed using a feature vector.
The waveform feature amount will be described with reference to
The waveform feature amount calculation unit 153 may also perform pre-processing including a first derivative and/or a second derivative on the blood pressure waveform in order to specify feature points such as the points T0, T1, T2, T3, and T4. Processing for specifying the feature points is simplified by using the first derivative and/or the second derivative of the blood pressure waveform.
Also, the waveform feature amount calculation unit 153 may also calculate an R-R interval (RRI), which is an interval between R waves in a cardiogram, based on the blood pressure waveform of a period including a blood pressure surge, perform frequency spectrum analysis on the RRI, calculate a low-frequency component LF and a high-frequency component HF, and calculate the ratio between the low-frequency component LF and the high-frequency component HF as the feature amount. For example, the waveform feature amount calculation unit 153 can calculate a power spectrum density for the RRI, calculate the power spectrum density using a self-regression model, calculate the integrated value of the power over the frequency region of 0.05 Hz to 0.15 Hz as the LF, and calculate the integrated value of the power over the frequency region of 0.15 Hz to 0.40 Hz as the HF. It is known that the ratio LF/HF indicates the balance of the autonomic nerves. For this reason, it is possible to determine whether or not the blood pressure surge is caused by REM sleep by using the ratio LF/HF as the feature amount.
The causal factor identification unit 154 identifies the causal factor of the blood pressure surge from among the predetermined causal factors, based on the waveform feature amount calculated by the waveform feature amount calculation unit 153. The causal factor identification unit 154 uses the causal factor identification data generated by the causal factor identification data generation unit 155 in order to perform identification. The causal factor identification data will be described before specific description of the causal factor identification unit 154 is given.
The blood pressure surge waveform storage unit 156 stores data of a typical blood pressure surge waveform. A blood pressure surge waveform in this context refers to the blood pressure waveform of one heartbeat, as shown in
The causal factor identification data generation unit 155 generates data (causal factor identification data) to be used in order for the causal factor identification unit 154 to perform identification, based on the blood pressure surge waveform data stored in the blood pressure surge waveform storage unit 156. The causal factor identification data generation unit 155 can generate the causal factor identification data by learning the blood pressure surge waveform data stored in the blood pressure surge waveform storage unit 156.
In one example, the causal factor identification data generation unit 155 determines a boundary in a feature space as follows for each of the three classes. The causal factor identification data generation unit 155 calculates the waveform feature amount based on the blood pressure surge waveform belonging to each class. Calculation of the waveform characteristic amount can be performed using a method similar to that described regarding the waveform feature amount calculation unit 153. The causal factor identification data generation unit 155 determines the boundary lines or planes for identifying the classes in the feature space, based on the calculated waveform feature amount. The causal factor identification data generation unit 155 determines the boundary lines or boundary planes in which approximately 95.4% or approximately 99.7% of the data is included, as with a 2σ method or a 3σ method, in the feature space. The boundaries can be determined using, for example, the Mahalanobis distance, one-class support vector machine (SVM), or the like. If using two types of waveform feature amounts, the boundary lines of the three classes are determined as shown in
The causal factor identification unit 154 identifies which causal factor among the predetermined causal factors caused the blood pressure surge based on the waveform feature amount calculated by the waveform feature amount calculation unit 153 and the boundaries set in the feature space. Specifically, the causal factor identification unit 154 determines which causal factor caused the blood pressure surge based on the boundary of the class in which the feature vector including the waveform feature amount as an element is located. If multiple blood pressure waveforms of one heartbeat have been extracted by the blood pressure waveform extraction unit 152, the causal factor identification unit 154 can perform identification through majority decision, for example. Specifically, the causal factor identification unit 154 determines the causal factor corresponding to the class with the largest number of feature vectors located inside of the boundary in the feature space as the causal factor of the blood pressure surge.
As described above, a boundary may overlap with another boundary. For this reason, a feature vector may also be located within the boundaries of two or more classes. In this case, the causal factor identification unit 154 may also determine the causal factors corresponding to these classes as elements of the blood pressure surge.
In another example, the causal factor identification unit 154 may also calculate the Mahalanobis distance between the feature vector and the center of mass (center) of each class, and perform identification based on the calculated Mahalanobis distances. The causal factor identification unit 154 may also determine the causal factor corresponding to the class with the smallest Mahalanobis distance between it and the feature vector as the causal factor of the blood pressure surge. In this case, the causal factor identification data includes the position of the center of mass in the feature space and an inverse covariance matrix for each class.
In yet another example, the causal factor identification unit 154 may also perform identification using the support vector machine. In this case, the causal factor identification data generation unit 155 generates the support vector machine based on the blood pressure surge waveform data stored in the blood pressure surge waveform storage unit 156.
Next, operations of the blood pressure data processing apparatus 10 will be described.
In step S103, the causal factor determination unit 15 sets a target segment in the blood pressure surge. In step S104, the causal factor determination unit 15 extracts the blood pressure waveform of one or more heartbeats from the waveform of the blood pressure surge of the target segment. In step S105, the causal factor determination unit 15 calculates the waveform feature amount based on the extracted blood pressure waveform of one or more heartbeats. In one example, the causal factor determination unit 15 calculates the waveform characteristic amount for each blood pressure waveform of one heartbeat isolated from the blood pressure waveform of one or more heartbeats. In another example, the causal factor determination unit 15 may also calculate the waveform feature amount for the average blood pressure waveform obtained by averaging the blood pressure waveforms of one heartbeat isolated from the blood pressure waveform of one or more heartbeats. In step S106, the causal factor determination unit 15 identifies which class the extracted blood pressure waveform of one heartbeat belongs to, based on the calculated waveform feature amount. For example, if a feature vector including the calculated waveform feature amount as an element is located within the boundary of a certain class set in the feature space, the causal factor determination unit 15 determines that the extracted blood pressure waveform of one heartbeat belongs to that class. The causal factor determination unit 15 adds one point to the score of the causal factor corresponding to the class to which it was determined that the extracted blood pressure waveform of one heartbeat belongs.
If multiple blood pressure waveforms of one heartbeat are included in the blood pressure waveform of the target segment extracted in step S103, the processing of steps S104 to S106 is performed on the blood pressure waveforms of one heartbeat.
In step S107, the causal factor determination unit 15 determines which of the predetermined causal factors caused the blood pressure surge based on the result of the identification, which was executed repeatedly (step S106). Specifically, the causal factor determination unit 15 determines the causal factor with the highest score as the causal factor of the blood pressure surge.
As described above, the blood pressure data processing apparatus 10 according to the first embodiment acquires the blood pressure data, detects a blood pressure surge based on the blood pressure data, extracts the blood pressure waveform of one or more heartbeats from the blood pressure surge, calculates the waveform feature amount for each blood pressure waveform of one heartbeat isolated from the blood pressure waveform of one or more heartbeats, or for an average blood pressure waveform obtained by averaging the blood pressure waveforms of one heartbeat isolated from the blood pressure waveform of one or more heartbeats, and identifies the causal factor of the blood pressure surge from among predetermined causal factors, based on the waveform feature amount. Accordingly, it is possible to identify a causal factor of a blood pressure surge based on blood pressure data, without using a high-cost and large-scale device such as PSG. As a result, it is possible to provide information relating to a blood pressure surge that occurs due to a specific causal factor such as apnea. By clarifying the causal factor that caused the blood pressure surge, it is possible to clarify points to be treated in the patient.
An example of a hardware configuration of the blood pressure data processing apparatus 10 will be described with reference to
The blood pressure data processing apparatus 10 includes a CPU 31, a ROM 32, a RAM 33, an auxiliary storage apparatus 34, an input apparatus 35, an output apparatus 36, and a transceiver 37, and these elements are connected to each other via a bus system 38. The above-described functions of the blood pressure data processing apparatus 10 can be realized by the CPU 31 reading out and executing a program stored in a computer-readable storage medium (the ROM 32 and/or the auxiliary storage apparatus 34). The RAM 33 is used as a work memory by the CPU 31. For example, the auxiliary storage apparatus 34 includes a hard disk drive (HDD) or a solid-state drive (SSD). The auxiliary storage apparatus 34 is used as the blood pressure data storage unit 12 (
In the above-described embodiment, the causal factor identification data generation unit 155 and the blood pressure surge waveform storage unit 156 are provided in the causal factor determination unit 15 of the blood pressure data processing apparatus 10. In another embodiment, the causal factor identification data generation unit 155 and the blood pressure surge waveform storage unit 156 may also be provided in an apparatus different from the blood pressure data processing apparatus 10. In other words, the causal factor identification data may also be generated in an external apparatus, and the causal factor identification data may be provided to the blood pressure data processing apparatus 10.
Also, in the above-described embodiment, the blood pressure data processing apparatus 10 is provided separate from the blood pressure measurement apparatus 20. In another embodiment, a portion or all of the functions of the blood pressure data processing apparatus 10 may also be provided in the blood pressure measurement apparatus 20.
In short, the present invention is not limited to the above-described embodiment as-is, and can be realized with modifications to the constituent elements without departing from the gist in the implementation stage. Also, various aspects of the invention can be formed through suitable combinations of the multiple constituent elements disclosed in the above-described embodiment. For example, several constituent elements may also be removed from all of the constituent elements shown in the embodiment. Furthermore, the constituent elements of different embodiments may also be combined as appropriate.
A portion or the entirety of the above-described embodiment can be described as in the following supplementary notes as well, but there is no limitation to the following description.
Supplementary Note 1
A blood pressure data processing apparatus comprising:
a hardware processor; and
a memory coupled to the hardware processor,
wherein the hardware processor is configured to
Supplementary Note 2
A blood pressure data processing method comprising:
acquiring blood pressure data using at least one hardware processor;
detecting a blood pressure surge based on the blood pressure data using at least one hardware processor;
extracting a blood pressure waveform of one or more heartbeats from the blood pressure surge using at least one hardware processor;
calculating a waveform feature amount for each blood pressure waveform of one heartbeat isolated from the blood pressure waveform of one or more heartbeats or for an average blood pressure waveform obtained by averaging the blood pressure waveforms of one heartbeat isolated from the blood pressure waveform of one or more heartbeats, using at least one hardware processor; and
identifying a causal factor of the blood pressure surge from among predetermined causal factors based on the waveform feature amount, using at least one hardware processor.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2017-048954 | Mar 2017 | JP | national |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/JP2018/009580 | Mar 2018 | US |
| Child | 16571946 | US |
