Mammal cardiopulmonary arrest prediction device

Abstract

A heart-lung stop prediction device includes: right and left sound detection sections for detecting sounds generated by heartbeat fluctuation at right and left corneal wind sites, respectively, and detecting sounds generated by breathing fluctuation at the right and left corneal wind sites, respectively; a calculation section for calculating a stress index of a mammal based on the sounds generated by heartbeat fluctuation and the sounds generated by breathing fluctuation detected by the right and left sound detection sections; a prediction section for predicting a heart-lung stop period of the mammal based on the stress index calculated by the calculation section; and a notification section for notifying the heart-lung stop period predicted by the prediction section.

Description

Technical Field

[0001] This disclosure relates to a cardiopulmonary arrest prediction device capable of predicting the duration of cardiopulmonary arrest in mammals. Background Technology

[0002] For example, Patent Document 1 discloses a contact-type biological sound sensor fixed in a state of insertion into the external auditory canal as an example of a sensor for acquiring biological information. The contact-type biological sound sensor of Patent Document 1 includes a biological sound sensor for acquiring biological sound as biological information, and a sound conduction part made of a material with an acoustic impedance similar to that of skin.

[0003] In addition, Patent Document 2 discloses a biological information acquisition device that is used when inserted into the external auditory canal and extracts vascular sound waveforms and respiratory sound waveforms based on sound signals obtained by a bone conduction sensor.

[0004] Existing technical documents Patent documents Patent Document 1: Japanese Patent Application Publication No. 2021-74464 Patent Document 2: Japanese Patent No. 7287612 Summary of the Invention

[0005] The technical problem that the invention aims to solve For example, in cases where patients admitted to the hospital due to illness, injury, or old age have no possibility of recovery and are nearing death, there is a need to predict when the patient's heart and lungs will stop functioning.

[0006] By acquiring and analyzing various biological information from patients, the duration of cardiopulmonary arrest can be predicted. At this point, it is considered to utilize sensors inserted into the external auditory canal, as in Patent Documents 1 and 2, to acquire biological sound or vascular sound waveforms. However, when using this biological information to predict the duration of cardiopulmonary arrest, improving the accuracy of the acquired information becomes a problem. Furthermore, the need for cardiopulmonary arrest prediction also exists in mammals other than humans.

[0007] This disclosure was made in view of this purpose, and its aim is to enable the prediction of the period of cardiopulmonary arrest in mammals with high accuracy.

[0008] Means for solving technical problems To achieve the above objectives, one aspect of this disclosure includes a cardiopulmonary arrest prediction device that can predict the duration of cardiopulmonary arrest in mammals. The cardiopulmonary arrest prediction device comprises: a right sound detection unit and a left sound detection unit for detecting sounds generated by heartbeat fluctuations in the right and left pterygoid regions of the mammal, respectively, and sounds generated by respiratory fluctuations in the right and left pterygoid regions, respectively; a calculation unit for calculating a stress index of the mammal based on the sounds generated by heartbeat fluctuations and respiratory fluctuations detected by the right and left sound detection units; a prediction unit for predicting the duration of cardiopulmonary arrest in the mammal based on the stress index calculated by the calculation unit; and a notification unit for notifying the duration of cardiopulmonary arrest predicted by the prediction unit.

[0009] That is, the right and left pterygium regions of mammals are close to arteries and the respiratory tract, so they can detect sounds related to heartbeat and breathing with high precision. Moreover, they can detect changes in heartbeat-related sounds as sounds generated by heartbeat fluctuations with high precision, and they can detect changes in breathing-related sounds as sounds generated by breathing fluctuations with high precision.

[0010] Therefore, by using the right and left sound detection units to accurately detect heartbeat and respiratory fluctuations as information about mammalian organisms, the stress index calculated by the calculation unit becomes an accurate indicator of the mammal's current state. The prediction unit uses this accurate stress index to predict the duration of cardiopulmonary arrest in the mammal, thus improving the accuracy of the predicted cardiopulmonary arrest duration. This cardiopulmonary arrest duration is communicated by the notification unit, thus enabling precise monitoring of the cardiopulmonary arrest duration.

[0011] Furthermore, the mammalian cardiopulmonary arrest prediction device may further include a right temperature detection unit and a left temperature detection unit, which are used to detect temperature fluctuations in the right and left ventricular regions, respectively. In this case, the calculation unit can calculate the mammalian stress index based on the temperature fluctuations detected by the right and left temperature detection units, and the sounds generated by heartbeat fluctuations and breathing fluctuations detected by the right and left sound detection units.

[0012] According to this configuration, body temperature-related temperatures can be detected with high precision at both the right and left pterygium sites, and changes in temperature at these sites can be detected with high precision as temperature fluctuations. Furthermore, since body temperature-related temperature fluctuations are also used to predict the duration of cardiopulmonary arrest in mammals, the accuracy of the predicted duration of cardiopulmonary arrest is further improved.

[0013] A mammalian cardiopulmonary arrest prediction device may include: a right sensor unit that integrates the right sound detection unit and the right temperature detection unit; and a left sensor unit that integrates the left sound detection unit and the left temperature detection unit. This allows for easy simultaneous detection of temperature fluctuations, heart rate fluctuations, and respiratory fluctuations.

[0014] The computation unit can also obtain the heart rate based on the sound generated by the heartbeat fluctuation, estimate the heart rate variation based on the obtained heart rate, obtain the respiratory rate based on the sound generated by the respiratory fluctuation, estimate the respiratory variation based on the obtained respiratory rate, and calculate the stress index of mammals based on the estimated heart rate variation and the respiratory variation.

[0015] In addition, the calculation unit can also estimate body temperature changes based on temperature fluctuations, and perform calculations based on the body temperature changes when calculating the stress index of mammals.

[0016] Invention Effects As explained above, heart rate and respiratory fluctuations are detected at the right and left pterygium sites, respectively. The stress index calculated based on these detection results is used to predict the duration of cardiopulmonary arrest, thus enabling high-precision prediction of the duration of cardiopulmonary arrest in mammals. Attached Figure Description

[0017] Figure 1 This is a diagram showing the usage status of the mammalian cardiopulmonary arrest prediction device according to an embodiment of the present invention.

[0018] Figure 2 This is a block diagram of a cardiac arrest prediction device for mammals.

[0019] Figure 3 This is a graph showing the correlation between stress index and time before cardiopulmonary arrest (death).

[0020] Figure 4 This is a chart showing the relationship between the difference in temperature between the left and right pterygium in PCR-positive mild cases and the presence or absence of symptoms.

[0021] Figure 5 It is a chart showing the forehead temperature, left and right lateral canthal temperature, and axillary temperature of PCR-positive mild cases.

[0022] Figure 6 This is a diagram illustrating VR rehabilitation therapy for phantom limb pain. Detailed Implementation

[0023] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the following description of preferred embodiments is merely illustrative and is not intended to limit the invention, its applications, or its uses. Furthermore, the scope of the present invention is not limited to the following embodiments, but also includes other embodiments for which those skilled in the art can appropriately substitute the following embodiments.

[0024] Figure 1 This is a diagram showing the usage state of the mammalian cardiopulmonary arrest prediction device (hereinafter referred to as "cardiopulmonary arrest prediction device 1") according to an embodiment of the present invention. Figure 1 Symbol 100 in the diagram represents the person (the person being predicted) whose cardiopulmonary arrest duration is predicted by the cardiopulmonary arrest prediction device 1. Figure 1 In the middle, when observing the subject 100 from the rear, the right side of the subject 100 becomes... Figure 1 The right side of the predicted 100 becomes the left side of the predicted 100. Figure 1 On the left side of the face. Right Yifeng (Yìfēng) 101 and left Yifeng 102 (approximate locations shown by dotted lines), located directly behind the earlobe and ear root of the person being predicted (in Eastern medicine), respectively. Yifeng is the part that concaves when the mouth is opened wide.

[0025] It should be noted that the cardiopulmonary arrest prediction device 1 involved in this invention can be applied to humans, as well as to mammals other than humans. In mammals other than humans, there are also right pterygium 101 and left pterygium 102, just like in humans.

[0026] The cardiopulmonary arrest prediction device 1 includes a right sensor unit 2, a left sensor unit 3, and a main body unit 4. For example... Figure 2 As shown, the right sensor unit 2 has a right sound detection unit 21 and a right temperature detection unit 22, which are integrated into one unit. The right sensor unit 2 has sound and temperature detection surfaces, which are arranged to contact the surface (skin) of the right vent 101 portion. The left sensor unit 3 has a left sound detection unit 31 and a left temperature detection unit 32, which are integrated into one unit. The left sensor unit 3 also has sound and temperature detection surfaces, which are arranged to contact the surface (skin) of the left vent 102 portion. The right sensor unit 2 and the left sensor unit 3 can be identical.

[0027] In this embodiment, although it is not a necessary component, but as Figure 1As shown, a connecting portion 5 is provided to connect the right sensor section 2 and the left sensor section 3. The connecting portion 5 is, for example, a rod-shaped or plate-shaped part extending from the right sensor section 2 to the left sensor section 3, and is made of an elastic component. The connecting portion 5 is configured to generate a force in a direction that reduces the distance between the right sensor section 2 and the left sensor section 3. Therefore, if the detection surface of the right sensor section 2 is brought into contact with the surface of the right eccentricity 101 and the detection surface of the left sensor section 3 is brought into contact with the surface of the left eccentricity 102, the elasticity of the connecting portion 5 can press the detection surface of the right sensor section 2 against the surface of the right eccentricity 101 and the detection surface of the left sensor section 3 against the surface of the left eccentricity 102. This prevents the right sensor section 2 and the left sensor section 3 from detaching from the subject 100, thus enabling continuous detection of sound and temperature at the right eccentricity 101 and the left eccentricity 102 for a predetermined time.

[0028] The right sound detection unit 21 and the left sound detection unit 31 are each composed of a sound detection sensor using an ECM (electret condenser microphone) or the like. They are components used to detect sounds generated by heartbeat fluctuations at the right yoke 101 and left yoke 102, respectively, and to detect sounds generated by respiratory fluctuations at the right yoke 101 and left yoke 102, respectively. The right yoke 101 and left yoke 102 are close to a person's arteries and respiratory tract, thus enabling high-precision detection of sounds related to heartbeat and sounds related to respiration. Furthermore, in the event of changes in sounds related to heartbeat, these changes can be detected with high precision as sounds generated by heartbeat fluctuations, and in the event of changes in sounds related to respiration, these changes can be detected with high precision as sounds generated by respiratory fluctuations.

[0029] The sounds generated by heartbeat fluctuations and breathing fluctuations detected by the right sound detection unit 21 and the left sound detection unit 31 are converted into electrical signals (sound signals) and sent to the main unit 4. That is, the right sound detection unit 21 and the left sound detection unit 31 are connected to the main unit 4 in a communicative manner. The communication method is not particularly limited; it can be wired communication using a wired communication module or wireless communication using a wireless communication module.

[0030] The right temperature detection unit 22 and the left temperature detection unit 32 are both composed of temperature sensors capable of detecting temperature, and are components used to detect temperature fluctuations at the right eccentricity 101 and the left eccentricity 102, respectively. In both the right eccentricity 101 and the left eccentricity 102, temperatures related to body temperature can be detected with high precision, and changes in temperature at the right eccentricity 101 and the left eccentricity 102 can be detected with high precision as temperature fluctuations. The temperature at the right eccentricity 101 is referred to as the right eccentricity temperature, and the temperature at the left eccentricity 102 is referred to as the left eccentricity temperature.

[0031] Temperature fluctuations detected by the right temperature detection unit 22 and the left temperature detection unit 32 are converted into electrical signals and sent to the main unit 4.

[0032] like Figure 2 As shown, the main unit 4 includes an arithmetic unit 41, a prediction unit 42, a power supply unit 43, and a display unit 44. The arithmetic unit 41 and the prediction unit 42 are, for example, configured as a microcomputer including a processor and memory. The memory is configured to store computer-readable instructions (programs). For example, the memory can be configured as a ROM storing various programs, or a RAM having multiple working areas storing various programs executed by the processor. Alternatively, the memory can be configured as flash memory. The processor is, for example, a CPU, an MPU, and / or a GPU. The CPU can be composed of multiple CPU cores. The GPU can be composed of multiple GPU cores. Additionally, a storage unit (not shown) composed of storage devices such as HDDs, SSDs, and flash memory can also be provided in the main unit 4. Various types of data can be stored in the storage unit.

[0033] exist Figure 2 The arithmetic unit 41 and the prediction unit 42 are separately described, but they can also be configured as a microcomputer. The power supply unit 43 is the part that supplies power to the arithmetic unit 41, the prediction unit 42, and the display unit 44, for example, it receives power input from a battery or commercial power supply. The power supply unit 43 can also supply power to the right sensor unit 2 and the left sensor unit 3 as needed. The display unit 44 is configured as, for example, a liquid crystal display or an organic EL display.

[0034] The arithmetic unit 41 includes a sound signal processing unit 41a, a heart rate estimation unit 41b, and a respiratory rate estimation unit 41c. The sound signal processing unit 41a receives sound signals output from the right sound detection unit 21 and the left sound detection unit 31. When the sound signal processing unit 41a receives the right-side sound signal output from the right sound detection unit 21, it processes the sound signal for a predetermined time, for example, approximately 30 seconds. Specifically, the sound signal processing unit 41a performs a Fourier transform on the predetermined time sound signal to generate a right-side transformed signal. It also performs a Fourier transform on the predetermined time sound signal output from the left sound detection unit 31 to generate a left-side transformed signal.

[0035] The right-side transformation signal and the left-side transformation signal generated by the sound signal processing unit 41a are sent to the heart rate estimation unit 41b and the respiratory rate estimation unit 41c. The heart rate estimation unit 41b is equipped with a high-pass filter as a low-frequency component removal unit for removing low-frequency components. The right-side transformation signal and the left-side transformation signal are respectively input into the high-pass filter to remove signals with frequencies less than 0.8Hz, generating a right-side low-frequency removed signal and a left-side low-frequency removed signal. The heart rate estimation unit 41b determines a peak formed by a signal with a predetermined intensity or higher in the right-side low-frequency removed signal. The heart rate is obtained based on the peak determined from the right-side low-frequency removed signal. In addition, the heart rate estimation unit 41b determines a peak formed by a signal with a predetermined intensity or higher in the left-side low-frequency removed signal. The heart rate is obtained based on the peak determined from the left-side low-frequency removed signal. In this way, the calculation unit 41 obtains the heart rate based on the sounds generated by heartbeat fluctuations detected by the right sound detection unit 21 and the left sound detection unit 31, and estimates the heart rate variation based on the obtained heart rate.

[0036] The respiratory rate estimation unit 41c includes a low-pass filter as a high-frequency component removal unit. The transformed signals from the right and left sides are input to the low-pass filter to remove signals with frequencies above 0.5 Hz, generating a high-frequency removed signal from the right and a high-frequency removed signal from the left. The respiratory rate estimation unit 41c determines peaks formed by signals with a predetermined intensity or higher in the high-frequency removed signal from the right side. The respiratory rate is obtained based on the peaks determined from the high-frequency removed signal from the right side. Similarly, the respiratory rate estimation unit 41c determines peaks formed by signals with a predetermined intensity or higher in the high-frequency removed signal from the left side. The respiratory rate is obtained based on the peaks determined from the high-frequency removed signal from the left side. Thus, the calculation unit 41 obtains the respiratory rate based on the sounds generated by respiratory fluctuations detected by the right sound detection unit 21 and the left sound detection unit 31, and estimates respiratory changes based on the obtained respiratory rate.

[0037] The calculation unit 41 calculates the stress index of mammals based on temperature fluctuations detected by the right temperature detection unit 22 and the left temperature detection unit 32, and sounds generated by heartbeat fluctuations and breathing fluctuations detected by the right sound detection unit 21 and the left sound detection unit 31. When calculating the stress index (SI), it is based on heart rate changes estimated by the heart rate estimation unit 41b and breathing changes estimated by the respiratory rate estimation unit 41c.

[0038] Specifically, the stress index is calculated using the following formula (1).

[0039] Stress Index (SI) = Parasympathetic Activity / Sympathetic Activity Respiratory variability (σRR) / Heart rate variability (σHR) (Equation 1) In the above formula (1), σ is the variance.

[0040] When performing frequency analysis on an electrocardiogram (ECG) to obtain the power spectrum, it is divided into two regions: LF (Low Frequency) and HF (High Frequency). The LF component increases with activation of the sympathetic or parasympathetic nervous system, while the HF component increases with activation of the parasympathetic nervous system. Therefore, by using the LF / HF ratio as an indicator, a high value can be interpreted as stress, and a low value as relaxation.

[0041] Alternatively, the stress index can be evaluated by extracting the high-frequency component (HF component) corresponding to respiratory changes and the low-frequency component (LF component) corresponding to the Mayer wave, which represents blood pressure changes, and comparing their magnitudes. That is, the calculation unit 41 can also calculate the ratio of the high-frequency component corresponding to respiratory changes to the low-frequency component corresponding to the Mayer wave, which represents blood pressure changes, i.e., LF / HF, as the stress index.

[0042] For example, the LF / HF ratio can be evaluated by using the sum (integral value) of the intensities of the LF component region (0.05 Hz to 0.15 Hz) and the HF component region (0.15 Hz to 0.40 Hz) of the power spectrum.

[0043] In a relaxed state, i.e., when the parasympathetic nervous system is activated, both the high-frequency (HF) component reflecting respiratory changes and the low-frequency (LF) component reflecting blood pressure changes are present. However, in a stressed state, i.e., when the sympathetic nervous system is activated, the LF component is present, while the HF component decreases. Therefore, in a relaxed state, the HF component is relatively larger, resulting in a smaller LF / HF ratio; conversely, in a stressed state, the LF component is relatively larger than the HF component, resulting in a larger LF / HF ratio.

[0044] For example Figure 3 The graph shows the correlation between stress index and time before cardiopulmonary arrest (death), with the vertical axis representing the stress index and the horizontal axis representing the time of cardiopulmonary arrest. Absolutely unstable and absolutely stable refer to signs of death observed approximately 48 hours before cardiopulmonary arrest (death). The following patterns were observed in terminally ill patients using the cardiopulmonary arrest prediction device 1.

[0045] The following pattern is shown as a sign of death observed approximately 48 hours before death.

[0046] Stable stress index → ​​Absolute instability of stress index (moving upwards on the chart) → Absolute stabilization (moving downwards on the chart) → Absolute instability (moving upwards on the chart) → Progression of absolute instability (moving further upwards on the chart) → Cardiopulmonary arrest (death) That is, when the prediction unit 42 obtains the stress index calculated by the calculation unit 41 and analyzes the changes, it can use the above pattern to predict the time of cardiopulmonary arrest (death).

[0047] The calculation unit 41 can also estimate body temperature changes based on temperature fluctuations detected by the right temperature detection unit 22 and the left temperature detection unit 32. That is, the right pterygium 101 and the left pterygium 102 are areas where mammals are prone to body temperature changes, so by detecting the temperature of the right pterygium 101 and the left pterygium 102, the body temperature changes of mammals can be estimated with high accuracy. In this case, when calculating the stress index, the calculation unit 41 performs calculations based on heart rate changes estimated by the heart rate estimation unit 41b, respiratory changes estimated by the respiratory rate estimation unit 41c, and body temperature changes.

[0048] In cases involving changes in body temperature, the stress index is specifically calculated using the following formula (2). Stress Index (SI) = Respiratory variation (σRR) × Heart rate variation (σHR) × Body temperature variation (σBT) Equation (2) In the above formula (2), σ is the variance.

[0049] The prediction unit 42 predicts a portion of the period of cardiopulmonary arrest in mammals based on the stress index calculated by the calculation unit 41. For example, the higher the stress index calculated by the calculation unit 41, the closer the predicted period of cardiopulmonary arrest; conversely, the lower the stress index calculated by the calculation unit 41, the farther the predicted period of cardiopulmonary arrest. The period of cardiopulmonary arrest can be predicted in the form of several hours after the current time, or several minutes after the current time, or the date and time of cardiopulmonary arrest can be predicted.

[0050] Display unit 44 displays the cardiopulmonary arrest period predicted by prediction unit 42. For example, display unit 44 can display how many hours or minutes after the current time have elapsed since the last moment, or the date and time of cardiopulmonary arrest. This allows the user (medical professional, etc.) of the cardiopulmonary arrest prediction device 1 to know the cardiopulmonary arrest period of a mammal. Display unit 44 is an example of a notification unit that notifies the user of the cardiopulmonary arrest period predicted by prediction unit 42.

[0051] Additionally, the cardiopulmonary arrest prediction device 1 may include a speaker (not shown), in which case the user of the cardiopulmonary arrest prediction device 1 can be informed of the cardiopulmonary arrest period predicted by the prediction unit 42 by sound. The speaker is another example of a notification unit that informs the user of the cardiopulmonary arrest period predicted by the prediction unit 42.

[0052] Here, an example is given to accurately represent the state of the subject 100 by showing the temperature at the right yoke 101 and left yoke 102 locations. Figure 4 This chart shows the relationship between the difference in throat temperature and the presence or absence of symptoms (throat discomfort) in approximately 50 PCR-positive patients with mild symptoms, identified by PCR testing as infected with the Omega globus strain (a type of COVID-19). The measurements were taken at the Hiroshima Western Infusion Center. The vertical axis of the chart represents the left throat temperature (°C), and the horizontal axis represents the right throat temperature (°C). The values ​​in the chart represent the difference between the left and right throat temperatures (°C). Additionally, Figure 5 This is a chart showing the forehead temperature, left and right lateral canthal temperature, and axillary temperature of individuals with mild PCR-positive symptoms.

[0053] In the doctor's Figure 4 When examining 50 patients with mild PCR-positive symptoms in the chart, even those diagnosed with infection via PCR testing showed that, with a small difference in temperature between the left and right pterygium, regardless of whether there was a difference in throat discomfort, the symptoms caused by Omega globus infection were mild, and the vast majority were ultimately diagnosed as requiring only follow-up observation. On the other hand, for patients with a temperature difference of more than 1°C between the left and right pterygium and a difference in throat discomfort, the vast majority were judged to have severe symptoms caused by Omega globus infection and required immediate medication and other treatments.

[0054] Thus, the left and right pterygoid temperatures not only indicate temperatures related to mammalian body temperature but also provide highly accurate biological information about the mammalian's state. Therefore, by using fluctuations in left and right pterygoid temperatures to calculate the stress index, the accuracy of mammalian stress index calculations is improved. Furthermore, left and right pterygoid temperatures can also be used to monitor infections. Additionally, left and right pterygoid temperatures can be used for remote medical monitoring of patients with triglyceride-depleted cardiomyopathy (TGCV).

[0055] Figure 6 This diagram illustrates VR rehabilitation therapy for phantom limb pain. VR rehabilitation refers to rehabilitation where patients, wearing virtual reality goggles, move their hands and feet according to images projected through the goggles. It can be used not only for phantom limb pain but also for hyperesthesia.

[0056] By wearing the right sensor unit 2 and the left sensor unit 3 on the patient during rehabilitation, the patient's right pterygium temperature and left pterygium temperature can be measured simultaneously. The following shows the measurement results of blood pressure, pulse, oxygen saturation, right pterygium temperature, and right pterygium temperature before and after rehabilitation for patients 1 to 3 during advanced brain function rehabilitation.

[0057] Patient 1 Advanced Brain Function Training (Before) Advanced Brain Function Training (After) Blood pressure (systolic / diastolic): 197 / 73, 164 / 80 Pulse 70 73 Oxygen saturation 99% 100% The temperature in the right wing area is 34.6℃ to 36.0℃. The temperature in Zuoyifeng was 34.4℃ and 36.1℃. Patient 2 Advanced Brain Function Training (Before) Advanced Brain Function Training (After) Blood pressure (systolic / diastolic): 195 / 83, 182 / 76 Pulse 75 69 Oxygen saturation 97% 99% The temperature in the right wing area is 35.4℃ and 36.3℃. The temperature in Zuoyifeng is 34.5℃ to 36.0℃. Patient 3 Advanced Brain Function Training (Before) Advanced Brain Function Training (After) Blood pressure (systolic / diastolic): 165 / 71 156 / 72 Pulse 63 64 Oxygen saturation: 98% - 96% The temperature in the right wing area is 35.3℃ to 35.4℃. The temperature in Zuoyifeng is 35.3℃ to 35.6℃. As mentioned above, the temperatures of both the right and left pterygium were higher after rehabilitation compared to before. This is because increased blood flow to the brain through brain activity leads to higher temperatures in both the right and left pterygium. Therefore, the temperatures of the left and right pterygium are biological information that can accurately reflect the state of mammals.

[0058] The above embodiments are merely illustrative in all respects and should not be interpreted as limiting. Furthermore, all modifications and alterations falling within the equivalent scope of the claims are within the scope of this invention.

[0059] Industrial utilization potential As described above, the mammalian cardiopulmonary arrest prediction device of the present invention can be used, for example, to predict the duration of cardiopulmonary arrest in humans.

[0060] Explanation of reference numerals in the attached figures 1. Cardiopulmonary arrest prediction device for mammals 2. Right sensor section 3. Left sensor section 21 Right Sound Detection Section 22 Right Temperature Detection Unit 31 Left Sound Detection Section 32 Left Temperature Detection Section 41. Arithmetic Unit 42 Forecasting Department 44 Display Department (Notification Department). Claims (as amended under Article 19 of the Treaty) 1. [Modified] A device for predicting cardiopulmonary arrest in mammals, characterized in that it predicts the duration of cardiopulmonary arrest in mammals, the cardiopulmonary arrest prediction device comprising: The right sound detection unit and the left sound detection unit are used to detect the sounds produced by heartbeat fluctuations in mammals at the right and left pterygium sites, respectively, and to detect the sounds produced by respiratory fluctuations at the right and left pterygium sites, respectively. The computation unit calculates the stress index of mammals based on the sounds generated by heartbeat fluctuations and sounds generated by breathing fluctuations detected by the right sound detection unit and the left sound detection unit. The prediction unit predicts the period of cardiopulmonary arrest in mammals based on the stress index calculated by the calculation unit. The notification department notifies the cardiopulmonary arrest period predicted by the prediction department; and The right temperature detection unit and the left temperature detection unit are used to detect temperature fluctuations in the right and left vent areas, respectively. The calculation unit calculates the stress index of mammals based on the temperature fluctuations detected by the right and left temperature detection units, and the sounds generated by heartbeat fluctuations and breathing fluctuations detected by the right and left sound detection units. 2. [Modified] The mammalian cardiopulmonary arrest prediction device according to claim 1, wherein the cardiopulmonary arrest prediction device comprises: The right sensor unit integrates the right sound detection unit and the right temperature detection unit; and The left sensor unit integrates the left sound detection unit and the left temperature detection unit. 3. [Modified] The mammalian cardiopulmonary arrest prediction device according to claim 1, wherein, The computation unit obtains the heart rate based on the sound generated by the heartbeat fluctuation, estimates the heart rate variation based on the obtained heart rate, obtains the respiratory rate based on the sound generated by the respiratory fluctuation, estimates the respiratory variation based on the obtained respiratory rate, and calculates the stress index of the mammal based on the estimated heart rate variation and the respiratory variation. 4. [Modified] The mammalian cardiopulmonary arrest prediction device according to claim 1, wherein, The calculation unit estimates body temperature changes based on temperature fluctuations, and performs calculations based on these body temperature changes when calculating the stress index of mammals. 5. [Delete]

Claims

1. A mammalian cardiopulmonary arrest prediction device, characterized in that, The cardiopulmonary arrest prediction device for predicting the duration of cardiopulmonary arrest in mammals includes: The right sound detection unit and the left sound detection unit are used to detect the sounds produced by heartbeat fluctuations in mammals at the right and left pterygium sites, respectively, and to detect the sounds produced by respiratory fluctuations at the right and left pterygium sites, respectively. The computation unit calculates the stress index of mammals based on the sounds generated by heartbeat fluctuations and sounds generated by breathing fluctuations detected by the right sound detection unit and the left sound detection unit. The prediction unit, based on the stress index calculated by the calculation unit, predicts the duration of cardiopulmonary arrest in mammals; and The notification department notifies the cardiopulmonary arrest period predicted by the prediction department.

2. The mammalian cardiopulmonary arrest prediction device according to claim 1, wherein, The cardiopulmonary arrest prediction device also features: The right temperature detection unit and the left temperature detection unit are used to detect temperature fluctuations in the right and left vent areas, respectively. The calculation unit calculates the stress index of mammals based on the temperature fluctuations detected by the right and left temperature detection units, and the sounds generated by heartbeat fluctuations and breathing fluctuations detected by the right and left sound detection units.

3. The mammalian cardiopulmonary arrest prediction device according to claim 2, wherein, The cardiopulmonary arrest prediction device includes: The right sensor unit integrates the right sound detection unit and the right temperature detection unit; and The left sensor unit integrates the left sound detection unit and the left temperature detection unit.

4. The mammalian cardiopulmonary arrest prediction device according to claim 1, wherein, The computation unit obtains the heart rate based on the sound generated by the heartbeat fluctuation, estimates the heart rate variation based on the obtained heart rate, obtains the respiratory rate based on the sound generated by the respiratory fluctuation, estimates the respiratory variation based on the obtained respiratory rate, and calculates the stress index of the mammal based on the estimated heart rate variation and the respiratory variation.

5. The mammalian cardiopulmonary arrest prediction device according to claim 2, wherein, The calculation unit estimates body temperature changes based on temperature fluctuations, and performs calculations based on these body temperature changes when calculating the stress index of mammals.

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