Medical device and snoring detection method

Abstract

To accurately detect an occurrence of snoring.SOLUTION: A medical device includes: a generation device for generating a high-frequency data (302) from a flow data (301) representing a time series of a respiratory flow of a patient; a determination part for determining an evaluation value of the high-frequency data in each period for each of a plurality of periods (304_1-304_n); a selection part for selecting one period of the plurality of periods based on the evaluation values of the plurality of periods; and a detection part for detecting an occurrence of snoring of the patient by analyzing a portion of the selected one period of the flow data.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a medical device and a method for detecting snoring. [Background technology]

[0002] Nasal continuous positive airway pressure (CPAP) is known as a treatment for sleep apnea syndrome (SAS). CPAP is a treatment that continuously delivers air at an appropriate pressure to the patient's airway through a nasal mask, preventing airway obstruction during sleep and thereby suppressing the occurrence of apnea and hypopnea. Snoring can be a precursor to the onset of apnea or hypopnea. In Patent Document 1, snoring is detected when the value obtained by subtracting the flow rate during the expiratory period from the flow rate during the inhalation period exceeds a threshold value. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2009-522026 Summary of the Invention [Problem to be solved by the invention]

[0004] It is difficult to accurately detect snoring with the method of Patent Document 1. An object of some aspects of the present invention is to provide a technique for accurately detecting the occurrence of snoring. [Means for solving the problem]

[0005] According to some embodiments, there is provided a medical device comprising: a generating means for generating high frequency data from flow data representing a time series of a patient's respiratory flow; a determining means for determining an evaluation value of the high frequency data for each of a plurality of periods; a selecting means for selecting one of the plurality of periods based on the evaluation value of the plurality of periods; and a detecting means for detecting the occurrence of snoring in the patient by analyzing a portion of the flow data for the selected one period. [Effects of the Invention]

[0006] According to some embodiments, the occurrence of snoring can be detected with high accuracy. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram illustrating an example of the configuration of a CPAP device according to some embodiments. [Figure 2] 1 is a flow diagram illustrating an example of a snore detection method according to some embodiments. [Figure 3] FIG. 10 is a schematic diagram illustrating an example of a method for determining an analysis period according to some embodiments. [Figure 4] Schematic diagrams illustrating examples of detection conditions according to some embodiments. [Figure 5] 1 is a flow diagram illustrating an example method for varying therapeutic pressure according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention as claimed, and not all combinations of features described in the embodiments are necessarily essential to the invention. Two or more of the features described in the embodiments may be combined in any desired manner. Furthermore, the same reference numerals are used to designate identical or similar components, and redundant descriptions will be omitted.

[0009] Referring to FIG. 1, a configuration example of a continuous positive airway pressure device (hereinafter referred to as a CPAP device) 100 according to some embodiments will be described. The CPAP device 100 is an example of a medical device having a snoring detection function. The CPAP device 100 includes a main body 101, a mask 125, and a tube 122 connecting the main body 101 and the mask 125. The operation of the CPAP device 100 is realized by a central processing unit (CPU) 112 reading a program stored in a read-only memory (ROM) 113 into a random access memory (RAM) 114 and executing the program. In this manner, a device including the CPU 112, the ROM 113, and the RAM 114 may be considered as a computer. Note that functional blocks 117 to 121 described within the CPU 112 are schematic representations of major functions among various functions realized by the CPU 112 executing the program. Therefore, the operations described mainly based on the functional blocks 117 to 121 are actually realized by the CPU 112 executing the program. Alternatively, one or more of the functional blocks may be implemented using hardware circuits other than the CPU 112.

[0010] First, the components present in the air flow path will be described. Filter 102 is provided at the air intake port and removes pollen, bacteria, dust, etc. Temperature sensor 103 measures the temperature of the air that has flowed in. The measurement value by temperature sensor 103 is supplied to temperature control unit 119. Humidity sensor 104 measures the humidity of the air that has flowed in. The measurement value by humidity sensor 104 is supplied to temperature control unit 119.

[0011] The flow (differential pressure) sensor 105 (hereinafter simply referred to as flow sensor 105) is, for example, a differential pressure type flow sensor, which measures the flow rate of air in the flow path based on the pressure difference between the upstream and downstream sides. Here, it is assumed that a positive measurement value is obtained when the upstream pressure is higher than the downstream pressure, and a negative measurement value is obtained when the downstream pressure is higher than the upstream pressure. Therefore, the measurement value of the flow sensor 105 can also be used to determine the direction of air flow in the flow path. The measurement value of the flow sensor 105 is supplied to the respiration analysis unit 117.

[0012] The blower 106 has an impeller and a motor that drives the impeller inside. The treatment pressure control unit 118 controls the rotation speed of the motor through the motor driver 108, thereby adjusting the flow rate and supply pressure of the air supplied to the patient.

[0013] Pressure sensor 107 is provided downstream of blower 106 in the flow path and measures the pressure in the flow path. The measurement value of pressure sensor 107 is supplied to treatment pressure control unit 118. Treatment pressure control unit 118 controls the supply pressure assuming that air is being supplied to the patient at the pressure measured by pressure sensor 107.

[0014] The humidifier 109 has a water storage tank and humidifies the air supplied to the tube 122. Here, the amount of water evaporated from the water storage tank, i.e., the degree of humidification, is controlled by a temperature control unit 119 controlling the temperature of a heater 110 provided in the humidifier 109. A temperature sensor 111 measures the temperature of the heater 110 and supplies it to the temperature control unit 119. In this embodiment, the heater 110 is used in the humidifier 109 to humidify and adjust the temperature of the air supplied to the patient. Note that the temperature of the air can also be adjusted by other methods, such as using a heater 123 provided in the tube 122. If humidification and temperature adjustment are performed separately, the heater 110 and temperature sensor 111 do not need to be provided in the humidifier 109. Alternatively, the air may be blown onto the surface of the water in the water storage tank, or a flow path may be arranged so that the air passes through the water.

[0015] Tube 122 connects main body 101 and mask 125. Tube 122 is stretchable and flexible so that it can easily follow the movement of mask 125. Tube 122 is provided with a temperature sensor 124 that measures the temperature of the air supplied to the patient. The measurement value of temperature sensor 124 is supplied to temperature control unit 119.

[0016] The mask 125 is sized and shaped to cover the patient's nose and mouth, and is attached to the patient by adjustable straps or bands.

[0017] The display unit 115 is, for example, a display provided on the housing of the main body 101, and displays messages regarding the use of the CPAP device 100, various menu screens for setting the CPAP device 100, measurement values ​​of various sensors, etc. The display on the display unit 115 is controlled by the input / output control unit 120.

[0018] Operation unit 116 is a general term for input devices that can be operated by the user, such as buttons and switches provided on the housing of main body 101. If display unit 115 is a touch display, display unit 115 and operation unit 116 are integrated into one unit. An operation on operation unit 116 is detected by input / output control unit 120, and CPU 112 executes an operation according to the detected operation.

[0019] The respiration analysis unit 117 detects the occurrence of a predetermined event based on the flow rate measured by the flow sensor 105. When the respiration analysis unit 117 detects the occurrence of a predetermined event, it notifies the therapeutic pressure control unit 118. In this embodiment, the respiration analysis unit 117 detects the occurrence of snoring by the patient.

[0020] Based on the measurement value of pressure sensor 107, therapeutic pressure control unit 118 controls the operation of blower 106 so that the supply pressure becomes the target value. Furthermore, therapeutic pressure control unit 118 switches between controlling the supply pressure when the patient is inhaling and controlling the supply pressure when the patient is exhaling, in response to a notification from breathing analysis unit 117. The therapeutic pressure control unit 118 controls the supply pressure by controlling the rotation speed of the impeller of blower 106, for example, by providing motor driver 108 with a duty ratio for the pulse voltage applied to the motor. The therapeutic pressure control unit 118 notifies temperature control unit 119 of the currently set supply pressure.

[0021] Temperature control unit 119 controls the temperature and humidity of the air supplied to the patient by controlling the operation of heater 110 in accordance with the measured values ​​of temperature sensor 103, humidity sensor 104, temperature sensor 111, and temperature sensor 124 and the supply pressure notified by treatment pressure control unit 118. The temperature and humidity of the air supplied to the patient may be the temperature and humidity set by the user via operation unit 116. The user setting via operation unit 116 is notified to temperature control unit 119 via, for example, input / output control unit 120. The supply pressure is taken into consideration because, even if the temperature of heater 110 is constant, the degree of increase in air temperature is lower when the flow rate is high than when the flow rate is low.

[0022] The communication control unit 121 executes processes related to communication between the main body 101 and the external system 130. The communication control unit 121 can execute communication with the external system 130 in accordance with, for example, one or more well-known wireless and / or wired communication standards. The external system 130 may be, for example, an in-hospital management system for diagnosis and treatment data or a remote management system for the CPAP device 100.

[0023] A method executed by the CPAP device 100 to detect snoring will be described with reference to FIG. 2. Snoring may be a vibration sound generated in the nasopharynx when air passes through a narrowed airway while the patient is sleeping. In the following description, each step of the method of FIG. 2 is executed by the CPU 112 (e.g., its respiration analysis unit 117). Specifically, each step is performed by the CPU 112 executing a program read into the RAM 114. Alternatively, at least some of the steps of the method of FIG. 2 may be executed by a dedicated circuit such as an ASIC (Application Specific Integrated Circuit). The method of FIG. 2 may be started in response to a patient's instruction to start an operation by the CPAP device 100 (e.g., a therapeutic operation during sleep), in response to the CPAP device 100 detecting that the patient has fallen asleep, or in response to some other event.

[0024] During execution of the method of FIG. 2, values ​​measured by the flow sensor 105 are continuously supplied to the CPU 112 (e.g., its respiration analysis unit 117). As described above, the values ​​measured by the flow sensor 105 represent the patient's respiratory flow rate. The CPU 112 samples the flow rate supplied by the flow sensor 105 at a predetermined sampling interval (e.g., 2 ms) and stores the flow rate at each time in the RAM 114 as flow rate data. This flow rate data represents a time series of the patient's respiratory flow rate. As will be described in detail below, the CPU 112 analyzes this flow rate data to detect the occurrence of snoring.

[0025] In S201, the CPU 112 determines whether the patient's current breathing has ended. If it is determined that the patient's current breathing has ended ("YES" in S201), the CPU 112 transitions the process to S202, and otherwise ("NO" in S201), repeats S201. In this way, the CPAP device 100 waits until the patient's current breathing has ended.

[0026] The CPU 112 may detect the end of the current breath based on the flow rate data. The start and end of a breath may be arbitrary. In the following description, the start of a breath is defined as the start of a patient's inhalation, and the end of a breath is defined as the end of a breath. In this case, the CPU 112 may detect the end of a breath based on a change in the flow rate from a value smaller than a predetermined threshold to the threshold. The predetermined threshold may be zero or a non-zero value (positive or negative). Alternatively, the start of a breath may be defined as the start of a patient's exhalation, and the end of a breath may be defined as the end of a breath. In this case, the CPU 112 may detect the end of a breath based on a change in the flow rate from a value larger than a predetermined threshold to the threshold. The predetermined threshold may be zero or a non-zero value (positive or negative).

[0027] The period during which one breath is taken is referred to as a breathing period. The period during which inhalation is taken during the breathing period is referred to as an inhalation period. The inhalation period may be a period during which flow rate data is greater than a predetermined threshold (e.g., zero or a positive value). The period during which exhalation is taken during the breathing period is referred to as an exhalation period. The exhalation period may be a period during which flow rate data is less than a predetermined threshold (e.g., zero or a negative value).

[0028] In S202, CPU 112 determines a period of time during which flow data is to be analyzed to detect the occurrence of snoring, from among the most recently completed breathing periods. In the following description, the period during which flow data is to be analyzed is referred to as an analysis period. Details of how to determine the analysis period will be described later.

[0029] In S203, CPU 112 analyzes the portion of the flow data corresponding to the analysis period to determine whether the detection condition for detecting snoring is met. If CPU 112 determines that the detection condition is met (YES in S203), it transitions the process to S204, and otherwise (NO in S203), it transitions the process to S207. Details of the detection condition will be described later.

[0030] In S204, the CPU 112 increments by 1 a counter for counting the number of consecutive breaths for which the detection condition is determined to be satisfied. In the following description, this counter will be referred to as a consecutive detection counter. The consecutive detection counter is initialized to zero at the start of FIG. 2. In S207, the CPU 112 resets the consecutive detection counter to zero.

[0031] In S205, CPU 112 determines whether the value of the consecutive detection counter is equal to or greater than a predetermined threshold number of times. If it is determined that the value of the consecutive detection counter is equal to or greater than the predetermined threshold number of times (YES in S205), CPU 112 transitions the process to S206, and otherwise (NO in S205), transitions the process to S201.

[0032] In S206, the CPU 112 detects that snoring occurred in the most recently completed breath. At this time, the CPU 112 may store the duration of this breath in the RAM 114 as the snoring duration. When snoring is detected in multiple consecutive breaths, the CPU 112 may store the total duration of these multiple breaths as the snoring duration. Alternatively, the CPU 112 may store the total duration of multiple consecutive breaths for which it is determined that the detection condition is satisfied as the snoring duration. The snoring duration stored in the RAM 114 may be presented to the patient or a doctor after the patient wakes up.

[0033] The threshold number of times used in S205 may be, for example, one time. In this case, CPU 112 detects the occurrence of snoring when breathing that satisfies the detection condition occurs once. Alternatively, the threshold number of times used in S205 may be, for example, two or more times (for example, three times). In this case, CPU 112 detects the occurrence of snoring when breathing that satisfies the detection condition occurs two or more times (for example, three times) in succession. In this way, by detecting the occurrence of snoring when the detection condition is satisfied multiple times in succession, it is possible to suppress erroneous detection due to body movement, mask slippage, etc.

[0034] As described above, in the method of FIG. 2, CPU 112 executes steps S202 to S206 each time the patient finishes breathing. Alternatively, CPU 112 may execute steps S202 and subsequent steps each time two or more predetermined number of breaths are completed. For example, CPU 112 (e.g., breathing analysis unit 117) counts the number of breaths taken by the patient since the most recent execution of steps S202 and subsequent steps. CPU 112 executes steps S202 and subsequent steps when the counted value of the number of breaths reaches a predetermined number. CPU 112 may execute steps S203 to S206 for each of the most recent predetermined number of breaths, starting with the most recent breath. Even in this case, the same results as when steps S202 to S206 are executed each time the patient finishes breathing can be obtained. Alternatively, CPU 112 may execute steps S203 to S206 for a portion of the most recent predetermined number of breaths. For example, the CPU 112 may execute steps S203 to S206 for the most recent one breath among the most recent predetermined number of breaths. In this case, steps S203 to S206 are executed for the discrete breaths.

[0035] Next, the method for determining the analysis period in S202 of Fig. 2 will be described in detail with reference to Fig. 3. Graph 301 in Fig. 3 shows an example of flow data. The horizontal axis of graph 301 represents time, and the vertical axis of graph 301 represents flow rate. Although Fig. 3 shows the flow rate data continuously, as mentioned above, the flow rate data may be digital data. Graph 301 focuses on the period of one breath in the flow rate data.

[0036] In the example of graph 301, inspiration begins at time t1. Inspiration ends and expiration begins at time t4. Graph 301 includes high-frequency components caused by snoring from time t2 to t3. Snoring generally has a frequency of 10 Hz or higher. When snoring occurs, high-frequency components of 10 Hz or higher are superimposed on the flow rate.

[0037] The CPU 112 may determine the inhalation period 303 (times t1 to t4) of one breathing period as the analysis target period. It is known that snoring is more likely to occur during inhalation than exhalation. Exhalation may contain noise caused by air turbulence caused by the patient's exhalation (for example, air turbulence caused by the collision of air supplied from the CPAP device 100 with the patient's exhaled air). Therefore, if snoring is detected based on the difference between the amount of noise during inhalation and the amount of noise during exhalation, it may not be possible to accurately detect snoring. In this embodiment, the CPU 112 includes the inhalation period 303 in the analysis target period and excludes the exhalation period, thereby enabling accurate detection of snoring.

[0038] The CPU 112 may determine the entire inhalation period 303 as the analysis period. Alternatively, the CPU 112 may determine only a part of the inhalation period 303 where snoring is thought to occur as the analysis period. This can reduce calculation costs and improve the accuracy of detecting the occurrence of snoring compared to when the entire inhalation period 303 is used as the analysis period.

[0039] A method for determining only a portion of the inspiration period 303 as the analysis period will be described below. The CPU 112 applies a filter that blocks low frequency components to the flow rate data. The data generated by this filtering is referred to as high frequency data. The filter that blocks low frequency components may be a high pass filter or a band pass filter. For example, the filter that blocks low frequency components may block frequency components below a specific frequency (e.g., 4.3 Hz) within the range of 4 to 10 Hz, and pass frequency components above this frequency.

[0040] When the flow rate data is digital data, the CPU 112 may perform filtering by subtracting a moving average from the flow rate data. For example, when the sampling interval of the flow rate data is 2 ms, the CPU 112 may generate high-frequency data by subtracting a 51-point moving average from the flow rate data.

[0041] Graph 302 in Figure 3 represents high-frequency data generated by applying a high-pass filter with a cutoff frequency of 4.3 Hz to the flow rate data represented by graph 301. The horizontal axis of graph 302 represents time, and the vertical axis of graph 302 represents flow rate.

[0042] The CPU 112 may determine the analysis period based on the high-frequency data represented by the graph 302. For example, the CPU 112 may determine the analysis period to be a period having a predetermined time length (a part of the inhalation period 303 of one breath, for example, a value within a range of 400 ms to 1500 ms, for example, 1024 ms) centered on the time when the absolute value of the high-frequency data is maximum. If the period having the predetermined time length centered on the time when the absolute value of the high-frequency data is maximum includes a portion that is not included in the inhalation period 303, the CPU 112 may shift this period so that the entire period is included in the inhalation period 303. In this case, the CPU 112 may shift the analysis period while maintaining the predetermined time length so that the start point of the analysis period coincides with the start point of the inhalation period 303 or so that the end point of the analysis period coincides with the end point of the inhalation period 303. In addition, the CPU 112 may not maintain a predetermined time length, but may change (shorten or extend) the analysis period including the portion before the start of the inhalation period 303 or the analysis period including the portion after the end of the inhalation period 303 so that the start of the analysis period coincides with the start of the inhalation period 303 or so that the end of the analysis period coincides with the end of the inhalation period 303.

[0043] Alternatively, the CPU 112 may determine one of the plurality of candidate periods 304_1 to 304_n included in the inspiration period 303 of one breath as the analysis target period. The plurality of candidate periods 304_1 to 304_n are collectively referred to as candidate periods 304. The following description of one candidate period 304 also applies to all of the plurality of candidate periods 304_1 to 304_n. The subscript of the candidate period 304 has a smaller value the closer the starting point of the candidate period 304 is to the starting point of the inspiration period 303 (i.e., the further in the past the time is). Hereinafter, the plurality of candidate periods 304_1 to 304_n will simply be referred to as the plurality of candidate periods 304.

[0044] In the example of FIG. 3, multiple candidate periods 304 having the same time length are arranged at equal intervals so as to cover the entire inhalation period 303. In other words, all of the multiple candidate periods 304 have the same time length. The time length of the candidate periods 304 may be, for example, a value within a range of 400 ms to 1500 ms, such as 1024 ms. The starting points of the multiple candidate periods 304 are aligned at equal time intervals. This interval may be wider than or equal to the sampling interval of the flow rate data (for example, 2 ms). This interval may be, for example, a value within a range of 30 ms to 150 ms, such as 64 ms. If this interval is too short, the number of candidate periods 304 will increase, increasing the calculation cost of the processing described below. On the other hand, if this interval is too long, the number of candidate periods 304 will decrease, resulting in fewer options for the analysis period. The interval between the starting points of the multiple candidate periods 304 may be a divisor of the above-mentioned analysis period. The ending points of the multiple candidate periods 304 are also aligned at the same time intervals. The start point of the first candidate period 304_1 coincides with the start point of the inspiration period 303. Alternatively, the start point of the first candidate period 304_1 may be after the start point of the inspiration period 303. The end point of the last candidate period 304_n coincides with the end point of the inspiration period 303. Alternatively, the end point of the last candidate period 304_n may be before the end point of the inspiration period 303.

[0045] In the example of FIG. 3 , the interval between the multiple candidate periods 304 is shorter than the time length of one candidate period 304. Therefore, each of the multiple candidate periods 304 partially overlaps with another candidate period of the multiple candidate periods 304. For example, candidate period 304_1 partially overlaps with candidate periods 304_2, 304_3, etc. This allows more candidate periods 304 to be included in the inspiration period 303, thereby enabling the inspiration period 303 to be analyzed with high accuracy. Alternatively, the interval between the multiple candidate periods 304 may be equal to or longer than the time length of one candidate period 304. In this case, each of the multiple candidate periods 304 does not overlap with another candidate period of the multiple candidate periods 304.

[0046] Instead of the example of Figure 3, the multiple candidate periods 304 may have different time lengths. Furthermore, the starting points of the multiple candidate periods 304 do not have to be aligned at the same time intervals. It is believed that snoring is more likely to occur near the center of inspiration than at the ends of inspiration. Therefore, for example, shorter candidate periods 304 may be densely arranged near the center of the analysis target period than at the ends of the analysis target period.

[0047] In the above example, the multiple candidate periods 304 are included in the inspiration period 303 of one breath. Alternatively, the multiple candidate periods 304 may include a portion that is not included in the inspiration period 303 of one breath. For example, the multiple candidate periods 304 may include a candidate period 304 that is included in an expiration period. Furthermore, the multiple candidate periods 304 may be distributed among the inspiration periods 303 of multiple breaths.

[0048] Next, a method for selecting one candidate period 304 to be used as an analysis target period from the plurality of candidate periods 304 will be described. The CPU 112 determines an evaluation value of the high-frequency data for each of the plurality of candidate periods 304. Hereinafter, the evaluation value of the high-frequency data for one candidate period will be simply referred to as the evaluation value of this candidate period. The evaluation value may be a value for evaluating the likelihood that the patient is snoring during one candidate period 304. Specifically, the evaluation value of a candidate period 304 is larger the greater the likelihood that the patient is snoring during this candidate period 304.

[0049] The CPU 112 may determine the maximum absolute value of the high-frequency data in one candidate period 304 as the evaluation value for that candidate period 304. Alternatively, the CPU 112 may determine the evaluation value based on the integral of the absolute values ​​of the high-frequency data in one candidate period 304. Specifically, the CPU 112 may determine the integral of the absolute values ​​of the high-frequency data in one candidate period 304 as the evaluation value. It is known that snoring continues for a certain period of time during one inhalation. Therefore, by determining the evaluation value based on the integral of the absolute values ​​of the high-frequency data, the occurrence of snoring can be accurately detected. When multiple candidate periods 304 include different time lengths, the evaluation value may be determined as the integral of the absolute values ​​of the high-frequency data divided by the time length of the candidate period 304.

[0050] An example of a method for determining an evaluation value for each of multiple candidate periods 304 when determining the integral of the absolute values ​​of the high frequency data for one candidate period 304 as the evaluation value will be described below. First, CPU 112 calculates the integral of the absolute values ​​of the high frequency data for candidate period 304_1. This integral is the evaluation value for candidate period 304_1. Then, CPU 112 calculates the integral of the absolute values ​​of the high frequency data from the start point of candidate period 304_1 to the start point of candidate period 304_2 (hereinafter referred to as the start point integral value) and the integral of the absolute values ​​of the high frequency data from the end point of candidate period 304_1 to the end point of candidate period 304_2 (hereinafter referred to as the end point integral value). Then, CPU 112 subtracts the start point integral value from the evaluation value for candidate period 304_1 and adds the end point integral value to determine the evaluation value for candidate period 304_2. Then, CPU 112 similarly determines the evaluation value for each of candidate periods 304_3 to 304_n. Instead of the method of calculating only the difference as described above, the CPU 112 may individually calculate the integral value of the absolute value of the high frequency data for each of the plurality of candidate periods 304.

[0051] After determining the evaluation value of each of the plurality of candidate periods 304, the CPU 112 compares the evaluation values ​​of the plurality of candidate periods 304 with each other to select one candidate period from the plurality of candidate periods 304.

[0052] In one example, CPU 112 may select one candidate period 304 with the highest evaluation value from among multiple candidate periods 304. If there are multiple candidate periods 304 with the highest evaluation value, CPU 112 may select one candidate period 304 that is located in the center on the time axis from among these multiple candidate periods 304. In another example, CPU 112 may select a predetermined number (e.g., five) of multiple candidate periods 304 in descending order of evaluation value from among the multiple candidate periods 304, and select one candidate period 304 that is located in the center on the time axis from among them.

[0053] Instead of the above-described method, CPU 112 may select one candidate period from the plurality of candidate periods 304 without comparing the evaluation values ​​of the plurality of candidate periods 304. For example, CPU 112 may identify one or more candidate periods 304 that have evaluation values ​​exceeding a predetermined threshold, and arbitrarily select one candidate period 304 from the one or more candidate periods 304. If none of the evaluation values ​​of the plurality of candidate periods 304 exceeds the predetermined threshold, CPU 112 may determine that snoring is not occurring in this breath.

[0054] Next, the detection condition of S203 in Fig. 2 will be described. In one example, the detection condition may be that the evaluation value for the analysis period is equal to or greater than a predetermined threshold. In another example, the detection condition may be that the result of frequency analysis of the flow data for the analysis period satisfies a predetermined condition. By detecting the occurrence of snoring using the result of frequency analysis in this way, the occurrence of snoring can be detected with high accuracy.

[0055] An example of frequency analysis of flow rate data will be described with reference to Fig. 4. Graphs 301 and 302 in Fig. 4 are the same as graphs 301 and 302 in Fig. 3. As a result of S202 in Fig. 2, it is assumed that a period 304_t from time t5 to time t6 is determined as the period to be analyzed.

[0056] The CPU 112 generates frequency data by performing signal processing, including a Fourier transform, on the portion of the flow rate data for the period 304_t. This signal processing may include filtering before the Fourier transform. For example, the CPU 112 may directly perform a Fourier transform on the portion of the flow rate data for the period 304_t. Alternatively, the CPU 112 may generate high-frequency data by applying a filter that blocks low-frequency components to the flow rate data, and then perform a Fourier transform on the portion of the high-frequency data for the period 304_t. The Fourier transform may be performed by a fast Fourier transform (FFT).

[0057] Graph 401 in FIG. 4 represents frequency data generated by Fourier transform. The horizontal axis of graph 401 represents frequency, and the vertical axis of graph 401 represents spectral amplitude. The detection condition may be a condition related to the frequency data and the reference data. For example, the detection condition may be a condition related to the relationship between the frequency data and the reference data. The reference data is data having a frequency distribution, and has a frequency distribution that the frequency data can have when it is assumed that snoring is not occurring. Graph 402 in FIG. 4 represents the reference data.

[0058] 2 (for example, before the patient starts to sleep). For example, the manufacturer of the CPAP device 100 may generate reference data based on respiratory flow data from multiple subjects while they are sleeping and not snoring, and store this reference data in the ROM 113 of the CPAP device 100. For example, the manufacturer may Fourier transform the respiratory flow data (or its high-frequency data) of each of the multiple subjects to generate a frequency spectrum, and use the average value plus twice the standard deviation as the reference data.

[0059] Alternatively, the reference data may be generated while the patient is sleeping and stored in RAM 114. It is considered that the patient is less likely to snore immediately after falling asleep. Therefore, CPU 112 may perform a Fourier transform on the flow rate data (or high frequency data thereof) of each breath of the patient for a predetermined period (e.g., 15 minutes or 30 minutes) after falling asleep to generate a frequency spectrum, and may use the average value obtained by adding twice the standard deviation as the reference data.

[0060] The detection conditions may include a condition regarding the integral value of the difference between frequency data and reference data in one or more bands where the frequency data in a specific frequency band exceeds the reference data. This condition is referred to as the integral value condition. The specific frequency band in the integral value condition may be a frequency band that includes air vibrations caused by snoring, and may be, for example, 10 Hz or higher. The specific frequency band may have no upper limit, or may be 100 Hz or 200 Hz. The specific frequency band may also be an upper limit determined by the sampling theorem. For example, if the flow rate data is sampled at 500 Hz, the upper limit of the frequency band is 250 Hz.

[0061] In the example of Fig. 4, in the frequency band of 10 Hz or more, the frequency data exceeds the reference data in four bands 403_1 to 403_4. The multiple bands 403_1 to 403_4 are collectively referred to as band 403. The following description of one band 403 also applies to all of the multiple bands 403_1 to 403_4. Hereinafter, the multiple bands 403_1 to 403_4 will simply be referred to as multiple bands 403.

[0062] The integral value of the difference between the frequency data and the reference data in the plurality of bands 403 represents the total area of ​​the regions 404_1 to 404_4. The integral value condition may include that the above-mentioned integral value is equal to or greater than a predetermined threshold value. This threshold value may be stored in the ROM 113 before starting the method of FIG. 2 (for example, when the CPAP device 100 is manufactured). It is known that snoring frequencies are distributed over a certain range. Therefore, by including the integral value condition in the detection conditions, the occurrence of snoring can be detected with high accuracy.

[0063] The detection condition may include a condition regarding a relative value of the frequency data with respect to the reference data in any one of one or more bands in which the frequency data exceeds the reference data in a specific frequency band. The relative value in each of the one or more bands may be the maximum value of the difference of the frequency data with respect to the reference data in one band (i.e., the value obtained by subtracting the reference data from the frequency data) or the maximum value of the ratio of the frequency data with respect to the reference data in the one band (i.e., the value obtained by dividing the frequency data by the reference data).

[0064] The detection condition may include a condition regarding a relative value in one of one or more bands in which the frequency data in a specific frequency band exceeds the reference data. This condition is referred to as a primary relative value condition. The specific frequency band in the primary relative value condition may be the same as the specific frequency band in the integral value condition.

[0065] A band used in the primary relative value condition may be a band with the largest relative value among one or more bands in which frequency data exceeds reference data in a specific frequency band. For example, band 403_4 may be used among multiple bands 403 in the primary relative value condition. The primary relative value condition may include the relative value of this band being equal to or greater than a predetermined threshold. This threshold may be stored in ROM 113 before the start of the method of FIG. 2 (e.g., during manufacture of CPAP device 100).

[0066] The detection conditions may include a condition regarding the relative value in one band different from the band used in the primary relative value condition among one or more bands in which the frequency data exceeds the reference data in a specific frequency band. This condition is referred to as a secondary relative value condition. The specific frequency band in the secondary relative value condition may be the same as the specific frequency band in the integral value condition.

[0067] A band used in the secondary relative value condition may be the band with the second largest relative value among one or more bands in which the frequency data exceeds the reference data in a specific frequency band. For example, the secondary relative value condition may use band 403_2 among multiple bands 403. The secondary relative value condition may include the relative value of this band being equal to or greater than a predetermined threshold. This threshold may be stored in ROM 113 before the start of the method of FIG. 2 (e.g., during the manufacture of CPAP device 100).

[0068] The CPU 112 may use only one of the integral value condition, the primary relative value condition, and the secondary relative value condition as the detection condition, or may use a combination of two or more of these three conditions as the detection condition. For example, the CPU 112 may determine whether each of the integral value condition, the primary relative value condition, and the secondary relative value condition is satisfied, and determine that the detection condition is satisfied based on at least one of these three conditions being satisfied. For example, the CPU 112 may determine whether each of the integral value condition and the primary relative value condition is satisfied, and determine that the detection condition is satisfied based on at least one of these two conditions being satisfied.

[0069] 2, the CPU 112 may adjust the value of the reference data and determine whether the detection condition is satisfied using the adjusted reference data. It is known that the magnitude of the frequency data (i.e., the spectrum amplitude) is proportional to the flow rate. Therefore, the CPU 112 may adjust the value of the reference data based on the flow rate of the inhaled air of each breath.

[0070] For example, CPU 112 determines a representative value of the flow rate data during the inhalation period of each breath. The representative value may be the maximum value of the flow rate data during the inhalation period, the average value of the flow rate data during the inhalation period, the integrated value of the flow rate data over the inhalation period, or some other value. ROM 113 or RAM 114 stores a reference flow rate associated with the reference data. CPU 112 may adjust the value of the reference data by multiplying the reference data by a value obtained by dividing the representative value of the flow rate data during the inhalation period of each breath by the reference flow rate (i.e., by multiplying the spectral amplitude at each frequency of the reference data).

[0071] A method executed by the CPAP device 100 to adjust the therapeutic pressure will be described with reference to FIG. 5. Detection of snoring may be used to adjust the temperature or humidity supplied to the patient, or may be used for other processing, in addition to or instead of adjusting the therapeutic pressure. In the following description, each step of the method of FIG. 5 is executed by the CPU 112 (e.g., its therapeutic pressure control unit 118). Specifically, each step is performed by the CPU 112 executing a program loaded into the RAM 114. Alternatively, at least some of the steps of the method of FIG. 5 may be performed by a dedicated circuit such as an ASIC. The method of FIG. 5 may be initiated in response to a patient's instruction to start an operation by the CPAP device 100 (e.g., a therapeutic operation during sleep), in response to the CPAP device 100 detecting that the patient has fallen asleep, or in response to some other event. In the method of FIG. 5, the therapeutic pressure is changed in response to the detection of snoring. In addition, the therapeutic pressure may be changed in response to the detection of some other event (e.g., the passage of a predetermined time, the occurrence of apnea).

[0072] In S501, the CPU 112 determines whether snoring has been detected. If it is determined that snoring has been detected ("YES" in S501), the CPU 112 transitions the process to S502, and otherwise ("NO" in S501), repeats S501. In this way, the CPAP device 100 waits until snoring is detected. The snoring of the patient is detected in real time while the patient is sleeping.

[0073] In S502, the CPU 112 increases the current treatment pressure by a predetermined value or ratio. The treatment pressure may refer to the pressure of the air supplied to the patient.

[0074] In S503, CPU 112 determines whether snoring is continuing. If it is determined that snoring is continuing ("YES" in S503), CPU 112 transitions the process to S504, and otherwise ("NO" in S503), transitions the process to S501.

[0075] When the snoring ceases (i.e., when the snoring stops), it is considered that the increase in the therapeutic pressure has resolved the airway obstruction. Therefore, the CPU 112 maintains this therapeutic pressure. On the other hand, when the snoring continues even with the increase in the therapeutic pressure, it is considered that the snoring is not caused by airway obstruction. Therefore, in S504, the CPU 112 executes a predetermined process. For example, the CPU 112 may decrease the therapeutic pressure. For example, the CPU 112 may return the therapeutic pressure to the value before the increase in S502. In addition to or instead of decreasing the therapeutic pressure, the CPU 112 may output an alert that snoring not caused by airway obstruction has occurred. The alert may be output to a doctor or the like. Snoring not caused by airway obstruction can be caused by nasal congestion, mouth breathing, or the like. Based on this alert, a doctor or the like may provide guidance on relieving nasal congestion and wearing a mask.

[0076] The therapeutic pressure may have an upper limit set by, for example, a doctor. When the therapeutic pressure has reached the upper limit, the CPU 112 may not increase the therapeutic pressure even if snoring is detected in S501. The therapeutic pressure may be reset to an initial value after each treatment (for example, each day). This initial value may be, for example, a minimum therapeutic pressure set by a doctor. Furthermore, the CPU 112 may reduce the therapeutic pressure if no respiratory disorder events such as snoring, apnea, or hypopnea are detected for a certain period of time or longer.

[0077] In the above-described embodiment, the snoring detection method of Fig. 2 is performed by the CPAP device 100. Alternatively, the snoring detection method of Fig. 2 may be performed by a sleep apnea syndrome (SAS) testing device. Such a testing device does not have the function of supplying compressed air to a patient. The SAS testing device that performs the snoring detection method of Fig. 2 is also a medical device with a snoring detection function.

[0078] In the above-described embodiment, the occurrence of snoring by a patient is detected while the patient is sleeping. Alternatively, the snoring detection method of FIG. 2 may be performed after the patient wakes up using flow data acquired while the patient is sleeping. That is, the occurrence of snoring by a patient may be detected after the patient wakes up. This allows the occurrence of snoring periods to be identified and used for diagnosing the patient.

[0079] <Summary of the embodiment> [Item 1] A medical device, generating means for generating high frequency data from flow data representing a time series of a patient's respiratory flow; a determination means for determining an evaluation value of the high-frequency data for each of a plurality of periods; a selection means for selecting one of the plurality of periods based on the evaluation values ​​of the plurality of periods; detection means for detecting the occurrence of snoring in the patient by analyzing the portion of the flow data for the selected period. According to this item, the flow rate data for the selected period is analyzed, so that the occurrence of snoring can be detected with high accuracy. [Item 2] Item 2. The medical device according to item 1, wherein the selection means selects one of the plurality of periods by comparing the evaluation values ​​of the plurality of periods. According to this item, an appropriate period can be selected by comparing the evaluation values, so that the occurrence of snoring can be detected with high accuracy. [Item 3] 3. The medical device according to item 1 or 2, wherein the plurality of periods is included in an inhalation period. According to this item, the occurrence of snoring can be detected with high accuracy by analyzing the flow rate data during the inspiration period, which is likely to include snoring. [Item 4] 4. The medical device according to any one of items 1 to 3, wherein each of the plurality of periods partially overlaps with another of the plurality of periods. According to this item, the number of multiple periods within a certain time length can be increased, so that multiple evaluation values ​​can be determined with fine granularity. [Item 5] 5. The medical device according to any one of items 1 to 4, wherein the multiple periods are all the same length of time. According to this item, the evaluation value can be determined on the same scale. [Item 6] 6. The medical device according to any one of items 1 to 5, wherein the start points of the multiple periods are aligned at the same time intervals. This item allows you to select multiple periods from the target period in a balanced manner. [Item 7] the flow rate data is digital data; 7. The medical device according to item 6, wherein the interval between the start points of the multiple periods is wider than the sampling interval of the flow rate data. According to this item, the processing load for detecting the occurrence of snoring can be reduced. [Item 8] The evaluation value has a larger value as the possibility of snoring increases, 8. The medical device according to any one of items 1 to 7, wherein the selection means selects one period from the plurality of periods that has the largest evaluation value. This item allows for accurate selection of a period during which snoring is likely to occur. [Item 9] 9. The medical device according to any one of items 1 to 8, wherein the determining means determines the evaluation value based on an integral value of the absolute value of the high-frequency data in each period. According to this item, the evaluation value can be determined with high accuracy based on the characteristic of snoring that it continues for a certain period of time. [Item 10] Item 2. The medical device according to item 1, wherein the detection means detects the occurrence of snoring by the patient in real time while the patient is sleeping. According to this item, treatment for snoring can be performed in real time while sleeping. [Item 11] the medical device is a continuous positive airway pressure device; Item 11. The medical device according to item 10, further comprising a pressure control means for increasing the pressure of the air supplied to the patient based on detection of the occurrence of snoring by the patient. According to this item, the occurrence of respiratory disorders can be prevented. [Item 12] A program for causing a computer to function as each means of the medical device described in any one of items 1 to 11. According to this item, a program for realizing a medical device having the above-mentioned effects is provided. [Item 13] 1. A computer-implemented method for detecting snoring, comprising: generating high frequency data from flow data representing a time series of a patient's respiratory flow; a determination step of determining an evaluation value of the high-frequency data for each of a plurality of periods; a selection step of selecting one period from the plurality of periods based on the evaluation values ​​of the plurality of periods; detecting the occurrence of snoring by analyzing the portion of the flow data for the selected period. According to this item, the flow rate data for the selected period is analyzed, so that the occurrence of snoring can be detected with high accuracy.

[0080] The invention is not limited to the above-described embodiment, and various modifications and variations are possible within the scope of the gist of the invention. [Explanation of symbols]

[0081] 100 CPAP device, 303 inspiratory period, 304 candidate period, 403 band

Claims

1. A medical device, generating means for generating high frequency data from flow data representing a time series of a patient's respiratory flow; a determination means for determining an evaluation value of the high-frequency data for each of a plurality of periods; a selection means for selecting one of the plurality of periods based on the evaluation values ​​of the plurality of periods; detection means for detecting the occurrence of snoring in the patient by analyzing the portion of the flow data for the selected period.

2. The medical device of claim 1 , wherein the plurality of periods are included in an inspiration period.

3. A medical device as described in claim 2, wherein the evaluation value is larger the more likely it is that snoring is occurring.

4. the flow rate data is digital data; The medical device according to claim 3 , wherein the interval between the start points of the plurality of periods is wider than the sampling interval of the flow rate data.

5. The medical device according to claim 4 , wherein the detection means detects occurrence of snoring by the patient in real time while the patient is sleeping.

6. the medical device is a continuous positive airway pressure device; The medical device according to claim 5 , further comprising a pressure control means for increasing the pressure of the air supplied to the patient based on detection of the occurrence of snoring by the patient.

7. The medical device described in Claim 6, wherein the selection means selects one period from the plurality of periods for which the evaluation value is maximum.

8. The medical device according to claim 6 , wherein the determining means determines the evaluation value based on an integral value of the absolute value of the high-frequency data in each period.

9. The medical device according to claim 6 , wherein the selection means selects one of the plurality of periods by comparing the evaluation values ​​of the plurality of periods with each other.

10. The medical device of claim 6 , wherein each of the plurality of time periods partially overlaps with another of the plurality of time periods.

11. The medical device of claim 6 , wherein the multiple periods are all the same length of time.

12. The medical device of claim 6 , wherein the start points of the multiple periods are aligned at equal time intervals.

13. A medical device as described in claim 2, wherein the multiple periods include only the inhalation period.

14. A program for causing a computer to function as each of the means of the medical device according to any one of claims 1 to 13.

15. 1. A computer-implemented method for detecting snoring, comprising: generating high frequency data from flow data representing a time series of a patient's respiratory flow; a determination step of determining an evaluation value of the high-frequency data for each of a plurality of periods; a selection step of selecting one period from the plurality of periods based on the evaluation values ​​of the plurality of periods; detecting an occurrence of snoring in the patient by analyzing the portion of the flow data for the selected period.

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