Electronic device and method for detecting periodic respiration
The electronic device receives and processes the respiratory signal, calculates the degree of variation and performs change point detection, and combines machine learning algorithms to determine whether it is periodic respiration, which solves the problem of difficulty in detecting periodic respiration in the prior art, and realizes accurate detection of periodic respiration and effective monitoring of physiological state.
Patent Information
- Application Number
- CN202311824237.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2023-12-05
- Filing Date
- 2023-12-27
- Publication Date
- 2025-06-06
AI Technical Summary
The prior art is difficult to effectively detect the abnormal breathing pattern of periodic breathing, which affects the monitoring of sleep quality and physiological status.
Receive breathing signals through electronic devices, calculate the degree of variation, perform change point detection to obtain interval signals, and determine whether it is periodic breathing based on machine learning algorithms or specific rules.
Accurate detection of periodic respiration is achieved, important basis for the subject's physiological status, and improved the monitoring ability of sleep quality.
Smart Images
Figure CN120093270A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a detection technology, and in particular to an electronic device and method for detecting periodic breathing (PB). Background Art
[0002] With the development of sleep medicine, people pay more and more attention to sleep quality. There are many products on the market that monitor the sleep status of the subjects. Most of these products are used to monitor the sleep status of the subjects, and are rarely used to monitor the breathing status of the subjects. However, the breathing status of the subjects is also an important basis for judging the physiological status of the subjects. Periodic breathing is a breathing pattern in which the breathing volume increases first and then decreases, which is an abnormal breathing pattern. How to detect this abnormal breathing pattern is one of the important topics in this field. Summary of the invention
[0003] The invention provides an electronic device and method for detecting periodic breathing, which can detect whether the breathing pattern of a subject is periodic breathing according to a radar signal.
[0004] An embodiment of the present invention provides an electronic device for detecting periodic breathing, comprising a processor and a transceiver. The transceiver receives a breathing signal. The processor is coupled to the transceiver and is configured to perform: calculating the degree of variation of the breathing signal; performing a first change point detection on the degree of variation to obtain a first interval; obtaining a first interval signal from the breathing signal according to the first interval; detecting the first interval signal to generate a detection result corresponding to at least one periodic breathing; and outputting the detection result through the transceiver.
[0005] An embodiment of the present invention provides a method for detecting periodic breathing, which is used for an electronic device for detecting periodic breathing, comprising: receiving a breathing signal through an electronic device; calculating the degree of variation of the breathing signal; performing a first change point detection on the degree of variation to obtain a first interval; obtaining a first interval signal from the breathing signal according to the first interval; detecting the first interval signal to generate a detection result corresponding to at least one periodic breathing; and outputting the detection result.
[0006] Based on the above, the electronic device of the present invention can determine whether the subject has a periodic breathing pattern based on a machine learning algorithm or a specific rule. BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Figure 1 A schematic diagram of an electronic device for detecting periodic breathing according to an embodiment of the present invention is shown.
[0008] Figure 2A flow chart of a method for detecting periodic breathing is shown according to an embodiment of the present invention.
[0009] Figure 3 A schematic diagram of a variation signal is shown according to an embodiment of the present invention.
[0010] Figure 4 A schematic diagram of a first interval signal is shown according to an embodiment of the present invention.
[0011] Figure 5 A schematic diagram of a conversion signal is shown according to an embodiment of the present invention.
[0012] Figure 6 A schematic diagram showing sampling of a second interval signal according to an embodiment of the present invention is shown.
[0013] Figure 7 A schematic diagram of a breathing signal of periodic breathing is shown according to an embodiment of the present invention.
[0014] Figure 8 A schematic diagram of a breathing signal of Cheyne-Stokes respiration is shown according to an embodiment of the present invention.
[0015] Fig. 9 A flow chart of a method for detecting periodic breathing is shown according to an embodiment of the present invention.
[0016] Description of reference numerals:
[0017] 100: Electronic devices
[0018] 110: Processor
[0019] 120: Storage Media
[0020] 130: Transceiver
[0021] 21, 22, 23: Second interval signal
[0022] 300: Degree of variation
[0023] 31, 32: The third interval signal
[0024] 400: First interval signal
[0025] 41, 42, 43: Fourth interval signal
[0026] 500: conversion signal
[0027] 51, 52: Fifth interval signal
[0028] 700, 800: breathing signal
[0029] S201, S202, S203, S204, S205, S206, S207, S208, S901, S902, S903, S904, S905, S906: Steps
[0030] T1, T2, T3, T4, T5, T6: Interval DETAILED DESCRIPTION
[0031] Figure 1 A schematic diagram of an electronic device 100 for detecting periodic breathing according to an embodiment of the present invention is shown. The electronic device 100 may include a processor 110, a storage medium 120, and a transceiver 130.
[0032] The processor 110 is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose micro control unit (MCU), microprocessor, digital signal processor (DSP), programmable controller, application specific integrated circuit (ASIC), graphics processing unit (GPU), image signal processor (ISP), image processing unit (IPU), arithmetic logic unit (ALU), complex programmable logic device (CPLD), field programmable gate array (FPGA), or other similar elements or combinations thereof. The processor 110 can be coupled to the storage medium 120 and the transceiver 130, and access and execute multiple modules and various applications stored in the storage medium 120.
[0033] The storage medium 120 is, for example, any form of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk drive (HDD), solid state drive (SSD) or similar elements or a combination of the above elements, and is used to store multiple modules or various applications that can be executed by the processor 110.
[0034] The transceiver 130 transmits or receives signals in a wireless or wired manner. The transceiver 130 may also perform operations such as low noise amplification, impedance matching, mixing, up or down frequency conversion, filtering, amplification, and the like.
[0035] Figure 2 According to an embodiment of the present invention, a flow chart of a method for detecting periodic breathing is shown, wherein the method may be performed as follows: Figure 1 The electronic device 100 shown is implemented. The periodic breathing may include but is not limited to general periodic breathing (PB) or Cheyne-Stokes breathing (CSB).
[0036] In step S201, the processor 110 may receive a breathing signal of the subject through the transceiver 130. For example, the processor 110 may be communicatively connected to a continuous wave (CW) radar for measuring the breathing of the subject through the transceiver 130, and receive the breathing signal from the continuous wave radar, wherein the continuous wave radar is, for example, a frequency modulated continuous wave (FMCW) radar.
[0037] In step S202, the processor 110 may calculate the degree of variation of the respiratory signal, such as Figure 3 The degree of variation shown is 300.
[0038] In step S203, the processor 110 may perform change point detection (CPD) on the variation level 300 to obtain one or more intervals, such as Figure 3The interval T1 shown. Generally speaking, if the sleeping position of the subject has not changed, the lengths of the intervals obtained by the processor 110 performing change point detection on the degree of variation will be very similar. In one embodiment, before performing change point detection on the degree of variation 300, the processor 110 may first perform filtering on the degree of variation 300. It should be noted that the processor 110 performing filtering on the degree of variation 300 can speed up the speed of the entire process operation. However, if the processor 110 does not perform filtering on the degree of variation 300, the result after the process operation can be more accurate.
[0039] In step S204 , the processor 110 may obtain a first interval signal from the respiration signal according to the interval T1 . Figure 4 A schematic diagram of a first interval signal 400 is shown according to an embodiment of the present invention. Figure 3 and Figure 4 The processor 110 may obtain the breathing signal during the interval T1 from the original breathing signal as the first interval signal 400. If the subject has a breathing pattern of periodic breathing, the first interval signal 400 may be used to generate a detection result corresponding to one or more periodic breathings.
[0040] In step S205, the processor 110 may perform a fast Fourier transform (FFT) on the first interval signal 400 to generate a transformed signal, such as Figure 5 A conversion signal 500 is shown.
[0041] In step S206, the processor 110 may perform change point detection on the conversion signal 500 to obtain one or more intervals, such as Figure 5 The interval T2, interval T3 or interval T4 shown, wherein interval T2 is adjacent to interval T3, and interval T3 is adjacent to interval T4. The interval (e.g., interval T2, T3 or T4) obtained by the processor 110 in step S206 can be identified as a periodic breathing region (PB region). In one embodiment, before performing change point detection on the conversion signal 500, the processor 110 may first perform filtering on the conversion signal 500. It should be noted that the processor 110 performing filtering on the degree of variation 300 can speed up the speed of the entire process operation. However, if the processor 110 does not perform filtering on the degree of variation 300, the result after the process operation can be more accurate.
[0042] In step S207 , the processor 110 may obtain an interval signal from the conversion signal 500 or the respiration signal 400 according to the interval (eg, interval T2 , T3 , or T4 ) obtained in step S206 .
[0043] In one embodiment, the interval signal acquired in step S207 may include a second interval signal. Figure 6 As shown, the interval signal may include a second interval signal 21 obtained from the breathing signal or the first interval signal 400 according to interval T2, a second interval signal 22 obtained from the breathing signal or the first interval signal 400 according to interval T3, or a second interval signal 23 obtained from the breathing signal or the first interval signal 400 according to interval T4.
[0044] In one embodiment, the interval signal acquired in step S207 may include a third interval signal. Figure 6 As shown, the interval signal may include a third interval signal 31 obtained from the breathing signal or the first interval signal 400 according to the interval T5 between the interval T2 and the interval T3, or a third interval signal 32 obtained from the breathing signal or the first interval signal 400 according to the interval T6 between the interval T3 and the interval T4. The third interval signal 31 and the third interval signal 32 are equivalent to the front-end signal and the rear-end signal of the second interval signal 22. The starting point of the interval T5 (i.e., the third interval signal 31) may be located at the end point of the interval T2, and the end point of the interval T5 may be located at the starting point of the interval T3. The starting point of the interval T6 (i.e., the third interval signal 32) may be located at the end point of the interval T3, and the end point of the interval T6 may be located at the starting point of the interval T4.
[0045] In one embodiment, the interval signal acquired in step S207 may include a fourth interval signal. Figure 5 As shown, the interval signal may include a fourth interval signal 41 obtained from the conversion signal 500 according to the interval T2, a fourth interval signal 42 obtained from the conversion signal 500 according to the interval T3, or a fourth interval signal 43 obtained from the conversion signal 500 according to the interval T4.
[0046] In one embodiment, the interval signal acquired in step S207 may include a fifth interval signal. Figure 5 As shown, the interval signal may include a fifth interval signal 51 obtained from the conversion signal 500 according to the interval T5 or a fifth interval signal 52 obtained from the conversion signal 500 according to the interval T6. The fifth interval signal 51 and the fifth interval signal 52 are equivalent to the front-end signal and the rear-end signal of the fourth interval signal 42. The starting point of interval T5 (i.e., the fifth interval signal 51) may be located at the end point of interval T2, and the end point of interval T5 may be located at the starting point of interval T3. The starting point of interval T6 (i.e., the fifth interval signal 52) may be located at the end point of interval T3, and the end point of interval T6 may be located at the starting point of interval T4.
[0047] In step S208, the processor 110 may determine whether the second interval signal (eg, the second interval signal 21, 22, or 23) corresponds to a general periodic breathing (eg, Figure 7 700) or tidal breathing (eg Figure 8After generating the detection result, the processor 110 may output the detection result through the transceiver 130 for reference by the user of the electronic device 100. In addition to indicating whether the specific second interval signal belongs to periodic breathing or tidal breathing, the detection result may further include information such as the ratio of periodic breathing to sleep time, the average cycle length of periodic breathing, or the maximum cycle length of periodic breathing.
[0048] In one embodiment, the processor 110 may input the second interval signal into a machine learning model to determine whether the second interval signal corresponds to periodic breathing. The machine learning model may be, for example, a supervised machine learning model.
[0049] In one embodiment, the processor 110 may calculate a score corresponding to the second interval signal based on a characteristic value of the second interval signal (or the third interval signal, the fourth interval signal, or the fifth interval signal corresponding to the second interval signal) based on a specific rule to determine whether the second interval signal corresponds to periodic breathing or tidal breathing. Taking the second interval signal 22 as an example, Table 1 is an example of the characteristics of the second interval signal (or the third interval signal, the fourth interval signal, or the fifth interval signal corresponding to the second interval signal), and Tables 2 and 3 are examples of scoring conditions corresponding to feature 1 and feature 2 of Table 1, respectively. The characteristics of Table 1 may include but are not limited to power spectral density (PSD), variance, median, first quartile, third quartile, maximum value, peak width, peak count, skewness, or kurtosis. The processor 110 may determine whether the second interval signal 22 corresponds to periodic breathing or tidal breathing based on one or more scores of the second interval signal 22, the third interval signal 31 (or 32), the fourth interval signal 42 or the fifth interval signal 51 (or 52).
[0050] Table 1
[0051]
[0052]
[0053] Table 2
[0054]
[0055] Table 3
[0056]
[0057]
[0058] Fig. 9 According to an embodiment of the present invention, a flow chart of a method for detecting periodic breathing is shown, wherein the method can be performed as follows: Figure 1 The electronic device 100 shown is implemented. In step S901, a breathing signal of a subject is received. In step S902, the degree of variation of the breathing signal is calculated. In step S903, a first change point detection is performed on the degree of variation to obtain a first interval. In step S904, a first interval signal is obtained from the breathing signal according to the first interval. In step S905, the first interval signal is detected to generate a detection result corresponding to at least one periodic breathing. In step S906, the detection result is output.
[0059] In summary, the electronic device of the present invention can obtain the breathing signal of the subject through a non-contact sensor and perform signal processing on the breathing signal. The electronic device can detect the processed breathing signal based on a machine learning algorithm or a specific rule to determine whether the breathing pattern of the subject is periodic breathing.
Claims
1. An electronic device for detecting periodic breathing, include: a transceiver, receiving a breathing signal; as well as a processor, coupled to the transceiver, and configured to execute: Calculating a degree of variation of the respiratory signal; Performing a first change point detection on the variation degree to obtain a first interval; Acquire a first interval signal from the breathing signal according to the first interval; detecting the first interval signal to generate a detection result corresponding to at least one periodic breathing; and The detection result is outputted through the transceiver.
2. The electronic device of claim 1, wherein the processor is further configured to execute: Performing a fast Fourier transform on the first interval signal to generate a transformation signal; Performing a second change point detection on the conversion signal to obtain a second interval; Acquire a second interval signal from the respiration signal according to the second interval; and It is determined whether the second interval signal corresponds to a periodic breathing to generate the detection result.
3. The electronic device of claim 2, wherein the processor is further configured to execute: The second interval signal is input into a machine learning model to determine whether the second interval signal corresponds to the periodic breathing.
4. The electronic device of claim 2, wherein the processor is further configured to execute: determining whether a first characteristic value of the second interval signal matches a first condition to calculate a first score corresponding to the second interval signal; and A determination is made based on the first score as to whether the second interval signal corresponds to the periodic breathing. 5 . The electronic device as claimed in claim 4 , wherein the first eigenvalue is associated with one of the following features: power spectral density, variance, median, first quartile, third quartile, and maximum value.
6. The electronic device of claim 4, wherein the processor is further configured to execute: Performing the second change point detection on the conversion signal to obtain a third interval adjacent to the second interval; Acquire a third interval signal from the respiration signal according to the second interval and the third interval; and Determine whether a second characteristic value of the third interval signal matches a second condition to calculate the first score. 7 . The electronic device as claimed in claim 6 , wherein the second feature value is associated with one of the following features: variance, median, first quartile, third quartile and maximum value.
8. The electronic device of claim 4, wherein the processor is further configured to execute: Acquire a fourth interval signal from the conversion signal according to the second interval; determining whether a second characteristic value of the fourth interval signal matches a second condition to calculate a second score corresponding to the fourth interval signal; and It is determined whether the second interval signal corresponds to the periodic breathing according to the first score and the second score.
9. The electronic device as claimed in claim 8, wherein the second characteristic value is associated with one of the following characteristics: variance, median, first quartile, third quartile, maximum value, peak width, number of peaks, skewness and kurtosis.
10. The electronic device of claim 8, wherein the processor is further configured to execute: Performing the second change point detection on the conversion signal to obtain a third interval adjacent to the second interval; Acquire a fifth interval signal from the conversion signal according to the second interval and the third interval; and Determine whether a third characteristic value of the fifth interval signal matches a third condition to calculate the second score. 11 . The electronic device as claimed in claim 10 , wherein the third feature value is associated with one of the following features: variance, median, first quartile, third quartile and maximum value.
12. A method for detecting periodic breathing, an electronic device for detecting periodic breathing, include: Receiving a breathing signal through the electronic device; Calculating the variation number of the respiratory signal to generate a variation degree; Performing a first change point detection on the variation degree to obtain a first interval; Acquire a first interval signal from the breathing signal according to the first interval; detecting the first interval signal to generate a detection result corresponding to at least one periodic breathing; and The detection result is outputted.
13. The method of claim 12, wherein the step of detecting the first interval signal to generate the detection result corresponding to the at least one periodic breathing include: Performing a fast Fourier transform on the first interval signal to generate a transformation signal; Performing a second change point detection on the conversion signal to obtain a second interval; Acquire a second interval signal from the breathing signal according to the second interval; as well as It is determined whether the second interval signal corresponds to a periodic breathing to generate the detection result.
14. The method according to claim 13, wherein the step of determining whether the second interval signal corresponds to the periodic breathing to generate the detection result include: The second interval signal is input into a machine learning model to determine whether the second interval signal corresponds to the periodic breathing.
15. The method according to claim 13, wherein the step of determining whether the second interval signal corresponds to the periodic breathing to generate the detection result include: determining whether a first characteristic value of the second interval signal matches a first condition to calculate a first score corresponding to the second interval signal; as well as A determination is made based on the first score as to whether the second interval signal corresponds to the periodic breathing.
16. The method of claim 15, wherein the first feature value is associated with one of the following features: power spectral density, variance, median, first quartile, third quartile, and maximum value.
17. The method of claim 15, wherein the step of determining whether the first characteristic value of the second interval signal matches the first condition to calculate the first score corresponding to the second interval signal include: Performing the second change point detection on the conversion signal to obtain a third interval adjacent to the second interval; Acquire a third interval signal from the respiration signal according to the second interval and the third interval; and Determine whether a second characteristic value of the third interval signal matches a second condition to calculate the first score.
18. The method of claim 17, wherein the second feature value is associated with one of the following features: variance, median, first quartile, third quartile, and maximum value.
19. The method of claim 15, wherein the step of determining whether the second interval signal corresponds to the periodic breathing according to the first score is: include: Acquire a fourth interval signal from the conversion signal according to the second interval; Determining whether a second characteristic value of the fourth interval signal matches a second condition to calculate a second score corresponding to the fourth interval signal; as well as It is determined whether the second interval signal corresponds to the periodic breathing according to the first score and the second score.
20. The method of claim 19, wherein the second feature value is associated with one of the following features: variance, median, first quartile, third quartile, maximum value, peak width, number of peaks, skewness, and kurtosis.
21. The method of claim 19, further comprising: include: Performing the second change point detection on the conversion signal to obtain a third interval adjacent to the second interval; Acquire a fifth interval signal from the conversion signal according to the second interval and the third interval; as well as Determine whether a third characteristic value of the fifth interval signal matches a third condition to calculate the second score.
22. The method of claim 21, wherein the third feature value is associated with one of the following features: variance, median, first quartile, third quartile, and maximum value.