CN109691999A - Respiratory rate detection method, device, storage medium and computer equipment - Google Patents
Respiratory rate detection method, device, storage medium and computer equipment Download PDFInfo
- Publication number
- CN109691999A CN109691999A CN201910055015.0A CN201910055015A CN109691999A CN 109691999 A CN109691999 A CN 109691999A CN 201910055015 A CN201910055015 A CN 201910055015A CN 109691999 A CN109691999 A CN 109691999A
- Authority
- CN
- China
- Prior art keywords
- signal
- respiratory rate
- breath signal
- frequency
- detection method
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Surgery (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Physiology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Pulmonology (AREA)
- Molecular Biology (AREA)
- Physics & Mathematics (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Signal Processing (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The present invention discloses a kind of respiratory rate detection method comprising: obtain distance change signal of at least one the calibration position of human chest in default collection period；Variable signal of adjusting the distance is normalized, to obtain normalization data；Normalization data is filtered to obtain the first breath signal；Wavelet transformation is carried out to the first breath signal and obtains the second breath signal, and respiratory rate is determined according to the second breath signal.The fluctuations that the present invention passes through record human chest specific region, distance change signal is converted into breath signal, original signal is handled to obtain respiratory rate in conjunction with normalization, filtering and wavelet transformation technique, has the advantages that signal acquisition real-time is high, calculated result is accurate and reliable and detection is easy to operate.
Description
Technical field
The present invention relates to technical field of medical equipment, and in particular to a kind of respiratory rate detection method, device, storage medium
And computer equipment.
Background technique
In the prior art, the monitoring means of breath state is usually used touch sensor and is detected, and needs pair
Sensor is installed, is connected, inconvenient to use.In touch sensor detection scheme, the side of including wired and wireless connections
Formula, such as a kind of existing disclosed wireless respiration monitoring device, including control circuit, piezoelectric film sensor and antenna, control
Circuit is electrically connected with piezoelectric film sensor and antenna respectively, incudes respiratory movement using piezoelectric film sensor, and by its
It is changed into corresponding charge signal, the charge signal is handled by control circuit, obtains corresponding monitoring result, then by day
Line sends the monitoring result.The program is wirelessly transferred despite the use of, and sensor still needs closely to be attached to trouble
On person's body, there are still disadvantages inconvenient for use.
Summary of the invention
The main object of the present invention is to propose a kind of respiratory rate detection method, it is intended to solve to connect because using in the prior art
Touch sensor acquires breath signal and leads to problem inconvenient for use.
To achieve the above object, the present invention proposes a kind of respiratory rate detection method, comprising:
Obtain distance change signal of at least one the calibration position of human chest in default collection period；
The distance change signal is normalized, to obtain normalization data；
The normalization data is filtered to obtain the first breath signal；
Wavelet transformation is carried out to first breath signal and obtains the second breath signal, and according to second breath signal
Determine respiratory rate.
Preferably, the distance change signal for obtaining at least one calibration position of human chest in default collection period
Include:
Distance change signal of the human chest four calibration positions in default collection period is acquired using laser sensor,
Wherein four calibration position includes thoracic cavity and the thorax side of sternum area.
Preferably, the sample frequency of the laser sensor is greater than or equal to 6.67Hz.
Preferably, it is described the normalization data is filtered to obtain the first breath signal include:
Using Gaussian functionThe normalization data is filtered；Wherein, wherein μ
It is preset constant for 0, σ, x is the input signal of gaussian filtering, and f is output signal after gaussian filtering.
Preferably, described wavelet transformation is carried out to first breath signal to obtain the second breath signal and include:
Frequency range layering, meter are carried out according to Shannon-nyquist sampling principle and the sample frequency of the distance change signal
Calculate every layer of frequency range；
Wavelet decomposition and reconstruct institute are determined according to every layer of frequency range and preset band connection frequency in frequency range layering
The number of plies needed；
Signal decomposition is carried out according to the number of plies needed for the wavelet decomposition and pre-selected morther wavelet, is obtained by frequency range
The multi-layer corrugated of division；
The multi-layer corrugated obtained according to coefficient corresponding with the number of plies needed for the wavelet reconstruction and decomposition carries out letter
Number reconstruct, obtain the second breath signal.
Preferably, the upper cut-off frequency of the band connection frequency is 0.37Hz, the lower-cut-off frequency of the band connection frequency
It is 0.
Preferably, described to determine that respiratory rate includes: according to second breath signal
Small echo wave crest or small echo trough are extracted from second breath signal；
Respiratory rate is calculated according to the time interval of adjacent small echo wave crest or the time interval of small echo trough.
The present invention also provides a kind of respiratory frequency detection devices characterized by comprising
Signal acquisition module becomes for obtaining distance of at least one the calibration position of human chest in default collection period
Change signal；
Signal pre-processing module, for the distance change signal to be normalized, to obtain normalization data；
Signal filter module obtains the first breath signal for being filtered to the normalization data；
Wavelet transformation module obtains the second breath signal for carrying out wavelet transformation to first breath signal；
Respiratory rate computing module, for determining respiratory rate according to second breath signal.
The present invention also provides a kind of computer program memory mediums, are stored thereon with computer program, the computer program
The step in above-mentioned respiratory rate detection method is realized when being executed by processor.
The present invention also provides a kind of computer equipment, which includes memory, processor and is stored in institute
The computer program that can be run on memory and on the processor is stated, the processor executes real when the computer program
Step in existing above-mentioned respiratory rate detection method.
Respiratory rate detection method provided by the present invention records human chest specific region by contactless mode
Fluctuations, distance change signal is converted into breath signal, then by using normalization and filter function to original signal
The first breath signal for obtaining low noise and calculating convenient for the later period is handled, wavelet transformation technique is recycled to believe the first breathing
It number is handled to obtain the second breath signal, respiratory rate is finally calculated according to the second breath signal, there is signal acquisition
The advantage that real-time is high, calculated result is accurate and reliable and detection is easy to operate.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of one embodiment of signal detection apparatus used in the present invention；
Fig. 2 is the flow diagram of one embodiment of respiratory rate detection method of the invention；
Fig. 3 is " to obtain second to the first breath signal progress wavelet transformation to exhale in respiratory rate detection method of the invention
The flow diagram of suction signal " implementation procedure preferred embodiment；
Fig. 4 is the implementation procedure that " determines respiratory rate according to the second breath signal " in respiratory rate detection method of the invention
The flow diagram of preferred embodiment；
Fig. 5 is the functional block diagram of one embodiment of device for detecting respiratory of the invention.
Specific embodiment
The embodiment of the present invention is described more fully below, the example of embodiment is shown in the accompanying drawings, wherein phase from beginning to end
Identical element or element with the same function are indicated with label.Embodiment below with reference to attached drawing description is exemplary
, it is intended to it is used to explain the present invention, and is not considered as limiting the invention, based on the embodiments of the present invention, this field
Those of ordinary skill's every other embodiment obtained without making creative work, belongs to protection of the present invention
Range.
In order to solve the above technical problems, the present invention proposes a kind of respiratory rate detection method, referring to fig. 2 comprising:
Step S10 obtains distance change signal of at least one the calibration position of human chest in default collection period.
In this step, the distance change signal that human chest specific position is acquired by contactless equipment, from
And improve the convenience of respiratory rate detection.Referring to Fig. 1, as a preferred embodiment, human body chest is acquired using laser sensor
The distance change signal of portion's specific position, keeps certain distance, and during signal acquisition between laser sensor and human body
It generally remains unchanged, signal acquisition accuracy is impacted with reducing.According to the signal acquisition mode of the present embodiment, tested person
Member 10 lies down and remains stationary, and the signal detection apparatus 20 configured with laser sensor (21,22) is placed on human chest region
Side makes laser sensor be directed at predetermined calibration position, then enabling signal detection device 20, the position of record calibration in real time
The distance signal of fluctuations or lateral displacement, such as with 120 distance signals of acquisition for a collection period.
Further, distance of the human chest four calibration positions in default collection period is acquired using laser sensor
Variable signal, wherein this four calibration positions include thoracic cavity and the thorax side of sternum area, i.e. two laser sensors 21 are used
In the lateral displacement signal of acquisition left and right thorax side, other two laser sensor 22 is located above chest, for acquiring chest
The thoracic cavity fluctuations signal in bone region.In addition, according to Shannon-nyquist sampling principle, setting signal detection device 20
Reasonable sample frequency, people's normal respiratory rate are about 16~22 times per minute, and being converted into frequency is 0.27-0.37Hz, laser
The sample frequency of sensor is 6.67Hz, meets sample frequency and is greater than by twice of requirement of sampling signal frequency, it is fixed to meet sampling
Reason, therefore breath signal is able to detect that in principle.In other embodiments, the sample frequency of laser sensor can also be big
In 6.67Hz, suitable numerical value can be selected under concrete application scene.In addition, in addition to using signal detection apparatus shown in FIG. 1
Standby 20 acquisition distance change signals can also use ultrasonic sensor in other embodiments or other any suitable non-connect
Touch acquires distance change signal, the invention is not limited in this regard apart from sensing apparatus.By the way that multiple signal sampling points are arranged, and
Data processing will be independently carried out per signal all the way, the robustness of data processing can be improved.
In addition, preceding N (1~N) a sample of extraction signal first is handled, then when processing signal in real time
First sample of this N number of sample is rejected by collected data in real time, that is, the data that next moment is used to handle become
1 newest moment collected data is added for a sample of N-1 (2~N) in previous sample.
Step S20, variable signal of adjusting the distance are normalized, to obtain normalization data.
It is usually that can have certain jump signals, and be based on the collected distance change signal of abovementioned steps S10 institute
The universality of algorithm is improved, flow chart of data processing is simplified, the variable signal that needs to adjust the distance is pre-processed, and dimensionless is converted to
Data.In the present embodiment, variable signal of preferably being adjusted the distance by the way of normalized is handled, to obtain normalizing
Change data.Z-score standardized method is used also, as better embodiment, at this, converts function are as follows:
Wherein σ ≠ 0
In the conversion function, z is treated signal, and x is original signal, and μ is the average value of original signal, and σ is original
The standard deviation of beginning signal.
Normalized fluctuates signal in the range of a standard, is convenient for later period calculation processing.
Step S30 is filtered normalization data to obtain the first breath signal.
During acquiring user distance variable signal, it is difficult to will appear with avoiding user's body position generate change with
And the non-uniform situation of breathing, therefore can include some noises, such as white noise in signal, it is therefore desirable to by normalization
The signal of reason is filtered, and realizes the smoothing processing of signal.In a better embodiment, filtering algorithm is gaussian filtering, tool
Body can be one-dimensional gaussian filtering, algorithm are as follows:
Wherein, wherein μ can be 0, σ be preset constant, x be gaussian filtering input signal, f be gaussian filtering after export
Signal, i.e. the first breath signal.
And in its other embodiments, such as limit filtration method, middle position value filtering method or arithmetic also can be used in filtering algorithm
Average filter method etc. can specifically be selected according to the acquisition situation of distance change signal.
Step S40 carries out wavelet transformation to the first breath signal and obtains the second breath signal.
Carrying out processing to the first breath signal using wavelet transformation technique is to obtain the key link of respiratory rate, by stretching
Contracting shift operations gradually carry out multi-scale refinement to signal (function), are finally reached high frequency treatment time subdivision, frequency fine at low frequency
Point, the automatic requirement for adapting to time frequency signal analysis, to improve the accuracy for calculating respiratory rate, the present embodiment is specifically used
Algorithm are as follows:
Wherein, scale a controls the flexible of wavelet function, and translational movement τ controls the translation of wavelet function.Scale corresponds to frequency
(inverse ratio), translational movement τ then correspond to the time.
Specifically, referring to Fig. 3, abovementioned steps S40 can include:
Step S41 carries out frequency range point according to Shannon-nyquist sampling principle and the sample frequency of distance change signal
Layer calculates every layer of frequency range.
The present embodiment using 6.67Hz use frequency progress signal sampling, according to Shannon-nyquist sampling principle,
Frequency is divided into 0~3.33Hz and 3.33-6.667Hz range by first layer；The second layer by frequency be divided into 0~1.66Hz and
1.66-3.33Hz range；And frequency is divided into 0~0.83Hz and 0.83~1.66Hz range in third layer.
Step S42, according to frequency range be layered in every layer frequency range and preset band connection frequency determine wavelet decomposition and again
The number of plies needed for structure.
Band connection frequency is determined according to the frequency range of people's eupnea, for example people's normal respiratory rate is about per minute 16
~22 times, being converted into frequency is 0.27-0.37Hz, then the upper cut-off frequency of band connection frequency is 0.37Hz, under band connection frequency
Limiting cutoff frequency is 0.Therefore, the useful signal component extraction in the first breath signal can be come out, with 0~0.83Hz frequency
The corresponding layer of range carrys out reconstruction signal.
Step S43, the number of plies according to needed for wavelet decomposition and pre-selected morther wavelet carry out signal decomposition, obtain by
The multi-layer corrugated that frequency range divides.
On extracting mode, coif3 or dmey small echo can be used as morther wavelet, there is preferable extraction effect.When
It so, also can be used other, for example db small echo is as morther wavelet, the invention is not limited in this regard.When being decomposed, obtain more
Layer waveform, is reconstructed by extracting selected waveform.
Step S44, the multi-layer corrugated obtained according to coefficient corresponding with the number of plies needed for wavelet reconstruction and decomposition carry out letter
Number reconstruct, obtain the second breath signal.
Step S50 determines respiratory rate according to the second breath signal.
In the present embodiment, the waveform of the second breath signal is focused on high frequency or low frequency region, can use phase on waveform
Adjacent two wave crests or trough determine respiratory rate.Specifically, referring to fig. 4, step S50 includes:
Step S51 extracts small echo wave crest or small echo trough from the second breath signal；
Step S52 calculates respiratory rate according to the time interval of adjacent small echo wave crest or the time interval of small echo trough.
That is respiratory rateAt the time of wherein T represents n-th of wave crest (trough).Also, it will can repeatedly count
Obtained respiratory rate is averaged, and to improve the robustness of algorithm, excludes to interfere caused by user's body change in location etc..
It can be seen that breathing detection method of the invention records human chest specific region by contactless mode
Distance change signal is converted to breath signal by fluctuations, then by using normalization and filter function to original signal into
The first breath signal that row processing obtains low noise and calculates convenient for the later period, recycles wavelet transformation technique to the first breath signal
It is handled to obtain the second breath signal, respiratory rate is finally calculated according to the second breath signal, have signal acquisition real
The advantage that Shi Xinggao, calculated result are accurate and reliable and detection is easy to operate.
The present invention also proposes a kind of respiratory frequency detection device, referring to Fig. 5 comprising:
Signal acquisition module 100, for obtain at least one calibration position of human chest in default collection period away from
From variable signal；
Signal pre-processing module 200, for the distance change signal to be normalized, to obtain normalization number
According to；
Signal filter module 300 obtains the first breath signal for being filtered to the normalization data；
Wavelet transformation module 400 obtains the second breath signal for carrying out wavelet transformation to first breath signal；
Respiratory rate computing module 500, for determining respiratory rate according to second breath signal.
Specifically, wavelet transformation module 400 includes:
Frequency range division unit, for according to the sample frequency of Shannon-nyquist sampling principle and distance change signal into
The layering of line frequency section, calculates every layer of frequency range；
Number of plies determination unit determines small echo for every layer in being layered according to frequency range of frequency range and preset band connection frequency
The number of plies needed for decomposing and reconstructing；
Signal decomposition unit carries out signal point for the number of plies according to needed for wavelet decomposition and pre-selected morther wavelet
Solution obtains the multi-layer corrugated divided by frequency range；
Signal reconstruction unit, the multilayer wave for being obtained according to coefficient corresponding with the number of plies needed for wavelet reconstruction and decomposition
Shape carries out signal reconstruction, obtains the second breath signal.
Specifically, respiratory rate computing module 500 includes:
Extraction unit, for extracting small echo wave crest or small echo trough from the second breath signal；
Frequency computing unit, for being calculated according to the time interval of adjacent small echo wave crest or the time interval of small echo trough
Respiratory rate.
Modules in above-mentioned respiratory frequency detection device can come real fully or partially through software, hardware and combinations thereof
It is existing.Above-mentioned each module can be embedded in the form of hardware or independently of in the computer equipment in server, can also be with software shape
Formula is stored in the memory in server, is called in order to computer equipment and is executed the corresponding operation of the above modules.It should
Computer equipment can be central processing unit (CPU), microcomputer apparatus, single-chip microcontroller etc..Above-mentioned each functional module is played
Working principle and play the role of can be found in Fig. 2~4 shown in breathing detection method realization process, do not go to live in the household of one's in-laws on getting married herein
It states.
The present invention also provides a kind of computer readable storage mediums, are stored thereon with computer program, and the program is processed
Device realizes following steps when executing:
Obtain distance change signal of at least one the calibration position of human chest in default collection period；
Variable signal of adjusting the distance is normalized, to obtain normalization data；
Normalization data is filtered to obtain the first breath signal；
Wavelet transformation is carried out to the first breath signal and obtains the second breath signal；
Respiratory rate is determined according to the second breath signal；
The computer program also achieves other steps of respiratory rate detection method when being executed by processor, can specifically join
See the explanation of above-mentioned Fig. 3,4 corresponding embodiments, therefore not to repeat here.
In one embodiment, it the present invention also provides a kind of computer equipment, including memory, processor and is stored in
On memory and the computer program that can run on a processor, realizes when which executes program and mentioned in the various embodiments described above
A kind of the step of respiratory rate detection method supplied.
Specifically, which can be personal computer or server.The computer equipment includes total by system
Processor, memory and the network interface of line connection.Wherein, processor supports entire meter for providing calculating and control ability
Calculate the operation of machine equipment.Memory includes non-volatile memory medium and built-in storage.It is stored in non-volatile memory medium
Operating system and and computer program, a kind of respiratory rate detection method is realized when which is executed by processor.
Built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.Network interface is used for
It is communicated with external server or terminal by network connection.
Above is only part or preferred embodiment of the invention, therefore either text or attached drawing cannot all limit this
The range of protection is invented to be made under all designs with an entirety of the invention using description of the invention and accompanying drawing content
Equivalent structure transformation, or directly/be used in other related technical areas indirectly and be included in the scope of protection of the invention.
Claims (10)
1. a kind of respiratory rate detection method characterized by comprising
Obtain distance change signal of at least one the calibration position of human chest in default collection period；
The distance change signal is normalized, to obtain normalization data；
The normalization data is filtered to obtain the first breath signal；
Wavelet transformation is carried out to first breath signal and obtains the second breath signal, and is determined according to second breath signal
Respiratory rate.
2. respiratory rate detection method according to claim 1, which is characterized in that the acquisition human chest at least one
Calibration position includes: in the distance change signal preset in collection period
Distance change signal of the human chest four calibration positions in default collection period is acquired using laser sensor, wherein
Four calibration position includes thoracic cavity and the thorax side of sternum area.
3. respiratory rate detection method according to claim 2, which is characterized in that the sample frequency of the laser sensor
More than or equal to 6.67Hz.
4. respiratory rate detection method according to claim 1, which is characterized in that described to be carried out to the normalization data
Filtering obtains the first breath signal and includes:
Using Gaussian functionThe normalization data is filtered；Wherein, wherein μ is 0,
σ is preset constant, and x is the input signal of gaussian filtering, and f is output signal after gaussian filtering.
5. respiratory rate detection method according to claim 1, which is characterized in that it is described to first breath signal into
Row wavelet transformation obtains the second breath signal
Frequency range layering is carried out according to Shannon-nyquist sampling principle and the sample frequency of the distance change signal, is calculated every
The frequency range of layer；
It is determined needed for wavelet decomposition and reconstruct according to every layer of frequency range and preset band connection frequency in frequency range layering
The number of plies；
Signal decomposition is carried out according to the number of plies needed for the wavelet decomposition and pre-selected morther wavelet, obtains dividing by frequency range
Multi-layer corrugated；
According to coefficient corresponding with the number of plies needed for the wavelet reconstruction and decompose obtained multi-layer corrugated progress signal weight
Structure obtains the second breath signal.
6. respiratory rate detection method according to claim 5, which is characterized in that the higher cut-off frequency of the band connection frequency
Rate is 0.37Hz, and the lower-cut-off frequency of the band connection frequency is 0.
7. respiratory rate detection method according to claim 1, which is characterized in that described according to second breath signal
Determine that respiratory rate includes:
Small echo wave crest or small echo trough are extracted from second breath signal；
Respiratory rate is calculated according to the time interval of adjacent small echo wave crest or the time interval of small echo trough.
8. a kind of respiratory frequency detection device characterized by comprising
Signal acquisition module, for obtaining distance change letter of at least one the calibration position of human chest in default collection period
Number；
Signal pre-processing module, for the distance change signal to be normalized, to obtain normalization data；
Signal filter module obtains the first breath signal for being filtered to the normalization data；
Wavelet transformation module obtains the second breath signal for carrying out wavelet transformation to first breath signal；
Respiratory rate computing module, for determining respiratory rate according to second breath signal.
9. a kind of computer program memory medium, which is characterized in that it is characterized in that, when the computer program is executed by processor
The step of realizing respiratory rate detection method described in any one of claims 1 to 7.
10. a kind of computer equipment, which includes memory, processor and is stored on the memory and can
The computer program run on the processor, which is characterized in that the processor is realized when executing the computer program
Described in any one of claims 1 to 7 the step of respiratory rate detection method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910055015.0A CN109691999A (en) | 2019-01-21 | 2019-01-21 | Respiratory rate detection method, device, storage medium and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910055015.0A CN109691999A (en) | 2019-01-21 | 2019-01-21 | Respiratory rate detection method, device, storage medium and computer equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109691999A true CN109691999A (en) | 2019-04-30 |
Family
ID=66234235
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910055015.0A Pending CN109691999A (en) | 2019-01-21 | 2019-01-21 | Respiratory rate detection method, device, storage medium and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109691999A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112932460A (en) * | 2021-02-01 | 2021-06-11 | 重庆大学 | Respiratory rate monitoring device and method |
| CN113205022A (en) * | 2021-04-23 | 2021-08-03 | 湖南万脉医疗科技有限公司 | Respiratory anomaly monitoring method and system based on wavelet analysis |
| CN114305482A (en) * | 2021-12-29 | 2022-04-12 | 杭州堃博生物科技有限公司 | Lung sound segmentation processing method and device, electronic equipment and storage medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102014737A (en) * | 2008-05-09 | 2011-04-13 | 皇家飞利浦电子股份有限公司 | Contactless respiration monitoring of a patient and optical sensor for a photoplethysmography measurement |
| CN102508256A (en) * | 2011-11-23 | 2012-06-20 | 中国科学院微电子研究所 | Non-contact real-time physiological signal monitoring system |
| CN202568219U (en) * | 2012-03-01 | 2012-12-05 | 北京麦邦光电仪器有限公司 | Sleeping heart rate and breath monitoring system |
| CN204147031U (en) * | 2014-04-03 | 2015-02-11 | 上海伟赛智能科技有限公司 | Non-interference intelligent breathing detector |
| US20150196205A1 (en) * | 2014-01-10 | 2015-07-16 | Electronics And Telecommunications Research Institute | Contactless cardiopulmonary signal estimation method and apparatus |
| CN104783799A (en) * | 2015-04-01 | 2015-07-22 | 上海兆观信息科技有限公司 | Short-distance non-contact type single objective breathing rate and breathing amplitude detection method |
| CN105578593A (en) * | 2015-12-23 | 2016-05-11 | 华南师范大学 | Wavelet transform prediction based mobile node location method |
| CN105813551A (en) * | 2013-10-08 | 2016-07-27 | 林内特斯波尔有限公司 | Device for contactless monitoring of patient's vital signs |
| CN106073784A (en) * | 2016-08-17 | 2016-11-09 | 广州视源电子科技股份有限公司 | A kind of breathing rate extracting method and device |
| CN107110994A (en) * | 2014-09-27 | 2017-08-29 | 英特尔公司 | Integrated terahertz sensor |
| CN107928674A (en) * | 2017-12-12 | 2018-04-20 | 北京大学（天津滨海）新代信息技术研究院 | A kind of contactless breathing detection method based on sound ranging |
| CN108175382A (en) * | 2018-01-10 | 2018-06-19 | 深圳先进技术研究院 | Contactless sleep evaluation method and device based on CPC |
-
2019
- 2019-01-21 CN CN201910055015.0A patent/CN109691999A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102014737A (en) * | 2008-05-09 | 2011-04-13 | 皇家飞利浦电子股份有限公司 | Contactless respiration monitoring of a patient and optical sensor for a photoplethysmography measurement |
| CN102508256A (en) * | 2011-11-23 | 2012-06-20 | 中国科学院微电子研究所 | Non-contact real-time physiological signal monitoring system |
| CN202568219U (en) * | 2012-03-01 | 2012-12-05 | 北京麦邦光电仪器有限公司 | Sleeping heart rate and breath monitoring system |
| CN105813551A (en) * | 2013-10-08 | 2016-07-27 | 林内特斯波尔有限公司 | Device for contactless monitoring of patient's vital signs |
| US20150196205A1 (en) * | 2014-01-10 | 2015-07-16 | Electronics And Telecommunications Research Institute | Contactless cardiopulmonary signal estimation method and apparatus |
| CN204147031U (en) * | 2014-04-03 | 2015-02-11 | 上海伟赛智能科技有限公司 | Non-interference intelligent breathing detector |
| CN107110994A (en) * | 2014-09-27 | 2017-08-29 | 英特尔公司 | Integrated terahertz sensor |
| CN104783799A (en) * | 2015-04-01 | 2015-07-22 | 上海兆观信息科技有限公司 | Short-distance non-contact type single objective breathing rate and breathing amplitude detection method |
| CN105578593A (en) * | 2015-12-23 | 2016-05-11 | 华南师范大学 | Wavelet transform prediction based mobile node location method |
| CN106073784A (en) * | 2016-08-17 | 2016-11-09 | 广州视源电子科技股份有限公司 | A kind of breathing rate extracting method and device |
| CN107928674A (en) * | 2017-12-12 | 2018-04-20 | 北京大学（天津滨海）新代信息技术研究院 | A kind of contactless breathing detection method based on sound ranging |
| CN108175382A (en) * | 2018-01-10 | 2018-06-19 | 深圳先进技术研究院 | Contactless sleep evaluation method and device based on CPC |
Non-Patent Citations (1)
| Title |
|---|
| 博古斯拉夫·赛干内克: "《三维计算机视觉技术和算法导论》", 31 October 2014, 国防工业出版社 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112932460A (en) * | 2021-02-01 | 2021-06-11 | 重庆大学 | Respiratory rate monitoring device and method |
| CN112932460B (en) * | 2021-02-01 | 2022-11-04 | 重庆大学 | Respiratory rate monitoring device and method |
| CN113205022A (en) * | 2021-04-23 | 2021-08-03 | 湖南万脉医疗科技有限公司 | Respiratory anomaly monitoring method and system based on wavelet analysis |
| CN113205022B (en) * | 2021-04-23 | 2022-10-11 | 湖南万脉医疗科技有限公司 | Respiratory anomaly monitoring method and system based on wavelet analysis |
| CN114305482A (en) * | 2021-12-29 | 2022-04-12 | 杭州堃博生物科技有限公司 | Lung sound segmentation processing method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111035367B (en) | Signal detection system for judging sleep apnea | |
| CN109691999A (en) | Respiratory rate detection method, device, storage medium and computer equipment | |
| WO2019174027A1 (en) | Physiological information monitoring method, physiological information monitoring mat, and mattress | |
| US20230037703A1 (en) | Posture determination apparatus | |
| CN108888271B (en) | Physiological parameter measuring system and intelligent seat with same | |
| CN208876492U (en) | A kind of physiologic information monitoring pad and mattress | |
| CN105662345B (en) | heartbeat signal processing method, device and system | |
| WO2019205841A1 (en) | Method and system for acquiring respiration information | |
| CN105249925B (en) | A kind of traditional Chinese medical pulse manifestation collecting device and noise reduction system and noise-reduction method | |
| CN110115574A (en) | The method and apparatus of rhythm of the heart | |
| CN108992053B (en) | Method for real-time non-binding detection of heart rate and heartbeat interval | |
| JP4863047B2 (en) | Respiratory heart rate monitoring device | |
| CN112089405A (en) | Pulse wave characteristic parameter measuring and displaying device | |
| CN112806977A (en) | Physiological parameter measuring method based on multi-scale fusion network | |
| CN110115583A (en) | The method and apparatus of monitoring of respiration | |
| CN114052693B (en) | Heart rate analysis method, device and equipment | |
| CN113017559A (en) | Vital sign extraction algorithm and system based on piezoelectric film sensor | |
| CN107495939A (en) | Live biometric monitoring method, device and system | |
| CN113729653A (en) | Human body pulse wave signal acquisition method | |
| CN113598758A (en) | Non-contact sleep monitoring method and system | |
| CN106236041A (en) | A kind of measure in real time and accurately heart rate and the algorithm of breathing rate and system | |
| Li et al. | Unobtrusive monitoring of cardiorespiratory signals during sleep based on PVDF sensor and singular spectrum analysis | |
| CN102894964A (en) | Method and device for non-invasively measuring blood pressure | |
| KR20190103626A (en) | A portable ECG electrode and an ECG measurement system for small animals | |
| KR101744691B1 (en) | Method and Apparatus for Detecting Heartbeat using Ballistocardiogram |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190430 |
|
| RJ01 | Rejection of invention patent application after publication |