CN102499651A - Alarm method for monitoring system - Google Patents
Alarm method for monitoring system Download PDFInfo
- Publication number
- CN102499651A CN102499651A CN2011103248823A CN201110324882A CN102499651A CN 102499651 A CN102499651 A CN 102499651A CN 2011103248823 A CN2011103248823 A CN 2011103248823A CN 201110324882 A CN201110324882 A CN 201110324882A CN 102499651 A CN102499651 A CN 102499651A
- Authority
- CN
- China
- Prior art keywords
- forecast
- monitor system
- alarm method
- alarm
- parameter
- 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
Images
Landscapes
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention discloses an alarm method for a monitoring system, which is based on Daubechies wavelets to decompose the monitoring information time-series data into a high-frequency signal and a low-frequency signal, wherein a forecast model of the high-frequency signal and a forecast model of the low-frequency signal are respectively constructed through adopting a least squares support vector machine arithmetic, and the wavelet inverse transformation is utilized to obtain the final forecast result as the forecast models determine forecast values. The alarm method adopts a particle swarm optimization algorithm to real-timely and automatically adjust model parameters according to the observed data and the estimation result, so the tracking of the 'slow' time varying physiological parameter time series is realized, and the model accuracy is ensured. According to the modeling result, the alarm method can forwardly forecast, ensures the forecast values and the upper and the lower thresholds and automatically set an alarm threshold, accordingly, different alarm models can be built according to different monitored people, and the alarm threshold is automatically set. The alarm method can be applied in a central monitoring system, an intensive care unit and the coronary heart disease monitoring of a hospital and a community remote monitoring system.
Description
Technical field
The invention belongs to the processing of biomedical signals field, relate in particular to a kind of alarm method that is used for monitor system.
Background technology
Existing monitor system mainly adopts following two kinds analysis mode: the analytical method based on time series modeling that proposes based on people such as the analysis mode method of preset threshold value and M.Imboff.In use all there is certain problem in this dual mode.
Analysis mode based on preset threshold value is meant: monitor system is set bound respectively to each item vital sign parameter of monitoring and (as: is preestablished for hrv parameter and to be limited to 120, is limited to 50 down; When monitor value exceeds this scope; Promptly send warning message), this analysis mode exists increase system operation complexity, be prone to false alarm information, can't reflect shortcoming such as guardianship health variation.
Common multi-parameter physiology monitor can realize even reach tens kinds of physiological parameter monitoring functions that guardianship content relates to cardiovascular system, respiratory system several.Because the health of guardianship is different, requires preset threshold value also to there are differences.Therefore often need be the various physiological parameters difference preset threshold values of each guardianship.The complexity of this operation of increase system undoubtedly.
Usually the process data of continuous detecting is concentrated and is all included 0.5%～10%, even 20% exceptional value, and 0.01%～0.05% exceptional value is also often arranged in the high-quality data.The form that these exceptional values show as away from most of observed values occurs.These abnormity point can exceed preset threshold value usually simultaneously, trigger warning message and produce.In the physiological parameter continuous monitor system of CICU (ICUs), this phenomenon is same often to be occurred.ICUs originates from during the Second World War, in ambulance, the soldier after undergoing surgery under the general anesthesia situation is guarded, and this technology obtains to use widely during 1947-1952.The medical monitoring technology of ICUs is very perfect at present, but its alarm method that is used for monitor system but causes correlational study personnel's concern always at recent two decades.Wherein main cause is exactly owing in the on-line monitor process, owing to adopt the analysis mode of preset threshold value to bring a large amount of false alarm information, bring a large amount of extra work burdens to medical personnel.And this mainly is because the detecting instrument error and since the exceptional value that guardianship moves generation cause.
The purpose of monitor system the more important thing is the variation that can find health as soon as possible.In the monitor system situation of monitoring information reduce as Fig. 1, Fig. 2, Fig. 3, four kinds shown in Figure 4.
Fig. 1 is the sketch map that monitoring information is in steady statue.Fig. 2 is the sketch map that monitoring information has exceptional value to occur; Be presented at 100,200,350 positions and exceptional value occurs; These abnormity point often are because monitor system or environment cause; Be not that reflection guardianship health changes really,, obviously need not to send warning message though therefore measured value exceeds preset threshold value.The ASSOCIATE STATISTICS data is presented at and exceeds 50% false alarm information among the ICUs and have exceptional value to cause.Fig. 3 is the sketch map that guardianship goes out the present condition skew; Fig. 4 is that the sketch map that trend changes appears in guardianship; Though Fig. 3 and Fig. 4 detected value possibly also not reach preset threshold value; But unusual condition appears in the expression guardianship, requires to find early, submits to medical personnel that the situation of guardianship is paid close attention to.Therefore to realize real-time online identification for the two states among Fig. 3 and Fig. 4, and send warning message.
The analytical method based on time series modeling that people such as M.Imboff propose is:
(1) adopts autoregression model
(2) adopting data length is 90 data window, and is that 30 data move forward with step-length;
(3) in each time window, preceding 60 data are used for parameter
,
of identification
model; After this model is done the forecast in 30 steps forward, probability is limited to 95%, confirms that forecast goes up lower threshold value PI;
(4) back 30 data and PI are done comparison;
Steady statue (Fig. 1): all data all do not exceed the PI scope;
Exceptional value (Fig. 2) is arranged: exceed 5 of PI scope point numbers
continuously;
State skew (Fig. 3): exceed PI scope point number continuously>5.
The core concept based on the analytical method of time series modeling that people such as M.Imboff propose is: through adopting lower-order model; Reduce the needed data volume of modeling as far as possible; The implementation model quick identification, but this can make model accuracy reduce, thus cause inaccurate forecast.
Domestic and international many scholars once studied for the alarm method problem that is used for monitor system, and proposed relevant imagination.Propositions such as Korhonen I when health monitoring system operate under the secular condition as: surpass one month even 1 year, the automatic processing of sensing data will become very crucial so; Processed content relates to the feature extraction of detection and compensation, monitored parameters of error like the discovery of long-term trend and necessary warning message issue.Yang Jie etc. also speak of the guardianship of all ages and classes, sex, body constitution, disease; The model that its identification is reported to the police should be inequality; Monitor system should be able to be according to the guardianship of all ages and classes, sex, body constitution, disease; Carry out modeling through self study, and can discern reasoning according to multiple model of cognition.But they do not have have total solution to propose to this problem.
Summary of the invention
The object of the present invention is to provide a kind of alarm method that is used for monitor system; The research of the alarm method through being used for monitor system; Guardianship to all ages and classes, sex, body constitution, disease; The monitoring warning model that foundation varies with each individual, the abnormal information that occurs in the real-time discovery monitoring process.
For this reason, the technical scheme steps of the present invention's employing is following:
(1) monitor system on-line operation obtains data, as sample data collection A;
(2) adopt the Daubechies wavelet basis to decompose to sample data collection A;
(3) above-mentioned decomposition is obtained each layer signal and carry out modeling, the modeling type is least square method supporting vector machine (LS-SVM);
(4) use particle swarm optimization algorithm and confirm the LS-SVM model parameter;
(5) with the LS-SVM model that obtains each layer data of wavelet decomposition is forecast;
(6) adopt the Mallat algorithm, synthetic each minute solution sequence the forecast result, and synthetic low frequency coefficient and high frequency coefficient carried out wavelet reconstruction respectively, obtain final forecast result;
(7) do forward direction forecast and confirm that forecast goes up lower threshold value PI, and which kind of state guardianship is in makes judgement;
(8) make forward recursion correction model parameter, and restart forecast and judge process.
Obtaining data in the said step (1) is to obtain 100 data.
The client of monitor system is made up of three physiological parameter detection modules in the said step (1), is respectively 630A non-invasive blood pressure module, 811 electrocardios/breathing/body temperature module, 9003 blood oxygen modules.
Sample data collection A comprises blood pressure, electrocardio, breathing, body temperature and blood oxygen in the said step (1).
The Daubechies wavelet basis decomposition number of plies is 3 layers in the said step (2).
Modeling procedure in the said step (3) is following:
The sample set of monitoring information
; Wherein
;
, construct linear regression LS-SVM model at feature space
:
Wherein,
is kernel function;
is for to be mapped to the vector in the feature space
with input vector
;
, parameter
,
are confirmed by following formula:
Wherein
;
LS-SVM model parameter in the said step (4) comprises kernel function parameter σ, ordering parameter γ.
The state of guardianship comprises steady statue, exceptional value and state skew is arranged in the said step (7).
The step number of forward direction forecast was 20 ~ 30 steps in the said step (7).
The step number of forward recursion was 20 ~ 30 steps in the said step (8).
The method that the present invention proposes will be guarded the information time series data based on the Daubechies wavelet basis and will be decomposed into high-frequency signal and low frequency signal; Adopt the least square method supporting vector machine algorithm, set up the forecasting model of high-frequency signal and low frequency signal respectively; Confirm predicted value by forecasting model, utilize wavelet inverse transformation to acquire final forecast result, and the algorithm frame of alarm threshold value is set automatically.
The present invention compared with prior art has following advantage and beneficial effect:
(1) the present invention uses the Daubechies wavelet basis and will guard the information time series data and be decomposed into high-frequency signal and low frequency signal; Adopt the least square method supporting vector machine algorithm, set up the forecasting model of high-frequency signal and low frequency signal respectively, to improve the precision of model.
(2) Using P SO particle swarm optimization algorithm of the present invention comes the self-optimizing model parameter according to observed data and estimated result in real time, becomes the physiological parameter seasonal effect in time series when realizing " slowly " to follow the trail of the accuracy of assurance model.
(3) the present invention can use prediction theory according to modeling result, does the forward direction forecast; Confirm predicted value, and forecast thresholding up and down, and it is set to the thresholding of reporting to the police automatically; Thereby realize and to set up the warning model that varies with each individual according to different guardianships, alarm threshold value is set automatically.
Description of drawings
Fig. 1 is the sketch map that guardianship is in steady statue;
Fig. 2 is the sketch map that monitor system has exceptional value to occur;
Fig. 3 is the sketch map that guardianship goes out the present condition skew;
Fig. 4 is that the sketch map that trend changes appears in guardianship;
Fig. 5 is the sketch map of monitor system block diagram;
Fig. 6 is the client sketch map of monitor system;
Fig. 7 is the algorithm flow chart that originally is used for the alarm method of monitor system;
Fig. 8 uses the modeling and forecast result of this method to heart rate data;
Fig. 9 uses the prediction error of this method to heart rate data.
The specific embodiment
In order to understand the present invention better, the present invention is done to describe further below in conjunction with accompanying drawing.
Fig. 5 is the sketch map of monitor system block diagram; The client of monitor system is made up of three physiological parameter detection modules; Be respectively 630A non-invasive blood pressure module, 811 electrocardios/breathing/body temperature module, 9003 blood oxygen modules, the physiological parameters such as blood pressure, electrocardio, breathing, body temperature and blood oxygen of guardianship are carried out online, continuous detecting; Three monitoring modules make up the client monitor system through the PC104 bus system, connect through the network interface on the PC104 bus between client and the community's remote monitoring system.
Fig. 6 is the client sketch map of monitor system; Novel intelligent physiological parameter monitor detects the physiological parameter of guardianship in real time; The alarm method that is used for monitor system through embedded carries out analyzing and processing to data; Identify the guardianship unusual condition, and data are passed to community long distance monitoring center through network interface, community makes real-time response in the long distance monitoring center to this.
Fig. 7 is the alarm method flow chart that originally is used for monitor system.The information time series data be will guard based on the Daubechies wavelet basis and high-frequency signal and low frequency signal will be decomposed into; Adopt the least square method supporting vector machine algorithm, set up the forecasting model of high-frequency signal and low frequency signal respectively; Confirm predicted value by forecasting model, utilize wavelet inverse transformation to acquire final forecast result, and the algorithm frame of alarm threshold value is set automatically.Wherein,
is the low frequency signal of original monitored signal through obtaining after the wavelet decomposition, and
,
and
are the high-frequency signal of original monitored signal through obtaining after the wavelet decomposition.
Use this method the data in PhysioBank physiological parameter data storehouse are carried out analyzing and processing; Fig. 8 uses the modeling and forecast result of this method to the heart rate data of heart rate data; Fig. 9 uses the prediction error analysis result of this method to heart rate data, and draw as drawing a conclusion: (1), modeling result satisfy the model testing method based on residual analysis; (2), the forecast result compares through the forecast result that the analytical method that proposed with M.Imboff, be used for the CICU monitor system obtains, prove this method be a kind of performance better, the method for optimization more; (3), the method can carry out analyzing and processing to the physiological parameter time series that continuous monitoring obtains, and realizes online, Realtime Alerts.
Based on the alarm method that is used for monitor system; The community's remote monitoring system that makes up can be realized for the old people, the people with disability that lack self-care ability monitoring function being provided; Make them recover partly self care ability, alleviate household's nursing burden, make them incorporate family life better.Place some guardianships that are distributed in certain community, hospital or the local area within the network monitoring system simultaneously; Old people, the relevant physiological parameter of people with disability's health are detected in real time; And the alarm method that is used for monitor system carries out analyzing and processing; Identify the guardianship unusual condition, and send warning message.
The present invention can carry out analyzing and processing to the vital sign data that continuous monitoring in the monitor system obtains, and comprises electrocardio, blood pressure, blood oxygen, pulse, breathing, body temperature, and the sample rate of monitor system>1S; Modeling result of the present invention satisfies the model testing method based on residual analysis, and analysis result of the present invention in addition and relevant medical expert's analysis result conforms to basically, more than the rate of accuracy reached to 95%.
The present invention can be applicable in central monitoring system, CICU (ICU), coronary heart disease Intensive Care Therapy (CCU) and the community's remote monitoring system in the hospital, and the vital sign parameter of continuous monitoring is carried out analyzing and processing, and information notes abnormalities.
Claims (10)
1. alarm method that is used for monitor system is characterized in that step is following:
(1) monitor system on-line operation obtains data, as sample data collection A;
(2) adopt the Daubechies wavelet basis to decompose to sample data collection A;
(3) above-mentioned decomposition is obtained each layer signal and carry out modeling, the modeling type is least square method supporting vector machine (LS-SVM);
(4) use particle swarm optimization algorithm and confirm the LS-SVM model parameter;
(5) with the LS-SVM model that obtains each layer data of wavelet decomposition is forecast;
(6) adopt the Mallat algorithm, synthetic each minute solution sequence the forecast result, and synthetic low frequency coefficient and high frequency coefficient carried out wavelet reconstruction respectively, obtain final forecast result;
(7) do forward direction forecast and confirm that forecast goes up lower threshold value PI, and which kind of state guardianship is in makes judgement;
(8) make forward recursion correction model parameter, and restart forecast and judge process.
2. the alarm method that is used for monitor system according to claim 1, it is characterized in that obtaining in the said step (1) data is to obtain 100 data.
3. the alarm method that is used for monitor system according to claim 1; The client that it is characterized in that monitor system in the said step (1) is made up of three physiological parameter detection modules, is respectively 630A non-invasive blood pressure module, 811 electrocardios/breathing/body temperature module, 9003 blood oxygen modules.
4. the alarm method that is used for monitor system according to claim 1 is characterized in that sample data collection A comprises blood pressure, electrocardio, breathing, body temperature and blood oxygen in the said step (1).
5. the alarm method that is used for monitor system according to claim 1 is characterized in that the Daubechies wavelet basis decomposition number of plies is 3 layers in the said step (2).
6. the alarm method that is used for monitor system according to claim 1 is characterized in that the modeling procedure in the said step (3) is following:
The sample set of monitoring information
; Wherein
;
, construct linear regression LS-SVM model at feature space
:
Wherein,
is kernel function;
,
For input vector
is mapped to the vector in the feature space
;
, parameter
,
are confirmed by following formula:
Wherein
;
7. the alarm method that is used for monitor system according to claim 1 is characterized in that the LS-SVM model parameter in the said step (4) comprises kernel function parameter σ, ordering parameter γ.
8. the alarm method that is used for monitor system according to claim 1 is characterized in that the state of guardianship in the said step (7) comprises steady statue, exceptional value and state skew is arranged.
9. the alarm method that is used for monitor system according to claim 1, the step number that it is characterized in that forward direction forecast in the said step (7) were 20 ~ 30 steps.
10. the alarm method that is used for monitor system according to claim 1, the step number that it is characterized in that forward recursion in the said step (8) were 20 ~ 30 steps.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011103248823A CN102499651A (en) | 2011-10-24 | 2011-10-24 | Alarm method for monitoring system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011103248823A CN102499651A (en) | 2011-10-24 | 2011-10-24 | Alarm method for monitoring system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102499651A true CN102499651A (en) | 2012-06-20 |
Family
ID=46211833
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011103248823A Pending CN102499651A (en) | 2011-10-24 | 2011-10-24 | Alarm method for monitoring system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102499651A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473480A (en) * | 2013-10-08 | 2013-12-25 | 武汉大学 | Online monitoring data correction method based on improved universal gravitation support vector machine |
| CN104905770A (en) * | 2014-03-13 | 2015-09-16 | 南京理工大学 | Human health state detection system based on internet of things and SVM analysis |
| CN105204413A (en) * | 2015-08-19 | 2015-12-30 | 武汉联中科技有限公司 | Information processing system and method used for wearable device |
| CN107928631A (en) * | 2017-12-21 | 2018-04-20 | 哈尔滨工业大学 | Near-infrared Brain function signal processing method based on the estimation of the differential path factor |
| CN110013243A (en) * | 2018-01-08 | 2019-07-16 | 映泰股份有限公司 | Electrocardiosignal alarming device |
| WO2020132799A1 (en) * | 2018-12-24 | 2020-07-02 | 深圳迈瑞生物医疗电子股份有限公司 | Method and apparatus for setting alarm limit value for monitoring device |
| CN111938607A (en) * | 2020-08-20 | 2020-11-17 | 中国人民解放军总医院 | Intelligent monitoring alarm method and system based on multivariate parameter fusion |
| CN117042031A (en) * | 2023-10-08 | 2023-11-10 | 深圳曼瑞德科技有限公司 | Mobile communication terminal and remote blood oxygen monitoring system |
| CN117275648A (en) * | 2023-11-21 | 2023-12-22 | 南通大学附属医院 | Intelligent nursing method for intensive care unit patient based on Internet of things |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7058712B1 (en) * | 2002-06-04 | 2006-06-06 | Rockwell Automation Technologies, Inc. | System and methodology providing flexible and distributed processing in an industrial controller environment |
| CN1803088A (en) * | 2006-01-20 | 2006-07-19 | 华南理工大学 | Alarming method of intelligent monitoring system |
| CN101329697A (en) * | 2008-06-11 | 2008-12-24 | 电子科技大学 | Method for predicting analog circuit state based on immingle algorithm |
| EP2369529A1 (en) * | 2010-03-24 | 2011-09-28 | Alcatel Lucent | A method of detecting anomalies in a message exchange, corresponding computer program product, and data storage device therefor |
-
2011
- 2011-10-24 CN CN2011103248823A patent/CN102499651A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7058712B1 (en) * | 2002-06-04 | 2006-06-06 | Rockwell Automation Technologies, Inc. | System and methodology providing flexible and distributed processing in an industrial controller environment |
| CN1803088A (en) * | 2006-01-20 | 2006-07-19 | 华南理工大学 | Alarming method of intelligent monitoring system |
| CN101329697A (en) * | 2008-06-11 | 2008-12-24 | 电子科技大学 | Method for predicting analog circuit state based on immingle algorithm |
| EP2369529A1 (en) * | 2010-03-24 | 2011-09-28 | Alcatel Lucent | A method of detecting anomalies in a message exchange, corresponding computer program product, and data storage device therefor |
Non-Patent Citations (6)
| Title |
|---|
| 《测试技术学报》 20071230 邹焱飚 监护信息系统中异常值的识别和处理方法 531-535 1-10 第21卷, 第6期 * |
| 熊伟丽: "粒子群算法在支持向量机参数选择优化中的应用研究", 《2007中国控制与决策学术年会论文集》, 31 July 2007 (2007-07-31), pages 447 - 452 * |
| 蒋贤海: "一种强噪声下的监护信息降噪方法", 《华南理工大学学报（自然科学版）》, vol. 39, no. 4, 30 April 2011 (2011-04-30), pages 66 - 69 * |
| 蒋贤海: "小波分析和神经网络在社区健康监护系统中的应用", 《机械设计与制造》, no. 3, 30 March 2011 (2011-03-30), pages 75 - 77 * |
| 邹焱飚: "监护信息系统中异常值的识别和处理方法", 《测试技术学报》, vol. 21, no. 6, 30 December 2007 (2007-12-30), pages 531 - 535 * |
| 陈帅: "最小二乘支持向量机的参数优化及其应用", 《华东理工大学学报（自然科学版）》, vol. 34, no. 2, 30 April 2008 (2008-04-30), pages 278 - 282 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473480A (en) * | 2013-10-08 | 2013-12-25 | 武汉大学 | Online monitoring data correction method based on improved universal gravitation support vector machine |
| CN104905770A (en) * | 2014-03-13 | 2015-09-16 | 南京理工大学 | Human health state detection system based on internet of things and SVM analysis |
| CN105204413A (en) * | 2015-08-19 | 2015-12-30 | 武汉联中科技有限公司 | Information processing system and method used for wearable device |
| CN107928631A (en) * | 2017-12-21 | 2018-04-20 | 哈尔滨工业大学 | Near-infrared Brain function signal processing method based on the estimation of the differential path factor |
| CN110013243A (en) * | 2018-01-08 | 2019-07-16 | 映泰股份有限公司 | Electrocardiosignal alarming device |
| WO2020132799A1 (en) * | 2018-12-24 | 2020-07-02 | 深圳迈瑞生物医疗电子股份有限公司 | Method and apparatus for setting alarm limit value for monitoring device |
| CN113164075A (en) * | 2018-12-24 | 2021-07-23 | 深圳迈瑞生物医疗电子股份有限公司 | Alarm limit value setting method and device for monitoring equipment |
| CN111938607A (en) * | 2020-08-20 | 2020-11-17 | 中国人民解放军总医院 | Intelligent monitoring alarm method and system based on multivariate parameter fusion |
| CN117042031A (en) * | 2023-10-08 | 2023-11-10 | 深圳曼瑞德科技有限公司 | Mobile communication terminal and remote blood oxygen monitoring system |
| CN117042031B (en) * | 2023-10-08 | 2023-12-26 | 深圳曼瑞德科技有限公司 | Mobile communication terminal and remote blood oxygen monitoring system |
| CN117275648A (en) * | 2023-11-21 | 2023-12-22 | 南通大学附属医院 | Intelligent nursing method for intensive care unit patient based on Internet of things |
| CN117275648B (en) * | 2023-11-21 | 2024-02-09 | 南通大学附属医院 | Intelligent nursing method for intensive care unit patient based on Internet of things |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102499651A (en) | Alarm method for monitoring system | |
| CN100340207C (en) | Alarming method of intelligent monitoring system | |
| JP6541738B2 (en) | Device for monitoring a patient for acute lung injury (ALI) and method of operation thereof | |
| US9700218B2 (en) | Systems and methods for reducing nuisance alarms in medical devices | |
| CN105765584B (en) | For dividing the Clinical Decision Support Systems and computer storage medium of examining decision-making | |
| CN101194271B (en) | Method and apparatus for distinguishing between clinically significant changes and artifacts in patient physiological information | |
| CN104838382B (en) | For optimizing method, system, medium and the monitoring station of data collection frequencies | |
| US9600990B2 (en) | System and methods for generating predictive combinations of hospital monitor alarms | |
| US20120220846A1 (en) | System and method for diagnosing sleep apnea | |
| US20120330565A1 (en) | System and method for evaluating physiological parameter data | |
| US11051768B1 (en) | Determining when to emit an alarm | |
| CN104823195A (en) | Method and system to reduce nuisance alarm load in clinical setting | |
| US20200178903A1 (en) | Patient monitoring system and method having severity prediction and visualization for a medical condition | |
| CA2862333A1 (en) | Systems and methods for automatic segment selection for multi-dimensional biomedical signals | |
| CN109564586A (en) | System monitor and system monitoring method | |
| Kenneth et al. | Data fusion of multimodal cardiovascular signals | |
| Lan et al. | Poster: DeePTOP: Personalized Tachycardia Onset Prediction Using Bi-directional LSTM in Wearable Embedded Systems. | |
| CN103169459A (en) | Medical device and achieving method | |
| Wang et al. | Single-lead ECG based multiscale neural network for obstructive sleep apnea detection | |
| Agbo et al. | A scalable patient monitoring system using Apache Storm | |
| Hassan et al. | A multi-sensor based automatic sleep apnea detection system for adults using neural network inference on FPGA | |
| Ranjan et al. | Automated Detection of Blood Pressure using CNN | |
| Wong et al. | Probabilistic detection of vital sign abnormality with Gaussian process regression | |
| CN113261924A (en) | Intelligent stroke early warning system and method | |
| Zhang et al. | An ECG intelligent monitoring system with MSP430 microcontroller |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20120620 |