CN104188649B - A method for real-time signal is a linear combination of the multi-point monitoring physiological electrical protection - Google Patents

A method for real-time signal is a linear combination of the multi-point monitoring physiological electrical protection Download PDF

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CN104188649B
CN104188649B CN201410471105.5A CN201410471105A CN104188649B CN 104188649 B CN104188649 B CN 104188649B CN 201410471105 A CN201410471105 A CN 201410471105A CN 104188649 B CN104188649 B CN 104188649B
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CN104188649A (en
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刘红星
闫华文
黄晓林
肇莹
司峻峰
宁新宝
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南京大学
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一种多点生理电监测中保障信号线性合成实时性的方法,包括步骤,(1)根据需要布置好体表的多个测点、连接好监测系统、准备监测,(2)预监测记录一段多点生理电信号,用周期成分分析法或者独立分量分析法或者其他方法,求解相应的优化问题,得到相应的信号最优线性组合向量,(3)进入正式监测,用第(2)步确定的信号最优线性组合向量,对每一段新监测的多点生理电信号进行线性合成,以合成出需要的纯净信号,其特征是:把第(2)步预监测阶段确定的信号最优线性组合向量,直接用于了第(3)步正式监测过程中的信号线性合成,而第(3)步正式监测阶段不再求解优化问题,从而保证了整个正式监测阶段线性合成处理的实时性。 The method of real-time physiological electrical multi-point monitoring of a linear combination of protection signal, comprising the steps of, (1) arranged in a plurality of measuring points required good body surface, connected monitoring system, monitoring of preparation, (2) pre-record a monitoring multipoint physiological electrical signals, using the periodic component analysis or independent component analysis, or other methods, solving the corresponding optimization problem to obtain the optimal linear combination signal corresponding to the vector, (3) into the formal monitoring, (2) determined by the first step optimal linear combination vector signal, multi-point monitoring physiological electrical signals of each section of the new linear synthesis to synthesize pure desired signal, characterized in that: the second step (2) the monitoring phase the pre-determined optimal linear signal combining vector, it was used directly in the (3) step formal linear synthesis signal monitoring process, and (3) no longer monitoring phase step formal optimization problem is solved, thereby ensuring real-time monitoring of the entire stage of the formal linear synthesis process.

Description

多点生理电监测中保障信号线性合成实时性的一种方法 A method for real-time signal is a linear combination of the multi-point monitoring physiological electrical protection

技术领域 FIELD

[0001] 本申请涉及一种多点生理电监测中保障信号线性合成实时性的方法。 [0001] The present application relates to a method of real-time physiological point of a multi-signal electrical monitoring security linear synthesis.

[0002] 动态监测心电图、动态监测脑电图、动态监测肌电图等,这些人体生理电信号的监测应用,往往都是在体表的多个点进行监测。 [0002] Dynamic ECG, EEG monitoring dynamic, dynamic monitoring EMG, etc. These applications monitoring physiological electrical signals, are often monitored at a plurality of points of the body surface. 由体表各点监测的生理电信号实质上都是各种生理电信号的混合信号;譬如,头部检测的脑电信号中往往混有眼电、心电及其它干扰和噪声,从孕妇腹部检测的胎心电信号中混有很大的母体心电信号、还有呼吸波、肌电及其它干扰和噪声等。 Physiological monitoring the electrical signal generated by each of the surface points of the mixed signal are substantially physiological electrical signal; for example, the head detecting EEG is often mixed with EOG, ECG, and other interference and noise, from the pregnant belly fetal detected electrical signal is mixed with a large parent ECG, and respiration wave, EMG and other noise and interference. 人们通过对在体表多点监测的多路生理电信号进行线性组合,可以合成出更为干净单纯的生理电信号,大大提高信噪比。 It physiological electrical signals multiplexed by the multi-point monitoring of the surface linear combination, can be synthesized much cleaner mere physical electrical signal, greatly improved signal to noise ratio.

[0003] 对多点生理电信号进行线性合成涉及最优线性组合向量的确定。 [0003] multipoint physiological electrical signal directed to determining the optimal linear synthesis of a linear combination of vectors. 所谓确定最优线性组合向量,顾名思义,就是要求解一个给定目标函数下的优化问题,而且往往是非凸的多极值优化问题,计算量非常大。 Called to determine the optimal linear combination of vectors, by definition, be solved optimization problem under a given objective function, but more often non-convex optimization problem extremum, a very large amount of calculation. 在多点生理电监测中,如果对监测的每个时间段信号都先求解一个优化问题,确定对应的最优线性组合向量,然后再据此对该时间段信号进行线性合成处理,则实时性难以保证。 In multi-point electrical physiological monitoring, if each signal period are monitored to solving an optimization problem to determine the optimal linear combination of vectors corresponding to, and then processed accordingly the period of linear synthesis signal, the real-time difficult to guarantee. 目前,研究的各种多点生理电信号线性合成算法,都是利用每个时间段信号确定自己的最优线性组合向量,适于离线非实时处理;在实际的多点生理电监测系统中,尚未见信号线性合成算法被应用于实时处理。 Currently, various multi-linear electrical signal synthesis algorithm physiological studies, are signals with each period to determine their optimal linear combination of vectors, suitable for off-line non-real time processing; the actual physiological electrical multi-monitor system, has not been applied to the signal is a linear combination of real-time processing algorithm.

[0004] 本申请就是要提出一种解决方案,以使信号线性合成处理能应用到多点生理电监测系统中去,进行实时处理。 [0004] The present application is to provide a solution, so that the signal processing can be applied to a linear combination of the multi-point electrical physiological monitoring system to perform real-time processing.

背景技术 Background technique

[0005] 对多点生理电信号进行线性合成,有周期成分分析法(Periodic Component Analysis)、独立分量分析法(independent component analysis,ICA)等。 [0005] multipoint linear synthesis physiological electrical signals, there are periodic component analysis (Periodic Component Analysis), independent component analysis (independent component analysis, ICA) and the like. 这些方法,确定最优线性组合向量时的优化目标函数不尽相同,但如前所述,都是求解优化问题并最终确定针对具体合成应用的最优线性组合向量。 These methods, optimizing the objective function to determine the optimal linear combination of vectors vary, but as described above, and finally solving the optimization problem is to determine the optimal linear combination of the vectors for the synthesis of specific applications. 比如,有从孕妇腹壁M个点监测的M路混合信号^(11)11=1,〜,^ = 1,〜,1氺为监测时间段的采样点数,现要用这1路信号线性合成出一路干净的母体心电信号mECG和一路干净的胎儿心电信号fECG,则不管用什么方法,最终都确定了一个合成母体心电图的最优线性组合向量1和一个合成胎儿心电图的最优线性组合向量Wf。 For example, there is a mixed signal path abdominal M M points from the monitoring of pregnant women ^ (11) 11 = 1, ~, ^ = 1, ~, 1 Shui period to monitor the number of sampling points, one signal which is now to use linear synthesis clean out all the way to the mother and starts with a clean ECG mECG fetal ECG the fECG, any method is not effective, eventually determining the optimal linear combination of the optimal linear combination of a vector and a synthesis of a synthesis of the parental fetal electrocardiogram ECG vector Wf. 若记Wm= [Wml,Wm2,…,Wium] Wf = [Wfl,Wf2,…,WfjJ,则有: If the note Wm = [Wml, Wm2, ..., Wium] Wf = [Wfl, Wf2, ..., WfjJ, there are:

Figure CN104188649BD00031

[0008]前已述及,现有多点生理电信号线性合成的算法,都是利用每个时间段信号确定自己的最优线性组合向量,适于进行离线非实时处理;在实际的多点生理电实时监测系统中,由于计算量大的原因,尚未见这些算法被应用于实时处理。 [0008] mentioned before, the conventional multi-point electrical physiologically linear synthesis algorithm is to determine their optimal linear combination of vectors, suitable for use off-line non-real time processing period for each signal; the actual multi- Real-time monitoring physiological electrical system, due to the computationally intensive, yet to see these algorithms are applied to real-time processing. 要提高系统的实时性,可以依靠提高计算机硬件的处理速度,也可以对具体的优化算法进行改进,但目前看来,在可预见的未来,这些途径都无法根本解决问题。 To improve the system in real time, it can rely on to improve the processing speed of the computer hardware can also be modified for specific optimization algorithm, but now it seems, in the foreseeable future, these approaches can not fundamentally solve the problem. 必须另辟蹊径提出方案,以保障多点生理电信号线性合成的实时性,使其能够应用到生理电实时监测系统中去。 Proposed scheme must look for other ways to protect the multi-linear electrical signal physiological synthesis of real-time, so that it can be applied to real-time monitoring physiological electrical system to go.

[0009] 参考文献: [0009] References:

[0010] [1] Mohammed Assam Ouali , and Kheireddine Chafaa,“Separation of composite maternal ECG using SVD de compos it ion,',Computer Applications Technology (ICCAT),2013International Conference on,pp.1-4,Jan.2013. [0010] [1] Mohammed Assam Ouali, and Kheireddine Chafaa, "Separation of composite maternal ECG using SVD de compos it ion, ', Computer Applications Technology (ICCAT), 2013International Conference on, pp.1-4, Jan.2013.

[0011] [2]A.Van Oosterom,“Spatial filtering of the fetal electrocardiogram,” J.Perinat.Med,vol·14,pp.411_419,1986. [0011] [2] A.Van Oosterom, "Spatial filtering of the fetal electrocardiogram," J.Perinat.Med, vol · 14, pp.411_419,1986.

[0012] [3] Zhenwei Shi,and Changshui Zhang,“Semi-blind source extraction for fetal electrocardiogram extraction by combining non-Gaussianity and time-correlation /iNeurocomputingiVol .70?pp.1547-1581? 2007. [0012] [3] Zhenwei Shi, and Changshui Zhang, "Semi-blind source extraction for fetal electrocardiogram extraction by combining non-Gaussianity and time-correlation / iNeurocomputingiVol .70? Pp.1547-1581? 2007.

[0013] [4] Reze Sameni,Christ ian Jutten,and Mohammad B.ShamsolIahi ? “Multichannel Electrocardiogram Decomposition Using Periodic Component Analysis,”IEEE Bio.Med.Eng.,vol.55,No.8,pp.1935-1940,Aug.2008. [0013] [4] Reze Sameni, Christ ian Jutten, and Mohammad B.ShamsolIahi? "Multichannel Electrocardiogram Decomposition Using Periodic Component Analysis," IEEE Bio.Med.Eng., Vol.55, No.8, pp.1935-1940 , Aug.2008.

[0014] [5]Thato Tsalaile,Reza Sameni ? Saeid Sanei,et al·,“Sequential blind source extraction for quasi-periodic signals with time-varying period,',IEEE Bio.Med.Eng.,vo1.56,No.3,pp.646-655,Mar.2009. [0014] [5] Thato Tsalaile, Reza Sameni? Saeid Sanei, et al ·, "Sequential blind source extraction for quasi-periodic signals with time-varying period, ', IEEE Bio.Med.Eng., Vo1.56, No .3, pp.646-655, Mar.2009.

[0015] [6]Aapo Hyvarinen and Erkki Oja,“A fast fixed-point algorithm for independent component analysis,',Neural Comput .vo 1.9?pp. 1483-1492? 1997. [0015] [6] Aapo Hyvarinen and Erkki Oja, "A fast fixed-point algorithm for independent component analysis, ', Neural Comput .vo 1.9? Pp. 1483-1492? 1997.

[0016] [7] Jean-Francois Cardoso,“Source Separation Using Higher Order Moments,',ICASSP,proceedings,vol .4,May 1989. [0016] [7] Jean-Francois Cardoso, "Source Separation Using Higher Order Moments, ', ICASSP, proceedings, vol .4, May 1989.

[0017] [8] Wei Lu?and Jagath C· Rajapakse,“Approach and Applications of Constrained ICA,”IEEE TNeuralNetwor·,vol·16,No·I,pp·203-212,Jan.2005· [0017] [8] Wei Lu? And Jagath C · Rajapakse, "Approach and Applications of Constrained ICA," IEEE TNeuralNetwor ·, vol · 16, No · I, pp · 203-212, Jan.2005 ·

[0018] [9] Wei Lu,and Jagath C.Rajapakse,“ICA with Reference,”Neurocomputing, vo1.69,pp.2244-2257,2006. [0018] [9] Wei Lu, and Jagath C.Rajapakse, "ICA with Reference," Neurocomputing, vo1.69, pp.2244-2257,2006.

发明内容 SUMMARY

[0019] 发明目的。 [0019] The object of the invention.

[0020] 现有的多点生理电信号线性合成算法,都是基于每个监测时间段信号本身确定该段信号的最优线性组合向量,适于对监测的多点生理电信号进行离线的非实时处理。 [0020] The conventional multi-point linear synthesis algorithm physiological electrical signals, each of the monitoring period is based on the signal itself to determine the optimal linear combination of vectors of the signal segment, suitable for multi-point monitoring physiological electrical signals of the non-offline real-time processing. 本发明的目的就是另辟蹊径提出一种能保障多点生理电信号线性合成处理之实时性的方案,以满足实际生理电监测系统对信号线性合成处理实时性的需求。 Object of the present invention is another way to provide a real-time embodiment can protect the electrical physiological process of multi-point linear synthesis to meet the needs of the actual physiological monitoring system electrical signal a linear combination of real time processing.

[0021] 技术方案。 [0021] Technical Solution.

[0022] 一种多点生理电监测中保障信号线性合成实时性的方法,包括步骤,(1)根据需要布置好体表的多个测点、连接好监测系统、准备监测,(2)预监测记录一段多点生理电信号, 用周期成分分析法或者独立分量分析法,求解相应的优化问题,得到相应的信号最优线性组合向量,(3)进入正式监测,用第(2)步确定的信号最优线性组合向量,对每一段新监测的多点生理电信号进行线性合成,以合成出需要的纯净信号,其特征是:把第(2)步预监测阶段确定的信号最优线性组合向量,直接用于了第⑶步正式监测过程中的信号线性合成,而第(3)步正式监测阶段不再求解优化问题,从而保证了整个正式监测阶段线性合成处理的实时性。 [0022] A multi-point real-time method of monitoring physiological electrical signal is a linear combination of security, comprising the steps of, (1) arranged in a plurality of measuring points required good body surface, connected monitoring system, monitoring of preparation, (2) pre- monitoring physiological electrical record a multi-point, using the periodic component analysis or independent component analysis, solving the corresponding optimization problem to obtain the optimal linear combination signal corresponding to the vector, (3) into the formal monitoring, (2) determined by the first step optimal linear combination vector signal, multi-point monitoring physiological electrical signals of each section of the new linear synthesis to synthesize pure desired signal, characterized in that: the second step (2) the monitoring phase the pre-determined optimal linear signal combining vector, it was used directly in the first step a linear combination of the formal ⑶ signal monitoring process, and (3) no longer monitoring phase step formal optimization problem is solved, thereby ensuring real-time monitoring of the entire stage of the formal linear synthesis process. 方法的流程框图如附图1所示。 The method of the flow diagram as shown in Figure 1.

[0023]以上保障信号线性合成实时性的方案不是随意臆想,而是基于一些基本规律的研究和认识提出的。 [0023] real-time signal above guarantee scheme is not a linear combination of random conjecture, but on the study and understanding of the basic rules proposed. 这些规律包括:(1)周期分量分析、独立分量分析等多点信号线性合成方法,可以用于多点混合信号的盲源分离,通过线性合成分离出相对纯净的各个源信号,这些方法已经从理论到实践得到证实;(2)理论上,周期分量分析、独立分量分析等线性合成方法中的最优线性组合向量,本质上是由各信号源产生点到体表各监测点传递路径的特性来决定的,对多点生理电监测而言,就是由心脏、大脑、眼睛等生理电产生源到体表各监测点的传递路径特性来决定的;(3)在多点生理电监测过程中,心脏、大脑、眼睛等生理电产生源到体表各监测点的位置一般是固定不变的、传递路径是相对稳定的。 These rules include: (1) periodic component analysis, independent component analysis, the multi-linear signal synthesizing methods, can be used for blind source separation multi-mix signal, the separated relatively pure source signals by linear synthesis, these methods have the confirmed theory to practice; (2) in theory, the periodic component analysis, independent component analysis, the optimal linear combination vector linear synthesis methods, essentially by the signal source generates the characteristic surface points of each monitoring point in the transmission path determined, multipoint monitoring physiological electrical terms, is generated by a physiological electrical cardiac, brain, eyes and other sources to the transmission channel characteristics of each monitoring point determined body; (3) electrically physiological monitoring process in a multi-point , physiological electrical cardiac, brain, eyes and other sources to produce a surface position of each monitoring point is generally fixed, the transmission path is relatively stable. 因此,将预监测阶段确定的最优线性组合向量直接用于后面的正式监测过程中的线性合成从道理上是可行的。 Thus, the monitoring phase the pre-determined optimal linear combination of the linear vector was used directly in the synthesis of formal monitoring process latter within reason is possible. 发明人也通过大量实验验证了这一结论,相关论文即将发表。 The inventors also verified this conclusion by a large number of experiments, the relevant papers to be published.

[0024] 有益效果。 [0024] benefits.

[0025] 首先,本申请方案显然与现有的利用当前监测信号段确定该段最优线性组合向量的方法不同,因此,具有新颖性。 Different methods [0025] First, the application program determines that the segment is clearly the optimal linear combination of vectors of the current segment using a conventional monitoring signal, therefore, novel. 第二,将预监测阶段确定的最优合成向量直接用于后面的正式监测过程,使正式监测过程不再涉及求解优化问题,只单纯地对多点信号进行线性加权求和,计算量大大减小,可绝对地保障信号线性合成的实时性,因此,这是一个实质进展。 Second, the pre-determined optimal monitoring phase resultant vector was used directly in the back of the official monitoring process, so that no formal monitoring process involves solving the optimization problem, only simply linear multi-point signal weighted sum calculation amount greatly reduced small, can absolutely guarantee real-time signal synthesis of linear, so this is a real progress.

[0026] 附图2为网上公开的一孕妇腹壁电监测数据,图2 (a) (b) (c)分别对应3个腹壁测点的生理电信号数据(第0-10秒段),它们都可看成是母体心电、胎心电及噪声等的混合信号。 [0026] The Internet is a pregnant woman's abdominal electrocardiogram monitoring data disclosed in Figure 2, FIG. 2 (a) (b) (c) respectively correspond to three abdominal physiological electrical signals of data measurement points (0-10 second period, in seconds), which ECG can be seen as the mother, fetal heart rate and electrical noise mixed signal. 为简单起见,这里以合成干净的母体心电源信号为例:发明人将0-10秒段多点信号作为预监测阶段数据,通过独立分量分析方法确定了该段数据的最优线性组合向量为1!11=[-0.4748,0.0523,-0.8786],用此向量合成的该段母体心电图结果如附图3(&)所示,显然比原腹壁电混合信号更为纯净;然后,将预监测阶段确定的最优线性组合向量Wm= [-0.4748, 0.0523,-0.8786]直接用于后来各段数据的线性合成,同样获得了成功,附图3(b)-(h)分别示出了第20-30秒段、70-80秒段、120-130秒段、170-180秒段、220-230秒段、270-280秒段以及300-310秒段的合成结果,都得到了比较“干净”的母体心电。 For simplicity, where the precursor to synthesize a clean signal power heart Case: 0-10 seconds inventors multi-segment signal as a pre-stage monitoring data, independent component analysis method to determine the optimal linear combination of vectors of the segment of data ! 111 = [- 0.4748,0.0523, -0.8786], the segment with this vector results electrocardiogram precursor synthesized as indicated by reference 3 (& amp;) shown, it is more pure than the original abdominal electrical mixed signal; then, the pre- monitoring phase determining the optimal linear combination vector Wm = [-0.4748, 0.0523, -0.8786] was used directly in subsequent linear synthesis of each segment data, equally successful, reference 3 (b) - (h) illustrate the first paragraph 20-30 seconds, 70-80 seconds segments, second segments 120-130, 170-180 second section, second section 220-230, 270-280 300-310 second segment and second segment synthesis result of comparison have been "clean" maternal ECG. 此例说明了本发明方案的有效性。 This example illustrates the effectiveness of the present invention.

附图说明 BRIEF DESCRIPTION

[0027] 图1,本发明多点生理电监测中保障信号线性合成实时性方法的框图。 [0027] FIG. 1, a physiological electrical signal in real-time security monitoring method of synthesis of linear multi-block diagram of the present invention.

[0028] 图2,实施例中预监测阶段体表3点生理电监测数据 [0028] FIG. 2, the electrical physiological monitoring data according to pre-monitor phase surface 3:00 embodiment

[0029] 图3,实施例中整个监测阶段信号若干线性合成结果示意图 [0029] FIG. 3, a schematic view of a plurality of linear synthesis results for the entire monitor phase signals embodiment

具体实施方式 Detailed ways

[0030] 利用网上公开的数据库MIT Non-Invasive Fetal Electrocardiogram Database 的数据作为多点监测数据进行示例。 [0030] for example using data from a database MIT Non-Invasive Fetal Electrocardiogram Database Online disclosed as a multi-point monitoring data. 该数据库中共包括55组数据,采样率为1kHz,每组数据中包括2路胸导信号和3或者4路腹壁电信号。 The CCP database 55 includes data set, a sampling rate of 1kHz, chest lead signals comprise 2-way and 3-way or 4 each abdominal electrical data. 为了方便,只拿出一组编号为“eCgca748”的数据的3点腹壁电信号进行具体说明,见附图(2)所示;它们都是混合信号,包括了母体心电、 胎心电以及肌电等噪声,除母体心电外,其他成分较弱;本实施例中,拟通过对它们的线性合成得到一路“干净”的母体心电为例。 For convenience, only one out of the group number will be specifically described abdominal 3:00 electrical signal "eCgca748" data, see reference (2); they are mixed signal, including maternal ECG, fetal heart rate and electrical EMG and other noise, in addition to maternal ECG, other ingredients weak; embodiment according to the present embodiment, the quasi linear synthesis by way thereof to give a "clean" parent ECG Example.

[0031]该组数据的总监测长度为318秒,前已述及,按本方案的步骤,以其前10秒的数据作为预监测阶段数据,后续数据作为正式监测阶段数据,以每10秒作为一个信号段(共划出30段)。 [0031] Monitoring of the total length of the set of data for 318 seconds, before mentioned, the steps in this scheme, the first 10 seconds of data thereof as a pre-stage monitoring data, the subsequent data as the formal data monitoring phase, to every 10 seconds as a signal section (co drawn. 30). 在预监测阶段,利用独立成分分析法确定得到该段数据母体心电的最优线性组合向量为Wm= [-0.4748,0.0523,-0.8786];然后,将预监测阶段确定的最优线性组合向量Wm = [-0.4748,0.0523,-0.8786]直接用于后来数据的线性合成。 In the pre-stage monitoring, using independent component analysis method to determine the optimal linear combination of vectors of the segment of ECG data matrix Wm = [-0.4748,0.0523, -0.8786]; and the monitoring phase the pre-determined optimal linear combination of vectors Wm = [-0.4748,0.0523, -0.8786] linear synthesis was used directly in subsequent data. 附图3〇3)-〇!)分别示出了第20-30 秒段、70-80 秒段、120-130 秒段、170-180 秒段、220-230 秒段、270-280 秒段以及300-310秒段的合成结果,都是比较“干净”的母体心电,合成取得了成功,说明了方法的可行性。 BRIEF 3〇3) -〇!) Show the first paragraph 20-30 seconds, 70-80 seconds segments, second segments 120-130, 170-180 second section, second section 220-230, 270-280 second segment as well as the synthesis of the results of 300-310 seconds segment, are relatively "clean" maternal ECG, synthesis success, illustrate the feasibility of the method.

Claims (1)

1.一种多点生理电监测中保障信号线性合成实时性的方法,包括步骤,(1)根据需要布置好体表的多个测点、连接好监测系统、准备监测,(2)预监测记录一段多点生理电信号,用周期成分分析法或者独立分量分析法,求解相应的优化问题,得到相应的信号最优线性组合向量,(3)进入正式监测,用第(2)步确定的信号最优线性组合向量,对每一段新监测的多点生理电信号进行线性合成,以合成出需要的纯净信号,其特征是:把第(2)步预监测阶段确定的信号最优线性组合向量,直接用于了第(3)步正式监测过程中的信号线性合成,而第(3)步正式监测阶段不再求解优化问题,从而保证了整个正式监测阶段线性合成处理的实时性。 A multi-point real-time physiological electrical signals of the linear synthesis processes security monitoring, comprising the steps of, (1) a plurality of measuring points are arranged according to the needs a good surface, good connection monitoring system, monitoring preparation, (2) pre-monitoring multipoint recording physiological electrical signals, analysis or independent component analysis, solving the corresponding optimization problem to obtain the optimal linear combination signal corresponding to the vector, (3) into the formal monitoring, (2) determined by the first step with periodic component optimal linear combination of the signal vector, the multi-point monitoring physiological electrical signals of each section of the new linear synthesis to synthesize pure desired signal, characterized in that: the optimal linear combination of the signals determined in (2) pre-monitor phase step vector, was used directly in the (3) step formal linear synthesis signal monitoring process, and (3) no longer monitoring phase step formal optimization problem is solved, thereby ensuring real-time monitoring of the entire stage of the formal linear synthesis process.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003003905A2 (en) * 2001-07-05 2003-01-16 Softmax, Inc. System and method for separating cardiac signals
WO2005044101A1 (en) * 2003-10-31 2005-05-19 The Board Of Trustees Of The University Of Illinois Separation of one or more fetal heart component signals from heart signal information obtained from a pregnant female
CN101596108A (en) * 2009-06-19 2009-12-09 南京大学 Nonlinear separation and extract methods of fetal electrocardiography
CN104027105A (en) * 2014-04-23 2014-09-10 河南科技大学 Novel maternal and fetal electrocardiogram separation method

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7333850B2 (en) * 2004-05-28 2008-02-19 University Of Florida Research Foundation, Inc. Maternal-fetal monitoring system
CN1951320A (en) * 2006-11-16 2007-04-25 上海交通大学 Abnormal electrocardiogram recognition method based on ultra-complete characteristics
CN201005696Y (en) * 2007-07-17 2008-01-16 中国人民解放军南京军区南京总医院 Brain inducing electrical signal tester based on separate component analysis technique
CN101488189B (en) * 2009-02-04 2012-01-18 天津大学 Brain-electrical signal processing method based on isolated component automatic clustering process
CN102008300A (en) * 2010-12-10 2011-04-13 吉林大学 Wearable multiple physiological parameter recording device
CN103431859B (en) * 2012-01-10 2014-12-10 西安交通大学 Experimental method for determining brain load in multitask visual cognition
CN102835955B (en) * 2012-09-08 2014-02-26 北京工业大学 Method of automatically removing ocular artifacts from electroencephalogram signal without setting threshold value
CN104688220B (en) * 2015-01-28 2017-04-19 西安交通大学 A method for EEG artifact removal Ocular

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003003905A2 (en) * 2001-07-05 2003-01-16 Softmax, Inc. System and method for separating cardiac signals
WO2005044101A1 (en) * 2003-10-31 2005-05-19 The Board Of Trustees Of The University Of Illinois Separation of one or more fetal heart component signals from heart signal information obtained from a pregnant female
CN101596108A (en) * 2009-06-19 2009-12-09 南京大学 Nonlinear separation and extract methods of fetal electrocardiography
CN104027105A (en) * 2014-04-23 2014-09-10 河南科技大学 Novel maternal and fetal electrocardiogram separation method

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