Address correspondence to
Epileptic Seizures are Characterized
by Changing Signal Complexity
Gregory K. Bergey and Piotr J. Franaszczuk
Department of Neurology
Johns Hopkins Epilepsy Center
Johns Hopkins University School of Medicine
Baltimore, MD
Key words: seizures, epilepsy, termination, complexity, signal analysis, EEG Address correspondence to:
Gregory K. Bergey, M.D.
Department of Neurology, Meyer 2-147
Johns Hopkins Hospital
600 North Wolfe St
Baltimore, MD 21287
Tel.: 410-955-7338
Fax : 410-614-1569
email: gbergey@https://www.sodocs.net/doc/d26388819.html,
Abstract
Objective: Epileptic seizures are brief episodic events resulting from abnormal synchronous discharges from cerebral neuronal networks. Traditional methods of signal analysis are limited by the rapidly changing nature of the EEG signal during a seizure. Time-frequency analyses, however, such as those produced by the matching pursuit method can provide continuous decompositions of recorded seizure activity. These accurate decompositions can allow for more detailed analyses of the changes in complexity of the signal that may accompany seizure evolution.
Methods: The matching pursuit algorithm was applied to provide time-frequency decompositions of entire seizures recorded from depth electrode contacts in patients with intractable complex partial seizures of mesial temporal onset. The results of these analyses were compared with signals generated from the Duffing equation that represented both limit cycle and chaotic behavior.
Results: Seventeen seizures from 12 different patients were analyzed. These analyses reveal that early in the seizure, the most organized, rhythmic seizure activity is more complex than limit-cycle behavior and that signal complexity increases further later in the seizure.
Conclusions: Increasing complexity routinely precedes seizure termination. This may reflect progressive desynchronization.
1. Introduction
All cerebral activity detectable by electroencephalography (EEG) is a reflection of synchronous neuronal activity, so synchronous neuronal activity per se is not abnormal. Epileptic seizures, however, are abnormal, temporary manifestations of dramatically increased neuronal synchrony, either occurring regionally (partial seizures) or bilaterally (generalized seizures) in the brain. The mechanisms that may contribute to or cause this increased synchrony have been the subject of numerous studies focusing on cellular mechanisms of decreased inhibition and increased excitation. Recently there has been interest in examining macroscopic EEG changes in neural and cerebral synchrony using various nonlinear dynamic approaches (Blinowska and Malinowski, 1991; Casdagli et al., 1996; Elger and Lehnertz, 1998; Martinerie et al., 1998; Pijn et al., 1991, 1997; Pritchard and Duke, 1992; Pritchard et al., 1995; Schiff, 1998; Theiler and Rapp, 1996). There is presently no standard mathematical model of EEG activity. Therefore investigators have been using various methods of signal analysis to describe stochastic and deterministic features of these signals. The most orderly synchronous activity can be represented by strictly periodic signals of low complexity. Less synchronous activity, reflecting a less orderly state, can be represented by signals of increased complexity with multiple frequencies, quasiperiodic signals, and increasingly chaotic behavior of the signal. The period between seizures (interictal period) represents a relatively less orderly state of relatively low neuronal synchrony. Nonlinear analyses have suggested that seizure onset may represent a transition from this interictal period to one of increased
synchronous activity and that a more orderly state characterizes an epileptic seizure (Iasemidis and Sackellares, 1996). The changes occurring during a seizure, however, have not been as well studied because of the rapidly changing nature of the signal.
One of the problems inherent in applying these methods of signal analysis to the recordings of actual seizures is that most linear and nonlinear methods require long periods of relatively stationary activity. Epileptic seizures, however, are characteristically rapidly changing dynamic phenomena. For analysis of such signals with multiple frequencies, time-frequency analysis is perhaps the best approach. This analysis decomposes signals into elementary components (called atoms) localized in time and frequency (Cohen, 1989). Such time-frequency decompositions include older methods such as short time Fourier transforms and Wigner transforms (Cohen, 1989) and more recently applied methods such as wavelet (Clark et al., 1995; Daubechies, 1990; Schiff, 1998) and reduced interference distribution (RID) (Battison et al., 1996). The method that best represents signals in the time-frequency domain is the Gabor decomposition, in the sense that they possess the smallest product of effective duration by effective frequency width (Gabor, 1946). The Gabor Transform has been applied to study the power of traditional frequency bands during generalized seizures (Quian Quiroga et al., 1997). In 1993 Mallat and Zhang developed a relatively fast algorithm (the matching pursuit algorithm) to compute such decompositions (Mallat and Zhang, 1993). This matching pursuit (MP) method is particularly well suited to analyses of the rapidly changing signals that characterize epileptic seizures. Less complex signals are decomposed into relatively few elementary signals (atoms) representing most of the energy. An epoch of a strictly
periodic signal would be represented by a single atom or function. Complex signals need to be represented by correspondingly more elementary components to represent the same amount of energy. To test the ability of the MP method to differentiate signals of different complexity, we applied it to signals generated by the Duffing equation (Guckenheimer and Holmes, 1983). The Duffing equation provides a good example of a non-linear dynamical system exhibiting either limit cycle (low complexity signal) or chaotic behavior (high complexity signal), depending upon the initial conditions.
The MP method has been successfully applied to produce time-frequency distributions of entire seizures recorded from intracranial electrodes from multiple patients (Franaszczuk et al., 1998). These analyses facilitate identification of periods of seizure initiation, transitional rhythmic bursting activity, organized rhythmic bursting activity and intermittent bursting activity. Here we apply the MP method to intracranial recordings of seizures from 12 patients to assess changes in signal complexity during seizure evolution and prior to seizure termination.
2. Methods
2.1.Data Acquisition
Data from patients monitored prior to seizure surgery for intractable complex partial seizures were analyzed retrospectively. All patients had intracranial EEG (ICEEG) recordings from electrode arrays combining a 28 to 32-contact subdural grid over the lateral temporal neocortex and one or two multi-contact depth electrodes placed freehand through the grid so that the deepest contacts recorded from the mesial
temporal structures. Some patients had additional subdural strips over orbitofrontal, lateral frontal or basal temporal neocortex. Decisions to perform intracranial
monitoring were based on needs for functional mapping of eloquent cortex (e.g. language mapping of the dominant temporal lobe) and seizure localization. Only seizures from patients having good mesial temporal location of depth electrodes, as confirmed by MRI, were included. All seizures had mesial temporal onset as
determined by visual inspection of the combined recordings. A 64-channel Telefactor MODAC 64-BSS was used to digitize and store the EEG signals at a rate of 200 samples per second. For these MP analyses, recordings from the depth electrode contact showing the earliest seizure onset were selected. The entire seizure was
analyzed in each instance. A low pass digital filter with a 50 Hz cutoff frequency was employed.
2.2. Matching Pursuit Method
The matching pursuit algorithm is designed to compute a linear expansion of signal f over a set of elementary functions (called atoms) in order to best match its inner structures. This is done by successive approximations of f with orthogonal projections on elements of the dictionary of functions. The dictionary is composed of translated and modulated discrete Gaussians (Gabor functions), discrete Dirac
functions and discrete complex exponentials. After m iterations, a matching pursuit decomposes a signal f into:
f R f
g g R f n n n m n m =+=?∑,01
,
where R m f is the residual vector after m iterations, and
Ef t R f g Wg t n
n n n m (,),(,)ωω==?∑201,
where Wg n (t, ω) is the Wigner distribution of atom g n (t, ω). Unlike the Wigner and the Cohen class distributions of f , it does not include cross terms. The energy
distributions of atoms from this dictionary are displayed as horizontal lines for cosine functions, vertical lines for Dirac functions or ellipses with axes proportional to time and frequency spread for Gabor functions.
2.3. The Duffing Equation
The Duffing equation , d 2x/dt 2 +δ dx/dt +(x 3 -x )=γcos(ω t) is an example of a forced nonlinear oscillator. We used this equation to generate signals representing both limit cycle behavior and chaotic behavior. The parameters used to generate these signals are included in the figure captions (Figs. 1 and 2).
3. Results
Fig. 1 illustrates limit cycle behavior generated by the Duffing equation; the signal is very regular with a stable period. In phase space it is represented by a closed curve representing a stable attractor. The time-frequency energy distribution (TFED) plot of the MP analysis for this signal consists of horizontal lines representing the base frequency and harmonics. Here most of the energy of the signal (92%) is in two
waveforms, the base frequency and the third harmonic. All epochs are 1024 samples with 100 Hz sampling except for whole seizures.
Figure 1. Demonstration of a limit cycle behavior of the Duffing equation. The parameters of the equation were (γ,δ,ω) = (0.3, 0.15, 1). Initial values (x, dx/dt) = (1.6081, 0.8783). The insert shows phase plane representation of solution x on the horizontal axis and dx/dt on the vertical axis. The time-frequency energy plot distribution is computed from 1024 generated samples. The generated signal is shown below the plot. The left vertical axis shows frequency in Hz. The horizontal axis shows time. The effective sampling rate was chosen to be 100 Hz. The first 100 waveforms, representing 100% of the total energy are shown, but most of the energy (92%) is in the base frequency waveform represented by the horizontal line at 16 Hz and the third harmonic at 48 Hz.
Fig. 2 illustrates chaotic behavior of the solution of the Duffing equation. The signal is irregular and the phase space plot suggests a chaotic attractor. The energy plot of the MP analysis of this now consists of many time-frequency atoms. The energy of the signal is distributed among these atoms and 58 waveforms are necessary to account for 90% of the total energy. The MP analysis very clearly distinguishes the
limit cycle from chaotic behavior.
Figure 2. Demonstration of chaotic behavior of the Duffing equation. The parameters of the equation were (γ,δ,ω) = (0.3, 0.15, 1). Initial values (x, dx/dt) = (0, 0). The insert shows phase plane representation of solution x on the horizontal axis and dx/dt on the vertical axis. The time-frequency energy plot distribution is computed from 1024 generated samples. The left vertical axis shows frequency in Hz for an effective sampling rate of 100 Hz. The darkness of each black and white time-
ω. The effective sampling rate was 100 Hz. The first 78 frequency image is proportional to Ef t(,)
waveforms, representing 90% of the total energy, are shown. The generated signal is shown under the plot.
Fig. 3 illustrates the matching pursuit analysis of an entire mesial temporal complex partial seizure (lasting about 60 seconds) recorded from one patient with intracranial electrodes. Fig. 4 shows the time-frequency energy distribution of a short (29 second) mesial temporal onset simple partial seizure (aura) from another patient. In this report we are examining the complexity of the sequential seizure epochs,
applying the MP analysis to reveal changing time-frequency dynamics as the seizure evolves.
Figure 3. Matching pursuit analysis of an entire complex partial seizure originating from the mesial temporal lobe of one patient (no. 6). The first 300 waveforms representing 86.8% of the energy are shown. The two horizontal lines demarcate the ten second epochs from the period of organized activity early in the seizure (A) during very organized rhythmic activity and later in the seizure (B) when activity was more of an intermittent bursting character. These two epochs are expanded and analyzed below in Figs. 5 and 6. The intracranial EEG (ICEEG) recording from the hippocampal depth contact closest to seizure onset is shown below the plot.
Figure 4. Matching pursuit analysis of an entire simple partial seizure (aura) originating from the mesial temporal lobe of another patient. This seizure is much shorter than that shown in Fig. 3; it did not propagate regionally. Nevertheless the early pattern of organized rhythmic activity can be contrasted with the later pattern of bursting activity even in this brief event. The ICEEG recording from the hippocampal depth contact closest to the seizure onset is shown below the plot.
Figs. 5 and 6 are time-frequency energy distributions (TFED) produced by MP analyses of 10-second epochs early and late in the complete seizure illustrated in Fig.
3. During the early period of organized rhythmic seizure activity 90% of energy is represented by only 22 waveforms (atoms). A similar length epoch later during the seizure, at a time of intermittent bursting activity shortly before seizure termination requires many more waveforms (72) to account for 90% of the energy. Indeed in the
early epoch most of the energy (72%) is represented by seven atoms representing a
base frequency and two harmonics.
Figure 5. Matching pursuit analysis of the 10-second epoch (A) from the period of organized rhythmic activity from the seizure illustrated in Fig. 3. Relatively few waveforms (22) represent 90% of the energy. This portion of the ICEEG is of relatively low complexity as revealed by the MP analysis. The ICEEG recording from the hippocampal depth contact is shown below the plot.
In the epoch later in the seizure, the energy is more evenly distributed among the various atoms. The first seven atoms (with highest energy) here account for only 28%
of total energy of the signal.
Figure 6. Matching pursuit analysis of the right marked 10-second epoch (B) from the period of IBA from the seizure illustrated in Fig. 3. Many more waveforms (72) are needed to account for 90% of the energy, illustrating that this portion of the ICEEG signal is of much greater complexity than that shown in Fig. 5. The ICEEG recording from the hippocampal depth contact is shown below the plot. These differences are apparent from examination of the respective TFED plots (Figs.
5 and 6) and the cumulative percentage of energy can be displayed as a function of the
number of contributing atoms (Fig. 7).
Figure 7. Cumulative percent of energy as a function of number of atoms for each of the epochs (1024 points) illustrated in Fig. 1 (limit cycle behavior of the Duffing equation), Fig. 2 (chaotic behavior of the Duffing equation), Fig. 5 (early organized rhythmic activity [ORA] in an epileptic seizure and Fig.
6 (late intermittent bursting activity [IBA] of an epileptic seizure. The limit cycle has almost all of its energy represented by a single atom. Even the early most organized period of the seizure does not behave like the limit cycle and requires considerably more atoms to represent its energy. The simulated chaotic behavior and the later seizure epoch have similar plots suggesting similar degrees of complexity, more than the early seizure epoch.
These analyses indicate that the epoch later in the seizure is of greater complexity than the early epoch. As the seizure evolves there is a transition from higher to lower complexity and then back to higher complexity prior to seizure termination. Analyses of different seizures from the same patient revealed very similar TFED; indeed partial seizure durations were often remarkably similar from seizure to seizure in the same patient. Simple partial seizures (Fig. 4) that did not propagate to involve the regional
temporal lobe also revealed similar patterns of changes in signal complexity. All
mesial temporal onset seizures analyzed (17 seizures from 12 patients) showed
increasing complexity of the signal as the seizure progressed from the period of most
organized rhythmic activity to the period of intermittent bursting activity.
The Table shows the change in complexity of selected seizure epochs from
each of the 12 patients. Although the number of atoms necessary to represent 90% of
the energy varied from patient to patient, in each instance the number of atoms
necessary increased from the period of organized rhythmic activity, compared to the
period of intermittent bursting activity late in the seizure, prior to seizure termination.
The number of patients does not allow for measures of statistical significance.
Table
Number of Atoms Necessary to Represent 90% Signal Energy During Early and Late Seizure Epochs
Patient No. ORA IBA
1 45 70
2 3
3 79
3 15 37
4 5 20
5 32 59
6 22 72
7 24 68
8 8 33
9 18 50
10 15 48
11 15 58
12 10 56 (ORA) = organized rhythmic activity; the number shown is the number of atoms for a 10 second epoch
during from this period. (IBA) = intermittent bursting activity; the number shown is the number of
atoms for a 10 second epoch during this period, prior to seizure termination.
4.Discussion
Clearly the recorded EEG activity during the various periods of an epileptic seizure represents synchronous neuronal activity. Application of the matching pursuit method to the rapidly changing dynamic signal of an epileptic seizure allows for continuous decomposition of these signals and reveals that there are multiple components of these signals at various times. The matching pursuit method when applied to the Duffing equation can distinguish between limit-cycle and chaotic behavior. Similarly, when applied to recorded seizure activity, the MP method can distinguish between signals of different complexity. After seizure initiation, but still relatively early in the seizure, when organized rhythmic activity predominates, the signal is one of relatively low complexity. Indeed at this time the predominant component waveforms are typically of similar frequencies. As the seizure evolves further, the complexity of the signal increases and it is represented by components of more widely disparate frequencies (13). Higher degrees of synchronization are reflected in lower signal complexity and conversely desynchronization is accompanied by higher signal complexity.
To validate the quantification of time-frequency atoms as a measure of the complexity of the signal it was applied to signals of known complexity generated from the Duffing equation. The Duffing equation is not used as a model of the epileptic EEG, but as a convenient tool to generate signals of known complexity (Pijn et al., 1997). The first description of the complexity of an EEG signal described here using the number of atoms determined by the MP analyses shows that this method has
the properties necessary for such applications. It correctly reveals the complexity of known signals and correlates with visual analysis of energy plots of ictal EEG signals of all 17 seizures recorded from 12 patients. In each seizure analyzed the complexity of the signal increased as the seizure evolved.
The matching pursuit time-energy distributions are quantitative representations; each atom is described by four parameters. Yet the numeric values of these parameters are not the most descriptive features of the time-frequency plots. The most discriminating feature is the different number and distribution of atoms during different periods during the seizure. To quantify this observation we use the number of atoms required to represent the energy of certain portions of the signal as a convenient quantitative measure of signal complexity. At this point a suitable statistical test for assessing the significance of differences in numerical values has not been established. Additional data is being collected to estimate the probability distribution of this measure to choose an appropriate test. Applications of traditional statistical methods are not appropriate here since the distributions are not Gaussian.
In previous work (Franaszczuk and Bergey, 1999) we introduced a measure of synchrony based on a multichannel AR model. Each of these methods has its advantages and disadvantages. The results presented here use a new measure of complexity limited to single channel analyses. This measure does not use the information about synchrony between signals in different channels. The previous method based on the AR model is faster but requires longer periods of stationarity, and therefore is not ideally suited to rapidly changing ictal transitions. Both measures can be interpreted regardless of whether the signal is linear, nonlinear, stochastic or
deterministic in nature. These two methods complement each other in the sense that the MP method quantifies each channel separately, while the AR method emphasizes interchannel synchronization.
The behavior of the neural networks in the hippocampus and the brain are thought to be nonlinear in many regards. Indeed many investigators make this assumption and then apply various nonlinear methods of analysis to investigate EEG activity. In fact these applications of nonlinear dynamics result in no more than operational measures of signal complexity (Lehnertz and Elger, 1998). As mentioned above, these nonlinear methods are not ideally suited to analysis of rapidly changing signals such as epileptic seizures. The matching pursuit analysis is well suited for continuous analyses of dynamic signals and makes no initial assumptions regarding linearity or nonlinearity of the signal.
During the most organized seizure activity observed, the signal was of low complexity. As seizures evolve further, the MP analyses reveal the increasing complexity. Signal complexity may reflect the intricacy of the neuronal interactions (Lehnertz and Elger, 1995). Increasingly chaotic behavior of a dynamic generator is reflected in increased signal complexity. The MP analyses of epochs late in the seizure and of the epoch of chaotic behavior of the Duffing equation are quite similar. These comparisons are designed to illustrate that the complexity of this late seizure activity is more consistent with chaotic behavior, rather than with limit-cycle behavior, recognizing that increased signal complexity per se does not indicate nonlinear or chaotic behavior. As mentioned above, one of the desirable features of the MP method is that it can appropriately be applied to dynamic signals without
requiring assumptions of linearity or nonlinearity. The observed increasing signal complexity during seizure evolution is consistent with progressive desynchronization of the seizure activity.
While visual inspection of some ictal EEG recordings may suggest increased signal complexity later in the seizure, in other seizures visual analysis of the EEG signal is not sufficient to suggest these changes in signal complexity (Figure 5 and 6). In either instance the matching pursuit analysis provides for detail quantification of the changes present. Although the current studies are limited to a single channel, the potential exists for multichannel analyses using these techniques.
Examinations of intracranial interictal recordings from patients with temporal lobe epilepsy show that there is often neuronal complexity loss on the side of seizure onset (Weber et al., 1998). In in vitro models of epilepsy reduced signal complexity can precede onset of epileptiform activity in some models (xanthine and penicillin), but not in others (low-magnesium and veratridine) (Widman et al., 1999). In addition, these investigators comment that increased complexity is seen before the cessation of epileptiform activity in these models.
It is still not resolved whether the periods between seizures (interictal periods) represent deterministic activity of extremely high complexity or merely random stochastic colored noise. Seizure evolution leading to termination can be accurately described by a transition from synchronized neuronal activity of low complexity (possibly nonlinear) behavior to increasingly complex (possibly chaotic) network behavior, reflecting progressive desynchronization prior to seizure termination. Gradually changing complexity during seizures is more consistent with the nonlinear
dynamic model of unstable periodic orbits (UPOs) in seizures (So et al., 1998) rather than a bistable model proposed by other authors (Manuca et al., 1998). The results presented here suggest that, from the perspective of limiting or controlling seizures, increasing complexity may be advantageous. This may also suggest that beneficial brain stimulation should be designed to result in increased complexity of the signal. The cellular and synaptic mechanisms that correlate with these transitions and seizure cessation remain to be elucidated.
Acknowledgments
This research was supported by NIH grant NS 33732 to GKB and PJF.
References
Battison, J. J., Jr., Darcey, T. M. and Williamson, P. D. High resolution spatio-temporal seizure analysis using reduced interference time-frequency
techniques. Epilepsia 1996; 37(Suppl. 5):144.
Blinowska, K. J. and Malinowski, M. Non-linear and linear forecasting of the EEG time series. Biol Cybern 1991; 66:159-65.
Casdagli, M. C., Iasemidis, L. D., Sackellares, J. C., Roper, S. N., Gilmore, R. L. and Savit, R. S. Characterizing nonlinearity in invasive EEG recordings from
temporal lobe epilepsy. Physica D 1996; 99:381-399.
Clark, I., Biscay, R., Echeverria, M. and Virues, T. Multiresolution decomposition of non-stationary EEG signals: a preliminary study. Comput Biol Med 1995;
25:373-82.
Cohen, L. Time-frequency distributions - a review. Proc. IEEE 1989; 77:941-981. Daubechies, I. The wavelet transform, time-frequency localization and signal analysis.
IEEE Trans Inf Theory 1990; 36:961-1005.
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(一)基本材料: 1、企业变更《医疗器械经营企业许可证》的申请书(见范例一); 2、《医疗器械经营企业许可证》变更申请表(一式两份，必须打印，不得手写，其余材料均提交一份)(见范例二); 3、《医疗器械经营企业许可证》正、本原件(范例略); 4、所提交材料真实性的自我保证声明(见范例三); (二)必备材料: 1、变更质量管理人员的，提交基本材料的同时提交新任质量管理人员的身份 证(范例略)、学历证书或者职称证书复印件(范例略); 2、变更企业注册地址的，提交基本材料的同时提交变更后地址的产权证明(范例略)或者租赁协议复印件(见范例四)、地理位臵图(见范例五)、平面图(见范例六)及存储条件说明(见范例七); 3、变更经营范围的，提交基本材料的同时提交拟经营产品注册证的复印件(范例略)及相应存储条件的说明(范例略); 4、变更仓库地址的，提交基本材料的同时提交变更后仓库地址的产权证明(范例略)或者租赁协议复印件(见范例四)、地理位臵图(见范例五)、平面图(见范例六)及存储条件说明(见范例七); 5、变更企业名称需提供已变更的《营业执照》复印件(范例略)或《企业核名 通知书》(范例略); 6、变更法人、负责人的，提交基本材料的同时提交已变更的《营业执照》复 印件(范例略)。范例一: 申请书黑龙江省食品药品监督管理局:
公司地址变更申请书范文3篇
公司地址变更申请书范文3篇 第一步:申请人持相关材料向市政务服务中心工商局窗口提出申请，经受理审查员初审通过，开具《受理通知书》或者《申请材料接收单》;不符合受理条件的，在当场或者5个工作日内一次性告知申请人应当补正的全部材料(出具告知单)。 第二步:对申请人申请材料齐全、符合法定形式的，当场作出是否准予登记的决定并出具《登记决定通知书》;需要对申请材料的实质内容进行核实的，出具《企业登记材料需要核实事项告知书》，在10个工作日内作出核准或者驳回申请的决定。 第三步:在1个工作日内(申请材料的实质内容需核实的除外)，申请人可以凭《登记决定通知书》到发照窗口换发《企业法人营业执照》。 四、申请材料: (1)公司法定代表人签署的《公司变更登记申请书》(公司加盖公章); (2)公司签署的《指定代表或者共同委托代理人的证明》(公司加盖公章)及指定代表或委托代理人身份证复印件(本人签字)，应标明具体委托事项、被委托人的权限、委托 期限; (3)公司章程修正案(公司法定代表人签署); (4)法律、行政法规和国务院决定规定变更名称必须报经有关部门批准的，提交有关部门的批准文件或者许可证书复印件; (5)公司《企业法人营业执照》副本。 (6)《企业名称变更核准通知书》。
公司变更名称，应当向其公司登记机关提出申请，申请名称超出其公司登记机关管辖权限的，由其公司登记机关向有该名称登记权的公司登记机关申报。 注:依照《公司法》、《公司登记管理条例》设立的公司申请名称变更适用。 以上各项未注明提交复印件的，应当提交原件;提交复印件的，应由公司加盖公章并署明与原件一致。 公司地址变更申请书范文1 XXXX工商局: 我单位位于xx街(路)xx号，由于经营(其他原因)需要，现欲搬到xx街(路)xx 号。现申请将xx证上的经营地址进行相应变更，请给予办理为感。 申请人:xxx 时间:20xx年x月x日 公司地址变更申请书范文2 你局核准注册的第__ 号__商标，因注册人名称/地址已由________，变更为________，现申请变更。 申请人:______(盖章) 地址:______ 年月日 公司地址变更申请书范文3 XX工商局: 我单位位于南通市港闸区陈桥乡河口村11组，由于经营的需要，现欲搬到南通市通州区平潮镇颜港村3组。现申请将营业执照证上的经营地址进行相应变更，特此申请，望批准为盼。 申请人:xxxx设备有限公司 时间:20xx年x月x日
公司地址变更申请书范文3篇
公司地址变更申请书范文3篇 公司改地址需提出申请。要怎么写申请好呢？下面是公司地址变更申请书范文，希望我们的文章你能喜欢。 第一步：申请人持相关材料向市政务服务中心工商局窗口提出申请，经受理审查员初审通过，开具《受理通知书》或者《申请材料接收单》;不符合受理条件的，在当场或者5个工作日内一次性告知申请人应当补正的全部材料出具告知单。 第二步：对申请人申请材料齐全、符合法定形式的，当场作出是否准予登记的决定并出具《登记决定通知书》;需要对申请材料的实质内容进行核实的，出具《企业登记材料需要核实事项告知书》，在10个工作日内作出核准或者驳回申请的决定。 第三步：在1个工作日内申请材料的实质内容需核实的除外，申请人可以凭《登记决定通知书》到发照窗口换发《企业法人营业执照》。 四、申请材料: 1公司法定代表人签署的《公司变更登记申请书》公司加盖公章; 2公司签署的《指定代表或者共同委托代理人的证明》公司加盖公章及指定代表或委托代理人身份证复印件本人签字，应标明具体委托事项、被委托人的权限、委托期限; 3公司章程修正案公司法定代表人签署; 4法律、行政法规和国务院决定规定变更名称必须报经有关部门批准的，提交有关部门的批准文件或者许可证书复印件; 5公司《企业法人营业执照》副本。 6《企业名称变更核准通知书》。 公司变更名称，应当向其公司登记机关提出申请，申请名称超出其公司登记机关管辖权限的，由其公司登记机关向有该名称登记权的公司登记机关申报。 注：依照《公司法》、《公司登记管理条例》设立的公司申请名称变更适用。 以上各项未注明提交复印件的，应当提交原件;提交复印件的，应由公司加盖公章并署明与原件一致。 XXXX工商局： 我单位位于xx街路xx号，由于经营其他原因需要，现欲搬到xx街路xx号。现申请将xx证上的经营地址进行相应变更，请给予办理为感。
变更企业名称申请书
变更企业名称申请书 变更企业名称申请书 致总公司 内容 特此申请 申请单位及个人 年月日 --------- 原企业名称 注册号 拟变更企业名称 备选企业名称（请选用不同的字号）： 1 2 3 经营范围（只需填写与企业名称行业表述一致的主要业务项目）： 注册资本（金）（法人企业必须填写） 企业类型□公司制□非公司制□个人独资□合伙 企业住所（地址） 企业盖章及法定代表人（负责人）签字 2 申请人应提交的材料清单 选择项序号文件，证件名称说明 1企业名称变更申请书本表第1页 2企业授权委托意见本表第3页 3企业营业执照复印件须加盖公章 4主管部门或审批机关的批准文件 5其他有关材料 备注： 1，“选择项”栏由工商部门填写：“说明”栏应注明提交的文件，证件是原件还是复印件； 2，企业名称变更申请书中的签字（盖章）应由企业盖公章，法定代表人（负责人）签字； 3，申请人应当使用钢笔，毛笔或签字笔工整地填写表格或签字。 申请人谨此确认和承诺本次申请中提交的所有文件材 料和填写的内容是真实，合法，有效的，复印件与原件是一 致的，并对因材料虚假所引起的一切后果负法律责任。 申请人签字（盖章）： 3 企业授权委托意见 兹委托（我单位/代理机构/自然人股东）前来办理企业名称变更事宜。 授权期限为： 授权权限如下（同意的，在括号内签署“同意”；不同意的，在括号内签署
“不同意”。选择二项以上同意或有空括号未填写的，本授权委托意见无效。）：1，全权办理企业名称变更申请，但不得修改本申请书任何文字内容。（） 2，全权办理企业名称变更申请，授权修改本申请书出现的错别字，遗漏和误加的文字。（） 3，全权办理企业名称变更申请，如申请的企业名称未能核准，授权修改，增加或减少企业名称字词表述。（） 4，全权办理企业名称变更申请，授权修改本申请书任何内容和文字表述。（） 代办人或代理人身份证复印件粘贴处 代办人或代理人签字： 联系电话： 通信地址及邮政编码： （企业盖章处） 年月日 4 （同意使用）承诺书 今有拟变更企业 名称为，拟用 “”作为字号，主要从事行 业。恳请予以核准。 本企业特此承诺：该名称中的字号符合国家法律，法规的规定， 其行业用语与经营范围主营业务是相一致的，凡在今后的经营活动 中，若与其他企业因名称发生争议或登记注册的经营范围与名称申请 的经营范围或行业专用语有差异时，我们愿无条件服从工商行政管理 机关的处理决定，变更企业名称，并承担相应的法律责任。 申请人盖章： 相关企业意见，盖章： 年月日
公司更名申请书范文3篇
公司更名申请书范文3篇 尊敬的社保局 您好! 未来我公司对外需要开具的发票抬头以及所有的相关票据、合同、文档等都以最新的名称为准，此前名称将不再使用，特此申请，望 批准! 凹凸人网(北京)教育科技有限公司 20xx年8月10日 (1)法定代表人签署的《公司变更登记申请书》(领取，公司加盖公章); (2)《企业(公司)申请登记委托书》(领取，公司加盖公章)，应 标明具体委托事项和被委托人的权限; 股份有限公司提交股东会决议内容包括：决议事项、修改公司章程相关条款，由发起人盖章或出席会议的董事签字。 国有独资有限责任公司提交董事会决议，内容包括：决议事项、修改公司章程相关条款，由董事签字。 (5)公司章程修正案; 有限责任公司由股东盖章或签字(自然人股东); 股份有限公司由发起人盖章或出席会议的董事签字确认。 国有独资有限责任公司由投资人盖章。 (6)公司营业执照副本复印件。 以上各项未注明提交复印件的一般均应提交原件; 提交复印件的，应由公司加盖公章并署明与原件一致。
xx有限公司，因业务发展需要，于XXXX年8月31日变更为北京腾华巨业科贸有限公司。从即日起xx有限公司于贵单位发生的各项业务和结算，全部由更名后xx有限公司负责。 变更后所出现的一切问题与北京甘家口大厦有限责任公司无关，责任由更名后的北京腾华巨业科贸有限公司承担。 原厂编：现厂编： 原公司名称：xx有限公司现公司名称：xx有限公司 原公司地址：xxxx现公司地址：xxxx 原开户行账号：32985602xxxx现开户行账号：1109030104001xxxx 原开户行银行：中国银行北京通州滨河支行现开户行银行：中国农行北京通州支行城关分理处 原(公章，财务章)现(公章，财务章) 原法人章现法人章 年月日 看了公司更名申请书的还看了：
关于公司变更监事申请书
关于公司变更监事申请书 监事(supervisor )，是公司中常设的监察机关的成员，又称“监察人”，负责监察公司的财务情况，公司高级管理人员的职务执行情况，以及其他由公司章程规定的监察职责。下文是小编为大家收集的关于变更监事的请示范文，仅供参考! 关于公司变更监事申请书一 合肥美菱股份有限公司关于变更公司职工监事的公告 本公司职工监事齐敦卫先生因个人原因于20xx年1月26日请求辞去所任的公司职工代表监事职务，本公司监事会同意齐敦卫先生的辞职请求。前述有关事项本公司已于20xx月1月28日在《证券时报》、《香港商报》及巨潮资讯网上以公告形式(20xx-003号公告)进行了披露。 20xx年2月26日，本公司监事会收到公司工会的《推荐函》，经职工代表大会选举，同意尚文先生担任公司第六届监事会职工监事。 特此公告。 合肥美菱股份有限公司监事会
关于公司变更监事申请书二 鉴于有限公司经股东会决议更换了公司董事，因此，新一届董事会成员于200X年XX月XX日在XX市XX区XX路XX号(XX 会议室)召开董事会会议，本次会议是根据公司章程规定召开的临时会议。于召开会议前依法通知了全体董事，会议通知的时间及方式符合公司章程的规定。股东会选举产生的新一届董事会成员XXX、XXX、XXX、XXX、XXX出席了本次会议。原公司董事长XXX主持了会议，董事会一致通过并决议如下： 一、决定免去XXX的董事长职务，选举XXX为公司新一届的董事长。 二、继续聘任XXX为公司经理。 有限公司董事会成员(签字)：XXX、XXX、XXX、XXX、XXX 200X年XX月XX日 关于公司变更监事申请书三 会议时间：20xx年12月2日 会议地点：***************(速捷快递有限公司会议室) 会议性质：临时(或者定期)股东会议 参加会议人员：
地址变更申请书
地址变更申请书 篇一：地址变更申请书 地址变更申请书 申请书 ***(单位)： 我单位位于**街(路)**号，由于经营(其他原因)需要，现欲搬到**街(路)**号。现申请将**证上的经营地址进行相应变更，请给予办理为感。 申请人：***** 年月日 赵王婧仪 20XX-05-03 篇二：申请书(变更注册地址范例) 医疗器械经营企业变更所需提交形式材料及范例 （一）基本材料： 1、企业变更《医疗器械经营企业许可证》的申请书(见范例一)； 2、《医疗器械经营企业许可证》变更申请表(一式两份，必须打印，不得手写，其余材料均提交一份)(见范例二)； 3、《医疗器械经营企业许可证》正、本原件(范例略)； 4、所提交材料真实性的自我保证声明(见范例三)；（二）必备材料：
1、变更质量管理人员的，提交基本材料的同时提交新任质量管理人员的身份证(范例略)、学历证书或者职称证书复印件(范例略)； 2、变更企业注册地址的，提交基本材料的同时提交变更后地址的产权证明(范例略)或者租赁协议复印件(见范例四)、地理位臵图(见范例 五)、平面图(见范例六)及存储条件说明(见范例七)； 3、变更经营范围的，提交基本材料的同时提交拟经营产品注册证的复印件(范例略)及相应存储条件的说明(范例略)； 4、变更仓库地址的，提交基本材料的同时提交变更后仓库地址的产权证明(范例略)或者租赁协议复印件(见范例四)、地理位臵图(见范例 五)、平面图(见范例六)及存储条件说明(见范例七)； 5、变更企业名称需提供已变更的《营业执照》复印件(范例略)或《企业核名通知书》(范例略)； 6、变更法人、负责人的，提交基本材料的同时提交已变更的《营业执照》复印件(范例略)。 范例一：申请书 黑龙江省食品药品监督管理局： 我公司因原经营地址租期已到，依据《医疗器械监督管理条例》、《医疗器械经营企业许可证》等相关法律规定，申请变更医疗器械经营注册地址至：哈尔滨市XX区XX街XX号X层XXX室。 特此申请。 XXXXXXXXXXXXX公司 XXXX年XX月XX日
公司注册地址变更(程序)
公司注册地址变更 内资企业 一、办理程序 1、申请营业执照变更（迁入地工商局）； 2、办理工商转出（迁出地工商局）； 3、领取新营业执照（迁入地工商局）； 4、变更税务登记证； 5、变更组织机构代码证； 二、提交材料 （一）申请营业执照变更（迁入地工商局） 申报材料: 1、公司法定代表人签署的变更登记申请书； 2、公司委托代理人的证明（委托书）以及委托人的工作证或身份证复印件； 3、股东会决议； 4、修改过的公司章程（包括修正案）； 5、新住所证明，租赁房屋需提交租赁协议书，协议期限必须一年以上（附产权证复印件）； 6、营业执照正副本、IC卡（电子版营业执照）原件+复印件（加盖公章）； 7、其它有关文件。
（注：申请人向拟迁入地的登记机关提交上述变更材料后，拟迁入地的登记机关初审合格后出具《企业迁移通知书》） （二）办理工商转出（迁出地工商局） 申报材料: 《企业迁移通知书》 （注：企业将《企业迁移通知书》交到迁出地登记机关；迁出地登记机关将登记档案采用挂号邮递方式邮至迁入地登记机关。） （三）领取新营业执照（迁入地工商局） （四）变更组织机构代码证 申报材料： 1、代码变更申请表； 2、法人身份证明； 3、变更后的工商营业执照、各部门批复复印件； 4、原代码证书正本、副本，代码证IC卡。 办理时限： 换证3个工作日。 （五）变更税务登记证 申报材料： 1、原税务登记证件原件（正、副本）； 2、加盖公章的《变更税务登记申请表》； 3、营业执照/注册证/登记证/其他核准执业证书（原件+复印件）； 4、组织机构代码证（原件+复印件）；
5、注册地/经营地的房屋产权证/使用权证明（门牌号有变更的须提供公安局新编门牌号证明）、租赁合同（涉及转租的需提供转租协议）原件。 6、《房屋、土地、车船情况登记表》（1式3份）。 外资企业 一、办理程序 1、申请迁入（迁入地商务委）； 2、申请迁出，取得批复（迁出地商务委）； 3、申请营业执照变更（迁入地工商局）； 4、办理工商转出（迁出地工商局）； 5、领取新营业执照（迁入地工商局）； 6、变更批准证书（迁入地商务委）； 7、变更组织机构代码证； 8、变更税务登记证； 9、变更外汇登记证； 10、变更财政登记证； 11、变更海关登记证。 二、提交材料 （一）申请迁入（迁入地商务委） 申报材料: 1、企业变更注册地址及修改合同、章程相应条款的申请报告； 2、企业原批准证书、营业执照（复印件）； 3、企业股东会或董事会关于注册地址变更及修改合同、章程相应条
公司地址变更申请书
公司地址变更申请书 篇一：工商营业执照地址变更地址变更申请 ___________有限公司因业务需要，申请将室变更至。 特此申请。 XXXXXX 二〇一五年四月八日 中铁二十四局集团有限公司因业务需要，将中铁二十四局集团有限公司黑龙江工程分公司的营业场所由哈尔滨市道里区地德里小区312栋1层3号变更至哈尔滨市香坊区华山路10号万达商务楼3号楼1层105室，申请哈尔滨市道里区工商局予以调档。特此申请。 中铁二十四局集团有限公司 二〇一四年九月二日 篇二：地址变更申请书 地址变更申请书 哈尔滨市商务局： **公司于*年*月*日成立，企业经营注册地址为：*市*区*路*号。由于注册地房屋产权纠纷，已严重影响我单位正常经营。特申请变更经营地址，将原经营注册地址：****申请变更为：*市*区*街*号，请贵局予以批准为盼。 特此申请！ 申请人：*公司 *年*月*日 *
篇三：公司地址变更申请书 深圳注册公司 公司地址变更范文 公司地址变更申请书范文1 XXXX工商局： 一、本公司于XXXX年XX月XX日奉设立准登记，领到工商局设新字第XX号执照。 二、兹因增加营业项目申请变更登记： 迁移地址 改选董事监事 三、遵照公司法规定，检具有关文件，随缴登记费XX元，执照费XX元，缴销原领执照，敬请准予变更登记换发执照。 申请人：XXX股份有限公司 时间：20xx年x月x日 公司地址变更申请书范文2 XX工商局： 我单位位于南通市港闸区陈桥乡河口村11组，由于经营的需要，现欲搬到南通市通州区平潮镇颜港村3组。现申请将营业执照证上的经营地址进行相应变更，特此申请，望批准为盼。 申请人：xxxx设备有限公司 时间：20xx年x月x日 公司地址变更申请书范文3