FIELD OF INVENTION
[001] The present invention relates to a noise detection and removal system  and more particularly to a system and method for simultaneous removal of baseline wander and power-line interference of a signal.

BACKGROUND OF INVENTION
[002] A common problem in biosignals (electrocardiography (ECG)  electroencephalogram (EEG)  eletrogastrogram (EGG)  phonocardiogram (PCG)  electromyogram (EMG)) monitoring or acquisition is contamination of biosignal with different artifacts and noises such as power-line noise  wideband noise (or baseline wander or baseline drift)  dirty lead wire/electrode noise  patient movement or activities noise  and other noises. For example  these noises often corrupt the ECG signal and render it difficult to perform clinical evaluation in both visual inspection and computer aided ECG analysis. However  the baseline wander and power-line interference are the most significant noises that strongly affect overall performance of many ECG signal processing applications. Further  such noises may lead to inaccurate determination of endpoints  amplitudes peak  interval  duration  and mask shapes of local components such as P  T  QRS  and U waves. As a result  these noises reduce the diagnostic and recognition accuracy of any physiological signal acquisition or processing device.
[003] Different digital filtering systems and methods implement digital signal processing (DSP) techniques for removing baseline wander (or low-frequency artifact or baseline drift) and power-line interference. Such DSP techniques can include  for example  adaptive or digital filtering  blind source separation  extended or adaptive Kalman filtering  empirical mode decomposition  discrete wavelet or cosine transform  frequency domain filtering  fixed or adaptive notch filtering  high or low pass filtering  independent component analysis  least mean square filtering  multi-adaptive bionic wavelet transform  morphological filtering  non-linear filter banks  polynomial splining  statistical weighted moving average filtering  time-varying filtering  and various other techniques.
[004] Although the techniques described above are effective in reducing or eliminating interfering signals  but can also cause a certain amount of distortion of the ECG signal. Further  these techniques include both the advantages and disadvantages in terms of preservation of signal features  computational complexity  memory space  and reliability. Thus  there remains a need of a robust system and method for simultaneous removal of the baseline wander and power-line interference from a recorder or received signal.

OBJECT OF INVENTION
[005] The principal object of the embodiments herein is to provide a system and method for simultaneous removal of the baseline wander and power-line interference and its harmonics in a recorded or received signal.
[006] Another object of the invention is to provide a mechanism for constructing a dictionary or transform matrix for simultaneous removal of both the specific and complete noise features from a recorded or received signal.
[007] Another object of the invention is to provide a method and system for constructing a signal based on an estimated coefficient and a dictionary matrix of a recorded or received signal.
[008] Another object of the invention is to provide a method and system for removing trends and periodic signals from a received or recorded signal.
[009]

SUMMARY
[0010] Accordingly the invention provides a method for removing noise from a signal. The method includes dividing the signal into processing blocks and constructing a transform matrix based on the noise of the signal. Further  the method includes estimating a transform coefficient of the signal and the constructed transform matrix  reconstructing the signal using the estimated transform coefficient and the constructed transform matrix  and displaying the reconstructed signal. Furthermore  the method includes determining a regularization parameter to control a fidelity and sparse constraint of the noise of the signal and performing a mean subtraction of the signal.
[0011] In an embodiment  the noise can include a baseline wander  power-line interference  and harmonics of the power-line interference. The noise further includes a frequency component varying based on characteristics of a noise source. Furthermore  the method includes simultaneously removing the baseline wander  power-line interference  and harmonics of the power-line interference  of the signal. Furthermore  the method includes removing trend and periodic signal components in the signal.
[0012] In an embodiment  constructing the transform matrix further includes adjusting a set of elementary functions based on shapes of the signal and characteristics of the noise source. The transform matrix is constructed as over-complete  under-complete  or critical transformations.
[0013] In an embodiment  estimating a transform coefficient further includes using a L1-norm minimization algorithm or greedy algorithm to estimate the transform coefficient.
[0014] Accordingly the invention provides a system for removing noise from a signal. The system includes a data acquisition module configured to receive the signal from an electrode  a dictionary matrix generation module configured to construct a dictionary matrix based on the noise of the signal and a sparse coefficient estimation module configured to estimate a transform coefficient of the signal and the constructed dictionary matrix. Further  the system includes a digital signal processing module configured reconstruct the signal using the estimated transform coefficient and the constructed dictionary matrix  and a display module configured to display the reconstructed signal.
[0015] In an embodiment  the digital signal processing module is further configured to divide the signal into processing blocks and determine a length of the processing blocks based on cyclic duration of the signal. In addition  the digital signal processing module is further configured to determine a regularization parameter to control a fidelity and sparse constraint of the noise of the signal and perform a mean subtraction of the signal.
[0016] In an embodiment  the noise can include a baseline wander  power-line interference  and harmonics of the power-line interference. The noise further includes a frequency component varying based on characteristics of a noise source. Furthermore  the digital signal processing module is configured to simultaneously removing the baseline wander  power-line interference  and harmonics of the power-line interference  of the signal. Furthermore  the digital signal processing module is configured to remove trend and periodic signal components in the signal.
[0017] In an embodiment  the dictionary matrix generation module is further configured to determine shapes of the signal and adjust a set of elementary functions based on the shapes of the signal and characteristics of the noise source. The transform matrix is constructed as over-complete  under-complete  or critical dictionary.
[0018] In an embodiment  the sparse coefficient estimation module further includes a transform coefficient estimator configured to estimate the transform coefficient using L1-norm minimization algorithm or greedy algorithm.
[0019] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood  however  that the following descriptions  while indicating preferred embodiments and numerous specific details thereof  are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof  and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF FIGURES
[0020] This invention is illustrated in the accompanying drawings  throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings  in which:
[0021] FIG. 1 depicts a block diagram of an ECG monitoring and transmission system  according to embodiments as disclosed herein;
[0022] FIG. 2 depicts a block diagram of a digital signal processing (DSP) technique implemented by the system of the FIG. 1  according to embodiments as disclosed herein;
[0023] FIG. 3 depicts a general framework of sparse coefficient estimation module of the FIG. 2  according to embodiments as disclosed herein;
[0024] FIG. 4 depict graphs representing an example of experimental waveforms of a corrupted or noisy ECG signal  an estimated ECG signal  and an extracted baseline wander and 60 Hz power-line signal  according to embodiments as disclosed herein;
[0025] FIG. 5 depict graphs representing another example of experimental waveforms of a noisy or corrupted ECG signal  an estimated ECG signal  and an extracted baseline wander and 60 Hz power-line signal  according to embodiments as disclosed herein;
[0026] FIG. 6 depicts a flow diagram of the DSP technique for simultaneous removal of baseline wander and power-line interference  in accordance with various embodiments of the present invention; and
[0027] FIG. 7 depicts a computing environment implementing the application  in accordance with various embodiments of the present invention.

DETAILED DESCRIPTION OF INVENTION
[0028] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly  the examples should not be construed as limiting the scope of the embodiments herein.
[0029] The embodiments herein achieve a system and method for simultaneously removing baseline wanders (or low-frequency artifacts or baseline drifts) and power-interference of a recorded or received signal. The method includes constructing a composite dictionary matrix including a set of elementary functions (or basis functions or elementary atoms or elementary waveforms). The recorded or received signal is divided into non-overlapping blocks of length N for effective suppression of different shapes of the baseline wander. A transform coefficient of the signal is estimated by using L1-Norm minimization algorithm or matching greedy algorithm and the constructed composite dictionary matrix. The method includes reconstructing the recorder or received signal using the estimated transform coefficient and the composite dictionary matrix. The baseline wander and power-line inference is removed from reconstructed signal without distorting clinical features of the signal.
[0030] Referring now to the drawings  and more particularly to FIGS. 1 through 7  where similar reference characters denote corresponding features consistently throughout the figures  there are shown preferred embodiments.
[0031] Throughout the description  the term composite dictionary matrix and transform matrix (or representation matrix or sparse matrix) is used interchangeably. The composite dictionary matrix can be constructed as over-complete  under-complete and critical dictionaries.
[0032] Throughout the description  the term elementary functions and elementary waveform (or basis functions or elementary atoms or elementary waveforms) is used interchangeably.
[0033] FIG. 1 depicts a block diagram of an ECG monitoring and transmission system 100  according to embodiments as disclosed herein. The system 100 includes electrodes 102  a data acquisition (DAQ) module 104  a communication module 106  a display module 108  a control module 110  and a digital signal processing (DSP) module 112.
[0034] In an embodiment  the information about the physiological conditions of a patient can be measured by positioning the electrodes 102 on the patient body in specific locations. In an example  different channels can be used to monitor electrical activity from different horizontal and frontal planes. The electrodes 102 can be placed on specific locations  for example  arms  legs  chest  and any other specific location to record cardiac bio-potentials or signal of the patient. The output of the electrodes 102 is provided to the DAQ module 106. The output of the electrodes described herein may be for example  cardiac related electrical signals such as electrocardiogram (ECG) waveform signals  pacemaker pulse signals acquired by the electrodes  or any other physiological parameter of the patient.
[0035] In an embodiment  the DAQ module 106 is configured to be coupled to the electrodes 102 to receive the cardiac bio-potentials of the patient. The DAQ module 106 includes a multiple ECG electrode strip having a connector terminal at one edge. The strip includes a number of electrodes spaced for placement on the patient of different size. Further  the data acquisition module 106 can be configured to include an analog processing (AP) unit 114 and a data acquisition and control (DAQC) interface unit 116.
[0036] The DAQ module 106 provides the output received from the electrodes 102 to the AP unit 114. In an example  the output representing a respective analog signal from the respective electrode 102. The AP unit 114 is configured to include an analog amplifier 118  analog filter 120  and an analog to digital converter (ADC) 122. The AP unit 114 is configured to use the analog amplifier 118  the analog filter 120  and the ADC 122 to amplify  filter  and convert the analog signals into digital signals. In an example  an output signal of the electrodes 102 can be connected to an input of the analog amplifier 118 to amplify the signal. The output of the analog amplifier 118 may be filtered by the analog filter 120 and the output of the analog filter 118 may be digitized by the ADC 122.
[0037] The DAQC interface unit 116 is configured to provide a connecting and controlling mechanism for handling multi-channel outputs with electronic devices  for example  desktops  laptops  tablets  Smartphone’s  Personal Digital Assistants (PDAs)  communicators  wearable computers  or any other consumer electronic devices. The DAQC interface unit 116 can interface with the electronic devices to receive real-time data using RS-232 or TIA-232-F standard  serial interface  Bluetooth  Ethernet  USB  TCP/IP devices  or any other standard or interface. Further  the DAQC interface unit 116 can be configured to provide interface for coupling additional hardware  for example the electronic devices  which may be used for diagnosing and monitoring purposes.
[0038] In an embodiment  the communication module 106 is capable of communicating with local or remotely-located monitoring devices. The communication module 106 is configured for wired or wirelessly communicating the ECG signals  obtained from the patient  to the local or remotely-located monitoring devices. The local or remotely-located monitoring devices described herein can be wired or wirelessly connected using the techniques such as cellular network  Radio-frequency identification (RFID)  ZigBee  Bluetooth  Wi-Fi  Ultra-wideband (UWB)  Worldwide Interoperability for Microwave Access (WiMax)  or any other technique.
[0039] In an embodiment  the display module 108 can be configured to provide a graphical representation of the real-time multi-channel ECG signals on the local or remotely-located monitoring devices (for example  mobile communication devices). In an example  the display module 108 can provide a screen type display or can be embodied in any other known type devices. The control module 110  coupled to the communication module 106 and the display module 108  is configured to include instructions to control the operation performed by the system 100.
[0040] In an embodiment  the DSP module 112 is configured to receive the input signal (x[n]) from the DAQ module 104. The DSP module 112 is configured to remove artifacts and noises from the input signal. In an embodiment  the DAQ module 104 is configured to simultaneously remove a baseline wander  and 50/60 Hz power-line and its harmonics associated with the signal. The DSP module 112 implements a DSP technique for constructing a transform matrix ( ) with a set of appropriate elementary functions to estimate a desired signal (z[n]) from the noisy signal. The DSP module 112 is coupled to the communication module 106 and the display module 108 to transmit and display the desired signal on the local and remotely-located monitoring devices.
[0041] FIG. 2 depicts a block diagram of a digital signal processing (DSP) technique implemented by the system 100 of the FIG. 1  according to embodiments as disclosed herein. The DSP module 112 is configured to implement the DSP technique for simultaneous removal of the baseline wander and the 50/60 Hz power-lines and its harmonics in a recorded or received signal. The DSP technique includes an initialization module 202 configured to initialize the input signal (x[n])  a block length (N)  a regularization parameter (?)  and a dictionary matrix ( ).
[0042] In an example  the input signal x[n] described herein includes the baseline wander and 50/60 Hz power-line interference signals. A frequency component of the input signal may varies based on the characteristics of various noise sources  for example  patient coughing  patient breathing  physical exercise  poor electrode contacts  perspiration of the patient under the electrodes 102  dirty lead wire or electrode  patient movement  movements of cables  and any other noise source. These noises can vary the frequency component of the signal  which may introduce the baseline wander and power-line interference during the signal transmission. The DSP module 112 is configured to specify a value to the regularization parameter to control fidelity and sparse constraint of the signal.
[0043] The DSP module 112 is configured to construct the dictionary matrix ( ) using a dictionary matrix generation module 202. The dictionary matrix includes a set of elementary functions (or elementary waveforms) for the frequency components of the signal. In an example  the set of elementary functions described herein can generally include Dirac’s  Heaviside  Fourier  short-time Fourier transform  Discrete cosines  Discrete sine’s  Haar  Wavelets  Wavelet packets  Gabor filters  Curvelets  Ridgelets  Contourlets  Bandelets  Shearlets  Directionlets  Grouplets  Chirplets  Hermite polynomials  Cubic ploynomials  and any other function or prototype waveform. An appropriate and flexible dictionary matrix needs to be constructed for an efficient representation of the cardiac bio-potentials of the ECG signal. The choice of the dictionary matrix affects the accuracy of signal estimation and computational complexity.
[0044] In an embodiment  the dictionary matrix includes cosine or sine functions (or waveforms) for the frequency components of the ECG signal. In an example  the dictionary matrix include cosine waveforms with frequency components expect for the 50/60 Hz power-line interference and its harmonics with bandwidth of 1 Hz  and the frequencies from 0 Hz to the highest frequency (fh) Hz of the baseline wander.
[0045] The dictionary matrix ( ) with size of N X M (where M < N) is constructed from the discrete cosine functions or waveforms  which are computed as:

[0046] The DSP module 112 receives the input signal x[n] and divides the input signal into non-overlapping processing blocks of the length N with a certain time duration (for example  10 seconds). In an example  the DSP module 112 determines the length based on cyclic duration of input signal x[n]. The DSP module 112 performs the blocking of the input signal for effective suppression of different shapes of the baseline wander. The DSP module 112 performs a mean subtraction of the input signal x[n] and provide a zero-mean discrete-time signal xb[n]. The mean subtraction leads to a better estimation of a transform coefficient (a) of the signal.
[0047] The DSP module 112 provides the zero-mean discrete-time signal xb[n] to a sparse coefficient estimation module 206. The sparse coefficient estimation module 206 uses a L1-norm minimization algorithm or greedy algorithm to compute the transform coefficient (a) for the input signal x[n] and the constructed dictionary matrix ( ). The estimation of the transform coefficient using the sparse coefficient estimation module 206 is described in conjunction with FIG. 3.
[0048] In an embodiment  the DSP module 112 is configured to construct a desired signal z[n] by using the estimated transform coefficient (a) and the dictionary matrix ( ). The DSP module 112 outputs the desired signal z[n] by the removing the baseline wander and the 50/60 Hz power-line and its harmonics without distorting the clinical features of the signal x[n].
[0049] The exemplary 50/60 Hz frequencies of the power-line described herein are only for illustrative purpose and should be considered as by way of example  but not by way limitation. The present invention is used to remove any type of power-line interference and its harmonics for 50/60 Hz or any other power-line frequency without departing from the scope of invention
[0050] FIG. 3 depicts a general framework 300 of t sparse coefficient estimation module 206 of the FIG. 2  according to embodiments as disclosed herein. The sparse coefficient estimation module 206 is configured to include a transform coefficient (a) estimator 302 to compute the transform coefficient using the L1-norm minimization algorithm or greedy algorithm.
[0051] In an embodiment  for the input signal x[n] and the dictionary matrix ( )  the transform coefficient (a) need to be computed. In an example  the zero-mean discrete-time signal xb[n] is provided to the transform coefficient (a) estimator 302. The transform coefficient (a) estimator 302 uses the L1-norm minimization algorithm or greedy algorithm to compute the transform coefficient (a). The transform coefficient (a) can be estimated by solving the following optimization problem computed by solving the following minimization problem:

[0052] Where  is fidelity term  is a sparsity term  x is the signal to be decomposed  and ? is the regularization parameter that controls the relative importance of the fidelity and sparseness terms.
[0053] The filtered or output signal z[n] with size of N × 1 can be computed as:
.
[0054] The DSP module 112  in communication with the sparse coefficient estimation module 206  can construct the output signal z[n] by using the estimated transform coefficient (a) vector and the dictionary matrix ( ). The outputted signal z[n] is free from the baseline wander and power-line interference and its harmonic components. The outputted signal z[n] can be displayed on the local or remotely-located devices using the display module 108.
[0055] FIG. 4 depict graphs 400 representing an example of experimental waveforms of a corrupted or noisy ECG signal 402  an estimated ECG signal 406  and an extracted baseline wander and 60 Hz power-line signal 408  according to embodiments as disclosed herein. The performance of the proposed method and system is evaluated using the exemplary noisy or corrupted ECG signal 402. The noisy or corrupted ECG signal 402 represent the original waveform of 10 seconds received from electrodes positioned on a patient  where no DSP techniques are applied. The noisy or corrupted ECG signal 402 is a contamination of the baseline wander and 60 Hz power-line noises  which corrupt the original signal and render it difficult to read. The estimated ECG signal 406 represents the desired waveform constructed by using the proposed DSP technique. The baseline wander and the 60 Hz power-line noises are removed from the estimated ECG signal 406  using the DSP technique  without distorting the cardiac bio-potentials of the noisy or corrupted ECG signal 402. The shape of the extracted baseline wander is represented in the signal 408.
[0056] FIG. 5 depict graphs 500 representing another example of experimental waveforms of a noisy or corrupted ECG signal 502  an estimated ECG signal 504  and an extracted baseline wander and 60 Hz power-line signal 506  according to embodiments as disclosed herein. The noisy or corrupted ECG signal 502 represent the original waveform of 10 seconds received from electrodes positioned on a patient  where no DSP techniques are applied. The noisy or corrupted ECG signal 502 includes sharp P waves  QRS complexes  muscle artifacts  baseline wander  and 60 Hz power-line  which corrupt the original signal and render it difficult to read. The estimated ECG signal 506 represents the desired waveform constructed by using the proposed DSP technique. The baseline wander  the 60 Hz power-line  and the muscle artifacts are removed from the estimated ECG signal 506  using the proposed DSP technique  without distorting the cardiac bio-potentials of the noisy or corrupted ECG signal 502. The shape of the extracted baseline wander is represented in the waveform signal 508.
[0057] The performance of the proposed method and system is evaluated using the noisy ECG signals taken from the standard MIT-BIH arrhythmia database at “Moody GB  Mark RG  The impact of the MIT-BIH Arrhythmia Database” www.physionet.org/physiobank/database/mitdb/” (Please refer more details). The preliminary experimental results of the method are shown in Figs. 4 and 5. The visual inspection of the experimental results shows that the method removes the baseline wander and the power line interference noises without distorting the morphological content of the local waves of the ECG signal.
[0058] FIG. 6 depicts a flow diagram 600 of the DSP technique for simultaneous removal of baseline wander and power-line interference  in accordance with various embodiments of the present invention. At step 602  the DSP module 112 initializes design parameters. In an example  the DSP module 112 receives the input signal x[n] from the DAQ module 104. The DSP module 112 initializes the input signal (x[n])  a block length (N)  a regularization parameter (?)  and a transform matrix ( ).
[0059] At step 604  the DSP module 112 performs blocking and mean subtraction of the input signal x[n]. In an example  the DSP module 112 divides the input signal into non-overlapping processing blocks of length N based on cyclic duration (for example  10 seconds). The DSP module 112 performs the blocking of the signal x[n] for effective suppression of different shapes of the baseline wander. In an example  the DSP module 112 performs a mean subtraction of the signal x[n] and provide a zero-mean discrete-time signal xb[n] for better estimation of a transform coefficient (a) of the signal x[n]. In an example  the DSP module 112 is configured to specify a value to the regularization parameter to control fidelity and sparse constraint of the input signal x[n].
[0060] At step 606  the transform matrix or dictionary matrix ( ) generation module 204 constructs the transform matrix ( ) for the signal x[n]. In an example  the transform matrix includes cosine elementary functions (or waveforms) for the frequency components of the ECG signal. The transform matrix ( ) with size of N X M (where M < N) is constructed from the discrete cosine functions or waveforms  which are computed as:

[0061] In an example  depending on the temporal and spectral characteristics of input signal x[n] and the encountered noises  the dictionary matrix can be constructed by using the elementary waveforms and prototype waveforms as mentioned in the present invention.
[0062] At step 608  the sparse coefficient estimation module 206 estimates the transform coefficient (a) of the zero-mean discrete-time signal xb[n]. In an example  the sparse coefficient estimation module 206 uses the L1-norm minimization algorithm or greedy algorithm to compute the transform coefficient (a) for the input signal x[n] and the constructed transform matrix ( ). The transform coefficient (a) can be estimated by solving the following optimization problem computed by solving the following minimization problem:

[0063] Where  is fidelity term  is a sparsity term  x is the signal to be decomposed  and ? is the regularization parameter that controls the relative importance of the fidelity and sparseness terms.
[0064] At step 610  the DSP module 112  in communication with the sparse coefficient estimation module 206  constructs an output signal z[n] using the estimated the transform coefficient (a) and the transform matrix ( ). In an example  the DSP module 112 constructs the output signal z[n] by removing the baseline wander and power-line interference and its harmonics  without distorting the cardiac bio-potentials of the input signal x[n]. The output signal z[n] with size of N × 1 can be computed as:

[0065] At step 612  the display module 108 displays the outputted signal z[n] on the local or remotely-located devices.
[0066] Through the above description is described with respect to the ECG monitoring system  the person skilled in art can quickly identify that the present invention can be used in any DSP systems. The present invention is capable of removing comprises removing trend and periodic signal components from a signal. Further  the present invention is capable of simultaneously removing any type of specific or complete noise from a recorded or received signal  without departing from the scope of the invention.
[0067] In an example  a method of removing signal drifts in a speech communication system using the proposed DSP technique is described. Consider a real-valued  finite-length  one-dimensional  and discrete-time input signal x = [x[1]  x[2]  … x[N]]T  where T denotes a matrix transpose.
[0068] The signal vector x can be represented as a linear combination of the elementary waveforms as the column vectors in the dictionary matrix ( ). The signal vector x can be represented as:
.
[0069] Where  is the transform coefficient vector that can be computed as . For example  if the consists of elementary discrete cosine waveforms  then is the vector of a discrete cosine transform (DCT) coefficients. An appropriate and flexible dictionary matrix is constructed for an efficient representation of the signal x[n]. The dictionary matrix ( )  with size of N ×M (where M < N)  is constructed from the discrete cosine waveforms  which are computed as:

[0070] The blocking and mean subtraction of the signal x[n] can be performed for effective estimation of the transform coefficient (a). A value to the regularization parameter (?) is specified to control fidelity and sparse constraint of the signal x[n]. The transform coefficient (a) can be estimated by solving the following optimization problem:

[0071] A filtered or reconstructed signal z[n] with size of N × 1 can be computed as:

[0072] The output signal z[n] is constructed by removing the drifts and other artifacts from the signal x[n]  without distorting voice data of the signal x[n].
[0073] FIG. 7 illustrates a computing environment implementing the application  in accordance with various embodiments of the present invention. As depicted the computing environment comprises at least one processing unit that is equipped with a control unit and an Arithmetic Logic Unit (ALU)  a memory  a storage unit  a clock chip  plurality of networking devices  and a plurality Input output (I/O) devices. The processing unit is responsible for processing the instructions of the algorithm. The processing unit receives commands from the control unit in order to perform its processing. Further  any logical and arithmetic operations involved in the execution of the instructions are computed with the help of the ALU.
[0074] The overall computing environment can be composed of multiple homogeneous and/or heterogeneous cores  multiple CPUs of different kinds  special media and other accelerators. The processing unit is responsible for processing the instructions of the algorithm. The processing unit receives commands from the control unit in order to perform its processing. Further  any logical and arithmetic operations involved in the execution of the instructions are computed with the help of the ALU. Further  the plurality of process units may be located on a single chip or over multiple chips.
[0075] The algorithm comprising of instructions and codes required for the implementation are stored in either the memory unit or the storage or both. At the time of execution  the instructions may be fetched from the corresponding memory and/or storage  and executed by the processing unit. The processing unit synchronizes the operations and executes the instructions based on the timing signals generated by the clock chip.
[0076] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the elements. The elements shown in FIGS. 1-3 and 6 include blocks which can be at least one of a hardware device  or a combination of hardware device and software module.
[0077] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can  by applying current knowledge  readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept  and  therefore  such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore  while the embodiments herein have been described in terms of preferred embodiments  those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

STATEMENT OF CLAIMS
We claim:
1. A method for removing noise from a signal  the method comprising:
dividing said signal into at least one processing block;
constructing a transform matrix based on said noise of said signal;
estimating a transform coefficient of said signal and said constructed transform matrix;
reconstructing said signal using said estimated transform coefficient and said constructed transform matrix; and
displaying said reconstructed signal.
2. The method of claim 1  wherein the method further comprises determining a regularization parameter to control a fidelity and sparse constraint of said noise of said signal.
3. The method of claim 1  wherein the method further comprises performing a mean subtraction of said signal.
4. The method of claim 1  wherein said noise comprises at least one of baseline wander  power-line interference  and harmonics of said power-line interference.
5. The method of claim 1  wherein said signal comprises a frequency component  wherein said frequency component is varying based on characteristics of a noise source.
6. The method of claim 1  wherein the method further comprises determining a length of said at least one processing block based on cyclic duration of said signal.
7. The method of claim 1  wherein said transform matrix comprises a set of elementary functions for said frequency component of said signal.
8. The method of claim 6  wherein said set of elementary functions comprises at least one of cosine and sine function for removal of said noise from said signal.
9. The method of claim 1  wherein said method further comprises determining said set of elementary functions of said transform matrix based on said frequency of said signal.
10. The method of claim 1  wherein said transform matrix further comprises a plurality of column vectors  wherein said plurality of column vectors is less than said length of said signal.
11. The method of claim 1  wherein constructing said transform matrix in accordance to said noise of said signal further comprises adjusting said set of elementary functions for removing said noise of said signal.
12. The method of claim 8  wherein adjusting said set of elementary functions further comprises determining shapes of said signal  wherein said set of elementary functions is adjusted based on said shapes of said signal and characteristics of said noise source.
13. The method of claim 1  wherein said transform matrix is constructed as at least one of over-complete  under-complete  and critical transformation.
14. The method of claim 1  wherein said transform coefficient is estimated using at least one of L1-norm minimization algorithm and greedy algorithm.
15. The method of claim 1  wherein said method further comprises simultaneously removing said at least one of baseline wander  power-line interference  and harmonics of said power-line interference  of said signal.
16. The method of claim 1  wherein said method further comprises removing trend and periodic signal component in said signal.
17. A system for removing noise from a signal  the system comprising:
a data acquisition module configured to receive said signal from at least one electrode;
a dictionary matrix generation module configured to construct a dictionary matrix based on said noise of said signal;
a sparse coefficient estimation module configured to estimate a transform coefficient of said signal and said constructed dictionary matrix;
a digital signal processing module configured reconstruct said signal using said estimated transform coefficient and said constructed dictionary matrix; and
a display module configured to display said reconstructed signal.
18. The system of claim 1  wherein said digital signal processing module is further configured to:
divide said signal into at least one processing block 
determine a length of said at least one processing block based on cyclic duration of said signal 
determine a regularization parameter to control a fidelity and sparse constraint of said noise of said signal  and
perform a mean subtraction of said signal.
19. The system of claim 17  wherein said noise comprises at least one of baseline wander  power-line interference  and harmonics of said power-line interference.
20. The system of claim 17  wherein said signal comprises a frequency component  wherein said frequency component is varying based on characteristics of a noise source.
21. The system of claim 17  wherein said dictionary matrix comprises a set of elementary functions for said frequency component of said signal.
22. The system of claim 21  wherein said set of elementary functions comprises at least one of cosine and sine function for removal of said noise from said signal.
23. The system of claim 17  wherein said dictionary matrix generation module is further configured to determine said set of elementary functions of said dictionary matrix based on said frequency of said signal.
24. The system of claim 17  wherein said dictionary matrix generation module is further configured to:
determine shapes of said signal  and
adjust said set of elementary functions based on said shapes of said signal and characteristics of said noise source.
25. The system of claim 17  wherein said dictionary matrix further comprises a plurality of column vectors  wherein said plurality of column vectors is less than said length of said signal.
26. The system of claim 17  wherein said dictionary matrix is constructed as at least one of over-complete  under-complete  and critical dictionary.
27. The system of claim 17  wherein said sparse coefficient estimation module further comprises a transform coefficient estimator configured to estimate said transform coefficient using at least one of L1-norm minimization algorithm and greedy algorithm.

28. The system of 17  wherein said digital signal processing module is further configured to remove trend and periodic signal component in said signal.

Dated: 21st day of September  2012 Signature:
Dr Kalyan Chakravarthy
Patent agent


ABSTRACT
[0078] The present invention provides a method for removing noise from a signal. The method includes dividing the signal into processing blocks and constructing a transform matrix based on the noise of the signal. Further  the method includes estimating a transform coefficient of the signal and the constructed transform matrix  reconstructing the signal using the estimated transform coefficient and the constructed transform matrix  and displaying the reconstructed signal.

FIG. 2