minor errors fixed

hooman650 · Oct 17, 2018 · 4604b3a · 4604b3a
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 # Summary
-BioSigKit is a set of Matlab (The MathWorks Inc., Natick, USA) tools for analysis and visualization of bio-signals. Matlab is a widely used programming language among researchers thanks to its simple and flexible syntax. Biomedical signal processing is one of the main areas that has been benefiting from Matlab for research and rapid prototyping. BioSigKit is a collection of signal processing tools for analysis of ECG, EEG, EMG and 3 Channel Accelerometer recordings. While there are already tools such as ECG-Kit [@Soria2015] that offer specialized algorithms for ECG processing, BioSigKit is a more general purpose biosignal analysis toolbox that allows processing various biological signals. Many of the subroutines in BioSigKit are already being actively used in research such as [@sedghamiz2014completed, @sedghamiz2013online]. BioSigKit gathers these popular signal processing algorithms under one roof. For the ECG processing, BioSigKit offers several popular algorthims that with the exception of [@Pan1985] are only available in C language. BioSigKit offers the Matlab implementation of [@Lee2002;@Afonso1999;@Scholkmann2012] for QRS detection and analysis along with several other algorithms that are detailed in the next section. BioSigKit also provides subroutines for activity detection in EMG recordigs, posture estimation from 3 channel Accelerometers and several adaptive filtering routines as well. The object oriented implementation of BioSigKit makes it easy to update and add new algorithms to its collection. The ultimate goal of BioSigKit is to provide an easy to use interactive Matlab software that provides easy access to many standard bio-signal processing algorithms. 
+BioSigKit is a set of Matlab (The MathWorks Inc., Natick, USA) tools for analysis and visualization of bio-signals. Matlab is a widely used programming language among researchers thanks to its simple and flexible syntax. Biomedical signal processing is one of the main areas that has been benefiting from Matlab for research and rapid prototyping. BioSigKit is a collection of signal processing tools for analysis of ECG, EEG, EMG and 3 Channel Accelerometer recordings. While there are already tools such as ECG-Kit [@Soria2015] that offer specialized algorithms for ECG processing, BioSigKit is a more general purpose biosignal analysis toolbox that allows processing various biological signals. Many of the subroutines in BioSigKit are already being actively used in research such as [@sedghamiz2014completed, @sedghamiz2013online]. BioSigKit gathers these popular signal processing algorithms under one roof. For the ECG processing, BioSigKit offers several popular algorthims that, with the exception of [@Pan1985], are only available in C language. BioSigKit offers the Matlab implementation of [@Lee2002;@Afonso1999;@Scholkmann2012] for QRS detection and analysis along with several other algorithms that are detailed in the next section. BioSigKit also provides subroutines for activity detection in EMG recordigs, posture estimation from 3 channel Accelerometers and several adaptive filtering routines as well. The object oriented implementation of BioSigKit makes it easy to update and add new algorithms to its collection. The ultimate goal of BioSigKit is to provide an easy-to-use interactive Matlab software that provides easy access to many standard bio-signal processing algorithms. 
 
 ![Graphical User Interface of BioSigKit. The algorithm pop-up menu provides an easy way for the selection of the QRS detection algorithm. The statistics panel automatically computes mean, maximum and minimum detected intervals.](fig1.png)
 
@@ -43,7 +43,7 @@ The filter bank method combines several band-pass filters in order to better del
 This algorithm employs Multilevel Teager Energy Operator (MTEO) in order to locate the QRS complexes. MTEO has been successfully used in Electromyography signals for action potential detection [@7391510] since it is computationally much more efficient than wavelet transform (subroutine name : ```BioSigKit.MTEO_qrstAlg()```).
 
 ### 1.6. Foetal ECG extraction [@Schreiber1996]:
-Foetal-ECG extraction from multichannel and single channel maternal ecg recordings. BioSigKit implements a non-linear phase space filter that is able to extract foetal ecg recordings. This is based on delayed phase space reconstruction of the signal. For more details see [@Schreiber1996]. Futhermore, it is possible to extract the foetal ecg in real-time with the neural PCA offered in BioSigKit. See demo.m file for more details (```obj.nonlinear_phase_filt```).
+Foetal-ECG extraction from multichannel and single channel maternal ECG recordings. BioSigKit implements a non-linear phase space filter that is able to extract foetal ECG recordings. This is based on delayed phase space reconstruction of the signal. For more details see [@Schreiber1996]. Futhermore, it is possible to extract the foetal ECG in real-time with the neural PCA offered in BioSigKit. See demo.m file for more details (```obj.nonlinear_phase_filt```).
 
 ### 1.7. Artifact Removal.
 ECG artifact removal with Recursive Least Squares filter (RLS). BioSigKit also offers a subroutine to remove artefacts from ECG recordings by using a 3 channel Accelerometer recording with RLS filter (```obj.adaptive_filter```). BioSigKit also implements Adaptive Line Enhancer and its leaky version. For more details regarding motion artefact removal in ECG with ACC.