A methodology for timefrequency image processing applied to the classification of nonstationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals
 Boualem Boashash^{1, 2},
 Larbi Boubchir^{1}Email author and
 Ghasem Azemi^{2, 3}
https://doi.org/10.1186/168761802012117
© Boashash et al; licensee Springer. 2012
Received: 24 November 2011
Accepted: 29 May 2012
Published: 29 May 2012
Abstract
This article presents a general methodology for processing nonstationary signals for the purpose of classification and localization. The methodology combines methods adapted from three complementary areas: timefrequency signal analysis, multichannel signal analysis and image processing. The latter three combine in a new methodology referred to as multichannel timefrequency image processing which is applied to the problem of classifying electroencephalogram (EEG) abnormalities in both adults and newborns. A combination of signal related features and image related features are used by merging key instantaneous frequency descriptors which characterize the signal nonstationarities. The results obtained show that, firstly, the features based on timefrequency image processing techniques such as image segmentation, improve the performance of EEG abnormalities detection in the classification systems based on multiSVM and neural network classifiers. Secondly, these discriminating features are able to better detect the correlation between newborn EEG signals in a multichannelbased newborn EEG seizure detection for the purpose of localizing EEG abnormalities on the scalp.
Keywords
timefrequency signal analysis multichannel signal analysis instantaneous frequency timefrequency image processing image segmentation timefrequency feature extraction seizure detection EEG classification localization newborn EEG1 Introduction to nonstationary EEG signal processing for diagnostic of abnormalities
1.1 EEG abnormalities detection using DSP
The focus of this study is to analyze abnormal newborn EEG signals in order to detect the different abnormalities and to locate their spatial distribution on the scalp. This abnormality detection and localization problem can be solved by analyzing newborn EEG signals and extracting features, which are then classified.
A typical scheme for EEG abnormalities detection using DSP includes the following three steps: (1) signal analysis in either time, frequency, timescale or joint time frequency domain, (2) features extraction which characterize the different abnormalities and (3) classification of these features in order to assign the signal to one abnormality class with a relevant degree of severity: mild, moderate or severe abnormality.
For a single channel, EEG data would be represented by only the symbolic notation s_{1}(t). Then, the signal analysis permits to exploit all information necessary to characterize different abnormalities in the newborn EEG signal. However, this is in general only an approximation aimed at simplifying the problem and this leads to inaccuracies that are detrimental to the application, and it is therefore preferable to consider the multichannel case when possible.
Step 2, feature extraction, then extracts this information in order to assign it to one abnormality type using the classification method used in Step 3.
Research on EEG abnormalities detection is wide and varied, but almost in all previous studies, a single channel signal analysis approach is used (see Section 1.3). Although, the EEG abnormalities detection and classification system receives an input of more than one EEG channel, the analysis is made for a single channel only. Hence, one main goal of this study is to not only examine the EEG abnormality detection problem but also consider the localization of this abnormality in the brain and on the scalp, using a multichannel analysis of the multichannel EEG signal.
1.2 EEG abnormality localization using multichannel analysis
1.3 The TF approach to nonstationary EEG analysis
Several automated techniques have been proposed for detecting EEG seizures [5, 8–16]. These techniques are based on time, frequency, nonstationary and nonlinear characteristics of EEG signals. Most of these methods use single channel EEG as their input and are based on a methodology similar to the one previously defined and illustrated in Figure 4(a)[14–16].
Among these methods, we note those which use time domain statistics [8], spectral features [9], autoregressive modeling [17], and linear prediction error energy [18]. Other methods use a combination of time and frequency features [19], chaotic features [20–22], fast Fourier transform (FFT) coefficients [10]; more advanced methods use the coefficients of the discrete wavelet transform (DWT) of EEG signals [11], combination of DWT coefficients and chaotic measures [23], energy distribution of EEG signals in the TF representation [24, 25], and TF matched filtering methods [26, 27]. A comparison of the performance of different feature sets in classifying EEG signals can be found in [5]. In [12, 13], a combination of features in time, frequency, and timescale domains and chaotic measures are used for neonatal seizure detection.
Due to the nonstationarity of EEG signals, TF and instantaneous frequency (IF) based methods seem naturally more suitable for seizure detection and classification [7, 26, 28–32] and [[6], Section 15.5]. Recently, a study exploited the additional information provided by signal variations in terms of nonstationarity observed with the TF approach, and then, developed novel features extracted from the TF representation of EEG signals for the purpose of classifying them [14–16].
A variety of methods have already been applied for automatic seizure detection using multichannel EEG. This includes, for example, the use of TF matched filters [7, 33, 34], multichannel matching pursuit [35], spatial and temporal contextual information obtained from multichannel EEGs [36].
One of the aims of this article is to extend the proposed approach in [14–16] using the full multichannel EEG signal in order to define a new multichannel abnormalities detection and localization approach for the newborn EEG.
The article is organized as follows. Section 2 presents a brief review of TF signal analysis and IF estimation using quadratic timefrequency distributions (QTFDs). Section 3 describes a review of TF image processing techniques applied to the timefrequency distribution (TFD) considered as an image. The methodology employed to extract new TF features using TF image processing tools for classifying EEG signals is described in Section 4. Experimental results with discussion are reported in Section 5. Finally, the article concludes with Section 6.
2 TF analysis of multichannel nonstationary EEG signals
This section introduces the concept of TF signal analysis applied to both monochannel and multichannel nonstationary EEG Signals using quadratic TFDs.
2.1 Representing EEGs with TFDs and their IFs
2.1.1 EEG timefrequency based feature selection approach
A TF based feature selection approach can improve the classification of EEG signals (illustrated in Figure 4(a) acquired from healthy and epileptic subjects [14–16]. This is achieved by exploiting the link between the nonstationary features of the signal and considering their impact on the performance of the classification scheme. This new classification method is based on a feature vector composed of ten TF features extracted from the TFD of EEG segments. These include: the IF and singular value decomposition (SVD) based features, the TF complexity, and the energy concentration measures (see detail in Section 4).
Statistical analysis and experimental results given in [14–16], have shown that the proposed TF features extracted from all TFDs perform well and achieve a highly significant discrimination between the EEG signals collected from healthy and those from epileptic patients. Also, the use of reduced interference TFDs (RIDs)such as MBD, Smoothed WignerVille distribution (SWVD), Gaussian Kernel distribution (GKD), Spectrogram (SPEC), Separable Kernel based RID (SEPK) [[6], Chapter 2 & Section 5.7] slightly improves the performance of the proposed method. This is because RIDs have the ability to reduce the effects of the crossterms while still providing a good resolution [6].
2.1.2 Need for image processing techniques to extract new features
The improvements provided by the method described in [14–16] are attributed to the TF approach as this allows the use of extra information related to the nonstationarity of the signals, which is directly available in the TF domain. Such information includes the distribution of the signal energy over TF plane and the IF characterizing the variation of its frequency content over time. The impact of this new method is that such variations are the key critical information that describes the nonstationarity of the signals.
As all the TF features discussed in [14–16] were extracted from TFDs of the EEG segments, there is still room for improving the performance of the proposed approach by exploiting the information provided by the full TF representation viewed as an image.
The use of TF image techniques can then improve the method performance by selecting and extracting new TF features. Section 3.1 reviews the proposed approaches based on TF image techniques in order to select a new methodology to define new features.
2.1.3 Array processing and multichannel analysis
The abnormality localization problem needs to consider the multiple EEG channels simultaneously since EEG data often come as correlated time series from multiple electrodes on the scalp.
In practice, each channel of the 1D timedomain signal is first transformed to reveal its 2D TF representation. By stacking all the 2D TF arrays from all the channels, we form a 3D array in the space "TF domain". Then, this 3D array is processed in order to detect and locate with accuracy the abnormality source on the scalp, and also their propagation in the head. To do this, the techniques borrowed from radar and communications statistical signal processing can be applied on this 3D data by considering the correlation between 2D TF arrays. This processing can be made in two ways: (1) correlation between the 2D TF arrays corresponding to the TFDs of multiple EEG signals, or (2) correlation between the "actual features" extracted from these 2D TF arrays.
2.1.4 Multichannel TF approach for localization
The design of a novel approach for automatic abnormalities detection and localization using a multichannel EEG signal for newborn is necessary to help the specialists to detect the presence of different abnormalities by interpreting the EEG signal in terms of abnormality type and its source localization, and to decide on a suitable diagnosis.
The selected approach is to exploit the additional information provided by the signal variation in terms of nonstationarity observed in the TFD; and then, develop novel features extracted from the TFD of the EEG signals using the TF image processing techniques, for the purpose of detecting the different neonatal abnormalities and classifying the EEG signals. Also, the localization of the abnormality sources on the scalp is dealt with by analyzing the multichannel EEG signal.
2.2 TF formulation
Quadratic timefrequency distributions form a class of TFDs which distribute the signal energy in a joint TF domain from which we can generate features for detection and classification. Related backgrounds regarding QTFDs and IF estimation are presented in the next sections.
2.2.1 Formulation of quadratic TFDs
where ρ(t, f) denotes the TFD, W_{ z } (t, f) the WingerVille distribution (WVD), γ(t, f) the TF kernel of the distribution, and $\underset{\left(t,\phantom{\rule{0.3em}{0ex}}f\right)}{**}$ the 2D convolution operation in time and frequency. According to Equation 1, QTFDs can be considered as smoothed versions of the WVD, with γ(t, f) being a 2D smoothing filter. Different kernels in Equation 1 define different distributions in the class, that are most specifically adapted to particular classes of signals [[6], pp. 7176]. The above formulation can also be given in any of the other three joint domains (timelag (t, τ), dopplerfrequency (ν, f) and dopplerlag (ν, τ)) linked to the TF domain via Fourier transform [6].
where $z\left(t\right)=s\left(t\right)+\mathcal{H}\left(s\left(t\right)\right)$ is the analytic associate of s(t), stands for the Hilbert transform, and z*(t) its complex conjugate [[6], pp. 1315]. The WVD provides the best joint TF concentration among all QTFDs for linear frequency modulation (LFM) signals [6]. As EEG seizures were shown to be mostly Piecewise LFM (PWLFM), the WVD seems to be the ideal tool. However, being quadratic in nature, the WVD introduces artifacts, or crossterms, in the case of multicomponent signals and/or nonlinear FM signals. The presence of these artifacts can mask the true signal components and make the interpretation of the TFD a difficult task [40]. For those signals, reduced interference TFDs and polynomial WVDs may be used [6].
where $G\left(t,\phantom{\rule{0.3em}{0ex}}\tau \right)\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\underset{f\to \tau}{{\mathcal{F}}^{1}}\left\{\gamma \left(t,\phantom{\rule{0.3em}{0ex}}f\right)\right\}$ is the timelag kernel of the TFD.
There is a special class of kernels, referred to as separable kernel [6], whose members are expressed as the product of two singlevariable functions (i.e., γ(t, f) = g_{1}(t)G_{2}(f)). This formulation allows the smoothing in time direction and frequency direction to be quasiindependently performed. The length and shape of the smoothing window g_{1}(t) control outer crossterm reduction. And, G_{2}(f) is the Fourier transform of the analysis window g_{2}(τ) whose length and shape control innerartifacts reduction [[6], Chapter 3]. The MBD is one such method that was shown to be the best to represent newborn EEG signals using an objective measure for comparison [6, 29]. Figure 5 shows an example of a joint TF representation of newborn EEG signals using MBD which illustrates the nonstationary and multicomponent characteristics of the EEG signal. Compared to time domain and frequency domain (given in Figure 2), the TFD provides a more informative description of the EEG seizure by revealing the temporal variation of its frequency content through the IF.
The timeLag kernels of TFDs used in this article [6]
QTFD  G[n, m]  Kernel type 

WVD  δ[n]  Lag independent/Doppler independent 
SWVD  δ[n]w[m]  Doppler independent 
SPEC  w[n + m] w [n  m]  Nonseparable kernel 
GKD  $\frac{\sqrt{\pi \sigma}}{2\leftm\right}\phantom{\rule{0.3em}{0ex}}exp\left(\frac{{\pi}^{2}\sigma {n}^{2}}{4{m}^{2}}\right)$  Product kernel 
MBD  $\frac{{cosh}^{2\beta}}{{\sum}_{n}{cosh}^{2\beta}n}$  Lag independent 
SEPK  Hamm_{ M }[n] Hann_{ L }[n]  Separable kernel 
2.2.2 Instantaneous frequency
where a(t) and φ(t) are respectively the instantaneous amplitude and instantaneous phase of the signal. The concept of IF has been successfully used in many technical fields such as radar, sonar, seismic, biomedical applications and communications (e.g. [41]). Many IF estimating techniques have been developed and a detailed review can be found in [38].
For monocomponent signals, the peaks of the constanttime crosssections of the TFD as well as its first order moment give estimates of the IF law of the signal. The notion of a single IF becomes inappropriate for a multicomponent signal. To characterize this type of signals, an IF law is assigned to each signal component. Various IF estimation approaches for multicomponent signals have been proposed (see e.g., [[6], Chapter 10] and [42, 43]). Such methods first localize and extract signal components from the signal TF representation and then apply an IF estimation procedure. The methods have the advantage of not requiring prior knowledge about the signal under analysis except that its components are separated in the TF domain, a condition often satisfied by newborn EEG signals [6].
 (a)
TF transformation: The given signal is first mapped to the TF domain using a suitable TFD which is chosen based on the characteristics of the signal under analysis. For newborn EEG signals, due to their nonstationary and multicomponent nature, the selected TFD should provide high spectral resolution and have good crossterm reductioncapability. Previous studies have shown that separable kernel TFDs, i.e. γ(t, f) = g _{1}(t) G _{2}(f) are the most suitable ones [[6], Section 5.7]. We can use typical spectral analysis windows such as a Hanning window and a Gaussian window for g _{1}(t) and G _{2}(f) respectively, or the windows used in MBD which was shown to be best for representing EEG signals.
 (b)
TFD Local peaks detection & component linking: The IF estimation method proposed in [42], considers the resulted TFD (ρ(t, f)) as a 2D image with time and frequency as its row and column coordinates and identifies the Local maxima (with respect to frequency) using both the first and second derivative tests. Only those local maxima which are higher than a threshold (i.e. ρ(t, f) > 0.01 × max(ρ(t, f))) are considered as valid peaks. By assigning value 1 to the locations of valid peaks and value 0 to all others, the TF image can then be transformed to a binary image B(t, f) [42].
A component linking algorithm then detects linked components in B(t, f) by examining the pixel connectivity and the number of connected pixels. This is based on the fact that ideally, the IF of a signal component where signal energy concentrates will be observed in the TFD as a ridge describing the IF. Among the sets of connected pixels, only those with a number of pixels exceeding a preset threshold are identified as true linked components of seizure IFs. To remove the IFs of nonseizure components, this threshold is set to the minimum time duration of a seizure component in samples (equivalent to 10 s).
2.2.3 TF analysis of multichannel EEG using spatial TF distributions
The concept of spatial TF distribution (STFD) was introduced to analyze an array of nonstationary signals and has been successfully used in the area of array signal processing for source separation, for example (see [[6], Chapter 8]). STFDs are a generalization of TFDs to a vector of multisensor signals.
The offdiagonal elements of the STFD are the crossTFDs and the diagonal entries are the classical autoTFDs of the EEG signals. STFDbased methods exploit the nonstationary characteristics of the signals together with the spatial diversity provided by the multiple sensors. This property makes them suitable for analyzing multichannel EEGs.
2.2.4 Selecting a data adapted TFD
 (a)
Realness: ρ[n, k] must be real.
 (b)
IF estimation: The IF of the signal can be estimated using the peak of the TFD of the signal.
 (c)
Local energy: The energy of the signal in a certain region in the TF plane can be found by the integral of the TFD over that region.
 (d)
Reduced interference: The TFD attenuates the unwanted crossterms in the TF domain relative to the signal terms.
 (e)
High resolution: The reduced interference property is achieved while preserving a good TF resolution.
Several measures that take into account the above properties were proposed in [29, 44, 45]. Comparative studies of these measures indicated that the selection of TFDs for a particular application is datadependent [29].
Table 1 lists the expressions for the timelag kernels G[n, m] of some of these QTFDs known as RIDs which are used in this study. These RIDs were designed to suppress or attenuate the crossterms in the WVD [[6], Section 5.2]. The WVD is also considered in this study as it is a reference, for performance comparison. The exact formula for each method can be found in [[6], Section 6.1].
3 TF image processing methodology for newborn EEG analysis
As mentioned earlier, TF image processing techniques represent a set of image processing methods that process the TFD of a given signal considered as an image. Several studies applied image processing techniques to the signal TFD in order to select a methodology suitable for the problem considered, leading to an improvement in terms of precision, resolution, performance or robustness. The applications of these techniques include: segmentation [46–55], denoising and signaltonoise ratio (SNR) enhancement [56–59], classification [60–63], coding and compression [64, 65], watermarking [66–70], and IF estimation and crossterm reduction [42, 52, 71, 72]. A review of proposed approaches is given in the following section.
3.1 TF image processing techniques: overview
A comprehensive review of proposed TF image processing approaches shows that they can be grouped in six classes (corresponding to the numbering of the next subsections). The intent is to evaluate these methods to determine their suitability in the main application considered in this article, i.e. feature selection and extraction, and classification.
3.1.1 Denoising or SNR enhancement of TF images
The presence of noise and/or artifact (e.g. muscle activity and eye blinks) in the recorded EEG signals degrades the performance of any EEG based signal processing method. Image processing techniques can be used to reduce noise and artifact from the TFD of EEG signals while retaining key details such as edges and texture of the TFD. TF image techniques can then be adapted and used to enhance the quality of TF representations in order to better represent the TF features which characterize different abnormalities.
There are several denoising TF methods that use TF image processing techniques for the removal of noise from a signal [56]. An adaptive block thresholding denoising algorithm based on Stein Unbiased Risk estimator is proposed to remove noise from audio signals in the TF representation in [73]. Another approach uses thresholding denoising algorithms in conjunction with the Stransform [57]. A Bayesian image denoising method is also proposed in [58] which removes Gaussian noise from the TF representation of EEG signals [59]. These methods can be adapted following the approach based on LPARICI.^{a} The idea consists of transforming a 2D image into 1D array using the rowwise or diagonalwise transformation, and its denoising using the LPARICI method.
The above indicates that there is a choice of several algorithms for denoising methods which reduce noise and artifacts in the EEG signals from their TFDs considered as images. The power of noise and artifact in the signal resulting from reconstructing the enhanced TFDs are expected to be much less than those in the original signals. This in turn can significantly improve the performance of any newborn EEG classification/source localization method.
3.1.2 TF image processing for IF estimation and crossterm reduction
Image processing techniques can be used to estimate the signal IF and attenuate crossterms which appear in QTFDs due to their bilinear nature. One such image processing techniques estimates the IF laws of a multicomponent signal based on component linking [42]. Another approach uses morphological image processing operators to reduce crossterms in the WVD [71]. Alternative approaches are also proposed in [52, 72], all assuming a prior knowledge of the interference geometry.
The use of the above mentioned methods not only improves the performance of the extracted features in discriminating between different classes, but also facilitates the estimation of the IF laws of multicomponent signals. The statistics of the estimated IF laws of the signal using image processing techniques can be used as TF features to characterize the nonstationarity of the EEG signal.
3.1.3 Segmentation of TF images
Image segmentation and edge detection methods can be applied to TFDs to localize significant structures in them and extract TF signatures of different components of the signal under analysis [74]. The goal of a TF image segmentation is to create homogeneous regions that correspond to separate components of the signal. This is performed by a complete partitioning of image C (the original TFD) into n disjoint subregions, C_{1}, C_{2}, ..., C_{ n } . Morphological filtering was used in the analysis of TFDs in several applications, for example, musical transcription of audio recording [47] and analysis of submarine and seismic signals [48, 49]. Similar to the method presented in [49], authors in [46] apply the watershed segmentation procedure on TF images of nonstationary signals consisting of multiple closelyspaced components in the presence of noise. In the context of segmentation, it is not only important to extract desirable patterns from noisy background but also to separate the TF image subsets corresponding to different signal components. This can be achieved by computing an oversegmented watershed image and remerging the adjacent regions into pertinent structures, based on their statistical and geometry features [46]. An extension of the segmentation procedure based on morphological TF image processing techniques is also proposed in [50] for speech spectrogram segmentation.
In addition, a new approach for blind separation of nonstationary sources using their TFDs, was proposed in [75]. This approach is based on the observation that a monocomponent FM signal is represented by a linear feature corresponding to the energy concentration points in the TF image. The idea is based on a line detection algorithm, which is an adaptation of the road network extraction method [76], to extract separately all the components using a spatially averaged TF image of their mixtures. The line detection is done at the pixel level by determining whether a pixel belongs to a line crossing it along a particular direction.
These techniques are especially useful for analyzing EEG signals as they have the ability to provide us with new features which capture the nonstationarity and multicomponent nature of the EEG signals. These features can be grouped into the following four classes:

Topological features such as the number of components of the signals,

Morphometric features based on the geometric shapes of the TF components of the signals such as: size, position, and orientation of the components,

Intensity features based on the histograms of the TFDs over relevant zones (typically components) such as moments, median, and mode; and

Texture features such as statistics of spatial grayscale variations of the TFDs such as contrast, entropy, and energy.
The use of the abovementioned features is expected to improve the performance of any EEG abnormality detector.
3.1.4 Classification of TF images
Electroencephalogram signals can be classified based on their TFDs using image processing techniques which allocate a particular TF image belongs to a certain class. Several approaches have been proposed for different applications. In the context of ECG, three methods were proposed to classify the signals using the images obtained from their WVDs [60]. The first method uses features that are problem specific and highly sensitive to noise such as the frequency range and the multicomponent characteristics of the signal. The second one uses correlation in the TF domain. The third method is based on a decision theoretic pattern recognition which uses the SVD and the ambiguity function as features for the classification [60].
Another approach determines the modulation mode of the signal through a TF method with autoregressive modeling to detect phase shifts, frequency shifts and amplitude shifts [61]. Then the classification is performed through a simple decision tree. Another approach is to use the regional correlation between TFDs in order to achieve a better performance such as automatically selecting regions of interest before performing the correlation [62]. For audio applications, an approach considers the TF spectrograms related to music samples as textured images and extracts texturebased features to classify these music samples according to the predominant musical instrument [63]. Another method directly classifies each signal according to the best match of its TFD (such as Spectrogram, QTFDs or Gabor) with a set of ideal masks. The best fit is determined through Frobenius inner products as in [77].
Most of the classification methods proposed in the image processing literature can be adapted and applied to EEG classification. These classifiers may outperform conventional classifiers such as Neural Networks and SVMs in terms of classification accuracy, and can be useful in the context of EEG signal classification.
3.1.5 Coding and compression of TF images
Timefrequency image compression aims at reducing irrelevant and redundant information in the TF data in order to store or transmit nonstationary data in an efficient form. So, TF image compression and coding techniques can be applied to TFD records as a solution to the transmission and storage of complex nonstationary signals. For example, one can use a TF image coding technique by dividing the TF image into a combination of time and frequency components and then code them separately [64]. The key of this algorithm is to locate the sharp edges in both horizontal and vertical directions, and to select a threshold size for which the sharp edges are to be coded. Another approach is based on the estimation of the sequence duration using the autocorrelation of the TF image; it was used to detect and characterize a frequency hopping (FH) signal in the context of spectrum surveillance [65]. Since the obtained TF image is often very large, a compression step reduces the image before processing it. Following the above approach, an audio data compression algorithm based on the TF image of music signals could reproduce an audio signal which keeps only the main sense of music with smaller size [78]. The method can be used for tasks of identification of a signal, search of pieces of music, etc.
The above indicates that the TF representation or TF image can be used in general coding and compression algorithms of EEG signals. This implies that these TF methods can be adapted and used in the case of large and complex biomedical data, including EEG, as a possible innovative solution to the transmission and storage of key TF features describing specific abnormalities.
3.1.6 TF watermarking
The main goal of TF watermarking is to hide a message m (watermark) in a nonstationary signal using a TF representation of data w to obtain new data w' practically indistinguishable from w, by people, in such a way that an eavesdropper cannot remove or replace m in w'. This allows to develop tools for data hiding and copyright protection, using the characteristics of nonstationarity signals.
Timefrequency image watermarking methods can be classified in two traditional approaches: spatial and spectral domain techniques. In the spatial domain, the watermark is embedded in selected regions chosen based on the texture of the given TF image. In the spectral domain, the watermark is embedded in the transform domain using methods such as discrete cosine transform (DCT) and DWT, in the midfrequency range to ensure transparency and robustness of the watermark. A joint TFD of images can give a more comprehensive representation of the image compared to looking at each domain individually [67, 70]. Image watermarking in the TF domain can be done using the basic WVD [68]. In this method, the watermarked cells are selected based on their impact on image quality as well as resilience to JPEG.
Another watermarking algorithm based on the WVD [69] does not assume any particular structure for the embedded watermark and offers a theoretical framework for designing the minimum probability of error detector. The embedding algorithm in the joint TF domain is based on two aspects: the first one embeds the watermark directly into the WVD of the image, while the second one embeds the WVD of the watermark. This result reduces the computational complexity of embedding and detecting the watermark. This method is shown to be transparent and robust under attack. A variant of the above method led to a robust and highcapacity image watermarking algorithm using the discrete evolutionary transform (DET) calculated by the Gabor expansion to represent an image in the joint TF domain [70]. The watermark is then embedded onto selected coefficients of the TFD.
Another approach to watermarking of speech signals in the TF domain [66] is based on the crossterms free Stransform and the timevarying filtering used for watermark modeling. The main advantages and drawbacks of various TFDs applied to digital watermarking are discussed in [67].
The above indicates that image watermarking methods can be adapted and used for biomedical data. For example, watermarking methods are needed for EEG data if information such as acquisition measures and/or patient information, is confidential or registered. The idea is that this information can be embedded as a hidden message in the TF image of the EEG signals, using the methods proposed in [66, 67, 69, 70].
3.2 Range of applicability of TF image processing methods to EEG classification
TF image processing techniques and corresponding quality criteria
TF image techniques  Resolution  Performance  Precision  Robustness 

Segmentation  √  
Denoising/SNR enhancement  √  
Classification  √  √  
Coding and compression  √  √  
Watermarking  √  √  
TF signal estimation  √  √ 
TF image processing techniques and their applicability (or not) to the EEG abnormalities detection and classification problem
TF image processing  EEG abnormalities detection and classification system 

Segmentation  √ 
Denoising/SNR enhancement  √ 
Classification  √ 
Coding and compression  
Watermarking  
TF signal estimation  √ 
Selected TF image processing techniques and their applicability to the different steps of the EEG abnormalities detection and classification problem
TF image techniques  Preprocessing  Feature extraction  Features classification 

Segmentation  √  
Denoising/SNR enhancement  √  
Classification  √  
TF signal estimation  √  √ 
4 EEG abnormality detection and localization methodology
The proposed method for EEG abnormalities detection and classification, illustrated in Figure 4, includes three stages: monochannel or multichannel TF analysis, feature selection and extraction, and final classification.
As mentioned earlier, spectral analysis of nonstationary signals leads to TF analysis. It is thus necessary to perform a TF analysis on the EEG signals in the presence and the absence of abnormality in order to determine features such as the frequency bands in which the energy of the signals is concentrated for each case. The proposed TF analysis aims at extracting new key features from the EEG signals which carry information related in particular to their nonstationary nature. These features include the TF features proposed recently in [14–16], and also some features based on TF image processing techniques as detailed in the following subsection.
4.1 Feature extraction methodology
4.1.1 Signal related features
 (1)IFbased features: As mentioned earlier, the IF can be estimated either as the peak of the TFD of the signal (Equation 8)${f}_{i}\left[n\right]\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{{f}_{s}}{2M}\phantom{\rule{0.3em}{0ex}}arg\left\{\underset{k}{max}\phantom{\rule{0.3em}{0ex}}\rho \left[n,\phantom{\rule{0.3em}{0ex}}k\right]\right\}$(8)or the first order moment of the TFD (Equation 9).${f}_{i}\left[n\right]\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{{f}_{s}}{2M}\phantom{\rule{0.3em}{0ex}}\frac{{\sum}_{k=1}^{M}k\phantom{\rule{0.3em}{0ex}}\rho \left[n,\phantom{\rule{0.3em}{0ex}}k\right]}{{\sum}_{k=1}^{M}\rho \left[n,\phantom{\rule{0.3em}{0ex}}k\right]}$(9)The first selected feature, denoted as F_{S 1}, is chosen as the mean of f_{ i } [n] (MEIF), i.e. ${F}_{S1}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{1}{N}{\sum}_{n=1}^{N}{f}_{i}\left[n\right].$ The second feature F_{S 2}represents the deviation of the IF (DEIF) of the EEG signals, i.e.${F}_{S2}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\mathrm{\Delta}{f}_{i}\left[n\right]\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}max\left({f}_{i}\left[n\right]\right)\phantom{\rule{0.3em}{0ex}}\phantom{\rule{0.3em}{0ex}}min\left({f}_{i}\left[n\right]\right)$(10)
 (2)SVDbased features: The SVD is performed on the N × M matrix ρ. This divides the TFD matrix into two subspaces, signal subspace and an orthogonal alternate subspace of the form given as follows [14–16]$\rho \phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\mathbf{US}{\mathbf{V}}^{\mathbf{H}}$(11)
where U is an N × N matrix, S is an N × M diagonal matrix with nonnegative real numbers (S_{ i }, i = 1, 2, ..., N) on the diagonal, and V^{ H } (the conjugate transpose of V) is an M × M real unitary matrix. The diagonal entries of S are known as the singular values of ρ. The singular values and vectors have proved useful in characterizing EEG abnormalities [13, 74]. Here, the maximum and variance of the singular values, denoted as MASV and VASV respectively, are selected as characteristics of the singular values of ρ and chosen as features F_{S 3}and F_{S 4}, following [14–16].
 (3)TF complexity (TFCM): The TFCM feature denoted as F _{S 5}is an extension of the measure proposed in [12, 79] to the TF plane as proposed in [14–16]. It uses both SVD and Shannon entropy and is given by${F}_{S5}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\phantom{\rule{0.3em}{0ex}}\sum _{i=1}^{N}\overline{{S}_{i}}log\phantom{\rule{0.3em}{0ex}}\overline{{S}_{i}}$(12)
Where $\overline{{S}_{i}}$, i = 1, 2, ..., N are the normalized singular values,i.e.: $\overline{{S}_{i}}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{{S}_{i}}{{\sum}_{i=1}^{N}{S}_{i}}$
According to the basic theorem of information theory, F_{S 5}≤ log N, and the equality holds only when all the normalized singular values are equal, i.e. $\overline{{S}_{i}}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{1}{N}$; i = 1, 2, ..., N (see for example [[80], pp. 88]. TFCM therefore represents the magnitude and the number of the nonzero singular values of the TFD of the EEG signals. TFCM is a useful feature as the number of nonzero singular values of ρ and their magnitudes have a strong relationship with the information content in the TFD [14–16, 81].
 (4)Energy concentration measure (ECOME): For a multicomponent signal, the energy concentration measure, denoted as ECOME, determines the concentration of the dominant component at each location in the TF domain [82]. We use the definition given in [14–16, 83] as it does not discriminate between low concentrated components and highly concentrated ones. It is given by:${F}_{S6}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}{\left(\sum _{n=1}^{N}\sum _{k=1}^{M}\rho \left[n,\phantom{\rule{0.3em}{0ex}}k\right]\phantom{\rule{0.3em}{0ex}}{}^{\frac{1}{2}}\right)}^{2}$(13)
 (5)
Subbands energies: Subband energybased features represent the energy of the EEG signal x[n] in the subband where this energy is mostly concentrated. The subband interval is determined in the TF representation by analyzing the EEG signal. The energy is computed by proper frequency windowing of ρ[n, k] and is derived using the formula representing the partial information contained in a certain region of the TF domain [[6], pp. 46].
In [14–16], the use of four subbands energies features F_{ Si }, i = 7, 8, 9, 10 based respectively on 04 Hz (δ), 48 Hz (θ), 812 Hz (α), and 1230 Hz (β) subbands, were proposed. Their subbands have been chosen by analyzing the EEG signals taken from the EEG database [84]. This database is described in [85], and will be used in our experimental simulation in the next section.
These features are defined as follows [14–16]:${F}_{S7}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\sum _{n=1}^{N}\sum _{k=1}^{{M}_{\delta}}\rho \left[n,\phantom{\rule{0.3em}{0ex}}k\right]$(14)${F}_{S8}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\sum _{n=1}^{N}\sum _{k={M}_{\delta}}^{2{M}_{\delta}}\rho \left[n,\phantom{\rule{0.3em}{0ex}}k\right]$(15)${F}_{S9}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\sum _{n=1}^{N}\sum _{k=2{M}_{\delta}}^{3{M}_{\delta}}\rho \left[n,\phantom{\rule{0.3em}{0ex}}k\right]$(16)${F}_{S10}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\sum _{n=1}^{N}\sum _{k=3{M}_{\delta}}^{{M}_{\beta}}\rho \left[n,\phantom{\rule{0.3em}{0ex}}k\right]$(17)where M_{ δ } = [8M/f_{ s } ], M_{ β } = [60M/f_{ s } ] and [.] stands for the floor operator. Note that k = M corresponds to the maximum frequency component in the signal (f_{ s }/ 2). It follows that M_{ δ } and M_{ β } correspond to the frequency components at 4 and 30 Hz, respectively. We note that the features F_{S 7} F_{S 10}may also be estimated from the spectrum of the signal as they do not contain information about nonstationarity.
For the newborn EEG database used in this study (as described in Section 5.2 and also in [15, 16]), the energies of the EEG signals in 04 Hz (δ) and 48 Hz (θ) subbands were chosen as features. Only the features F_{S 7}and F_{S 8}will be used for this database in our experimental simulation in the next section.
4.1.2 TF image processingbased features
We now describe five morphometric features based on the geometric shape of the segmented regions.
1. Image grayscale moments: The moments of the TF binarysegmented image denoted ρ'[n, k] can be used to compute shape features, such as height, width, area, perimeter, compactness, centroid, etc. Using these shape descriptors we could further compute other ratios of geometric features, such as: $\mathsf{\text{Rectangularity}}\phantom{\rule{0.3em}{0ex}}\left(=\phantom{\rule{0.3em}{0ex}}\frac{\mathsf{\text{lengt}}{\mathsf{\text{h}}}_{\mathsf{\text{bounding}}}{}_{\mathsf{\text{box}}}}{\mathsf{\text{widt}}{\mathsf{\text{h}}}_{\mathsf{\text{bounding}}}{}_{\mathsf{\text{box}}}}\right)$, $\mathsf{\text{Aspect Ratio}}\phantom{\rule{0.3em}{0ex}}\left(=\frac{\mathsf{\text{Are}}{\mathsf{\text{a}}}_{\mathsf{\text{object}}}}{\mathsf{\text{Are}}{\mathsf{\text{a}}}_{\mathsf{\text{bounding}}}{}_{\mathsf{\text{box}}}}\right)$ and $\mathsf{\text{Form Factor}}\phantom{\rule{0.3em}{0ex}}\left(=\phantom{\rule{0.3em}{0ex}}\frac{4\mathrm{\pi}\times \mathsf{\text{Are}}{\mathsf{\text{a}}}_{\mathsf{\text{object}}}}{{\mathsf{\text{Perimeter}}}_{\mathsf{\text{object}}}^{\mathsf{\text{2}}}}\right)$.
where $\overline{n}\phantom{\rule{0.3em}{0ex}}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{{m}_{10}}{{m}_{00}}$ and $\overline{k}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{{m}_{01}}{{m}_{00}}$ are centroids of the TF image. The μ_{00} is the area of the segmented region, $\overline{n}$ and $\overline{k}$ give the x and y coordinates of the centroid for the segmented region. Higher order moments may give even more detailed shape characteristic, but they are not considered in this study.
2. Convex Hull: The convex Hull H(ρ') of a TF binarysegmented image subset ρ'[n, k] is defined as the smallest convex set containing a given subset [87]. The only parameter required in our study is the area of the convex Hull (i.e. the number of pixels, d(H(ρ')))). This can be calculated using the central moments of order (0, 0) defined in Equation 19.
In summary, the following five morphometric features based on the geometric shape of the segmented TF regions are considered in this study as a supplement to the signalbased features listed in the previous subsection:

Convex Hull/area: F_{I 1}= μ_{00}

Perimeter: F_{I 2}= (m_{30} + m_{12})^{2} + (m_{03} + m_{21})^{2}

Compactness^{b}: F_{I 3}= (F_{I 2})^{2}/F_{I 1}

Coordinates of the centroid for the segmented region: ${F}_{I4}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{{m}_{10}}{{m}_{00}}$ and ${F}_{I5}\phantom{\rule{0.3em}{0ex}}=\phantom{\rule{0.3em}{0ex}}\frac{{m}_{01}}{{m}_{00}}$
The total number of features for this study was therefore 15 for the adult EEG data ({F_{S 1}, F_{S 2}, ..., F_{S 10}, F_{I 1}, ..., F_{I 5}}) and 13 for the newborn EEG data ({F_{S 1}, F_{S 2}, ..., F_{S 8}, F_{I 1}, ..., F_{I 5}}). These features include both signal related features and image related features.
4.2 Classification
During the classification step, an EEG signal is allocated a certain class based on the location of its feature vector. The TF features extracted from EEG signals, and described above, are used to train a classifier. We opted for the classification algorithms which were recently used to classify the EEG signals in the TF domain in [14–16, 88]. In particular, two classification algorithms are used in this study:
These methods extract features from the TFD of the EEG signals under analysis and then feed them to the classifier as a vector. These classification methods are then compared by evaluating their performance in classifying the EEG signals using the selected TF features defined in the previous section, in terms of sensitivity, specificity and classification accuracy.
4.3 Localization
The classification step consists in detecting the presence or absence of the abnormality in the EEG signals and allocating it to a particular class. For the problem of localizing the spatial location of an abnormality on the scalp, it requires the simultaneous analysis of the multiple EEG signals which are provided from the different electrodes placed on the scalp.
 (1)
For each electrode, the EEG signal is transformed to reveal its TF representation, described by its TFD, using the MBD.
 (2)
Construct the 3D array data in the spaceTF domain by stacking all the 2D TF arrays corresponding to the TFDs (by concatenation of all 2D data). The stacking order corresponds to the location of electrodes on the scalp. This assigns the space direction in the spaceTF domain.
 (3)
Calculate the correlation between the 2D TF arrays in order to detect the presence of an abnormality. The correlation can be calculated between the 2D TF arrays or the TF features extracted from these 2D TF arrays [31].
 (4)
Determine the electrodes for which an abnormality is detected in their corresponding 2D TF arrays.
We assume here that the problem of EEG synchronization does not arise or has been already dealt with in a preprocessing stage. Furthermore, fusion approaches can be applied on the 3D array data for detecting and localizing the abnormalities [89–91].
5 Experimental results and discussion
This section assesses the performance of the EEG abnormalities detection and localization schemes using the TF image processing techniques, defined in the previous section in terms of features selection and extraction, localization and classification.
5.1 Adult EEG seizure detection and classification
The performance of TF features, defined in Section 4.1, for EEG abnormalities detection and classification (Figure 4(a)) is first assessed in the case of real adult EEG data.
The data set used is described in [85]; it consists of five sets denoted as A, B, C, D and E. Each set contains 100 single channel EEG segments with duration of 23.6 s. The signals have been recorded at f_{ s } = 173.6 Hz sampling rate and therefore each EEG signal in each set has f_{ s } × 23.6 = 4, 096 samples. Sets A and B have been taken from surface EEG recordings of five healthy volunteers with eye open and closed, respectively. EEG signals in sets C and D, respectively, were acquired in seizurefree intervals from five epileptic patients in the epileptogenic zone and from the hippocampal formation of the opposite hemisphere of the brain. Set E contains seizure activity intervals in the EEG signals acquired from the five patients. More details about the dataset can be found in [84]. Here, for practical reasons, the desired outcome of the classification stage is one of three different classes of the EEG signals in the dataset, namely: classes H, F, and S defined as follows:

Class H includes sets A and B which contain the EEG signals acquired from H ealthy volunteers.

Class F includes sets C and D which contain seizureF ree intervals acquired from epileptic volunteers, and

Class S includes set E which contains S eizureactivity intervals of the EEG signals collected from epileptic patients.
The TF feature set {F_{S 1}, F_{S 2}, ..., F_{S 10}, F_{I 1}, ..., F_{I 5}} was extracted from the TFD of each EEG segment of length T seconds. Only five TFDs are chosen in this simulation: MBD, SPEC, SWVD, GKD and WVD. The parameters of the MBD and GKD were respectively chosen as β = 0.01 and σ = 0.9. These values are typical ones for which the MBD and GKD have shown good performances in analyzing EEG signals [[6], Sections 7.4 and 15.5] and [15, 16]. The window w[n] for SWVD and SPEC distributions, was chosen to be a Hanning window of length ⌊N/ 4⌋ samples. Note that we did not optimize the window functions and lengths used for SPEC and SWVD. The simulations were carried out in Matlab.
For performance evaluation, the multiclass SVM and neural network classifiers were trained using the TF features extracted from the EEG signals. We compared the results of the classification for different TFDs. The original database was split in two parts, 60% of the data were used for training and 40% for testing the classifier.
Confusion matrices for EEG classification using TF features set {F_{S 1}, F_{S 2}, ..., F_{S 10}, F_{I 1}, ..., F_{I 5}} extracted from the TFD with SVM classifier and Neural Networkbased classifier.
Classifier outputs  Statistical parameters (%)  

TFD  Class  Total  H  F  S  Sensitivity  Specificity  Total accuracy 
Multiclass SVM classifier  
H  100  98 (98)  1 (2)  1 (0)  98 (98)  92 (90.5)  
MBD  F  100  6 (5)  89 (92)  5 (3)  89 (92)  96.5 (93.5)  94 (93) 
S  100  1 (2)  4 (9)  95 (89)  95 (89)  93.5 (95)  
H  100  97 (99)  1 (1)  1 (0)  97 (99)  93 (91)  
SPEC  F  100  5 (5)  89 (92)  6 (3)  89 (92)  97 (94.5)  94.33 (93.67) 
S  100  1 (2)  2 (7)  97 (90)  97 (90)  93 (95.5)  
H  100  99 (98)  0 (2)  1 (0)  99 (98)  93.5 (90)  
SWVD  F  100  3 (5)  93 (91)  4 (4)  93 (91)  96.5 (93.5)  95.33 (92.67) 
S  100  0 (2)  4 (9)  94 (89)  94 (89)  96 (94.5)  
H  100  99 (98)  1 (2)  0 (0)  99 (98)  90.5 (89.5)  
GKD  F  100  5 (5)  88 (91)  7 (4)  88 (91)  96 (93)  93.33 (92.33) 
S  100  1 (2)  5 (9)  93 (88)  93 (88)  93.5 (94.5)  
H  100  98 (98)  1 (2)  1 (0)  98 (98)  91.5 (90)  
WVD  F  100  4 (8)  89 (87)  7 (5)  89 (87)  96.5 (95.5)  93.67 (92.67) 
S  100  2 (2)  3 (4)  94 (93)  94 (93)  93.5 (92.5)  
Neural networkbased classifier  
H  100  99 (89)  1 (11)  0 (0)  99 (89)  90.5 (93.5)  
MBD  F  100  5 (8)  89 (88)  6 (0)  89 (88)  95.5 (94)  93.33 (92) 
S  100  0 (0)  8 (1)  92 (99)  92 (99)  94 (88.5)  
H  100  99 (91)  1 (7)  0 (0)  99 (91)  94 (95.5)  
SPEC  F  100  2 (4)  93 (92)  5 (4)  93 (92)  97 (95)  95.67 (94) 
S  100  2 (0)  3 (1)  95 (99)  95 (99)  96 (91.5)  
H  100  98 (94)  0 (4)  0 (0)  98 (94)  93 (92)  
SWVD  F  100  7 (7)  88 (89)  5 (4)  88 (89)  98 (94.5)  94.67 (92.67) 
S  100  0 (0)  2 (5)  98 (95)  98 (95)  93 (91.5)  
H  100  99 (98)  1 (0)  0 (0)  99 (98)  92 (91)  
GKD  F  100  4 (8)  88 (85)  8 (7)  88 (85)  97.5 (97.5)  94.33 (93.33) 
S  100  1 (0)  3 (3)  96 (97)  96 (97)  93.5 (91.5)  
H  100  98 (91)  2 (7)  0 (0)  98 (91)  91.5 (92.5)  
WVD  F  100  6 (7)  88 (88)  6 (5)  88 (88)  96.5 (94)  93.67 (92) 
S  100  0 (0)  4 (3)  95 (97)  95 (97)  93 (89.5) 
where TP, TN, FN, and FP, respectively represent the numbers of true positive, true negative, false negative, and false positive.
We notice that the classification results using the feature vector {F_{S 1}, F_{S 2}, ..., F_{S 10}, F_{I 1}, ..., F_{I 5}} with the SVMbased classifier, are better compared with the results using the feature vector {F_{S 1}, F_{S 2}, ..., F_{S 10}}. This is confirmed by the total classification accuracy calculated for each TFD. Also, the results given by the neural networkbased classifier are comparable with the SVM classification results. We note that the neural networkbased classifier is sensitive to overtraining compared to the SVMbased classifier. The total classification accuracy is in the range (93% to 95%) for 100 EEG segments. This can be improved by increasing the number of EEG segments in the trainingstep. These results indicate that TF features based on image processing techniques are also able to characterize nonstationary EEG signals, and therefore, detect the different abnormalities with accuracy.
5.2 Newborn EEG seizure detection and classification
The performance of the proposed TF features in newborn EEG seizure detection and classification (Figure 4(a)) is now assessed using real newborn EEG data.
The newborn EEG data used here includes 14channel EEG recordings of nine neonates using monopolar montages. The recordings have been marked by a neurologist for seizures. The EEG signals have been bandpass filtered in [0.750] Hz band with a notch filter at 50 Hz and collected at 256 Hz sampling frequency. From the dataset, we extracted two sets of seizure and nonseizure EEG epochs referred to as sets S and N respectively. Each set contains 50 segments. Each segment was bandpass filtered in the range 0.510 Hz and downsampled from 256 to 20 Hz. This was based on findings that neonatal EEG seizures have spectral activities mostly below 12 Hz [92]. The length of each segment is 12.8 s with 256 samples.
Confusion matrices for newborn EEG classification using TF features set {F_{S 1}, F_{S 2}, ..., F_{S 8}, F_{I 1}, ..., F_{I 5}} extracted from the TFD
Classifier outputs  Statistical parameters (%)  

TFD  Class  Total  N  S  Sensitivity  Specificity  Total accuracy 
N  50  48 (47)  2 (3)  96 (94)  96 (86)  96 (90)  
MBD  S  50  2 (7)  48 (43)  96 (86)  96 (94)  
N  50  49 (45)  1 (5)  98 (90)  96 (84)  97 (87)  
SPEC  S  50  2 (8)  48 (42)  96 (84)  98 (90)  
N  50  46 (45)  4 (5)  92 (90)  94 (84)  93 (87)  
SWVD  S  50  3 (8)  47 (42)  94 (84)  92 (90)  
N  50  43 (44)  7 (6)  86 (88)  72 (77)  79 (79)  
GKD  S  50  14 (15)  36 (35)  72 (70)  86 (88)  
N  50  46 (33)  4 (17)  92 (66)  92 (86)  90 (76)  
WVD  S  50  6 (7)  44 (43)  88 (86)  88 (66) 
5.3 Seizure localization in newborn EEG signals
The performance of TF features in EEG abnormalities detection and localization (Figure 4(b)) is now assessed using real multichannel EEG signals recorded from newborns.
For the problem of seizure detection and localization, we assume that the seizure was detected in the newborn EEG signal of channel A and then we want to locate other channels where the EEG signal have the same seizure pattern. We applied the methodology described in Section 4.3 which consists of calculating the correlation between signals in the TF domain.
Correlation between channel A and channels B, C, D and E in the spatial domain and TF domain
Channel A  

Time domain  TF domain  
Channel B  0.0562  0.2166 
Channel C  0.0105  0.1157 
Channel D  0.2527  0.5945 
Channel E  0.0502  0.6113 
Mahalanobis distance between channel A and channels B, C, D and E in the TF domain using the feature vector
Channel A  

8 TF features  13 TF features  
Channel B  0.5084  0.3883 
Channel C  0.5049  0.3856 
Channel D  0.1832  0.1399 
Channel E  0.1733  0.1324 
where $\overrightarrow{x}$ and $\overrightarrow{y}$ are two feature vectors and S is the covariance matrix.
The results show also that the correlation is better detected using the complete 15 TF features vector. This suggests that features based on TF image processing techniques are able to help characterize better the abnormality patterns in newborn EEG signals, and also to detect and locate them on the scalp.
6 Conclusion and perspectives
This article concludes that it is possible to improve the analysis, classification and localization of nonstationary signals by using a general methodology that combines techniques from three complementary fields: TF signal analysis, image processing and multichannel analysis. The approach is illustrated on the key problem of newborn EEG abnormality diagnostic and localization for which a solution would help improve health outcomes for newborns. In addition, the same problem was also considered for the case of adults. More precisely, this article reports the results of a study on EEG abnormalities detection and classification in the case of monochannel TF analysis first, and then, the EEG abnormalities sources localization in the case of multichannel TF analysis. The novelties of this article include: (1) a comprehensive review of TF image processing techniques, and (2) a review of methods to process the TF representation of EEG signal considered as an image, as well as (3) a novel approach that combines the above with TF features to characterize the nonstationary newborn EEG signal, following [14–16].
In particular, this article has adapted and applied a segmentation algorithm on the TF image in order to detect the region where all EEG information appear. Then, new morphometric features were defined based on the geometric shape of the segmented region. Two TF image classification methods were selected to evaluate the performance of the EEG classification using the selected TF features. These TF features were also able to evaluate the correlation between newborn EEG signals in order to locate the abnormality source on the scalp. The experimental results show that the features based on TF image processing techniques are able to improve the performance of both EEG classification and EEG abnormality source localization.
Finally, the above results allow further progress with a new focus on the adaptation of TF image processing techniques in order to better use them in the problem considered. This should result in a more advanced design for automatic abnormality diagnosis and localization systems based on a complete multichannel TF image processing approach. In addition, this work can be further refined by considering the TFD which is most appropriate for this TF image processing approach following the algorithmic perspective developed in [6, 94–96] and the selection criteria presented in [29].
Endnotes
^{a}Local polynomial approximationrelative intersection of confidence intervals (for more detail see [97]). ^{b}The compactness is the most popular shape feature, and is obtained by perimeter^{2}/area.
Declarations
Acknowledgements
The authors would like to thank Prof. Paul Colditz and his colleagues at the University of Queensland, Centre for Clinical Research, Brisbane, Australia for providing the Newborn data used in this article. This publication was made possible by a grant from the Qatar National Research Fund under its National Priorities Research Program award number NPRP 094652174.
Authors’ Affiliations
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