A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks
- Saman Sargolzaei^{1}Email author,
- Mercedes Cabrerizo^{1},
- Arman Sargolzaei^{1},
- Shirin Noei^{2},
- Anas Salah Eddin^{3},
- Hoda Rajaei^{1},
- Alberto Pinzon-Ardila^{4},
- Sergio M Gonzalez-Arias^{4},
- Prasanna Jayakar^{5} and
- Malek Adjouadi^{1}
https://doi.org/10.1186/1471-2105-16-S7-S9
© Sargolzaei et al.; licensee BioMed Central Ltd. 2015
Published: 23 April 2015
Abstract
Background
The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.
Methods
A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group.
Results
The study results show the existence of a statistically significant difference (p < 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis.
Conclusions
The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed.
Background
Epilepsy is a neurological disorder characterized by recurrent seizures with unspecified causes. The Center for Disease Control and Prevention (CDC) estimates that more than 2.3 million adults and half a million children in the United States are affected by Epilepsy [1]. This number is projected to dramatically increase every year by about 0.15 million newly diagnosed epilepsy cases [2]. Although the impact of seizures varies from person to person, physical and mental functions of the affected person could be severely altered. A systematic approach for epilepsy diagnosis could improve the planning for a treatment process and thus relieve the burden already imposed on the overall healthcare system. Scalp Electroencephalography (EEG) recording at resting state condition has been widely perceived as an effective preliminary tool for non-invasive study of the brain in individuals with epilepsy. Analysis of Scalp EEG during resting state condition, without performing a cognitive task and with the absence of external stimuli, has gained significant prominence for assessing brain function and related disorders. Applications include tasks that require assessing responses of the brain under the influence of different drug therapies [3], and tasks that rely on determining the 3D source localization of epileptic seizures which exploits techniques in the time/frequency domains for analysis of individual EEG electrode recordings [4, 5]. Assessment of brain functional connectivity network in patients suffering with various neurological disorders through modalities such as EEG recording, Magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) has elicited new findings in ways of underlying distinctions that delineate epileptic from control populations [6–14]. The high temporal resolution of EEG renders it as an indispensable tool in the primary diagnosis of epilepsy and in visualization of characteristic temporal events like interictal spikes which are closely associated with epileptic foci [7, 15]. Additionally, EEG has been utilized to distinguish focal and generalized neurophysiologic correlates of epilepsy [16]. Diagnosis of epilepsy by the means of scalp EEG visual inspection often involves long term recording and investigation by the EEG expert to search for abnormal activities.
However, visual inspection and interpretation of continuous temporal EEG recordings is tedious, time consuming and prone to human error. Furthermore, epilepsy diagnosis based on visual inspection of EEG has been shown to be very subjective to the expert opinion [17]. This has led to the general cohort of adopting various computer aided techniques with the help of machine learning for medical applications [18–20]. Artificial Neural Network (ANN) based classifiers have received the most attention towards epilepsy diagnosis using scalp EEG recordings [21–23] where the accuracy rate of 0.92 [21] and 0.8 [22, 24] are reported which involved the existence of training set and a priori knowledge. The general routine of ANN based techniques is to process each isolated EEG signal with the aim of extracting a set of discriminating features as input to train an ANN in the design of an optimal classifier and predictor of the diagnosis. Considering the fact that Epilepsy is a complex disease with multifactorial causes, makes the diagnostic process much more complicated than simply relying on solely model driven knowledge. Furthermore, the human brain includes a complex web of neuronal interconnectivity and discrete anatomical regions that function together to generate brain activity [11, 25]. This underlying functional brain infrastructure suggests that a methodology for enhanced epilepsy diagnosis needs to consider the whole brain network as described by its patterns of functional connectivity networks (FCNs). Thus, FCNs seek to define the patterns of cross-correlation between discrete functionally characterized brain regions to give statistical importance to anatomical connectivity (upon the existence of physical connection) and subsequently determining inter-regional neurophysiological inferences. FCNs could be grouped into two broad categories: Directed and Undirected. Undirected FCNs finds the dynamic associations among functional brain regions without considering the hypothetical causalities among them. Whereas Directed FCNs, sometimes referred to as effective connectivity [26], assesses the influence of one cerebral region upon another and therefore gives direction to the calculated associations. Current trends in adopting FCNs for understanding the complex brain are placed toward developing data driven methodologies for constructing FCNs which benefits from a robust parcellation of functional data of the brain and an objective formulation of the hypothesized association among functional parcels [6, 26]. The crucial role of time delay in the dynamics of large scale networks [27] such as brain networks is well motivated, due to the large scale property of brain connectivity networks including discrete sub-networks [28], but yet not fully understood and incorporated in constructing the brain networks and decision making processes [29]. Time delay is coupled with the fact that on large scale networks such as brain networks, the recorded signal at each electrode is in fact showing the summation of the variance of the brain area closer to the electrode and a lagged version of variances from other brain areas.
Methods
Subjects and data
Demographic characteristics of study subjects
Age | Female/Male | Number of Segments | |||
---|---|---|---|---|---|
PC (n = 7) | 12.86 ± 3.39* | 3/4 | 13 ± 6.98 | ||
PE (n = 11) | 9.09 ± 4.81 | 5/6 | 32.18 ± 22.41 | ||
p** | ns | ns | < 0.05 | ||
Subject ID | Age | Gender | Diagnosis | Sampling Rate (Hz) | Number of Segments |
PC01 | 12 | M | - | 200 | 11 |
PC02 | 15 | F | - | 512 | 20 |
PC03 | 12 | M | - | 200 | 14 |
PC04 | 15 | F | - | 512 | 18 |
PC05 | 10 | M | - | 512 | 3 |
PC06 | 18 | F | - | 512 | 20 |
PC07 | 8 | M | - | 512 | 5 |
PE01 | 10 | F | Left temporal dysplasia | 200 | 14 |
PE02 | 7 | F | Left frontal region | 512 | 67 |
PE03 | 4 | F | Right fronto-centro-temporal | 512 | 39 |
PE04 | 14 | M | Generalized | 512 | 18 |
PE05 | 8 | M | Right parietal | 200 | 30 |
PE06 | 7 | M | Left frontal pole, posterior frontal lobe | 512 | 77 |
PE07 | 15 | F | Left and right frontal | 500 | 40 |
PE08 | 4 | M | Right fronto-centro-temporal | 512 | 25 |
PE09 | 2 | F | Left temporal (posterior) | 512 | 25 |
PE10 | 14 | M | Generalized | 512 | 6 |
PE11 | 15 | M | Generalized | 512 | 13 |
Functional connectivity networks construction
Connectivity strength, which establishes a symmetric adjacency matrix. Following the symmetric property of undirected FCN, a geometric distance as shown in equation (2) is used to calculate the pairwise connectivity strength. The proposed geometric distance is a modification of the cosine similarity metric.
Each θ_{ ij }^{ w } value represents the pairwise connectivity strength between electrodes i and j for the corresponding window w with their electrical activity recordings denoted as x_{ i }[n] and x_{ j }[n], respectively. The value of n represents the discrete time sample number which ranges from 1 to N where N is the length of window w, and m is the number of electrodes considered for constructing the FCNs. The angular value of θ_{ ij }^{ w } of 0 radian specifies the highest connectivity strength which is most likely the case when calculating the distance among an electrode and itself, and a value of $\frac{\pi}{2}$ radian shows that the respective pair of electrode recordings is orthogonal and corresponds to the maximum distance (i.e., lowest connectivity strength).
Graph mapping of brain networks and feature extraction
Graph theoretical features of functional connectivity networks
Feature | Feature description | Feature calculation |
---|---|---|
ldg | Link density of the graph | (2 × ne)/(nn × (nn - 1)) |
acc | Average of closeness centrality | (1/nn) ∑_{ nn }(sum of reciprocal distances from a node to all other nodes) |
gcc | Graph clustering coefficient | (3 × number of triangles)/(number of connected triples of nodes) |
rcc | Rich club coefficient | (ne_k)/(nn_k × (nn_{ k } - 1)) |
smg | S-metric of graph | Sum of the nodal degree products for every edge. |
acg | Algebraic connectivity of graph | Second smallest eigenvalue of the Laplacian of adjacency matrix. |
eng | Energy of network graph | Sum of absolute values of the real components of eigenvalues of adjacency matrix. |
Decision making process
Gaussian Mixture Model (GMM) for EEG Segments Labeling
Where ω_{ i } elements are the mixture weights, and g(x|μ_{ i }, Σ_{ i }) represents the Gaussian densities calculated from D-variate Gaussian distribution with parameters μ_{ i } and Σ_{ i } (i = 1,2) as their respective means and covariance matrices [41–43]. Parameters of the model were estimated through maximum likelihood (ML) estimation method and EEG segments are then labelled based on their closeness to the either estimated means.
Probabilistic approach in the decision making process
Where E_{ s } is the number of segments from the given subject being labeled as epileptic out of total number of segments D_{ s } for the corresponding subject.
Results and discussion
Statistical analysis of features across PC and PE
Feature | PC | PE | p |
---|---|---|---|
ldg | 44.56 ± 7.77** | 57.74 ± 8.44 | < 0.000 |
acc | 0.0014 ± 0.0003 | 0.0010 ± 0.0003 | < 0.000 |
gcc | 1.18 ± 0..006 | 1.18 ± 0.005 | ns |
rcc | 44.56 ± 7.77 | 57.74 ± 8.44 | < 0.000 |
0smg | 2.38 ± 0.81 | 3.98 ± 1.04 | < 0.000 |
acg | 659.76 ± 135.45 | 913.24 ± 169.46 | < 0.000 |
eng | 1.68 ± 0.26 | 2.11 ± 0.28 | < 0.000 |
Connectivity strength for left hemisphere, right hemisphere and inter-hemispheres
Hemisphere Connection | Hemisphere Connection | ||||||
---|---|---|---|---|---|---|---|
Right | Left | Inter-hemisphere | Right | Left | Inter-hemisphere | ||
PC01 | 10.82 ± 2.6 | 9.09 ± 4.23 | 18 ± 11.4 | PE01 | 10.64 ± 4.41 | 3.42 ± 2.79 | 10 ± 7.1 |
PC02 | 19.3 ± 4.12 | 19.55 ± 4.16 | 34.1 ± 12.45 | PE02 | 9 ± 3.39 | 7.75 ± 2.3 | 9.75 ± 5.98 |
PC03 | 20 ± 1.62 | 15.71 ± 1.14 | 33.42 ± 5.62 | PE03 | 11.87 ± 4.93 | 16.28 ± 6.1 | 26.13 ± 15.4 |
PC04 | 16 ± 5.72 | 15.5 ± 4.74 | 27 ± 18.82 | PE04 | 15.28 ± 4.84 | 13.11 ± 7.5 | 22.16 ± 17.5 |
PC05 | 24.67 ± 1.5 | 15 ± 1.73 | 33.33 ± 16 | PE05 | 9.6 ± 6.5 | 11.5 ± 5.3 | 14 ± 16.2 |
PC06 | 18.7 ± 2.56 | 15.2 ± 3.67 | 35.75 ± 7.57 | PE06 | 9.7 ± 1.38 | 8.5 ± 1.7 | 19.25 ± 4.78 |
PC07 | 13.8 ± 3.96 | 12.8 ± 3.96 | 27 ± 11.62 | PE07 | 9.85 ± 1.72 | 11.97 ± 4.4 | 17.15 ± 9.1 |
Pooled statistics | 17.4 ± 3.45 | 15.42 ± 3.64 | 30.59 ± 11.5 | PE08 | 6.28 ± 2.82 | 7.7 ± 5.73 | 9.16 ± 11.43 |
PE09 | 14.36 ± 3.36 | 5.1 ± 1.1 | 4.64 ± 1.87 | ||||
PE10 | 5.6 ± 3 | 7 ± 4.7 | 11.17 ± 7.3 | ||||
PE11 | 14.15 ± 1.72 | 15.38 ± 5 | 34 ± 7.85 | ||||
Pooled statistics | 10.32 ± 3.19 | 9.82 ± 3.64 | 15.97 ± 8.77 |
Clustering results with no prior knowledge provided on diagnosis
Condition | |||
---|---|---|---|
Healthy | Epileptic | ||
Clustered as Epilepsy | 0 | 9 | Positive predictive value (%) 100 |
Clustered as Healthy | 7 | 2 | Negative predictive value (%) 77.8 |
Sensitivity (%) 81.8 | Specificity (%) 100 | Accuracy (%) 88.9 | |
Subject ID | E _{ s } | D _{ s } | Probability (%) |
PC01 | 0 | 11 | 0 |
PC02 | 0 | 20 | 0 |
PC03 | 0 | 14 | 0 |
PC04 | 2 | 18 | 11 |
PC05 | 0 | 3 | 0 |
PC06 | 0 | 20 | 0 |
PC07 | 1 | 5 | 20 |
PE01 | 12 | 14 | 86 |
PE02 | 67 | 67 | 100 |
PE03 | 10 | 39 | 25 |
PE04 | 12 | 18 | 66 |
PE05 | 21 | 30 | 70 |
PE06 | 66 | 77 | 86 |
PE07 | 30 | 40 | 75 |
PE08 | 23 | 25 | 92 |
PE09 | 25 | 25 | 100 |
PE10 | 5 | 6 | 83 |
PE11 | 3 | 13 | 23 |
This mis-identification could be due to different factors such as the window size, number of segments required to accurately diagnose or the type of epilepsy which needs more investigation.
The assumption of the existence of no priori knowledge on the diagnosis in the clinical environment could be relieved by assuming the existence of symptoms and the minimal knowledge of a training set of multi-channel EEG segments which could be considered as a tuning approach in the decision making process. A training set was composed including twenty randomly chosen multi-channel EEG segments from the total set of EEG segments and Support Vector Machine (SVM) with a linear kernel were trained to classify the segments. The testing set was then given to the system after self tuning and the results showed 100% accuracy in classification accuracy of the multi-channel EEG segments.
Conclusions
A novel decision support system for computer aided diagnosis of pediatric epilepsy using machine learning techniques was presented. The approach taken in the system was based on constructing functional connectivity networks (FCNs) of the brain and analyzing graph theoretical based features to identify the wiring pattern differences among pediatric control (PC) and pediatric epilepsy (PE) groups. The system is designed to provide clinicians with initial screening results about the likelihood of a given subject to be epileptic or not. The time-varying FCNs implementation increases the resolution by segmenting the multichannel EEG. This created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed. The main contribution of the study is the reliance of the algorithm on machine learning techniques to facilitate the screening process of potential epileptic patients without need of a priori knowledge and without need for a training phase. The suggested window length in constructing FCNs, the number of principal components (dimension of GMM) to be used, and the inspection of possible causal relationships among cortical brain regions are areas that need further investigation on the basis of a larger dataset.
Declarations
Acknowledgements
This work is supported by the National Science Foundation under grants CNS-0959985, CNS-1042341, HRD-0833093, and IIP-1230661. The support of the Ware Foundation is greatly appreciated.
Declarations
The publication costs for this article were partially funded by the FIU Open Access Publishing Initiative.
This article has been published as part of BMC Bioinformatics Volume 16 Supplement 7, 2015: Selected articles from The 11th Annual Biotechnology and Bioinformatics Symposium (BIOT-2014): Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/16/S7.
Authors’ Affiliations
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