Machine Learning based Diagnosis of Binge Eating Disorder ... · Binge eating disorder is the most...

Machine Learning based Diagnosis of Binge Eating Disorder

Twenty-Third Pacific Asia Conference on Information Systems, Dubai, UAE, 2020 1

Machine Learning based Diagnosis of Binge Eating Disorder using EEG recordings

Completed Research Paper

Dominik Raab Aalen University

[email protected]

Hermann Baumgartl Aalen University

[email protected]

Ricardo Buettner Aalen University

[email protected]

Abstract

Binge eating disorder is the most common eating disorder and therefore an important health problem worldwide often resulting in obesity. Current investigations on binge eating disorder’s impact on the human brain regarding electroencephalography data are limited to traditional approaches. In this study we make use of a Machine Learning method both for distinguishing individuals affected by a BED and healthy individuals with an overall accuracy of 81.25% and highlighting low theta sub-band in the range of 4.5 – 6 Hz as the most important distinctive feature. Individuals with a BED show significantly higher theta activity. Using Machine learning approaches based on EEG data is a promising approach in order to facilitate disorder identification and to provide novel insights for health scientists.

Keywords: Eating disorder, Binge eating disorder, electroencephalography, EEG, Machine Learning, Random Forests

Introduction

According to the National Eating Disorder Association (NEDA), up to 30 million people in the USA suffer from an eating disorder (ED) in the course of their lives, whereby it’s a mostly undetected issue in peoples’ mental health state (Fursland et al. 2014). EDs are characterized by atypical behavior towards food and unusual eating habits (American Psychiatric Association 2013) and are associated with increased mortality (Berkman et al. 2007). The first modern theory of EDs was proposed by Bruch (1962) containing reasons leading to the development of such unusual eating habits. Decisive factors are a perceptual disturbance of their own body image, an incapacity to interact with cues for hunger and low self-esteem. Cognitive-behavioral models are used to describe these interactions between behavior patterns, emotions and cognitions of EDs resulting in a conceptual framework to treat them. According to the American Psychiatric Association, the three major types of EDs are binge eating disorder (BED), anorexia nervosa (AN) and bulimia nervosa (BN). Among EDs, BED is the most common one - affecting more individuals than AN and BN combined - and consequently an important public health problem worldwide. Data gathered from the World Health Organization World Mental Survey Study 2008, which surveyed adults from 14 countries on 4 continents, discover a lifetime prevalence rate of BED to be 1.4% (Kessler et al. 2013). BED is characterized by repetitive episodes of eating abnormally large quantities of food within a discrete time interval proceeding no compensatory behaviors of eating, and a loss of control over eating during these episodes (American Psychiatric Association 2013).

Preprint of Raab, D.; Baumgartl, H.; Buettner, R.: Machine Learning based Diagnosis of Binge Eating Disorder using EEG recordings. In PACIS 2020 Proceedings: 24th Pacific Asia Conference on Information Systems, June 20-24, 2020, in Press. Copyright by AIS



In the diagnosis of a BED the so called ‘disinhibition’ of individuals’ eating behavior has been proven as an incisive characteristic. Disinhibition reflects a tendency towards overeating and eating opportunistically triggered by negative emotional states, inability to resist food cues, and good-tasting food (Stunkard et al. 1985) and is also regarded to be a behavioral indicator of a loss of control over eating resulting in consuming larger amounts of food (Stunkard et al. 1985; Forney et al. 2016). Individuals possessing higher disinhibition and hunger are related to a BED diagnosis (Vinai et al. 2015). Food craving as one of the leading characteristics of a BED have also been reported by those with a high disinhibition (van Gucht et al. 2014), where the craving subsequently leads to binge eating and obesity. One of the long-term negative impacts of a BED is obesity, which is regarded as one of the world’s major health problems (Dixon 2010; Stevens et al. 2009) and is associated with reduced expectancy of life (Orpana et al. 2010). In turn, the most frequent ED among obese patients is BED (American Psychiatric Association 2013).

Furthermore, binge eating is associated with certain forms of negative emotional feelings. According to the escape theory proposed by Heatherton and Baumeister (1991), individuals in a negative mood subconsciously regard binge eating as a means to cope with negative emotions or even to try to avoid them (Burton et al. 2017). By analyzing the occurring negative emotions in a BED, it has shown that a depressive mood is the most frequent one (Nicholls et al. 2016). Several studies suggest that a severe depression is related to a pronounced BED (Antony et al. 1994; Telch et al. 1996; Dingemans et al. 2015). Although the neural basis of specific emotions is poorly understood (Kragel and LaBar 2016), researchers related both positive and negative emotions with an amygdala activation by means of fMRI (Lindquist et al. 2012). In this respect, a BED can be proven indirectly via considering the impacts of negative emotions like depression on the human brain.

Non-detection of diseases consequently leads to non-treatment. Considering the annual expenditure of an untreated ED alone in Germany for BN of 617.69 million EUR and for AN of 2.38 billion EUR (Bode et al. 2017), emphasizes the importance of the EDs’ financial perspective in addition to the physiological and psychological impacts just mentioned. EDs are generally medicated with psychological therapies. But the complexity and expenditure needed to treat them (Simon et al. 2005) result in low treatment rates, which highlights the clinical importance of more frequent and intensified questioning of patients with specific questionnaires concerning their eating patterns (Kessler et al. 2013). While a questionnaire is a subjective measurement based on the analysis of subjective feedback provided by subjects, physiological measurements depend on the analysis of physiological indicators and reactions of the human body. In particular, the neuro-feedback of electroencephalography (EEG) can be supportive in the treatment of several EDs (Stunkard et al. 1985). EEG is the second most prevalent instrument in NeuroIS research and is widely applied in medicine (Riedl et al. 2017). EEG records the neuronal activity of the brain in order to determine its functional state. Gathering vast amounts of aperiodic, times series data from multi-channel EEG, enables experts to evaluate disorders and effects in the brain.

These interactions between BEDs, other serious mental disorders like depression, and the brain activity mentioned above justifies the relevance of using a more novel method by analyzing patients’ EEG data in order to make a statement about a potentially present BED, instead of only questioning patients about their eating habits. Lowe et al. (2019) highlighted the possibility of predicting future individuals’ states of health and behavior patterns associated by using neuroscientific data. IT-based healthcare has undergone a dramatic upswing in the past years, due to the combination of increased computational power and the availability of huge new datasets (Tsoi et al. 2018). This has led to the fact that the usage of Machine learning (ML) in healthcare is an emerging application field to predict disorders of any kind like the detection of alcoholism (Rieg et al. 2019), detection of heart disease (Buettner and Schunter 2019), detection of schizophrenia (Buettner et al. 2019, 2020), detection of sleeping disorders (Buettner et al. 2020a; Breitenbach et al. 2020), detection of epilepsy (Buettner et al. 2019a; Rieg et al. 2020), detection of internet addiction (Gross et al. 2020) and detection of stress (Baumgartl et al. 2020). At present, no studies based on EEG data have used ML approaches both to detect BEDs and to identify characteristic frequency bands and brain areas of a BED. For this reason, this study was aimed at deploying a validated supervised ML method both to classify participants affected by a BED and to define reliable EEG frequency bands useful to distinguish individuals with diagnosis of a BED from


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non-BED individuals. While the Random Forest (RF) approach was used to perform classification, we make use of the variable importance function in order to extract the most informative EEG features.

The most important contributions of this paper are:

• We developed an automatic classification method for an accurate and reliable classification of BED and non-BED individuals with an overall accuracy of 81.25 percent.

• With the help of RFs’ variable importance function, we provided a novel insight by pointing out that the low theta frequency bandwidth from 4.5 Hz to 6 Hz is highly relevant for distinguishing BED individuals from non-BED individuals.

• Individuals with a BED have a significantly higher activity in each of the three fine frequency bands according to the low theta sub-band.

The paper is organized as follows: First we provide an overview of related work. Next we detail the research methodology, including the ML method used, the RF, information about the dataset and the steps to preprocess the EEG data. After that we present the ML results concerning the performance indicators and the most important variables extracted by performing the variable importance function. Afterwards we discuss the results. Finally, we conclude including limitations and ideas for future research.

Related Work

A key feature of many mental health problems, beneath BED, is failure of self-regulation (Heatherton and Wagner 2011). Self-regulation is primarily conditional on the capacity of the Prefrontal Cortex (PFC) both performing control over food choices (Lowe et al. 2019) and functioning as one of the main neuroanatomical regions involved in cognitive control (Miller et al. 2001). The positioning of the PFC as the most anterior part of the frontal lobes and the functional characteristics of it correspond to the following key studies on the topics of EEG joined with BED. Obese women with a BED have shown greater frontal beta activity (14–20 Hz) with closed eyes than obese women without a BED (Tammela et al. 2010). Furthermore, an increase of lagged phase synchronization in the beta frequency band among the cortical areas, with which the PFC is highly interconnected, was also confirmed (Imperatori et al. 2015). The implications of a BED also affected subjects´ beta band (14–30 Hz) in the central regions of the brain (Blume et al. 2019). In non-EEG investigations, frontal regions of the brain were also pointed out as an area affected by a BED (Karhunen et al. 2000; Geliebter et al. 2006; Schienle et al. 2008).

While in our work EEG recordings are used to perform ML based diagnosis of BED, further instruments of NeuroIS research came into operation in other studies addressing the same issue. Cerasa et al. (2015) achieved an accuracy of 80.0% of classifying individuals with AN and BN utilizing structutal magnetic resonance images. Thereby, Support Vector Machine (SVM) technique was used for classification. Linardon et al. (2020) built a Decision Tree classifier only based on the subjective feedback provided by subjects in form of several questionnaires without any physiological measurements. They achieved an overall accuracy of 70% for classifying recurrent binge eating.

Methodology

This part of the paper covers the method used to obtain the upcoming results. The data preparation for the ML algorithm, the training of the model, and the label prediction were executed using R 3.6.1. The most important R packages used during the implementation were eegUtils, eegkit and caret.

Machine Learning Method

Classification

Taking this preprocessed data as input variables, we used the RF to classify whether a participant has a BED or not, based on their individual score of the factor 2 ‘disinhibition’ provided within the dataset together with their brain activity. The RF was introduced by Breiman (2001) and is a collection of




unpruned, binary decision trees. It can be used both for classification and regression tasks and can handle many input variables. This ML classifier is an ensemble method, which is often applied due to the conviction that an aggregated decision from various systems is often superior to a decision from a single system. Here, the final classification of the RF is built on the majority vote of the several decision trees. We used the RF from the R package ‘caret’.

Validation

A reliable result from the RF can be achieved by using k-fold cross-validation, which is a robust method for estimating the accuracy of a model. K-fold cross-validation divides the whole dataset into a user-specified number of k equal subsets (folds). One subset is chosen for testing the model and estimating the prediction error, while the remaining subsets are used for training. This process is repeated k times until each of the subsets has served once for testing. The average of the recorded errors of all rounds is computed (Kohavi 1995). For this work, we set k to 10.

Dataset

We used an EEG dataset consisting of 203 healthy participants in total including their psychological assessment through their completion of either 1 of 6 cognitive tests or 1 of 21 questionnaires covering several mental behaviors like eating behavior. 202 participants completed the procedure of the German version of TFEQ, called ‘Fragebogen zum Essverhalten’ (FEV), which was published by Pudel and Westenhöfer (1989). The Three-Factor Eating Questionnaire (TFEQ) can be used to address the lack of information about individuals’ eating habits. The TFEQ (Stunkard et al. 1985) is a widely used method for measuring three dimensions of human eating behavior. Containing 51 questions, the questionnaire indicates the following factors: cognitive restraint of eating (1), disinhibition (2) and hunger (3). Restraint refers to an individual’s willingness to address weight control and strategies needed to maintain body weight resulting in restricting eating. Therefore, supervising actions like eating small portions, avoiding fatty dishes and sweet desserts, and stopping eating before entering the sense of satiation are taken. The overall goal is to limit and control intake of food. Disinhibition reflects a tendency towards overeating and eating opportunistically triggered by negative emotional states, inability to resist food cues, and good-tasting food. Hunger is associated with the dimension of perceived hunger feelings and their impact on subsequent food intake (Stunkard et al. 1985). The calculation of these factors results from the quantification of each questions with one point and the respective allocation of them to the factors. 21 questions are assigned to factor 1, 16 questions to factor 2, and factor 3 is determined by the remaining 14 questions. According to the distribution of the questions, for factor 1 the minimum score is ‘0’ and the maximum score is ‘21’. The same condition applies for the other two factors.

The dataset itself is provided by Max Planck Institute Leipzig and is publicly available. The raw EEG data was acquired with a sampling frequency of 2,500 Hz, subsequently down sampled to 250 Hz by us, and the electrodes were placed according to the international standard 10–20 extended localization system, also known as the 10-10 system and referenced to FCz. Sixteen minutes of EEG were recorded per participant. The EEG session comprised 16 blocks, each 60 seconds long, 8 with eyes-closed (EC) and 8 with eyes-open (EO), where the recording started with the eyes-closed (Babayan et al. 2019).

Due to the lack of a clear diagnosis within the provided dataset as to whether a participant is affected by a BED or not, the numerical values factor 2 ‘disinhibition’ of the TFEQ function as indicators of a BED justified by the detailed line of arguments within our introduction. We will provide more details on how we separate the participants into healthy and unhealthy patients in the results section.

Data Preprocessing

Independent Component Analysis

A common issue of EEG data is their noise generated during data gathering. Muscle activities, eye movement, blinking and heartbeat are typical forms of noises, which should be removed before inputting to the ML model. Besides these so-called noncortical biological artifacts, environmental




noises like line noises, radio or electrical interference occur during recording (Fitzgibbon et al. 2007). These different manifestations of noises aren’t completely avoidable leading to the relevance of a conscientious preprocessing of the EEG data. Therefore, we applied the Independent Component Analysis (ICA) algorithm, which was introduced by neuroscientists Bell and Sejnowski (1995) to correct raw EEG data with that linear decomposition approach. In this paper we used the R package ‘eegUtils’, which is designed for an automatically executed ICA. We set the integrated band-pass filter from 0.5 to 50 Hz.

Spectral Analysis and Feature Extraction

As mentioned in the introduction, enormous amounts of aperiodic, time series data arises while recording brain activity with EEG. Before taking the EEG signals as input for the classifier after noise removal by ICA, it must be transformed into a frequency signal (Delorme and Makeig 2004). This goal is accomplished by EEG spectral analysis, which is a frequently used approach in the neurosciences. Spectral analysis is conducted by means of a Fast Fourier Transform. Here, the EEG signals are first subdivided into sinusoidal oscillations with a known wavelength. By convolution analysis, checking each wavelength for accordance with the EEG signal is now achievable. The power spectrum, as a result of the Fast Fourier Transformation, allows the distribution of the frequencies of the EEG signal to be estimated (van Vugt et al. 2007; Ahirwal and Londhe 2012). The power spectrum is traditionally divided into the five frequency bands: alpha (7.5 – 12.5 Hz), beta (12.5 – 30 Hz), theta (3.5 – 7.5 Hz), delta (0.5 – 3.5 Hz) and gamma (> 30 Hz). For our approach we take over the hypothesis by Rieg et al. (2019), that the information content of the traditional division into larger frequency bandwidth could be lower than dividing the power spectrum into finer frequency bands. For that, as feature extraction criterion, we divide the EEG power spectrum into 99 fine frequency bands in the range from 0.5 Hz to 50 Hz and the power spectrum of each 0.5 Hz frequency band was calculated. This leads to the possibility of identifying single, highly important frequency bands.

Results

The TFEQ’s minimum score for factor 2 ‘disinhibition’ is ‘0’ and the maximum score is ‘16’. High and low extreme values of factor 2 ‘disinhibition’ function as target variables assigned to two class labels. We assume that the differences in respective EEG spectrums are more significant when comparing subjects with extremely weak and extremely pronounced disinhibition. Considering that assumption and the request of training our model with an almost balanced dataset, we assigned participants as not affected by a BED (class 0) if their disinhibition scores are in the range from 0 – 2 and participants as affected by a BED (class1) if their disinhibition scores are >= 8. Participants their disinhibition scores range between 3 and 7 were excluded. This leads to an almost uniform allocation of 36 non-BED participants and 27 BED participants within the used dataset. The whole dataset was divided with a train-test-ratio of 60/40. So, we trained the RF with 39 participants and tested with 24 participants. In order to optimize our classifier performance, we make use of a hyperparameter tuning and set ntree = 500 and mtry = 2. Both a frequency bandwidth of 0.5 Hz and a 10-fold cross-validation were considered. Table 1 shows the resulting performance of our model in the form of a confusion matrix. We achieved an overall accuracy of 81.25 percent (balanced accuracy 80.50 percent) with a standard deviation of 0.04. Other relevant performance indicators are shown in table 2.




Table 1. Confusion matrix

Reference

non-BED BED

Predicted non-BED 11.9 (49.58%) 2.4 (10.00%)

BED 2.1 (8.75%) 7.6 (31.67%)

Table 2. Performance indicators

Performance indicator Value Standard deviation

Overall accuracy 81.25 % 0.04

True positive rate 84.99 % 0.07

True negative rate 76.00 % 0.13

Positive predictive value 84.18 % 0.08

Negative predictive value 79.17 % 0.06

Balanced accuracy 80.50 % 0.05 Kappa 61.13 % 0.09

Through implementation of the variable importance function included in the library caret, it has been shown that the frequency bands from 4.5 Hz to 6 Hz have a much bigger impact for an accurate classification than the remaining ones. The spectral power density for the most important frequency bands detecting a BED is shown in figure 1. As can be seen very clearly, the spectral power density of BED participants is significantly higher compared to the spectral power density of non-BED participants.

Figure 1. Mean spectral power density

In table 3, further statistical evaluations of the results are shown. It contains the most important frequencies with the mean spectral power density of non-BED participants and BED participants and respective Cohen’s d and p-value.

Discussion

Since our test dataset is not quite balanced the prevalence score is at 58.33%, the baseline model performance using a majority rule dummy classifier would also be 58.33%. With our RF approach we achieved a total lift in overall accuracy of 22.92% over the random baseline. The RF is efficient in handling many input variables. Therefore, each of the 61 electrodes are divided into 99 fine frequency bands, resulting in a total of 6039 input variables. This approach allows us to provide the model with




the full variety of information recorded by EEG. We deliberately avoid a preselection of certain electrodes or frequency bands. Instead, using the variable importance function of the algorithm, the most important variables could be extracted. The EEG frequency bands in the range of 4.5 – 6 Hz are notably more important than the other ones, which seem to be almost meaningless for the decision-making of our model. The relevant frequency bands belong to the theta band, which extends from 3.5 – 7.5 Hz and is characterized in particular by dream phases.

Table 3. Spectral power, effect size and significance for non-BED vs. BED

fb (Hz) non-BED BED Cohen’s d p-value 4.5-5 118,127.0 183,704.8 0.673 <0.01

5-5.5 116,373.6 190,367.9 0.654 <0.01

5.5-6 119,415.2 199,487.9 0.544 <0.05

Comparing the spectral power density of these frequency bands between participants without a BED and participants with a BED, a significant difference can be identified. As visualized in figure 1, the spectral power density in each of the three fine frequency bands (4.5 – 5 Hz, 5 – 5.5 Hz, 5.5 – 6 Hz) is significantly higher among participants with a BED than among participants without a BED. Expressed in numbers, the spectral power density of participants with a BED is increased by 64.30 % (4.5 – 5 Hz), by 61.13% (5 – 5.5 Hz) and by 58.86% (5.5 – 6 Hz). Furthermore, as shown in table 3, all these differences are significantly expressed by the corresponding one-tailed p-values (p<0.01, p<0.01 and p<0.05) while the effect sizes are middle (Cohen’s d < 0.8). These aspects underline the importance of these frequency bands for the decision-making of the RF highlighted by the variable importance function.

Since anorexia nervosa individuals are associated with higher theta activity (Grunwald et al. 2001), at first sight it seems, according to the literature on binge eating in connection with EEG, that increased theta activity is an unspecific finding for binge eating. Although theta waves are particularly involved in daydreaming and sleep, there are some indications in the literature suggesting that there could be such an interaction. Beyond the relaxing states of theta waves, neural activity of theta oscillation was found to be significantly higher during impulsive decisions (Gui et al. 2018). Therefore, a novel neuroimaging technique with a high spatial and temporal resolution has been used: Electrocorticography. An individual with a distinct impulsivity has a very pronounced tendency to an exorbitant and unrestricted intake of food causing a big impact on developing and maintaining obesity (Davis 2009; Joseph et al. 2011). Several studies indicate that individuals with BED show higher impulsive characteristics (Fassino et al. 2003; Davis et al. 2004; Galanti et al. 2007; Davis et al. 2010; Mobbs et al. 2010). Apart from this, scores on factor 2 ‘disinhibition’ of the TFEQ are associated with impulsivity (Yeomans et al. 2008; Lyke and Spinella 2004). These indications suggest that even among individuals with a BED there is an interaction between the characteristics resulting from their BED – such as high values on disinhibition – and increased theta activity, as found and verified in our investigation.

Conclusion

To date no relevant studies exist that have addressed an automatic detection of a BED by means of ML approaches using EEG recordings. EDs in general are often undetected and untreated, which leads to a worsening of the individuals’ symptoms and higher costs for medicating individuals in advanced disorder stages. BED is the most common ED and therefore an important health problem worldwide resulting in obesity. For this reason, it is essential to make use of novel ML approaches using EEG data in the context of a BED in order to facilitate identification of the disorder.

Hence, our approach uses a RF, and both classify with an accuracy of 81.25% and identify highly important variables for an accurate classification out of a huge number of input variables. Using the variable importance function, our ML model points out the frequency bands 4.5 – 5 Hz, 5 – 5.5 Hz and




5.5 – 6 Hz according to the low theta band as the most informative. A detailed comparison of the respective spectral power density of non-BED participants and BED participants validates the results of our model and makes them traceable. Participants with a BED show significantly higher activity in each of the three fine frequency bands. For distinguishing between participants affected by a BED and not affected by a BED our model highlighted these frequency bands as the most important ones.

The structure of our argumentation that a participant of the EEG dataset used for this study is affected by a BED or not is based on relevant key literature in the field of diagnosing a BED. In addition, it conforms to the decisive characteristics and influencing factors of a BED. For the participants’ division into affected by a BED and not affected by a BED, low and high extreme values of factor 2 ‘disinhibition’ of the TFEQ were used. Disinhibition reflects a tendency towards overeating and an inability to resist food cues, and appears to be a behavioral indicator of a loss of control over eating which is reflected in those who have a BED. For this reason, disinhibition is the characteristic factor of a BED. To sum up, participants who scored high values for disinhibition and so are grouped as affected by a BED, show a significantly higher theta activity in the range from 4.5 to 6 Hz.

While previous studies on BED using EEG data found greater beta activity in the frontal and central regions of the brain to be an indicator of a BED, we cannot confirm these findings in our case. Since higher theta activity seems an unspecific characteristic for a BED, on closer examination there are some indications in the literature suggesting that there could be such an interaction. On the one hand, several studies associate BED individuals with higher impulsive characteristics, while on the other hand, scores on factor 2 ‘disinhibition’ of the TFEQ are linked with impulsivity. Additionally, considering the fact of higher theta activity during impulsive decision, this comes full circle.

This new strategy for detecting the most informative frequency bands while a RF was used to perform the classification, results in completely new insights for health scientists researching in this field by highlighting frequency bands that have not been paid attention to before. Furthermore, we developed an algorithm which can distinguish between individuals affected by a BED and individuals not affected by a BED with a balanced accuracy of 80.50%. This leads to support for and relief of doctors, health insurance companies and other players in the health industry in diagnosing a BED. Our ML approach combining subjective measurements in the form of a questionnaire and physiological measurements in the form of EEG data to accomplish a complementary method for diagnosing a BED, which results in health cost savings, symptom relief and avoidance, and in the possibility of earlier disorder identification.

Limitations

The biggest limitation of our work is that only explicitly healthy individuals were selected as participants in the study. Since a BED over time often leads to obesity, the dataset could not include individuals with a very pronounced and advanced BED. Furthermore, we also had to handle the lack of clear diagnosis not provided within the dataset. But as mentioned in the introduction of our work, EDs are often undetected. Therefore, our classifier can be regarded as an early detection system by predicting extreme values of factor 2 ‘disinhibition’ with a good level of accuracy. For the sake of completeness, it must be mentioned that a questioning bias can always be present in any type of survey. For implementing our classifier in a day-to-day business, the system must be tested in a daily routine. Here, a future limitation is the lack of acceptance among doctors, who first must be convinced of its benefits.

Despite intensively evaluating other traditional ML approaches such as clustering (Baumann et al. 2018) and also most modern convolutional neural networks, which are outstanding in other domains such as image recognition (Baumgartl and Buettner 2020; Baumgartl et al. 2019, 2020; Buettner and Baumgartl 2019), we achieved the best results here with our novel method, originally proposed in (Buettner et al. 2019c; Rieg et al. 2019). However, future work should extend the application of further novel ML approaches. In addition, medication and personality (Buettner 2016, 2016a, 2017, 2017a; Baumgartl et al. 2020a) influence EEG data, and, as a result, our classifier. While the internal validity of our model is very high due to the rigorous k-fold-cross-validation, improving external validity by training with additional datasets is also an important step to improve the model.




Future work

From a data science perspective, in future investigations on the EEG data of individuals affected by a BED, theta activity should be considered in a focused way in order to validate our insights. Thereby, the analysis and interpretation of the correlation between a BED and increased theta activity is left to the specialists and health scientists in this field. Here, especially the potential long-term effects of impulsivity and higher theta activity should be considered in a detailed way. Because of the existence of other widely used questionnaires on EDs, like Eating Disorder Inventory (EDI) (Garner et al. 1983) and Eating Disorder Examination-Questionnaire (EDE-Q) (Fairburn et al. 1994), studies on EEG data with these screening tools should be conducted in order to examine if more significant results can be achieved.

Furthermore, we will also test the robustness of our algorithm against individual states and mental concepts such as cognitive workload (Buettner 2014, 2015a; Buettner et al. 2018a), concentration (Buettner et al. 2018b), and mindfulness (Sauer et al. 2015; 2018) in multi-agent-settings (Buettner 2006, 2009; Buettner and Kirn 2008; Landes and Buettner 2012). Furthermore, we will triangulate psychophysiological and physiological data (i.e., electroencephalographic data (Buettner et al. 2019; 2019b; 2019c; 2020; 2020a) and spectra (Buettner et al. 2019a; 2019b; Rieg et al. 2019), electrocardiographic data (Buettner et al. 2018; Buettner and Schunter 2019), electrodermal activity (Eckhardt et al. 2012), eye fixation (Buettner et al. 2013; Eckhardt et al. 2013), eye pupil diameter (Buettner 2013a; Buettner et al. 2013; 2015), facial data (Buettner 2018) to increase reliability. Furthermore, we will apply our classifier for diagnosing other EDs like anorexia nervosa and bulimia nervosa, which show different characteristics, though are equally relevant for investigation using novel ML approaches, in order to evaluate whether our model is robust across various EDs. To assess the preconditions for implementing the approach in real clinical environments, we will conduct an implementation study to evaluate acceptance (Buettner et al. 2013a; Buettner 2015, 2015b, 2016c) and trust (Meixner and Buettner 2012; Buettner 2009, 2020) by physicians and patients, and determine if the automated approach improves the coordination (Buettner 2006, 2006a) between doctors more efficiently. Furthermore, we want to extend the current machine learning scope by using a broader variety of models like convolutional neural networks (Baumgartl and Buettner 2020; Baumgartl et al. 2020b; Buettner and Baumgartl 2019; LeCun et al. 2015), XGBoost (Chen and Guestrin 2016) and Support Vector Machines.

Acknowledgement

We would like to thank the reviewers and track chairs, who provided very helpful comments on the refinement of this paper. This research is funded by the Carl Zeiss Foundation and the German Federal Ministry of Education and Research (13FH4E03IA, 13FH4E07IA, 13FH176PX8, 13FH4E05IA).

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