Hypoglycemia-induced EEG complexity changes in Type 1 diabetes assessed by fractal analysis algorithm

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Highlights

  • Hypoglycemia induces significant changes in EEG properties.

  • EEG features based on Higuchi’s fractal dimension are sensitive to hypoglycemia.

  • The proposed nonlinear indicators are computationally efficient.

  • Fractal analysis indicators might be suited for real-time applications.

Abstract

In recent years, hypoglycemia-induced changes in the EEG signal of patients with Type 1 diabetes (T1D) have been quantified and studied mainly by linear approaches. So far, sample entropy (SampEn) is the only nonlinear measure used in the literature. SampEn has the disadvantage of being computationally demanding and, hence, difficult to be used in real-time settings. The present study investigates whether other nonlinear indicators, less computationally demanding than SampEn, can be equally sensitive to changes in the EEG signal induced by hypoglycemia. For such a scope, we considered a database obtained from 19 T1D patients who underwent a hyperinsulinemic-hypoglycemic clamp while continuous EEG was recorded. We analyzed the P3-C3 EEG derivation data using three measures of signal complexity based on an approach originally proposed by Higuchi in the 80s: the original measure of fractal dimension and two new indexes based on the Higuchi’s curve. All the three indicators revealed a statistically significant decrease in EEG complexity in the hypo- versus euglycemic state, which is in line with the results previously obtained with SampEn. However, the lower computational cost of the proposed indicators (∼O(N) versus ∼O(N2)) makes them potentially more suited for real-time applications such as the use of EEG to trigger hypoglycemia alerts.

Introduction

Type 1 diabetes (T1D) is an autoimmune chronic disease, where the insulin-producing beta cells in the pancreas are destroyed. Thus, patients affected by T1D need daily insulin injections. One of the most important complications of insulin treatment is the risk of hypoglycemia (blood glucose, BG < 70 mg/dl), a severe condition that potentially progresses into coma without subject awareness. The brain is dependent upon glucose as a primary source of energy. Indeed, glucose is needed for neuronal and non-neuronal cellular maintenance and for the generation of neurotransmitters [1]. Low blood glucose levels affect the ionic currents within the neurons of the brain and impair brain function. The changes in these ionic currents generate voltage fluctuations, which can be measured by electroencephalogram (EEG).

In the last years, hypoglycemia-associated EEG changes have been investigated in several studies, mostly employing linear indicators like power spectral density and coherence. For instance, it has been shown that EEG power spectral density in the conventional theta ([4], [5], [6], [7], [8] Hz) and alpha ([8], [9], [10], [11], [12], [13] Hz) bands increases during hypoglycemia with respect to euglycemia [2], [3], [4]. A decrease of EEG coherence during the hypoglycemic state was also reported [5]. These results motivated the idea of employing the brain as a biosensor to detect hypoglycemia in real-time, and highlighted the need for a robust panel of EEG indicators to monitor the glycemic state [6].

In the investigation of the relationship between the glycemic state and the EEG signal, the use of nonlinear indexes can yield information complementary to that obtainable by linear methodologies, providing new insights into the effects of hypoglycemia on the brain and possibly improving the methodologies for the real-time detection of hypoglycemic events. However, to the best of our knowledge, application of nonlinear indicators to analyze hypoglycemia-associated EEG changes has only been reported by Fabris et al. [7]. They showed that a decrease of EEG complexity (here intended as signal irregularity) was shown to be induced by hypoglycemia by computing the sample entropy (SampEn) index [8] at various scale factors, employing the so-called multiscale entropy algorithm (MSE) [9].

Signal complexity can be assessed using various nonlinear indicators [10]. Among these, entropy-based algorithms are very popular and powerful for analyzing EEG and biomedical signals in general, e.g. [8], [11], [12], [13], [14], [15], [16], [17], [18], [19], but their high computational cost may render their use difficult, in particular in real-time applications. Other approaches based on signal quantization, such as the Lempel-Ziv method [20], may overlook some information related to changes in the brain function during hypoglycemia.

The aim of this paper is to determine whether other nonlinear complexity indicators, based on fractal analysis, can be used to detect EEG changes related to hypoglycemia. In particular, we considered the Higuchi’s measure of fractal dimension [21], already used in EEG analysis [22], [23], [24], and two new indexes reflecting both signal amplitude and frequency properties [25]. All the three indexes are characterized by a lower computational cost than SampEn, i.e. (∼O(N) versus ∼O(N2)) [10]. Although a comprehensive benchmarking of the signal processing methods applicable to investigate EEG changes induced by hypoglycemia is out of the scope of the present study, the comparison of Higuchi’s fractal dimension versus SampEn is of practical relevance, because the reduced computational cost of the former can allow the simultaneous computation of multiple features and on grouping their assessments obtained by temporally consecutive epochs.

Section snippets

Database

In order to investigate our hypothesis we consider the same data set used by Fabris et al. [7], obtained from 19 T1D patients who underwent a hyperinsulinemic-hypoglycemic clamp procedure while continuous EEG was recorded and blood glucose (BG) was frequently sampled.

Methods

As reported by Kaplan and Glass [28] while elaborating on the nonlinear analysis of EEG during sleep, not only entropy measures, but also fractal theory can be used for explaining temporal models of a time-series and predicting extreme events of human behavior. In this work, we investigate the effect of hypoglycemia on the EEG signal using indexes based on the algorithm proposed by Higuchi [21]. This algorithm is expected to be suitable to study nonlinear, non-stationary, multiscale variables

Results and discussion

For each 4-s EEG epoch, the fractal dimension features described in Section 3 were computed. The linear region was defined considering klin = 6, according to [23], while the two other features were computed considering also the nonlinear region up to kmax = 30 according to [25] and taking into account the different sampling frequency (i.e. since the actual sampling frequency is 200 Hz while in [25] it was 128 Hz, k max was proportionally increased from 18 to 30). Moreover, the results obtained by the

Conclusions

In recent years, EEG changes induced by hypoglycemia in T1D have been investigated mostly by linear time-series analysis methods. For a more in depth understanding of the functioning of the brain in hypoglycemia and also for evaluating a possible use of the EEG to detect hypoglycemia in real-time by means of a multiparameter classification algorithm [6], [34], it is worthwhile implementing also nonlinear time-series analysis methods. The only nonlinear measure investigated so far for such a

Acknowledgement

The authors thank Hyposafe A/S, Lynge, Denmark for assisting with providing the data.

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    1

    Present address: Campus Biotech, 1202, Genève, Switzerland.

    2

    Present address: Biovotion AG, Zurich, Switzerland.

    3

    Present addresses: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria and Department of Information Engineering, University of Padova, Padova, 35131, Italy.

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