# Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and
ECG Signal

MohammadReza Hosseinzadehketilateh, Banafsheh Adami, Nima Karimian  Lane
Department of Computer Science and Electrical Engineering
West Virginia University
Morgantown, USA
<EMAIL_ADDRESS><EMAIL_ADDRESS>and<EMAIL_ADDRESS>
###### Abstract

This paper presents a novel approach to non-invasive hyperglycemia monitoring
utilizing electrocardiograms (ECG) from an extensive database comprising 1119
subjects. Previous research on hyperglycemia or glucose detection using ECG
has been constrained by challenges related to generalization and scalability,
primarily due to using all subjects’ ECG in training without considering
unseen subjects—a critical factor for developing methods with effective
generalization. We designed a deep neural network model capable of identifying
significant features across various spatial locations and examining the
interdependencies among different features within each convolutional layer. To
expedite processing speed, we segment the ECG of each user to isolate one
heartbeat or one cycle of the ECG. Our model was trained using data from 727
subjects, while 168 were used for validation. The testing phase involved 224
unseen subjects, with a dataset consisting of 9,000 segments. The result
indicates that the proposed algorithm effectively detects hyperglycemia with a
91.60% area under the curve (AUC), 81.05% sensitivity, and 85.54% specificity.

###### Index Terms:

ECG, hyperglycemia, Deep learning

##  I Introduction

Continuous monitoring of hyperglycemia is crucial for individuals with
diabetes. It enables both individuals and healthcare providers to make real-
time adjustments to medication and insulin doses. Continuous glucose level
monitoring helps maintain blood glucose levels within the desired range,
reducing the risk of complications. According to CDC [1], 38.4 million people
in the US have diabetes, and 97.6 million people aged 18 years or older have
prediabetes. Thus, developing continuous monitoring is essential. Traditional
methods for monitoring blood glucose include finger-prick measurements and
continuous glucose monitoring (CGM) devices [2]. However, finger-prick
measurements are associated with pain, discomfort, and high costs.
Additionally, this approach fails to offer a continuous monitoring solution
for blood glucose levels. Recent studies have explored the use of
electrocardiograms for hyperglycemia detection, making them a viable option
for continuous monitoring. This data can be conveniently collected from
wearable devices like the Apple Watch or Fitbit. In contrast to alternative,
noninvasive methodologies, the acquisition of ECG signals through wearable
devices is readily achievable. This presents a cost-effective and comfortable
monitoring solution for individuals with diabetes [3, 4].

Ngyuen et al. [5] demonstrated that ECG exhibits notable changes during
hyperglycemia events. The study, involving five subjects who experienced both
hyperglycemia and hypoglycemia, revealed significant increases in heart rate,
QTc (corrected ECG QT interval), and RTc (corrected ECG RT interval).
Subsequently, a neural network was developed, utilizing 16 handcrafted
features with a dataset of 10 subjects for hyperglycemia identification [6].
The proposed model achieved a sensitivity of 70.6% and specificity of 65.4%.
Li et al. [7] proposed density-based spatial clustering applications with
noise and convolutional neural networks (DBSCAN-CNN), for glucose level
identification using ECG data from 21 subjects. The proposed model
demonstrated impressive classification accuracy, achieving 87.94% for low
glucose levels, 69.36% for moderate glucose levels, and 86.39% for high
glucose levels. Furthermore, the reported sensitivity and specificity were
98.48% and 76.75%, respectively. Following Li et al.’s work [8], the ResNet
architecture was employed, yielding similar results. While prior research has
conducted preliminary investigations into the potential utilization of ECG for
detecting blood glucose and hyperglycemia, the existing studies not only lack
comprehensive demonstrations of high accuracy and specificity but also suffer
from limited data, typically involving a maximum of 21 subjects. Our goal is
to address the aforementioned challenges and establish the feasibility of ECG
for blood glucose level detection or hyperglycemia by introducing a novel
database comprising 1116 subjects. Additionally, we propose a new deep
learning architecture that surpasses the performance of existing methodologies
even when confronted with a larger dataset.

The main contributions of this paper are summarized:

  * •

We propose a novel and generalized convolutional block attention module with
CNN for hyperglacemia detection using a non-invasive ECG signal.

  * •

We present a large ECG database consisting of 1,119 subjects, where each
subject is equally labeled for both high levels of hyperglycemia and non-
hyperglycemia.

  * •

We evaluated the performance of hyperglycemia detection in diverse scenarios,
including situations involving unseen subjects and individual-based analyses.

  * •

To showcase the effectiveness, robustness, and generalization capability of
our proposed methods, we conducted experiments on a novel ECG database
comprising 68,274 samples recorded from 1119 subjects with a sensitivity of
81.05% and a specificity of 85.54%.

  * •

Our proposed work achieved a significant improvement compared to results in
[8, 9]. Our proposed model improved the specificity by 10%.

##  II Experiment Setup

###  II-A Data Acquisition

The experiments were meticulously conducted in controlled settings to mitigate
potential external interference. Prior to the beginning of the experiment,
participants did not engage in any physical exercise or drug consumption. ECG
signals were captured using an Analog AD-8232 with a sampling rate of 1000 Hz,
and blood glucose values were acquired from participants’ fingers using a
minimally invasive BG meter (ACCU-CHEK). A total of 1119 subjects, including
386 females and 733 males aged between 38 and 80 years, participated in the
study. Fasting was not mandatory, and participants did not disclose their
overall health status. Each participant participated in two successive
recording sessions, both conducted in the morning. Each session consisted of
the recording of a 60-second single-lead ECG and blood glucose
concentration.Following this, a comprehensive analysis was conducted on each
ECG recording, leading to the exclusion of those with low quality, resulting
in a dataset comprising 1963 recordings. Recordings from subjects with glucose
concentrations exceeding 100 mg/dL were identified as characteristic of
hyperglycemia for the purposes of this study.

###  II-B Data Processing

As the ECG encompasses diverse noise sources such as baseline wander (BW),
motion artifact (MA), and electrode movement (EM), these can improve the
accuracy of proposed model by smoothing the ECG signal. To mitigate potential
artifacts arising from the setup and removal of electrodes on the subject, the
initial and final 2 seconds of the raw ECG signals are disregarded. The
remaining data undergoes filtration employing a Butterworth bandpass filter of
order 4, with a frequency range set at 1 Hz to 40 Hz [10].

ECG waveforms comprise a repetitive order characterized by five major peaks,
namely P, QRS, and T. Given that each ECG heartbeat contains redundant
information, repetitively processing heartbeats with a correlation is
inefficient. ECG segmentation emerges as a widely adopted signal preparation
method to reduce signal size for subsequent feature extraction. Essentially,
the goal of segmentation is to identify recurring patterns in the ECG signal,
specifically the P, QRS, and T waves, thereby significantly reducing the input
size of deep learning. In our study, segmentation is aligned with the
reference point, typically identifying an R-peak, and fixed distances before
and after the identified R-peaks. This involves extracting a partial ECG
signal (R-$t_{1}$, R+$t_{0}$) instead of the entire signal, where $t_{1}$ and
$t_{0}$) represent predefined fixed times covering the majority of the P-QRS-T
fragment. Subsequently, we illustrate the influence of using a single segment
of the ECG signal as input for deep learning, as well as employing five
consecutive subsequences of the ECG signal to assess our base model. Segments
across subjects do not share the same unit and require normalization to
standardize them. This process involves not only removing their mean but also
scaling them to achieve unit variance before feeding them into the deep
learning model. The normalization calculation, utilizing the mean and standard
deviation, was performed using the StandardScaler function available in the
Python Scikit-learn library.

|
---|---
(a) | (b)
Figure 1: (a) Demonstrate a recording of ECG waveform from a participant. (b)
Shows one segment of ECG signal.) Figure 2: proposed hyperglacemia detection
using CNN with block attention module. a) convolutional block attention module
(CBAM). b) channel attention module (CAM), c) spatial attention module (SAM),
d) convolutional neural network with attention mechanism.

###  II-C Evaluation Metrics

Three performance metrics were employed to assess the hyperglycemia detection
performance: true positive rate (TPR), false positive rate (FPR), and area
under the curve (AUC). FPR, or (1-Specificity), represents the percentage of
healthy ECG wrongly classified as hyperglycemia, while TPR, or sensitivity,
signifies the percentage of hyperglycemic events successfully identified as
hyperglycemia. The AUC of the receiver operating characteristic (ROC) curve
served as the performance metric for model comparison. In binary
classification problems like these, the threshold used to distinguish between
the two output labels (hyperglycemia and non-hyperglycemia) directly impacts
performance metrics. The ROC curve illustrates model performance in terms of
TPR versus FPR across different thresholds, and the area under these curves
offers a comprehensive performance measurement for all thresholds. Sensitivity
and specificity were also employed as additional metrics for classification
performance. Sensitivity gauges how accurately a test produces a positive
result for individuals with the hyperglycemia condition, while specificity
assesses a test’s ability to accurately generate a negative result for
individuals without hyperglycemia. 727 subjects with around 29,080 segments
are used for training, while 168 subjects, contributing 6,720 segments, are
allocated for validation. The testing phase involved 224 unseen subjects,
comprising a dataset of 9,000 segments.

###  II-D Model

Our proposed model is demonstrated in Figure. 2. It comprises four layers of a
convolutional neural network (CNN), followed by a convolutional block
attention module (CBAM) and max pooling [11, 12]. Our proposed CBAM
incorporates two attention mechanisms: the channel attention module (CAM) and
the spatial attention module (SAM). The channel attention module, as shown in
Figure 2-b. The channel attention module captures the interdependencies among
feature channels in the ECG segment through max pooling and average pooling
operations across two dense layers. The first dense layer, utilizing a ReLU
activation function, reduces the number of information channels, and the
second layer generates channel-wise attention using a sigmoid activation
function.

The Spatial Attention Module, illustrated in Figure 2-c, focuses on capturing
spatial relationships within feature maps of both hyperglycemia and non-
hyperglycemia representations in the ECG signal. This enhancement aims to
boost the model’s performance in classification tasks, enabling it to
concentrate on informative spatial regions within the feature maps. The module
calculates spatial attention for hyperglycemia input features by performing
average-pooling and max-pooling operations along the channel dimension of the
hyperglycemia feature map. This process generates a concise and informative
feature representation, which is then concatenated. Following concatenation,
convolution layers ($f^{7\times 7}$) are applied to construct a spatial
attention map ($M_{s}\in\mathbf{R}^{C\times H\times W}$), where $C$ is number
of channel which we set it to 1, $H$ is height, and $W$ is width. using
Equation 1:

$\displaystyle M_{spatial}(X)=\sigma(f^{7\times 7}[AvgPool(X);MaxPool(X)]),$
(1)

Where $\sigma$ is sigmoid function, $f^{7\times 7}$ is convolution operation
with filter size $7\times 7$. In our study, we fixed the values of Channel
($C$) and height ($H$) at one due to the sequential nature of the signal.
Additionally, $W$ was set to 600, representing the size of a single segment in
the ECG signal.

The channel attention module focuses on understanding the relationships or
dependencies that exist between different feature channels in the ECG signal.

$\displaystyle M_{Channel}(x)=\sigma(Maxpool(x))+\sigma(Avgpool(x))$ (2)

Where $x$ is the original feature map produced by a convolutional layer This
integration allows for the capture of both channel-wise and spatial-wise
attention, facilitating the learning of representations for hyperglycemia
levels in the ECG signal.

The proposed method tries to minimize the binary cross entropy (BCE) loss,
which measures the difference between the predicted probability of
hyperglycemia and the actual binary labels (indicating the presence or absence
of hyperglycemia). Given an ECG signal as input and the predicted probability
$p$ that hyperglycemia is present, along with the true label y (which is 1 if
hyperglycemia is present and 0 if not), the BCE loss is calculated as:

$\displaystyle\text{BCE}(y,p)=-[y\log(p)+(1-y)\log(1-p)].$ (3)

|
---|---
(a) | (b)
Figure 3: (a) ROC for testing dataset with AUC of 93%. (b) Depicts model
accuracy for both training and testing under different epoch.)

##  III Experimental Results

According to the results depicted in Figure 3, our proposed model, utilizing
data from 1119 subjects, achieved notable performance metrics. Specifically,
it attained a 91.60% area under the curve (AUC), 81.05% sensitivity, and
85.54% specificity. This evaluation was conducted by training the model on a
subset of subjects’ ECG data and validating it on unseen subjects. In contrast
to other approaches that involve using only a portion of subjects’ ECG for
training, our methodology reflects a more realistic scenario. Furthermore,
unlike previous studies that considered some ECG segments as outliers and
removed them, our study retained all segments and subjects, even those with
noisy data. This contributes to the robustness and real-world applicability of
our proposed model.

##  IV Discussion and comparison

In our previous investigations[9], we segmented the ECG data of each
participant into approximately 40 consecutive cardiac cycles, where each
segment represented a complete ECG waveform corresponding to a cardiac cycle.
Subsequently, we applied a random partitioning method, designating 85% of the
segments for the training subset and allocating the remaining as the testing
subset. Following this, the training and testing subsets were amalgamated to
create the respective training and testing datasets. The choice to employ a
segmentation method based on individual ECG data rather than participant-wise
stems from the personalized variations observed in both ECG and blood glucose.
While our earlier study shared similarities with Li et al. [8], it is not
realistic. The primary objective of our current study is to generalize the
model based on the current participants, enabling it to identify hyperglycemia
in unseen subjects. For instance, if an Apple Watch incorporates this feature,
the system should be trained on an existing database and possess the
capability to notify a user about hyperglycemia without requiring their data
under both conditions. In order to compare our proposed model with the
literature, we have implemented the same scenario for training and testing the
model, splitting the ECG segments of each subject into training and testing
with our proposed deep learning architecture. The model performance is
demonstrated in Table I. As can be seen in this table, our proposed model
outperforms other methods when compared with other methods, and it has been
evaluated on more subjects.

| Sensitivity | Specificity | AUC | #subjects
---|---|---|---|---
Linh et al.[6] | 65.64% | 56.21% | 61.68% | 10
cordeiro et al.[9] | 87.57% | 85.04% | 94.53% | 1119
Li et al.[8] | 98.4 | 76.75 | - | 21
Our work | 96.07 | 95.46 | 99 | 1119
TABLE I: Evaluating the proposed model by comparing it with various frameworks
using ECG data from all subjects in training. Measurement will be based on
sensitivity, specificity, and AUC.

##  V Conclusion

In this study, we introduce an innovative approach for non-invasive
hyperglycemia monitoring using electrocardiograms (ECG) from a remarkably
large database of 1119 subjects. Our methodology involves developing a
generalized model trained on a subset of subjects’ ECG data, facilitating
hyperglycemia prediction in unseen subjects. We developed a deep neural
network model capable of identifying significant features across various
spatial locations and examining interdependencies within each convolutional
layer. Our model underwent training with data from 727 subjects, encompassing
around 29,080 segments, while 168 subjects, contributing 6,720 segments, were
allocated for validation. The testing phase involved 224 subjects, comprising
a dataset of 9,000 segments. We ensured an equal distribution of data
containing hyperglycemia and normal instances in both the training and testing
sets. The results demonstrate the effectiveness of the proposed algorithm in
hyperglycemia detection, achieving a 91.60% area under the curve (AUC), 81.05%
sensitivity, and 85.54% specificity.

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