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Articial Intelligence In Medicine 115 (2021) 102059

Available online 26 March 2021

CEFEs: A CNN Explainable Framework for ECG Signals

Barbara Mukami Maweu

, Sagnik Dakshit

, Rittika Shamsuddin

Balakrishnan Prabhakaran

Erik Jonsson School of Eng. & Computer Science, University of Texas, Dallas, Richardson, TX, USA

Computer Science, Oklahoma State University, Stillwater, OK, USA

ARTICLE INFO

Keywords:

Deep learning

Convolution neural network

ECG Signals

Explainable AI

Explainable Framework

Synthetic healthcare data

ABSTRACT

In the healthcare domain, trust, condence, and functional understanding are critical for decision support sys-

tems, therefore, presenting challenges in the prevalent use of black-box deep learning (DL) models. With recent

advances in deep learning methods for classication tasks, there is an increased use of deep learning in

healthcare decision support systems, such as detection and classication of abnormal Electrocardiogram (ECG)

signals. Domain experts seek to understand the functional mechanism of black-box models with an emphasis on

understanding how these models arrive at specic classication of patient medical data. In this paper, we focus

on ECG data as the healthcare data signal to be analyzed. Since ECG is a one-dimensional time-series data, we

target 1D-CNN (Convolutional Neural Networks) as the candidate DL model. Majority of existing interpretation

and explanations research has been on 2D-CNN models in non-medical domain leaving a gap in terms of

explanation of CNN models used on medical time-series data. Hence, we propose a modular framework, CNN

Explanations Framework for ECG Signals (CEFEs), for interpretable explanations. Each module of CEFEs provides

users with the functional understanding of the underlying CNN models in terms of data descriptive statistics,

feature visualization, feature detection, and feature mapping. The modules evaluate a model’s capacity while

inherently accounting for correlation between learned features and raw signals which translates to correlation

between model’s capacity to classify and it’s learned features. Explainable models such as CEFEs could be

evaluated in different ways: training one deep learning architecture on different volumes/amounts of the same

dataset, training different architectures on the same data set or a combination of different CNN architectures and

datasets. In this paper, we choose to evaluate CEFEs extensively by training on different volumes of datasets with

the same CNN architecture. The CEFEs’ interpretations, in terms of quantiable metrics, feature visualization,

provide explanation as to the quality of the deep learning model where traditional performance metrics (such as

precision, recall, accuracy, etc.) do not sufce.

1. Introduction

Deep learning models in healthcare are traditionally evaluated in

terms of accuracy, precision, and recall over the entire test set. These

metrics tell us the degree to which a model can accurately classify the

data but does not provide an interpretable explanation to the strength or

lack of capacity of the model. By the capacity of a model, we refer to the

model’s capability to correctly classify hard and easy cases alike. The

classication accuracy, precision, and recall alone are not sufcient for

deep learning-based applications in settings such as ICU (Intensive Care

Units) where ECG (Electrocardiogram) signals are monitored

automatically. There also persists the question whether the model has

learned the correct classication features (for instance, ECG features and

not some random Noise). An interpretable explanation-based metric

provides an understanding of the working of a neural network model

which can help researchers improve the model to accommodate both

hard and easy cases. While explainable methods such as class activation

mapping [31], saliency maps [27], and GradCam [11] exist, these are

not used as metrics for evaluation of model capacity and most of these

existing methods work more efciently with images, NLP (Natural

Language Processing) tasks, and 2D non-medical data. Furthermore, it is

not feasible to study each test signal’s activation maps or similar

* Corresponding author.

E-mail addresses: barbara.maweu@utdallas.edu (B.M. Maweu), sdakshit@utdallas.edu (S. Dakshit), r.shamsuddin@okstate.edu (R. Shamsuddin), bprabhakaran@

utdallas.edu (B. Prabhakaran).

Authors have equal contribution.

Contents lists available at ScienceDirect

Articial Intelligence In Medicine

journal homepage: www.elsevier.com/locate/artmed

https://doi.org/10.1016/j.artmed.2021.102059

Received 5 July 2020; Received in revised form 18 January 2021; Accepted 23 March 2021

Articial Intelligence In Medicine 115 (2021) 102059

explanations and improve the model as the existing explanations are

qualitative and not quantitative.

Model accuracy is a measure of performance in CNN (Convolutional

Neural Networks) models and therefore, models are designed to produce

high accuracy in classication tasks. The design of CNN models com-

prises stacked components such as convolution, activation, normaliza-

tion, subsampling and fully connected layers which produce inherently

complex models. To achieve high model performance, accuracy ulti-

mately becomes a compromise for model complexity and abstractions.

Model complexity tends to reduce both mechanical (inside) and func-

tional (outside) human understanding of a model. The layered feature

abstractions in CNN increase progressively depending on the depth of

the network. Each of these abstraction levels encode knowledge repre-

sentation which can be described and interpreted [23] for better human

understanding. The healthcare settings are a source of both structured

and unstructured data stored in form of medical images, physiological

time-series, and genomic sequences [24]

A majority of healthcare data such as ECG, Photoplethysmogram

(PPG), A-Scans of Optical Coherence Tomography (OCT), Electroen-

cephalogram (EEG) are in the form of time-series signals. Time-series

healthcare datasets present several challenges which include small

dataset size and inconsistently collected data [25]. These limitations

hinder the use of the state-of-the art CNN models because the models

exhibit poor performance when trained on small datasets. In addition to

these challenges, when CNN models are applied to medical decision

support systems, healthcare users seek trustable and interpretable

models that explain model outcomes [26] and insight in the features

learned. Research is continuing to focus on nding methods of both

extending the available small healthcare datasets and explaining these

black-box CNN models.

The structure of ECG signals depends on semantic relationships be-

tween waveform features shown in (Fig. 1) to arrive at a diagnosis. This

waveform dependency highlights the importance of achieving model

explanations that precisely show learned ECG feature and their contri-

bution to ECG classication. Traditional qualitative measures such as

accuracy, selectivity and specicity do not articulate the learned fea-

tures of CNN models thus providing users with only high classication

accuracy without associated explanation or interpretations. Majority of

existing interpretation and explanations research has been on 2D-CNN

models, in non-medical domain (as discussed in Section 2). This cre-

ates a need in terms of explanation of CNN models used on medical time-

series data. In this paper, we seek to explain and interpret knowledge

encoded in 1D-CNN layers trained on highly structured electrocardio-

gram (ECG) signals.

1.1. Proposed approach

We propose a modular framework, named, CNN Explainability

Framework for ECG signals (CEFEs), as shown in Fig. 2. Through the

modules of our framework, we demonstrate how explanations and in-

terpretations of a model are achieved by:

a) Understanding the evolution of machine-learned features across

layers of a model.

b) Comparative analysis of machine learned class discriminant features

and actual ECG observable diagnostic waveform features.

c) Understanding the relationships between machine-learned features

and the features’ contributions to misclassication of target classes.

Each module of CEFEs provides users with the functional under-

standing of the CNN models in terms of data descriptive statistics,

feature visualization, feature detection, and feature mapping. We iden-

tied three possible evaluation paradigms for CEFE: (a) model trained

on different volumes of the same dataset; (b) different models trained on

the same dataset; and (c) different models trained on different datasets.

In this paper, we elected to evaluate CEFEs using one model trained on

varying quantities of training data. CEFEs explanations were derived

from a custom one-dimensional convolution neural network (1D-CNN)

model trained on ECG signals to classify ECG arrhythmias. Following

model training, we performed post-hoc evaluation of the model using

CEFEs’ modular tests. CEFEs-based explanations derived from our 1D-

CNN impart visual, metric and feature based analysis. CEFEs also pro-

vides explanations for model improvement, model degradation, and no-

change model in performance. Additionally, these model explanations

provide insights into how varying quantity of training data affects model

explanations. In other words, our proposed CEFEs method provides a

comprehensive modular technique for post-hoc explanations of the

highly structured ECG data. CEFEs employs observable ECG features and

learned features of target classes to provide explanation. 1D-CNN model

explanations are described in terms of descriptive statistics, overlay

plots and mapping between ECG observable features and learned

features.

1.2. Contribution

The proposed framework, CEFEs, delivers:

1 Interpretable explanations by means of functional understanding of

internal mechanism of CNNs when used in ECG analysis support

Fig. 1. ECG Clinical Features [30] (X axis represents Time and Y axis represents Amplitude).

B.M. Maweu et al.

Articial Intelligence In Medicine 115 (2021) 102059

systems and therefore, addressing the trust gap found in these black-

box deep networks.

2 We propose the use of interpretable explanations as metrics that

evaluate the quality of CNN models through a set of tri-fold modular

tests: a) descriptive statistics, b) visualization and c) detction and

mapping of input ECG signal in comparison to its learned feature

maps.

3 We provide a benchmark for interpretable explanation-based eval-

uation of CNN models trained for ECG signal classication task.

1.3. Organization of the paper

Our paper is organized as follows: Section 3 describes the proposed

CEFEs framework together with its explainability modules. Section 4

denes the 1D-CNN that we trained on original ECG signals and we

provide modular results of CEFEs for this model. In Section 5, we use

CEFEs on our 1D-CNN models and provide empirical results that show

model improvements as models are trained with different increments of

training data. Finally, in Section 6 we present discussions surrounding

our results and application of CEFEs followed by the paper conclusion in

Section 7.

2. Related works

In this section, we review research literature on explainable and

interpretable methods for deep networks in two categories:

(a) Explanation from interpretable models

(b) Explanations from post-hoc model analysis.

We present works that show how model explanations have previ-

ously been achieved in various data modalities and in different domains.

2.1. Interpretable models

Interpretable models incorporate design features that inherently

capture interpretable representations of a model’s internal mechanism.

Majority of interpretable models are motivated by image data with very

few applied to physiological timeseries data. The work of Zhang et al.

[18] proposed an automated method that maps higher level CNN lters

to an object-part (CNN semantics) instead of traditional input patterns

on image data. Their interpretable model applies techniques that modify

components of black-box deep learning models in such a way that

interpretable knowledge representations are easily obtained. Other

interpretable models assign a score to input data such as [19], which

uses Layer wise Relevance Propagation (LRP) method to capture

explanation. Although LRP analyzes electroencephalogram (EEG)

timeseries data, this method unlike CEFEs does not provide knowledge

on features or structure of signal learned by the model and only shows an

assigned relevance scores of data points and their contribution to model

prediction.

2.2. Post-hoc model explanations

Post-hoc explanations provide functional explanations that describe

trained models with respect to input data. An important aspect of model

explanation methods [9, 10, and 11] is exibility in use across different

deep learning models, as in our proposed CEFEs. Common post-hoc

explanation methods include data perturbation and feature scoring [9,

29], activation maximization [1,11], and backward propagation [27,

28]. Visual explanations method using activation maximization such as

Gradient-weighted Class Activation Mapping (GRAD-CAM) proposed in

[11], tags the discriminative area of a target class. The tagged discrim-

inative area is dened by computing class specic gradients in the nal

convolution layer and then outputs a localization heat map of the pre-

dicted class [32]. introduced a post-hoc interpretability method Testing

with Concept Activation Vector (TCAV) method of post-hoc interpret-

ability which optimizes a quantitative measure of explanation based on

high level domain concepts. Our proposed CEFEs framework also uses

high-level domain concepts similar to [32]. The difference is that our

goals are: (a) discover whether domain concepts such as ECG diagnostic

waveforms are learnt by deep learning models; (b) provide model ca-

pacity and classication explanations based on how well a trained model

has learned the domain concepts.

While GRAD-CAM localizes the class discriminative area, it does not

give a measure of the error of the model’s localized discriminative area

and relies simply on visual interpretation of the expert user. Image data

explanations found in Local Interpretable Model-agnostic Explanations

(LIME) by [9] are model-agnostic and employ localized data perturba-

tions to compute importance scores of image pixels. An importance

scores subsequently draws attention to image pixels with positive weight

for a specic class. We show through our experiments that visualization

alone is not sufcient and is not always interpretable for complex

medical signals specially in deeper layers.

Springenberg et al. [29], in addition to values that separate feature

by their importance, provides formal mathematical denition in the

axiomatic based method to extract explainable rules a model. Rule

extraction for deep learning models provide rules for diagnosis and class

discrimination but does not provide interpretation or explanation for a

model’s capacity. Furthermore, the extracted rules for medical decision

Fig. 2. CNN Explainability Framework for ECG signals (CEFEs).

B.M. Maweu et al.

Articial Intelligence In Medicine 115 (2021) 102059

systems require expert opinion and cannot be used as a metric for model

capacity and understanding performance. Another popular explan-

ability tool SHAP (Shapely Additive Explanations) [10] describe the

contribution of each input feature towards model outcomes. However,

the lack of feature dataset in clinical decision system such as ECG

diagnosis, continuous 1D nature of ECG signals make it unsuitable for

use. None of the methods illustrated are specic to be used as metric for

understanding model capacity and performance in time series medical

datasets.

Explanations for models trained on time-series data use extracted

shapelet [15,16] (time-series subsequences) which are suited for

discovering the best patterns that are representative of a target class.

Time-series tweaking in [16] is a method applied to time-series data

although not applied to provide explanation for deep networks.

Time-series tweaking nds the minimum number of changes needed in

order to change an input classication outcome in a random forest type

of classier. These time series explanations cannot be used as metric for

evaluation of model performance. While CEFEs does not use shapelets to

extract model learned features, it uses ECG waveform segmentation

techniques to discover, map and compare model learned features to

those in input ECG signals. The methods in [9–11] and [27–29] focused

input feature scoring and data perturbation differ with our proposed

CEFEs which provides interpretable insights on specic features learned

by a 1D-CNN model and explanations on how these learned features

affect CNN model capacity and outcomes.

In summary, literature survey shows majority of interpretation and

explanations research has been on 2D-CNN models in non-medical

domain, leaving a gap of explanation of medical time-series data.

Traditional metrics such as accuracy, sensitivity, and selectivity are not

sufcient for providing details of structural ECG features learned by a

CNN model. The challenges posed by medical signal datasets as dis-

cussed in the Introduction section, hinders ability of CNN to learn,

especially specic intricate structural clinical features for clinical diag-

nosis. Developing interpretable and explainable techniques for health-

care timeseries data creates supportive trust and condence in

automated decision support systems. CEFEs framework addresses these

gaps by providing interpretable explanations for CNN models trained on

ECG timeseries data, by focusing on post-hoc model interpretability in

terms of model capacity.

3. CEFEs

We aim to provide transparency and functional understanding of 1D-

CNN model using a layer-wise interpretation of relevant features learned

by the model. Denitions of Interpretation and Explanation in the context

of computation models are often used interchangeably. Montavon et al.

[1] denes Interpretation as the idea of mapping from feature space (e.g.,

predicted class) into a human comprehendible domain and Explanations

as a set of features in the interpretable domain that contribute towards

class discrimination.

Our proposed framework (Figs. 2 and 3) for ECG signals, is a post-hoc

tri-modular evaluation structure that provides local interpretations and

explanations from convolution neural networks. Local interpretations

and explanations of a model explain the “why” of individual test case

predictions. In this section, we present the details of CEFEs modules and

the process by which the framework achieves model interpretation and

explanations.

1 Descriptive Statistics: Descriptive statistics are summary analysis of

representative model features or input data. These representations

help users realize a model’s capacity to learn inherent statistical and

mechanical features of data such as waveform shape features of

signal. CEFEs descriptive statistics module uses task dependent tests

to analyze an input ECG signal and corresponding feature map

extracted from a convolution layer of a trained CNN model. Although

the choice of CNN layer for statistical analysis is not limited to a

specic layer, we were motivated to use the nal convolution layer

(Conv

nal

) because this layer incorporates both low level and high-

level data features and balances spatial and semantics information

contribute to explainable and interpretable class discrimination ar-

tifacts. Descriptive statistics tests are task dependent. We chose Dy-

namic Time Warping (DTW) algorithm to compute the similarities

between the input ECG signal and the CNN model learned features.

DTW enabled us to analyze and observe learned representation of the

rigid ECG signal morphology. DTW distance measures are organized

into intra-model distance (Eq. 1) and inter-model distance (Eq. 2).

Intra-model distance (d

intra

) is the warped Euclidean similarity

measure returned by DTW from an input ECG signal and feature map

projections. We dene (d

intra

) as a value that represents how well a

model has learned input ECG shape features. A low (d

intra

) value

explains that a model has adequately learned ECG shape features.

Once (d

intra

) values of several models are computed, we compute the

difference in learned ECG shape features between two CNN models

using the inter-model distance (d

inter

). The (d

inter

) values are used as a

comparative measure of ECG shape features learned between two

models trained on similar input ECG signals. A high (d

inter

) value

explains the differences in prediction outcomes of two models on a

xed test set [17].

intra





k=1



k,m

− y

k,n



∗



k,m

− y

k,n









(1)

inter

= |d

intra

− d

intra

| (2)

Where k represents the samples, m

data point of one input signal (ECG

Signal), n

data point of other input signal (Feature Map) and M

, M

represent the two models under comparison. We approximate d

inter

and

Fig. 3. CEFEs - Explainable Modules.

B.M. Maweu et al.

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