深度学习驱动的ECG信号可解释框架：提升医疗决策信任度
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本文主要探讨了人工智能在医学领域中的应用，特别是深度学习技术在心电图（ECG）信号检测和分类中的最新进展。随着深度学习方法在医疗决策支持系统中的广泛应用，如何确保模型的透明度和可解释性变得至关重要。在健康护理领域，信任、信心和对模型功能的理解对于决策支持系统的有效使用是必不可少的，因此，研究者们正在寻找方法来克服黑盒深度学习（DL）模型所带来的挑战。
文章标题"Artificial Intelligence in Medicine, 115(2021)102059"强调了深度学习在ECG信号处理中的重要性，作者们提出了一种名为CEFEs（ACNN Explainable Framework for ECG Signals）的可解释人工智能框架。该框架旨在通过增强深度学习模型的透明度，帮助领域专家理解模型的工作原理，从而提高其在临床诊断中的可信度和功能性。
具体而言，文章的核心内容包括以下几个方面：
1. **深度学习与ECG信号**：深度学习，尤其是卷积神经网络（Convolutional Neural Networks, CNN），因其在复杂数据如ECG信号中的出色性能，被广泛应用于医疗领域，尤其是在异常检测和分类任务中。
2. **可解释性AI的需求**：在医疗决策中，医生和患者需要对预测结果有深入的理解，因此，解释性AI（Explainable AI, XAI）成为关键需求。这涉及到开发方法，使模型不仅能够提供准确的结果，还能解释其决策背后的逻辑。
3. **CEFEs框架设计**：作者们介绍的CEFEs框架可能包含特征提取、模型训练和解释性组件，它可能使用了诸如注意力机制、局部可解释性方法或对抗性示例分析等技术，以可视化和量化模型决策过程，增强医生和用户的理解。
4. **合成医疗数据的应用**：为了提升模型的泛化能力和可靠性，文中可能还讨论了使用合成健康数据集在训练过程中扮演的角色，这有助于克服真实数据不足或隐私保护问题。
5. **结论与未来方向**：文章最后可能会总结当前的研究成果，同时指出未来可能的研究挑战，如提高模型解释性的有效性、结合更多领域专家的知识以及与传统医学知识的融合等。
这篇文章提供了深度学习在医疗决策支持系统中的一个实际案例，特别是针对ECG信号处理的可解释性框架，这对于推动AI在医疗领域的稳健应用和发展具有重要意义。
Articial Intelligence In Medicine 115 (2021) 102059
4
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 specic 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 classication outcome in a random forest type
of classier. 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 specic 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
sufcient 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 specic intricate structural clinical features for clinical diag-
nosis. Developing interpretable and explainable techniques for health-
care timeseries data creates supportive trust and condence 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. Denitions of Interpretation and Explanation in the context
of computation models are often used interchangeably. Montavon et al.
[1] denes 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
specic 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 dene (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].
d
intra
=
K
k=1
x
k,m
− y
k,n
∗
x
k,m
− y
k,n
(1)
d
inter
= |d
M
y1
intra
− d
M
y2
intra
| (2)
Where k represents the samples, m
th
data point of one input signal (ECG
Signal), n
th
data point of other input signal (Feature Map) and M
y1
, M
y2
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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