Explainable AI decision model for ECG data of cardiac disorders

Anand, Atul; Kadian, Tushar; Shetty, Manu Kumar; Gupta, Anubha

doi:10.1016/j.bspc.2022.103584

Cited by 48 publications

(21 citation statements)

References 31 publications

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“…Deep learning based agnostic methods have rarely been applied to predict hypotension, to the best of our knowledge. However, an attempt similar to our task was presented in [17] and [22], where electrocardiogram signal was employed as time-series input to predict arrhythmia. Among the three model-agnostic methods of SHAP, LIME, and Anchor applied in [17], only SHAP was evaluated to have an adequate interpretability in signal analysis.…”

Section: B Explainable Ai and Hypotension Prediction Model Interpreta...mentioning

confidence: 99%

Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model

Kim¹,

Kim²

2023

Preprint

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<p>Monitoring arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Thus, several efforts have been made to develop an artificial intelligence-based hypotension prediction index. Nevertheless, the use of these indices is limited because they may not provide a convincing interpretation of the association between predictors and hypotension. Herein, we developed an interpretable deep learning model that forecasts hypotension occurrences 10 min before a given 90 s ABP record. Internal and external validations of model performance reported the area under the receiver operating characteristic curve (AUC) as 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted by using predictors representing ABP trends that are automatically generated in the proposed model. Ultimately, we demonstrate high-applicability of a deep learning model that has a high accuracy performance and provides an interpretation of the association between ABP trends and hypotension in clinical practices.</p>

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Section: B Explainable Ai and Hypotension Prediction Model Interpreta...mentioning

confidence: 99%

Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model

Kim¹,

Kim²

2023

Preprint

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“…In existing state-of-the-art schemes, researchers have proposed explainability solutions in various practical healthcare applications such as COVID-19 detection and prediction [27], [28], cardio-vascular disease [29], [30], biomedical engineering [31], structural health monitoring [32], mental health diagnosis [33] and many more. The solutions also explore techniques to utilize DL techniques and the implementation of algorithms to provide global and local explanations.…”

Section: Novelty Of the Proposed Surveymentioning

confidence: 99%

“…The work in [92] describes existing AI techniques like ML/DL/NLP in and extends the survey to explain the importance of EXAI in future medicine and biomedical applications. Authors in [29] implements analyze the medical ECG data on proposing a generalized model, ST-CNN-GAP-5, that implements DNN algorithms using online available two ECG datasets with the achieved accuracy of 95.8% and AUC value of 99.46%. The dataset is analyzed using SHAP for explainability.…”

Section: State-of-the-artmentioning

confidence: 99%

Explainable AI for Healthcare 5.0: Opportunities and Challenges

et al. 2022

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In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital wellness. Healthcare 5.0 focuses on real-time patient monitoring, ambient control and wellness, and privacy compliance through assisted technologies like artificial intelligence (AI), Internet-of-Things (IoT), big data, and assisted networking channels. However, healthcare operational procedures, verifiability of prediction models, resilience, and lack of ethical and regulatory frameworks are potential hindrances to the realization of Healthcare 5.0. Recently, explainable AI (EXAI) has been a disruptive trend in AI that focuses on the explainability of traditional AI models by leveraging the decision-making of the models and prediction outputs. The explainability factor opens new opportunities to the black-box models and brings confidence in healthcare stakeholders to interpret the machine learning (ML) and deep learning (DL) models. EXAI is focused on improving clinical health practices and brings transparency to the predictive analysis, which is crucial in the healthcare domain. Recent surveys on EXAI in healthcare have not significantly focused on the data analysis and interpretation of models, which lowers its practical deployment opportunities. Owing to the gap, the proposed survey explicitly details the requirements of EXAI in Healthcare 5.0, the operational and data collection process. Based on the review method and presented research questions, systematically, the article unfolds a proposed architecture that presents an EXAI ensemble on the computerized tomography (CT) image classification and segmentation process. A solution taxonomy of EXAI in Healthcare 5.0 is proposed, and operational challenges are presented. A supported case study on electrocardiogram (ECG) monitoring is presented that preserves the privacy of local models via federated learning (FL) and EXAI for metric validation. The case-study is supported through experimental validation. The analysis proves the efficacy of EXAI in health setups that envisions real-life model deployments in a wide range of clinical applications.

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“…Hughes et al [33] proposed to use of Linear Interpretable Model-Agnostic Explanations (LIME). Zhang et al [22], Anand et al [34] applied SHapley Additive exPlanations (SHAP) analysis to test the interpretability of an ECG classification model. LIME and SHAP are both perturbation-based techniques that provide explanations based on the variation of output after applying perturbations to input.…”

Section: Reduced-lead Ecg Classificationmentioning

confidence: 99%

LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification

Le¹,

Pham²,

Nguyen³

et al. 2022

Preprint

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Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays a key role in successful treatment and intervention. Electrocardiogram (ECG) is the gold standard for identifying a variety of cardiovascular abnormalities. In clinical practices and most of the current research, standard 12-lead ECG is mainly used. However, using a lower number of leads can make ECG more prevalent as it can be conveniently recorded by portable or wearable devices. In this research, we develop a novel deep learning system to accurately identify multiple cardiovascular abnormalities by using only three ECG leads. Specifically, we use three separate Onedimensional Convolutional Neural Networks (1D-CNNs) as backbones to extract features from three input ECG leads separately. The architecture of 1D-CNNs is redesigned for high performance and low computational cost. A novel Lead-wise Attention module is then introduced to aggregate outputs from these three backbones, resulting in a more robust representation which is then passed through a Fully-Connected (FC) layer to perform classification. Moreover, to make the system's prediction clinically explainable, the Grad-CAM technique is modified to produce a high meaningful lead-wise explanation. Finally, we employ a pruning technique to reduce system size, forcing it suitable for deployment on hardware-constrained platforms. The proposed lightweight, explainable system is named LightX3ECG. We got classification performance in terms of F1 scores of 0.9718 and 0.8004 on two large-scale ECG datasets, i.e., Chapman and CPSC-2018, respectively, which surpassed current state-of-the-art methods while achieving higher computational and storage efficiency. Visual examinations and a sanity check were also performed to demonstrate the strength of our system's interpretability. To encourage further development, our source code is publicly available at https://github.com/lhkhiem28/LightX3ECG.

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Explainable AI decision model for ECG data of cardiac disorders

Cited by 48 publications

References 31 publications

Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model

Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model

Explainable AI for Healthcare 5.0: Opportunities and Challenges

LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification

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