Designing ECG monitoring healthcare system with federated transfer learning and explainable AI

Raza, Ali; Tran, Kim Phuc; Koehl, Ludovic; Li, Shujun

doi:10.1016/j.knosys.2021.107763

Cited by 94 publications

(54 citation statements)

References 56 publications

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“…In addition, to explain the causal relationship between input data and output results, an in-depth approach using technologies such as explainable AI, which has been recently studied, needs to be conducted. With respect to bio-signals, although explainable AI has been mainly applied to ECG ( Sanjana et al, 2020 ; Ganeshkumar et al, 2021 ; Jo et al, 2021 ; Maweu et al, 2021 ; Raza et al, 2021 ; Taniguchi et al, 2021 ), it is difficult to find a clear application case for medical purposes in PPG. Although it is difficult to say that the application of explainable AI to PPG has been generalized yet, it seems clear that explainable AI will be introduced into PPG analysis given the tendency for the development of machine learning to be introduced into other fields.…”

Section: Discussionmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

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Section: Discussionmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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“…Globally, researchers emphasize technology to be made more human-centric. For AI to be fair, work on explainability of AI [ 143 , 144 ] is on the rise to minimize challenges raised by centralized machine learning architectures such as privacy concerns and failure to explain and interpret the AI models. Additionally, scrutiny of AI algorithms is escalating due to the mistrust of AI, leading to the high demand for auditing frameworks [ 141 ].…”

Section: Rqn5: What Are the Limitations Of The Reviewed Studies In Mi...mentioning

confidence: 99%

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Nandutu

Atemkeng

Okouma

2022

Sensors

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Worldwide, the persistent trend of human and animal life losses, as well as damage to properties due to wildlife–vehicle collisions (WVCs) remains a significant source of concerns for a broad range of stakeholders. To mitigate their occurrences and impact, many approaches are being adopted, with varying successes. Because of their increased versatility and increasing efficiency, Artificial Intelligence-based methods have been experiencing a significant level of adoption. The present work extensively reviews the literature on intelligent systems incorporating sensor technologies and/or machine learning methods to mitigate WVCs. Included in our review is an investigation of key factors contributing to human–wildlife conflicts, as well as a discussion of dominant state-of-the-art datasets used in the mitigation of WVCs. Our study combines a systematic review with bibliometric analysis. We find that most animal detection systems (excluding autonomous vehicles) are relying neither on state-of-the-art datasets nor on recent breakthrough machine learning approaches. We, therefore, argue that the use of the latest datasets and machine learning techniques will minimize false detection and improve model performance. In addition, the present work covers a comprehensive list of associated challenges ranging from failure to detect hotspot areas to limitations in training datasets. Future research directions identified include the design and development of algorithms for real-time animal detection systems. The latter provides a rationale for the applicability of our proposed solutions, for which we designed a continuous product development lifecycle to determine their feasibility.

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“…Due to inherent smoothing in provided explanations, some XAI techniques such as Grad-CAM and its variants are recently more preferred. Vijayarangan et al [36], Raza et al [37] employed Grad-CAM on 1D-CNN for single-lead ECG classification. Ganeshkumar et al [38] further applied Grad-CAM on a multi-lead circumstance but generated the same class activation map for multiple input signals.…”

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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Designing ECG monitoring healthcare system with federated transfer learning and explainable AI

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 94 publications

References 56 publications

Photoplethysmogram Analysis and Applications: An Integrative Review

Photoplethysmogram Analysis and Applications: An Integrative Review

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

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

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