Deep Transferable Intelligence for Spatial Variability Characterization and Data-Efficient Learning in Biomechanical Measurement

Gangadharan, Kiirthanaa; Zhang, Qingxue

doi:10.1109/tim.2023.3265753

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…It will be promising to further enhance the system with more effective pattern extraction [ 37 , 38 , 39 , 40 ] studies. The proposed approaches could also be generalized to other applications or signals [ 41 , 42 , 43 , 44 ] for event detection, template signal learning, and signal quality purification.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

Zhang

Zhou

2023

Sensors

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Background: Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. Methods: A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. Results: With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. Conclusions: Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

Zhang

Zhou

2023

Sensors

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“…We have firstly built a residual CNN -ResNet [44][45][46] for ECG-based multi-class heart disease classification, as showin in Fig. 3(a).…”

Section: B Residual Convolutional Neural Networkmentioning

confidence: 99%

Ultra-Efficient Edge Cardiac Disease Detection Towards Real-Time Precision Health

Wong,

Nerbonne,

Zhang

2024

IEEE Access

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Nowadays, intensive interests are targeting the deep learning on edge precision health towards instantaneous disease measurements. However, edge inference usually has constrained computing resource, which poses a great challenge on running the heavy deep learning for real-time measurements. In this study, we propose to leverage a knowledge distillation methodology to enable ultra-efficient deep learning on edge. We take a special interest in Electrocardiogram (ECG)-based cardiac abnormality measurement. More specifically, we propose to train two deep learning models, including a heavy teacher model and a lightweight student model, and leverage the 'soft target distribution' learned by the teacher model to supervise the learning of the student model. So, the powerful teacher model can transfer learned knowledge to the student model and boost the latter's accuracy. Further, to mitigate the vulnerability of the deep learning model under adversarial attacks, we further introduce preserving-robustness learning to the student model, without needing extra computing resources, through enhancing its loss function under adversarial perturbations. Our experiments on real-time heart disease measurement have demonstrated that, the learned lightweight student model, with a model reduction of 45x and under adversarial attacks, can still achieve comparable disease detection performance. The proposed robust knowledge distillation methodology has effectively enabled light-weight and secure cardiac measurement. Significance: This study is expected to contribute to on-edge deep learning-powered disease detection, for real-time, long term, and secured cardiac precision health.

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“…Table 3 summarizes the important techniques (mainly data-related) that are vital while developing DC-AI-based systems. Further technical information about these techniques can be learned from recently published articles in reputed venues [28,[46][47][48][49][50][51][52][53][54][55][56][57]. In the coming years, many innovative techniques are expected in DC-AI to improve the quality of data enclosed in different modalities [58].…”

Section: Supportive Techniques For Dc-ai Development/implementationmentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

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Deep Transferable Intelligence for Spatial Variability Characterization and Data-Efficient Learning in Biomechanical Measurement

Cited by 6 publications

References 37 publications

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

Ultra-Efficient Edge Cardiac Disease Detection Towards Real-Time Precision Health

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

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