Resource Constrained CVD Classification Using Single Lead ECG On Wearable and Implantable Devices

Ukil, Arijit; Sahu, Ishan; Majumdar, Angshul; Racha, Sai Chander; Kulkarni, Gitesh; Choudhury, Anirban Dutta; Khandelwal, Sundeep; Ghose, Avik; Pal, Arpan

doi:10.1109/embc46164.2021.9630348

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…In Scire et al, however [ 60 ], all the above-mentioned beats except unknown beats are considered for classification. Another 5-class beat classification identifies normal (N), R-on-T premature ventricular contraction (Ron-T PVC), premature ventricular contraction (PVC), supraventricular premature or ectopic beat (SP or EB), and unclassified beat (UB) [ 61 , 62 ]. Other works determine the varying number of arrhythmia that can be used to detect heart malfunction and provide specific intervention insight.…”

Section: Resultsmentioning

confidence: 99%

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Shumba

Montanaro

Sergi

et al. 2023

Sensors

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Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

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Section: Resultsmentioning

confidence: 99%

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Shumba

Montanaro

Sergi

et al. 2023

Sensors

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“…For this reason, it is widely adopted in several fields where existing models are well-performing but unable to be deployed ''as they are'' in resource-constrained hardware. This is the case with large scaling requirements [89], bandwidth-limited domains [82], and healthcare applications, where the trade-off between accuracy and model size needs to produce a high accuracy model that can fit the hardware requirements [83].…”

Section: ) Knowledge Distillationmentioning

confidence: 99%

A Machine Learning-Oriented Survey on Tiny Machine Learning

Capogrosso,

Cunico,

Cheng

et al. 2024

IEEE Access

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The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly, we will examine the three different workflows for implementing a TinyML-based system, i.e., MLoriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications. Finally, we discuss the challenges and future directions.

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“…Due to the specificity of wearable devices, it is required that the model for classification occupies as little memory as possible. Traditional classifiers such as Support vector machine (SVM) and k-nearest neighbor (KNN) have been relatively saturated with classification results [26][27][28]. With the development of deep learning, many lightweight neural networks based on Deep neural network (DNN), Convolutional neural network (CNN) or Bi-directional LSTMs (Bi-LSTMs) are used for ECG signal classification.…”

Section: Introductionmentioning

confidence: 99%

ECG signal classification in wearable devices based on compressed domain

et al. 2023

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Wearable devices are often used to diagnose arrhythmia, but the electrocardiogram (ECG) monitoring process generates a large amount of data, which will affect the detection speed and accuracy. In order to solve this problem, many studies have applied deep compressed sensing (DCS) technology to ECG monitoring, which can under-sampling and reconstruct ECG signals, greatly optimizing the diagnosis process, but the reconstruction process is complex and expensive. In this paper, we propose an improved classification scheme for deep compressed sensing models. The framework is comprised of four modules: pre-processing; compression; and classification. Firstly, the normalized ECG signals are compressed adaptively in the three convolutional layers, and then the compressed data is directly put into the classification network to obtain the results of four kinds of ECG signals. We conducted our experiments on the MIT-BIH Arrhythmia Database and Ali Cloud Tianchi ECG signal Database to validate the robustness of our model, adopting Accuracy, Precision, Sensitivity and F1-score as the evaluation metrics. When the compression ratio (CR) is 0.2, our model has 98.16% accuracy, 98.28% average accuracy, 98.09% Sensitivity and 98.06% F1-score, all of which are better than other models.

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Resource Constrained CVD Classification Using Single Lead ECG On Wearable and Implantable Devices

Cited by 12 publications

References 9 publications

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

A Machine Learning-Oriented Survey on Tiny Machine Learning

ECG signal classification in wearable devices based on compressed domain

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