An ECG Delineation and Arrhythmia Classification System Using Slope Variation Measurement by Ternary Second-Order Delta Modulators for Wearable ECG Sensors

Tang, Xiaochen; Tang, Wei

doi:10.1109/tbcas.2021.3113665

Cited by 34 publications

(15 citation statements)

References 43 publications

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“…The automated classification model can only be studied if a large ECG database with annotations is available. The MIT-BIH, ST-T, and AHA databases are used in the majority of contemporary ECG research [6,19]. There is a single class for all of the ECG indications.…”

Section: Background Studymentioning

confidence: 99%

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ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

et al. 2022

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The advancement in deep learning techniques has helped researchers acquire and process multimodal data signals from different healthcare domains. Now, the focus has shifted towards providing end-to-end solutions, i.e., processing these data and developing models that can be directly implemented on edge devices. To achieve this, the researchers try to solve two problems: (I) reduce the complex feature dependencies and (II) reduce the complexity of the deep learning model without compromising accuracy. In this paper, we focus on the later part of reducing the complexity of the model by using the knowledge distillation framework. We have introduced knowledge distillation on the Vision Transformer model to study the MIT-BIH Arrhythmia Database. A tenfold crossvalidation technique was used to validate the model, and we obtained a 99.7% F1 score and 99.3% accuracy. The model was further tested on the Xilinx Alveo U50 FPGA accelerator, and it is found fit for any low-powered wearable device implementation.

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Section: Background Studymentioning

confidence: 99%

“…It is often the most prominent portion of the ECG and hence the easiest to identify. The P-wave indicates the sinus rhythm, whereas a prolonged PR interval generally indicates a first-degree heart blockage [4,6]. As a result, cardiologists consistently use ECG to assess the heart's condition and performance.…”

Section: Introductionmentioning

confidence: 99%

ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

et al. 2022

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“…Specifically, the authors in [ 82 , 95 ] used the MAX30100 blood oxygen sensor in addition to the ECG sensor, whereas the authors in [ 84 ] used the ADXL345 triaxial accelerometer, and the authors in [ 97 ] used the MAX30102 pulse oximeter sensor. Other studies also used different ECG sensors, with the authors in [ 87 ] building their portable devices using the “Ternary Second-Order Delta Modulator Circuits” to acquire ECG data. In the same context, the authors in [ 111 , 124 ] used the sensor ECG AFE and other tools to build a wearable device capable of acquiring the necessary vital signs data.…”

Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

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“…The measured pulses are then converted into an electric signal that can be used to measure irregularities in the patient’s heart rate [ 64 ]. Naturally, electrocardiograms are mainly used in medical facilities or by caregivers and nurses to monitor heart health [ 65 , 66 ], however, the sources used for this review have also utilized them for identifying epileptic seizures.…”

Section: Sensorsmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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An ECG Delineation and Arrhythmia Classification System Using Slope Variation Measurement by Ternary Second-Order Delta Modulators for Wearable ECG Sensors

Cited by 34 publications

References 43 publications

ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

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