A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines

Tang, Xiaochen; Ma, Ziwei; Hu, Qisong; Tang, Wei

doi:10.1109/tbme.2019.2926104

Cited by 64 publications

(37 citation statements)

References 41 publications

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“…The system may be combined with weather systems to have an early preparation of weather changes such as heavy rains or high temperatures. Furthermore, the drinking behavior data such as visit time and visit duration of the domestic animal could be applied to train machine learning algorithms to identify the abnormal features of animal health status [47][48][49][50][51]. Early detection and intervention could help save costs related to animal health issues.…”

Section: Discussionmentioning

confidence: 99%

A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

Tang

Biglari

Ebarb

et al. 2021

Sensors

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This paper presents a water intake monitoring system for animal agriculture that tracks individual animal watering behavior, water quality, and water consumption. The system is deployed in an outdoor environment to reach remote areas. The proposed system integrates motion detectors, cameras, water level sensors, flow meters, Radio-Frequency Identification (RFID) systems, and water temperature sensors. The data collection and control are performed using Arduino microcontrollers with custom-designed circuit boards. The data associated with each drinking event are water consumption, water temperature, drinking duration, animal identification, and pictures. The data and pictures are automatically stored on Secure Digital (SD) cards. The prototypes are deployed in a remote grazing site located in Tucumcari, New Mexico, USA. The system can be used to perform water consumption and watering behavior studies of both domestic animals and wild animals. The current system automatically records the drinking behavior of 29 cows in a two-week duration in the remote ranch.

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Section: Discussionmentioning

confidence: 99%

A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

Tang

Biglari

Ebarb

et al. 2021

Sensors

Self Cite

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“…Although automated arrhythmia detection from single-lead ECG has been extensively studied, its state-of-the-art algorithms usually rely on high-accuracy QRS and PT wave detection [ 139 ], which may not work effectively if used directly on data collected from wearable devices, considering the relatively poor signal quality and limited computational resources. End-to-end deep learning-based methods have been developed to solve this problem.…”

Section: Early Warning and Dysfunction Detectionmentioning

confidence: 99%

A review of wearable and unobtrusive sensing technologies for chronic disease management

Guo

Liu

Peng

et al. 2021

Computers in Biology and Medicine

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“…In the classification step, the extracted features are fed into some types of classifiers. Recently, there are a lot of ECG classification algorithms having been proposed, such as convolutional neural network (CNN) [11], support vector machines (SVM) [12], cluster analysis (CA) [13], random forests (RF) [14], optimum-path forest (OPF) [15], decision tree [16], logistic regression [17], neuro-fuzzy system [18], K-nearest neighbors (KNN) classification method [19], etc. After performing the two steps for the analysis of ECG signals, the recognition of LBBB, which is one of the predominant ECG heartbeats from MIT-BIH arrhythmia database, can be accomplished.…”

Section: The Related Work To Lbbb Detectionmentioning

confidence: 99%

Monitoring of driver’s biomedical signals using LoRa-based wireless communications

Lee

2021

IEICE Electron. Express

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This letter presents the design and implementation of a LoRabased wireless communication system aimed at monitoring of driver's biomedical signals in the car environments. The proposed system is composed of a sensor node, a LoRa gateway, and a cloud server. Each sensor node includes four parts as microcontroller unit, data collection unit, wireless communication unit, and supplied power unit. The microcontroller unit is mainly designed as the signal processing module to deal with the detection of abnormal ECG symptom such as left bundle branch block (LBBB), which is the most common symptoms of myocardial infarction. The data collection unit contains a sequential stage of the instrumentation amplifier and the filter blocks. The notification of LBBB detection is then transmitted to the LoRa gateway by wireless communication unit. The LoRa gateway with multiple wireless interfaces is designed to collect the information from the sensor node to transmit to cloud server. The experimental results are conducted to evaluate the detection performance of LBBB by means of MIT-BIH arrhythmia database.

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A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines

Cited by 64 publications

References 41 publications

A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

A review of wearable and unobtrusive sensing technologies for chronic disease management

Monitoring of driver’s biomedical signals using LoRa-based wireless communications

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