Automatic Atrial Fibrillation Detection Based on Heart Rate Variability and Spectral Features

Mei, Zhenning; Gu, Xiao; Chen, Hongyu; Chen, Wei

doi:10.1109/access.2018.2871220

Cited by 45 publications

(28 citation statements)

References 26 publications

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“…Assuming the driver's heart parameters, such as HR, HRV, and RR, can be measured reliably while driving, a medical check-up could be done in the car on a daily basis during commuting. ECG-or BCG-based monitoring of these parameters enables early detection of heart disorders, such as atrial fibrillation [70,71], the precursor of stroke. To this end, the possible routes are highlighted with blue lines in Figure 4.…”

Section: Discussionmentioning

confidence: 99%

Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

Wang

Warnecke

Haghi

et al. 2020

Sensors

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Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest.

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Section: Discussionmentioning

confidence: 99%

Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

Wang

Warnecke

Haghi

et al. 2020

Sensors

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“…Then, with an unsupervised dynamic time warping (DTW)-based learning approach using the K-medoids clustering method, the distorted heartbeats are identified and purified. SVM and bagging trees have been used in [ 11 ] to detect atrial fibrillation from features from ECG signals.…”

Section: Wearable Devices and Machine Learningmentioning

confidence: 99%

Machine Learning for Healthcare Wearable Devices: The Big Picture

Sabry

Eltaras

Labda

et al. 2022

Journal of Healthcare Engineering

145

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Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases’ diagnosis, and it can play a great role in elderly care and patient’s health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.

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“…3) Electrocardiogram ECG: Because of ECG is highly correlated with the diagnosis of many diseases, it has become an important part of the field of biomedical signal processing; research directions include signal monitoring, model algorithm optimization, disease screening, etc [34][35][36]. 4) Proposed model: According to the data characteristics of biological signals, an algorithm model for signal recognition is constructed [37][38].…”

Section: Distribution Of Scientific Research Forcesmentioning

confidence: 99%

International publication trends in Biomedical Signal Processing research: A bibliometric analysis (2009-2018)

Cui

Zhang

Yan

et al. 2019

Preprint

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Background This study aimed to analyze and assess the scientific outputs of biomedical signal processing by using bibliometric analysis.Methods Data were obtained from the WoSCC of Thomson Reuters, on January 21, 2019. VOSviewer (Leiden University, Van Eck and Waltman, Netherlands) and carrot 2 (Poznan University of Technology, Dawid Weiss, Poland) were used to analyze the knowledge maps and clusters of countries, research area and hot topics.Results A total of 335 articles on biomedical signal processing were identified. The number of publications increased only mildly during from 2009 (n=14) to 2018 (n=62). The majority of articles were published in the USA, and the leading institute was University of California System. Van Huffel S was the top authors on the topic, and the research area of “Engineering” generated the most publications. Cluter analysis (keywords and terms) indicated that “algorithm” and “extracted features” was the most hot topics on biomedical signal processing.Conclusion Overall, Through analysis of biosignal single processing related research in the past 10 years, the results found that the close international cooperation in this field, and the future research trends may be the signal acquisition methods and signal processing algorithms. They can provide reference for researchers in related field to choose research directions and find cooperative resources.

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Automatic Atrial Fibrillation Detection Based on Heart Rate Variability and Spectral Features

Cited by 45 publications

References 26 publications

Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

Machine Learning for Healthcare Wearable Devices: The Big Picture

International publication trends in Biomedical Signal Processing research: A bibliometric analysis (2009-2018)

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