Robust medical ad hoc sensor networks (MASN) with wavelet-based ECG data mining
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To remove frequency noise, methods in frequency domain such as PSD, FFT, and low-pass/high-pass filtering are common to remove fluctuations in sensor signals.
“To remove frequency noise, the other methods in frequency domain such as such as power spectral density (PSD) fast Fourier transforms (FFT), and low-pass/high-pass filtering tools are common to remove the fluctuations in sensor signals [51,62].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [62]
Hu et al. applied a binary classifier version of SVM to categorize ECG signals into normal and arrhythmia classes.
“It applied a binary classifier version of SVM to categorize ECG signals into normal and arrhythmia classes.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [62]
Hu et al. used SVM to find arrhythmia in ECG signals.
“Hu et al. [62] used SVM to find out arrhythmia in ECG signals.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [62]
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Vo, M.-T., Do, H. N., Mai, L., & Le, C.-T. (2017) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-4361-1_50
A Review of Different Techniques for Biomedical Data Security
Kaur, H. P., & Pahuja, S. K. (2019) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-5225-7952-6.ch005
Forensic Investigation of Digital Crimes in Healthcare Applications
Ellouze, N., Rekhis, S., & Boudriga, N. A. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-1204-3.ch089
A Review of Different Techniques for Biomedical Data Security
Kaur, H. P., & Pahuja, S. K. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-8954-0.ch055
M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease
Boursalie, O., Samavi, R., & Doyle, T. E. (2023) · doi:10.32920/24132879.v1
M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease
Boursalie, O., Samavi, R., & Doyle, T. E. (2023) · doi:10.32920/24132879
- Authors
- Hu, F., Jiang, M., Celentano, L., & Xiao, Y.
- Affiliations
- Rochester Institute of Technology (US)
- University of Alabama (US)
- Corresponding author
- Hu, Fei
- Venue
- Ad Hoc Networks
- Publisher
- Elsevier BV
- Published
- 2008-09
- Volume
- 6
- Issue
- 7
- Pages
- 986-1012
- ISSN
- 1570-8705 (ISSN-L)
- OpenAlex
- W1977577985
- Cited by
- 74
- References
- 45
- Citations by year
- 2025: 1 · 2023: 4 · 2022: 2 · 2021: 3 · 2020: 6 · 2019: 3 · 2018: 6 · 2017: 2 · 2016: 6 · 2015: 8 · 2014: 3 · 2013: 5 · 2012: 5
- FWCI
- 2.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ad Hoc Networks (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- ECG monitoring, telemedicine, wireless sensor networks, network security, wavelet feature extraction
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