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Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects

Hai-Cheng Wei, Ming-Xia Xiao, Hong-Yu Chen, Yun-Qin Li, Hsien-Tsai Wu, Cheuk-Kwan Sun
2018 Scientific Reports  
Six-second DVP signals from right index finger acquired through photoplethysmography were decomposed using ensemble empirical mode decomposition.  ...  Using one intrinsic mode function (IMF5), stiffness index (SI) and instantaneous energy of maximal energy (f Emax ) were obtained.  ...  Ensemble empirical mode decomposition (EEMD), one of the two components of the Hilbert-Huang transformation, is an adaptive time-frequency data analysis method found to be useful for extracting signals  ... 
doi:10.1038/s41598-018-34091-6 fatcat:atsjtxk6gfdojohvazzao6vt6q

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Muhammad Umar Khan, Sumair Aziz, Tallha Akram, Fatima Amjad, Khushbakht Iqtidar, Yunyoung Nam, Muhammad Attique Khan
2021 Sensors  
The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components.  ...  This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension.  ...  The authors in [18] proposed a method to detect ECG hypertensive signals using empirical mode decomposition (EMD) for preprocessing of the signals, yielding an accuracy of 97.7% through the KNN classifier  ... 
doi:10.3390/s21010247 pmid:33401652 fatcat:tawv33dnyfgs5phtydm5pur4qq

In-Body Electromagnetic Sensor Combined with AI-Enhanced Electrocardiography for Pacemaker Working Status Telemonitoring

Wu Lu, Ranran Ding, Bingjie Wu, Wenbin Zhao, Dong Huang, Xue Zhang, Yunze He
2021 Journal of Sensors  
A prototype of the sensor was implemented on a human torso, and the in vitro test results prove that the sensor can work properly for the 1-4-meter transmission range.  ...  A 16-bit high-resolution analog front-end (AFE) and an energy-efficient 32-bit CPU are used for instantaneous ECG recording.  ...  Acknowledgments This work is supported by the National Nature Science Foundation of China (Nos. 82172068 and 51707113).  ... 
doi:10.1155/2021/8444015 fatcat:3qfpfwecbzdu3mfaxcwigbsxj4

An Emotion Recognition Approach based on Wavelet Transform and Second-Order Difference Plot of ECG

A. Goshvarpour, A. Abbasi, A. Goshvarpour
2017 Journal of Artificial Intelligence and Data Mining  
The results of this study indicate the usefulness of the WT in combination with nonlinear technique in characterizing emotional states.  ...  Applying LS-SVM, the maximum classification rate of 80.24 % was reached for discrimination between rest and fear.  ...  Acknowledgment We gratefully acknowledge Computational Neuroscience Laboratory, where the data was collected, and all the subjects volunteered for the study.  ... 
doi:10.22044/jadm.2017.887 doaj:c34a1b356df1401cab3ee8c555c82f58 fatcat:o74wkjzxvffghh6wwu34ghyccm

Sleep Apnea Detection From Variational Mode Decomposed EEG Signal Using a Hybrid CNN-BiLSTM

Tanvir Mahmud, Ishtiaque Ahmed Khan, Talha Ibn Mahmud, Shaikh Anowarul Fattah, Wei-Ping Zhu, M. Omair Ahmad
2021 IEEE Access  
INDEX TERMS Sleep apnea, variational mode decomposition, computer-aided diagnosis, neural network, EEG signal.  ...  Unlike conventional methods of direct feature extraction from EEG signals, the variational mode decomposition (VMD) algorithm is utilized in the proposed method to decompose the EEG signals into a number  ...  Instead of frequency division, empirical mode decomposition is also used to analyze the characteristics of the EEG signal during apnea events.  ... 
doi:10.1109/access.2021.3097090 fatcat:o3jfzlllqrgerchpghoi266faa

Wearable Intelligent Systems for E-Health

Carmen C.Y. Poon, Qing Liu, Hui Gao, Wan-Hua Lin, Yuan-Ting Zhang
2011 Journal of Computing Science and Engineering  
Category: Smart and intelligent computing  ...  Therefore, the development of novel wearable intelligent systems to effectively monitor the vital signs continuously over a 24 hour period is in some cases crucial for understanding the progression of  ...  ACKNOWLEDGEMENTS The authors are grateful to Mr. Laurence Chan and Mr. Billy Leung for their contributions to the development of the prototype of the h-shirt, and Ms.  ... 
doi:10.5626/jcse.2011.5.3.246 fatcat:5pd42hhay5bstlu24knuwviwbm

Enhanced Automated Diagnosis of Coronary Artery Disease Using Features Extracted from QT Interval Time Series and ST–T Waveform

Lianke Yao, Changchun Liu, Peng Li, Jikuo Wang, Yuanyuan Liu, Wang Li, Xinpei Wang, Han Li, Huan Zhang
2020 IEEE Access  
To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed.  ...  The results of this study support the potential of information derived from the QT interval time-series and ST-T segment waveforms in ECG-based automated CAD detection.  ...  Jiao of the Institute of Biomedical Engineering at Shandong University, as well as all the nurses of the No. 45 inpatient area at Shandong Provincial Qianfoshan Hospital for their assistance with the data  ... 
doi:10.1109/access.2020.3008965 fatcat:no2q3jjjvzddzo3ty3iopcj2za

Structural and functional changes in COPD : What we have learned from imaging

Ilyes Benlala, François Laurent, Gael Dournes
2021 Respirology (Carlton South. Print)  
Chronic obstructive pulmonary disease (COPD) is the third leading cause of mortality worldwide. It is a heterogeneous disease involving different components of the lung to varying extents.  ...  Developments in medical imaging and image analysis techniques provide new insights in the assessment of the structural and functional changes of the disease.  ...  The acquisition is made in freebreathing mode, and there is no need for a contrast product injection.  ... 
doi:10.1111/resp.14047 pmid:33829593 fatcat:l46k7v6h2jhizji7xawmzguque

Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework

Honghao Dai, Xiaodong Jia, Laura Pahren, Jay Lee, Brandon Foreman
2020 Frontiers in Neurology  
In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome.  ...  We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  ... 
doi:10.3389/fneur.2020.00959 pmid:33013638 pmcid:PMC7496370 fatcat:xzou5khknvhhxmxfalmkkwiqva

A Multistage Deep Belief Networks Application on Arrhythmia Classification

2016 International Journal of Intelligent Systems and Applications in Engineering  
An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders  ...  The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years.  ...  ., 2014) used the SODP together with Empirical Mode Decomposition for diagnosis of congestive heart failure with an accuracy rate of 94.73% [28] .  ... 
doi:10.18201/ijisae.2016specialissue-146978 fatcat:sofr5ae3ivbuvicqcy73w5lshy

Semi-Skipping Layered Gated Unit and Efficient Network: Hybrid Deep Feature Selection Method for Edge Computing in EEG-Based Emotion Classification

Muhammad Adeel Asghar, Muhammad Jamil Khan, Humayun Shahid, Mohammad Shorfuzzaman, Neal Naixue Xiong, Raja Majid Mehmood
2021 IEEE Access  
With the development of neural networks for machine learning tools, the Electroencephalogram (EEG)-based classification of human emotions is increasingly important in providing health-care for Edge Computing  ...  To overcome the computational cost, an optimal function reduction method called the Bag of Visualized Characteristics (BoVC) is used.  ...  Many decomposition methods are used to deal with the non-linearity of the EEG signal. Reference [8] uses empirical mode decomposition (EMD) to classify emotions using the EEG signal.  ... 
doi:10.1109/access.2021.3051808 fatcat:iun4xhaf7fgutpnifmql5qea3u

Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

Yingchao Zou, Lean Yu, Kaijian He
2015 Entropy  
Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting  ...  For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based  ...  Our results show that wavelet entropy serves as a nontrivial tool for determining the optimal model specifications and parameters for multiscale analysis, such as wavelet analysis and empirical mode decomposition  ... 
doi:10.3390/e17107167 fatcat:odnhy7zrmfdb7cj26k6y4rjkp4

A Review of EMG Techniques for Detection of Gait Disorders [chapter]

Rajat Emanuel Singh, Kamran Iqbal, Gannon White, Jennifer K. Holtz
2019 Machine Learning in Medicine and Biology [Working Title]  
Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of  ...  EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities.  ...  [82] decomposed needle EMG from brachial biceps with ensemble empirical mode decomposition (EMD).  ... 
doi:10.5772/intechopen.84403 fatcat:dvv6bu4w65h3lboixghu322koy

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Liping Xie, Zilong Li, Yihan Zhou, Yiliu He, Jiaxin Zhu
2020 Sensors  
Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years.  ...  Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death.  ...  Empirical mode decomposition (EMD) serves as an alternative to wavelet analysis for ECG denoising [33] .  ... 
doi:10.3390/s20216318 pmid:33167558 pmcid:PMC7664289 fatcat:echda3mznbekrclhwj3e774gc4

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Luca Saba, Skandha S. Sanagala, Suneet K. Gupta, Vijaya K. Koppula, Amer M. Johri, Narendra N. Khanna, Sophie Mavrogeni, John R. Laird, Gyan Pareek, Martin Miner, Petros P. S kakis, Athanasios Protogerou (+13 others)
2021 Annals of Translational Medicine  
Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound  ...  Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally.  ...  (95) proposed a data mining framework for classifying symptomatic and asymptomatic plaque using bi-dimensional empirical mode decomposition and entropy features.  ... 
doi:10.21037/atm-20-7676 fatcat:2zuwvhfq3nbplangoriejpvg6a
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