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Heartbeat biometrics: a sensing system perspective

Steven A. Israel, John M. Irvine
2012 International Journal of Cognitive Biometrics  
He serves on planning committees for IEEE and SPIE, and has served on several advisory panels for the Department of Defence and the Department of Energy.  ...  The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors.  ...  The reason for alignment is that most techniques rely on features derived from the morphology, amplitude, and timing of the heartbeat, thus requiring segmentation of individual beats.  ... 
doi:10.1504/ijcb.2012.046514 fatcat:ukmzekdxjfasxpmibutiaxj5za

Artifact Reduction in Multichannel ECG Recordings Acquired with Textile Electrodes

D. Zelle, P. Fiedler, J. Haueisen
2012 Biomedical Engineering  
The acquired ECG recordings were preprocessed in Matlab using a wavelet decomposition algorithm to remove baseline drift and wavelet denoising to reduce power-line interference and high-frequency noise  ...  In order to obtain five Einthoven-I-leads ten horizontally aligned electrodes were located on the left and right side of the shoulders, the chest and the back.  ... 
doi:10.1515/bmt-2012-4401 fatcat:blx53xtrobe3jlfgkeped6jiva

ECG signal acquisition and analysis for telemonitoring

Emil Plesnik, Olga Malgina, Jurij F. Tasic, Matej Zajc
2010 Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference  
The algorithm was tested on real ECG signals acquired with a commercial monitoring system Alive Heart Monitor [1] and also for reference on signals from MIT-BIH online ECG signal database [2].  ...  Furthermore, we describe the fundamental architecture of an electrocardiograph and describe and overview the basic methods for ECG signal processing.  ...  ACKNOWLEDGMENT This work was supported in part by the Ministry of Higher Education, Science and Technology, Slovenia, under Scientific Program P2-0246.  ... 
doi:10.1109/melcon.2010.5475990 fatcat:ygv5dbbktncgvbcrzm2acqxgha

Activity-aware ECG-based patient authentication for remote health monitoring

Janani C. Sriram, Minho Shin, Tanzeem Choudhury, David Kotz
2009 Proceedings of the 2009 international conference on Multimodal interfaces - ICMI-MLMI '09  
However, perturbation of the ECG signal due to physical activity is a major obstacle in applying the technology in real-world situations.  ...  In this paper, we present a novel ECG and accelerometer-based system that can authenticate individuals in an ongoing manner under various activity conditions.  ...  The estimated baseline is then subtracted to align all beats within a window.  ... 
doi:10.1145/1647314.1647378 dblp:conf/icmi/SriramSCK09 fatcat:mfl3i6gpifaihlhcq4zdbotady

The North Sea Bicycle Race ECG Project: Time-Domain Analysis

Dominika Długosz, Trygve Eftestø, Aleksandra Królak, Tomasz Wiktorski, Stein Ørn
2018 Journal of Automation, Mobile Robotics & Intelligent Systems  
Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology.  ...  This paper presents results of a time-domain analysis of ECG data collected in 2014, implementing K-Means clustering.  ...  Data Pre-processing In the initial stage of processing, the lead-I ECG signal was subjected to filtering to suppress high-frequency noise and remove baseline drift.  ... 
doi:10.14313/jamris_1-2018/3 fatcat:26gwpfxt5jhhravx5eoxpdhbgu

An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks

Jingting Yao, S Tridandapani, W F Auffermann, C A Wick, P T Bhatti
2018 IEEE Journal of Translational Engineering in Health and Medicine  
Relying upon a three-layer artificial neural network that adaptively fuses individual ECG- and SCG-based quiescence predictions on a beat-by-beat basis, this framework yields a personalized quiescence  ...  The combination of ECG and SCG signals for quiescence prediction bears promise for a more personalized and reliable approach than ECG-only-based method to predict cardiac quiescence for prospective CCTA  ...  PRE-PROCESSING Raw signals were pre-processed to remove the noise and baseline drift [28] .  ... 
doi:10.1109/jtehm.2018.2869141 pmid:30405976 pmcid:PMC6204924 fatcat:prk7gve2kjas7hekigqknpk6ie

Heart failure grading using single-lead electrocardiography [article]

Eriko Hasumi, Katsuhito Fujiu, Ying Chen, Yu Shimizu, Tsukasa Oshima, Hiroshi Matsunaga, Jun Matsuda, Takumi James Matsubara, Nobuaki Fukuma, Liu Yuxiang, Junichi Sugita, Yukiteru Nakayama (+6 others)
2020 medRxiv   pre-print
We trained a CNN using ECG data and the HF classification from 7,865 patients with HF.  ...  Here we developed a CNN algorithm to classify the severity of HF based on single-lead ECG data, irrespective of EF.  ...  Before heartbeat segmentation, preprocessing of ECG recordings was conducted to eliminate baseline drift and noise. First, the baseline drift was removed using the wavelet decomposition method.  ... 
doi:10.1101/2020.10.08.20209700 fatcat:izx3rbk6lnh7vcfmhvwlnvah2a

An evaluation of the generalisability and applicability of the PhysioNet electrocardiogram (ECG) repository as test cases for ECG-based biometrics

Manal M. Tantawi, Kenneth Revett, Mohammed Fahmy Tolba, Abdel Badeeh Salem
2012 International Journal of Cognitive Biometrics  
The purpose of this study was to evaluate the applicability and/or suitability of the PhysioNet ECG data for deployment within biometrics.  ...  Because of the convenience afforded by the internet, literally thousands of ECG records can be downloaded and used for non-medical purposes, such as biometrics.  ...  Figure 2 shows an ECG segment before and after the filter application, which has corrected baseline drift by band pass filtering.  ... 
doi:10.1504/ijcb.2012.046515 fatcat:wmodfmwwlfacjgezt4ow5thfl4

A Comprehensive Survey on ECG signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Anthony Ngozichukwuka Uwaechia, Dzati Athiar Ramli
2021 IEEE Access  
To remove muscle noise, baseline wander, motion artifacts, baseline drift, frequency noise, and the power line interference, band-pass filter with cutoff frequencies spread in the range (0.1 − 100) Hz  ...  High-pass filters with cut-off frequencies of 0.5 Hz [134] , [135] , [ [140] have been employed to eliminate baseline wander and to suppress baseline drift.  ... 
doi:10.1109/access.2021.3095248 fatcat:6bdgelhmanb27ii25e25wkewey

PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier

Max Gordon, Cranos Williams
2018 Biocomputing 2019  
In many of these applications, the long-term nature of the monitoring required and the infrequency of PVCs make manual observation for PVCs impractical.  ...  In particular, systems using large numbers of trained parameters have the potential to require large amounts of training data and computation and may have issues generalizing due to their potential to  ...  We then removed the mean from each segmented QRS complex to reduce the impact of baseline drift, variations in instrumentation, and differences across patients.  ... 
doi:10.1142/9789813279827_0005 fatcat:oe7kfcsx65eqjepfkzxohqwe2a

Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents

Muhammad E. H. Chowdhury, Khawla Alzoubi, Amith Khandakar, Ridab Khallifa, Rayaan Abouhasera, Sirine Koubaa, Rashid Ahmed, Md Anwarul Hasan
2019 Sensors  
Heart attack is one of the leading causes of human death worldwide.  ...  Linear classification and several machine algorithms were trained and tested for real-time application.  ...  Conflicts of Interest: The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or  ... 
doi:10.3390/s19122780 fatcat:yfemqhcimrhy7pr3w5yirb6dai

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL [article]

Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, Wojciech Samek
2020 arXiv   pre-print
We also put forward benchmarking results for the ICBEB2018 challenge ECG dataset and discuss prospects of transfer learning using classifiers pretrained on PTB-XL.  ...  These results are complemented by deeper insights into the classification algorithm in terms of hidden stratification, model uncertainty and an exploratory interpretability analysis.  ...  present static noise (static noise) or local bursts of high voltage induced by external sources (burst noise)), DRIFT (baseline wandering).  ... 
arXiv:2004.13701v1 fatcat:youhas6lmrbbre7mo3x4xn27n4

Deep Learning in Cardiology

Paschalis Bizopoulos, Dimitrios Koutsouris
2019 IEEE Reviews in Biomedical Engineering  
The medical field is creating large amount of data that physicians are unable to decipher and use efficiently.  ...  Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention.  ...  [76] used WT to remove high frequency noise and baseline drift and biorthogonal spline wavelet for detecting the R-peak.  ... 
doi:10.1109/rbme.2018.2885714 fatcat:pa47trmskvflvig5cotth265q4

Adrenergic stimulation promotes T-wave alternans and arrhythmia inducibility in a TNF-α genetic mouse model of congestive heart failure

Vladimir Shusterman, Charles F. McTiernan, Anna Goldberg, Samir Saba, Guy Salama, Barry London
2010 American Journal of Physiology. Heart and Circulatory Physiology  
The magnitude of TWA increased significantly after the isoproterenol injection compared with the baseline in TNF-␣ mice (P ϭ 0.003) but not in FVB mice.  ...  The mean amplitude of the T wave and area under the T wave increased 60% and 80% in FVB mice (P ϭ 0.006 and 0.009) but not in TNF-␣ mice.  ...  Artifact and residual baseline drift control.  ... 
doi:10.1152/ajpheart.01024.2008 pmid:19940073 pmcid:PMC2822584 fatcat:4dhmjvzboje7jh6h7vkcfmdju4

ECG Biometric Authentication: A Comparative Analysis

Mohit Ingale, Renato Cordeiro, Siddartha Thentu, Younghee Park, Nima Karimian
2020 IEEE Access  
We explore the impact of filtering type, segmentation, feature extraction, and health status on ECG biometric by using the evaluation metrics.  ...  However, one of the main challenges in ECG-based biometric development is the lack of large ECG databases.  ...  ACKNOWLEDGMENT The authors would like to thank Chun-Min Chang and the Research Center for Applied Sciences, Academia Sinica, Taiwan for providing the ECG-BG dataset.  ... 
doi:10.1109/access.2020.3004464 fatcat:hhk7aumwpbfpzdgjsusu4xs23y
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