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https://www.readbyqxmd.com/read/28644795/a-novel-short-term-event-extraction-algorithm-for-biomedical-signals
#1
Sasan Yazdani, Sibylle Fallet, Jean-Marc Vesin
In this paper we propose a fast novel non-linear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal is multiplied, heightening events of interest. This algorithm is thoroughly assessed on benchmark datasets in three different biomedical applications namely, ECG QRS-complex detection, EEG K-complex detection, and imaging photoplethysmography (iPPG) peak detection...
June 21, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28644794/electrocardiogram-signal-quality-assessment-based-on-structural-image-similarity-metric
#2
Yalda Shahriari, Richard Fidler, Michele Pelter, Yong Bai, Andrea Villaroman, Xiao Hu
OBJECTIVE: We developed an image-based electrocardiographic (ECG) quality assessment technique that mimics how clinicians annotate ECG signal quality. METHODS: We adopted the Structural Similarity Measure (SSIM) to compare images of two ECG records that are obtained from displaying ECGs in a standard scale. Then a subset of representative ECG images from the training set was selected as templates through a clustering method. SSIM between each image and all the templates were used as the feature vector for the linear discriminant analysis (LDA) classifier...
June 21, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28635670/single-lead-fetal-ecg-extraction-based-on-a-parallel-marginalized-particle-filter
#3
Zhidong Zhao, Huiling Tong, Yanjun Deng, Wen Xu, Yefei Zhang, Haihui Ye
This paper presents a novel method for extracting the fetal ECG (FECG) from a single-lead abdominal signal. A dynamical model for a modified abdominal signal is proposed, in which both the maternal ECG (MECG) and the FECG are modeled, and then a parallel marginalized particle filter (par-MPF) is used for tracking the abdominal signal. Finally, the FECG and MECG are simultaneously separated. Several experiments are conducted using both simulated and clinical signals. The results indicate that the method proposed in this paper effectively extracts the FECG and outperforms other Bayesian filtering algorithms...
June 21, 2017: Sensors
https://www.readbyqxmd.com/read/28626057/abnormal-p-wave-axis-and-ischemic-stroke-the-aric-study-atherosclerosis-risk-in-communities
#4
Ankit Maheshwari, Faye L Norby, Elsayed Z Soliman, Ryan J Koene, Mary R Rooney, Wesley T O'Neal, Alvaro Alonso, Lin Y Chen
BACKGROUND AND PURPOSE: Abnormal P-wave axis (aPWA) has been linked to incident atrial fibrillation and mortality; however, the relationship between aPWA and stroke has not been reported. We hypothesized that aPWA is associated with ischemic stroke independent of atrial fibrillation and other stroke risk factors and tested our hypothesis in the ARIC study (Atherosclerosis Risk In Communities), a community-based prospective cohort study. METHODS: We included 15 102 participants (aged 54...
June 16, 2017: Stroke; a Journal of Cerebral Circulation
https://www.readbyqxmd.com/read/28624712/classification-of-ecg-heartbeats-using-nonlinear-decomposition-methods-and-support-vector-machine
#5
Kandala N V P S Rajesh, Ravindra Dhuli
Classifying electrocardiogram (ECG) heartbeats for arrhythmic risk prediction is a challenging task due to minute variations in the amplitude, duration and morphology of the ECG signal. In this paper, we propose two feature extraction approaches to classify five types of heartbeats: normal, premature ventricular contraction, atrial premature contraction, left bundle branch block and right bundle branch block. In the first approach, ECG beats are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD)...
June 15, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28622529/automatic-multimodal-detection-for-long-term-seizure-documentation-in-epilepsy
#6
F Fürbass, S Kampusch, E Kaniusas, J Koren, S Pirker, R Hopfengärtner, H Stefan, T Kluge, C Baumgartner
OBJECTIVE: This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients. METHODS: An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance...
May 25, 2017: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
https://www.readbyqxmd.com/read/28619766/diagnostic-performance-of-an-automatic-blood-pressure-measurement-device-microlife-watchbp-home-a-for-atrial-fibrillation-screening-in-a-real-world-primary-care-setting
#7
Pak-Hei Chan, Chun-Ka Wong, Louise Pun, Yu-Fai Wong, Michelle Man-Ying Wong, Daniel Wai-Sing Chu, Chung-Wah Siu
OBJECTIVE: To evaluate the diagnostic performance of a UK National Institute for Health and Care Excellence-recommended automatic oscillometric blood pressure (BP) measurement device incorporated with an atrial fibrillation (AF) detection algorithm (Microlife WatchBP Home A) for real-world AF screening in a primary healthcare setting. SETTING: Primary healthcare setting in Hong Kong. INTERVENTIONS: This was a prospective AF screening study carried out between 1 September 2014 and 14 January 2015...
June 15, 2017: BMJ Open
https://www.readbyqxmd.com/read/28604628/privacy-preserving-electrocardiogram-monitoring-for-intelligent-arrhythmia-detection
#8
Junggab Son, Juyoung Park, Heekuck Oh, Md Zakirul Alam Bhuiyan, Junbeom Hur, Kyungtae Kang
Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components...
June 12, 2017: Sensors
https://www.readbyqxmd.com/read/28603478/physiological-signal-based-method-for-measurement-of-pain-intensity
#9
Yaqi Chu, Xingang Zhao, Jianda Han, Yang Su
The standard method for prediction of the absence and presence of pain has long been self-report. However, for patients with major cognitive or communicative impairments, it would be better if clinicians could quantify pain without having to rely on the patient's self-description. Here, we present a newly pain intensity measurement method based on multiple physiological signals, including blood volume pulse (BVP), electrocardiogram (ECG), and skin conductance level (SCL), all of which are induced by external electrical stimulation...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28600268/slow-wave-sleep-estimation-for-healthy-subjects-and-osa-patients-using-r-r-intervals
#10
Hee Nam Yoon, Su Hwan Hwang, Jae Won Choi, Yu Jin Lee, Do Un Jeong, Kwang Suk Park
We developed an automatic slow-wave sleep (SWS) detection algorithm that can be applied to groups of healthy subjects and patients with obstructive sleep apnea (OSA). This algorithm detected SWS based on autonomic activations derived from the heart rate variations of a single sensor. An autonomic stability, which is an SWS characteristic, was evaluated and quantified using R-R intervals from an electrocardiogram (ECG). The thresholds and the heuristic rule to determine SWS were designed based on the physiological backgrounds for sleep process and distribution across the night...
June 7, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28596962/a-novel-ecg-eigenvalue-detection-algorithm-based-on-wavelet-transform
#11
Ziran Peng, Guojun Wang
This study investigated an electrocardiogram (ECG) eigenvalue automatic analysis and detection method; ECG eigenvalues were used to reverse the myocardial action potential in order to achieve automatic detection and diagnosis of heart disease. Firstly, the frequency component of the feature signal was extracted based on the wavelet transform, which could be used to locate the signal feature after the energy integral processing. Secondly, this study established a simultaneous equations model of action potentials of the myocardial membrane, using ECG eigenvalues for regression fitting, in order to accurately obtain the eigenvalue vector of myocardial membrane potential...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28593660/inhibition-of-left-ventricular-stimulation-due-to-left-ventricular-lead-failure-and-the-left-ventricular-t-wave-protection-algorithm-in-patient-with-cardiac-resynchronization-therapy-and-pacemaker-dependency
#12
Andrzej Ząbek, Barbara Małecka, Maciej Dębski, Krzysztof Boczar, Jacek Lelakowski
The electrocardiogram (ECG) interpretation in patients with cardiac resynchronization therapy (CRT) is often a perplexing problem. The difficulty in the device evaluation increases in the presence of unfamiliar timing cycles and a lead dysfunction. Authors describe a special function of a Biotronik CRT devices called the left ventricle T-wave protection (LVTP), and demonstrate its behavior in a patient with left ventricular (LV) lead failure. This report shows that sometimes it might be difficult to understand the loss of resynchronization in 12-lead ECG when LVTP feature is on, and a malfunction of left ventricular lead sensing occurs...
June 8, 2017: Annals of Noninvasive Electrocardiology
https://www.readbyqxmd.com/read/28582906/paroxysmal-atrial-fibrillation-recognition-based-on-multi-scale-r%C3%A3-nyi-entropy-of-ecg
#13
Yi Xin, Yizhang Zhao, Yuanhui Mu, Qin Li, Caicheng Shi
BACKGROUND: Atrial fibrillation (AF) is a common type of arrhythmia disease, which has a high morbidity and can lead to some serious complications. The ability to detect and in turn prevent AF is extremely significant to the patient and clinician. OBJECTIVE: Using ECG to detect AF and develop a robust and effective algorithm is the primary objective of this study. METHODS: Some studies show that after AF occurs, the regulatory mechanism of vagus nerve and sympathetic nerve will change...
May 19, 2017: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
https://www.readbyqxmd.com/read/28569241/atrial-fibrillation-detection-on-compressed-sensed-ecg
#14
Giulia Da Poian, Chengyu Liu, Riccardo Bernardini, Roberto Rinaldo, Gari Clifford
OBJECTIVE: Compressive Sensing (CS) approaches to electrocardiogram (ECG) analysis provide efficient methods for real time encoding of cardiac activity. In doing so, it is important to assess the downstream effect of the compression on any signal processing and classification algorithms. CS is particularly suitable for low power wearable devices, thanks to its low-complex digital or hardware implementation that directly acquires a compressed version of the signal through random projections...
June 1, 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28559848/efficient-fetal-maternal-ecg-signal-separation-from-two-channel-maternal-abdominal-ecg-via-diffusion-based-channel-selection
#15
Ruilin Li, Martin G Frasch, Hau-Tieng Wu
There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal and fetal ECG from a two channel signal acquired over maternal abdomen. The proposed algorithm is the first algorithm, to the best of the authors' knowledge, focusing on the fetal ECG analysis based on two channel maternal abdominal ECG signal, and we apply it to two publicly available databases, the PhysioNet non-invasive fECG database (adfecgdb) and the 2013 PhysioNet/Computing in Cardiology Challenge (CinC2013), to validate the algorithm...
2017: Frontiers in Physiology
https://www.readbyqxmd.com/read/28555426/a-novel-approach-to-the-extraction-of-fetal-electrocardiogram-based-on-empirical-mode-decomposition-and-correlation-analysis
#16
Peyman Ghobadi Azbari, Mostafa Abdolghaffar, Saeed Mohaqeqi, Mohammad Pooyan, Alireza Ahmadian, Niloofar Ghanbarzadeh Gashti
Fetal heart rate monitoring is the process of checking the condition of the fetus during pregnancy and it would allow doctors and nurses to detect early signs of trouble during labor and delivery. The fetal ECG (FECG) signal is so weak and also is corrupted by other signals and noises, mainly by maternal ECG signal. It is so hard to acquire a noise-free, precise and reliable FECG using the conventional methods. In this study, a combination of empirical mode decomposition (EMD) algorithms, correlation and match filtering is used for extracting FECG from maternal abdominal ECG signals...
May 29, 2017: Australasian Physical & Engineering Sciences in Medicine
https://www.readbyqxmd.com/read/28554512/functional-atrial-undersensing-associated-with-device-algorithm-promoting-av-conduction
#17
Pasquale Crea, Angela Nicotera, Dalia Di Nunzio, Giuseppe Picciolo
A 58-year-old woman received a dual chamber pacemaker (Medtronic) for sick sinus syndrome. Given intact AV conduction the Managed Ventricular Pacing mode algorithm (MVP) was programmed. The day after, she suffered from palpitations. Her ECG showed a possible loss of atrial capture accompanied by atrial undersensing. Telemetry-supported pacemaker control confirmed the loss of capture. Undersensing of atrial signal was functional, related to long atrial refractory period in MVP mode algorithm. Device algorithms could induce false suspicions...
May 18, 2017: Journal of Electrocardiology
https://www.readbyqxmd.com/read/28552419/a-new-algorithm-for-arrhythmia-interpretation
#18
Marzieh Mirtajaddini
BACKGROUND: Electrocardiogram (ECG) is an essential tool used to diagnose serious heart disease but its interpretation is challenging for undergraduate students and junior practitioners despite numerous methods that have been suggested to aid ECG interpretation. This paper aims to present a new algorithm for arrhythmia interpretation that is superior to current methods to be used as a supplement to lecture materials for medical students. METHODS: A new systematic algorithm is introduced in this paper...
May 18, 2017: Journal of Electrocardiology
https://www.readbyqxmd.com/read/28552121/research-and-improvement-of-ecg-compression-algorithm-based-on-ezw
#19
Ziran Peng, Guojun Wang, Huabin Jiang, Shuangwu Meng
Embedded zerotree wavelet (EZW) is an efficient compression method that has advantages in coding, but its multilayer structure information coding reduces signal compression ratio. This paper studies the optimization of the EZW compression algorithm and aims to improve it. First, we used lifting wavelet transformation to process electrocardiograph (ECG) signals, focusing on the lifting algorithm. Second, we utilized the EZW compression coding algorithm, through the ECG information decomposition to determine the feature detection value...
July 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28546863/ecg-signal-analysis-using-modified-s-transform
#20
Birendra Biswal
Accurate detection of QRS complexes is essential for the investigation of heart rate variability. Several transform techniques have been proposed and extensively used for the detection and analysis of QRS complexes. In this proposed work, the de-noised ECG signal is subjected to a modified S-transform for QRS complex detection.The performance analysis of the proposed work is evaluated using parameters such as sensitivity, positive predictivity and accuracy. The algorithm delivers sensitivity, positive predictivity and overall accuracy of 99...
April 2017: Healthcare Technology Letters
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