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ECG data mining

Daniela Husser, Petra Büttner, Laura Ueberham, Borislav Dinov, Philipp Sommer, Arash Arya, Gerhard Hindricks, Andreas Bollmann
BACKGROUND: Left atrial enlargement and persistent atrial fibrillation (AF) are well-known predictors for arrhythmia recurrence after AF catheter ablation (LRAF). In this study, by using pathway enrichment analysis of GWAS data, we tested the hypothesis that genetic pathways associated with these phenotypes are also associated with LRAF. METHODS: Samples from 660 patients with paroxysmal (n = 370) or persistent AF (n = 290) undergoing de-novo AF catheter ablation were genotyped for ~1,000,000 SNPs...
2016: PloS One
Rossana Castaldo, Paolo Melillo, Raffaele Izzo, Nicola De Luca, Leandro Pecchia
Falls are a major problem of later life having severe consequences on quality of life and a significant burden in occidental countries. Many technological solutions have been proposed to assess the risk or to predict falls and the majority is based on accelerometers and gyroscopes. However, very little was done for identifying first time fallers, which are very difficult to recognise. This paper presents a meta-model predicting falls using short term Heart Rate Variability (HRV) analysis acquired at the baseline...
March 18, 2016: IEEE Journal of Biomedical and Health Informatics
Paolo Melillo, Rossana Castaldo, Giovanna Sannino, Ada Orrico, Giuseppe de Pietro, Leandro Pecchia
Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection...
2015: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Xiang-Lin Yang, Guo-Zhen Liu, Yun-Hai Tong, Hong Yan, Zhi Xu, Qi Chen, Xiang Liu, Hong-Hao Zhang, Hong-Bo Wang, Shao-Hua Tan
The electrocardiogram (ECG) has broad applications in clinical diagnosis and prognosis of cardiovascular disease. Many researchers have contributed to its progressive development. To commemorate those pioneers, and to better study and promote the use of ECG, we reviewed and present here a systematic introduction about the history, hotspots, and trends of ECG. In the historical part, information including the invention, improvement, and extensive applications of ECG, such as in long QT syndrome (LQTS), angina, and myocardial infarction (MI), are chronologically presented...
July 2015: Journal of Geriatric Cardiology: JGC
Jian Zhang, Xin Niu, Xue-zhi Yang, Qing-wen Zhu, Hai-yan Li, Xuan Wang, Zhi-guo Zhang, Hong Sha
BACKGROUND: To design the pulse information which includes the parameter of pulse-position, pulse-number, pulse-shape and pulse-force acquisition and analysis system with function of dynamic recognition, and research the digitalization and visualization of some common cardiovascular mechanism of single pulse. METHODS: To use some flexible sensors to catch the radial artery pressure pulse wave and utilize the high frequency B mode ultrasound scanning technology to synchronously obtain the information of radial extension and axial movement, by the way of dynamic images, then the gathered information was analyzed and processed together with ECG...
September 2014: African Health Sciences
Juan A Lara, David Lizcano, Aurora Pérez, Juan P Valente
There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common...
October 2014: Journal of Biomedical Informatics
Kunal Karandikar, Trung Q Le, Akkarapol Sa-ngasoongsong, Woranat Wongdhamma, Satish T S Bukkapatnam
Obstructive sleep apnea (OSA) is a common sleep disorder that causes increasing risk of mortality and affects quality of life of approximately 6.62% of the total US population. Timely detection of sleep apnea events is vital for the treatment of OSA. In this paper, we present a novel approach based on extracting the quantifiers of nonlinear dynamic cardio-respiratory coupling from electrocardiogram (ECG) signals to detect sleep apnea events. The quantifiers of the cardio-respiratory dynamic coupling were extracted based on recurrence quantification analysis (RQA), and a battery of statistical data mining techniques were to enhance OSA detection accuracy...
2013: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Alfredo Rosado-Muñoz, José M Martínez-Martínez, Pablo Escandell-Montero, Emilio Soria-Olivas
Detection of ventricular fibrillation (VF) at an early stage is being deeply studied in order to lower the risk of sudden death and allows the specialist to have greater reaction time to give the patient a good recovering therapy. Some works are focusing on detecting VF based on numerical analysis of time-frequency distributions, but in general the methods used do not provide insight into the problem. However, this study proposes a new methodology in order to obtain information about this problem. This work uses a supervised self-organising map (SOM) to obtain visually information among four important groups of patients: VF (ventricular fibrillation), VT (ventricular tachycardia), HP (healthy patients) and AHR (other anomalous heart rates and noise)...
August 2013: Computer Methods and Programs in Biomedicine
Roohallah Alizadehsani, Jafar Habibi, Behdad Bahadorian, Hoda Mashayekhi, Asma Ghandeharioun, Reihane Boghrati, Zahra Alizadeh Sani
Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies. Many methods are used to diagnose CAD so far. These methods reduce cost and deaths. But a few studies examined stenosis of each vessel separately. Determination of stenosed coronary artery when significant ECG abnormality exists is not a difficult task...
July 2012: Journal of Medical Signals and Sensors
Jana Faganeli Pucer, Janez Demšar, Matjaž Kukar
Coronary artery disease is the developed world's premier cause of mortality and the most probable cause of myocardial ischaemia. More advanced diagnostic tests aside, in electrocardiogram (ECG) analysis it manifests itself as a ST segment deviation, targeted by both exercise ECG and ambulatory ECG. In ambulatory ECG, besides ischaemic ST segment deviation episodes there are also non-ischaemic heart rate related episodes which aggravate real ischaemia detection. We present methods to transform the features developed for the heart rate adjustment of ST segment depression in exercise ECG for use in ambulatory ECG...
2012: Studies in Health Technology and Informatics
J L Rodríguez-Sotelo, D Peluffo-Ordoñez, D Cuesta-Frau, G Castellanos-Domínguez
The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration...
October 2012: Computer Methods and Programs in Biomedicine
Dewar D Finlay, Chris D Nugent, Haiying Wang, Mark P Donnelly, Paul J McCullagh
Decision support systems (DSS) are software entities that assist the physician in the decision making process. They have found application in medicine due to the large amounts of data (e.g. laboratory measurements such as blood pressure, heart rate, body-mass index) and information (e.g. patient history, population statistics based on age and sex) that must be considered before diagnosing any disease or recommending a therapy. A well known example is the embedded software in defibrillators which allows a 'shock' to be delivered, by analyzing the electrocardiogram for known conditions (heart attack)...
2010: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
Fahim Sufi, Ibrahim Khalil
Usage of compressed ECG for fast and efficient telecardiology application is crucial, as ECG signals are enormously large in size. However, conventional ECG diagnosis algorithms require the compressed ECG packets to be decompressed before diagnosis can be performed. This added step of decompression before performing diagnosis for every ECG packet introduces unnecessary delay, which is undesirable for cardiovascular diseased (CVD) patients. In this paper, we are demonstrating an innovative technique that performs real-time classification of CVD...
January 2011: IEEE Transactions on Information Technology in Biomedicine
Leandro Pecchia, Paolo Melillo, Mario Sansone, Marcello Bracale
In this study, we investigated the discrimination power of short-term heart rate variability (HRV) for discriminating normal subjects versus chronic heart failure (CHF) patients. We analyzed 1914.40 h of ECG of 83 patients of which 54 are normal and 29 are suffering from CHF with New York Heart Association (NYHA) classification I, II, and III, extracted by public databases. Following guidelines, we performed time and frequency analysis in order to measure HRV features. To assess the discrimination power of HRV features, we designed a classifier based on the classification and regression tree (CART) method, which is a nonparametric statistical technique, strongly effective on nonnormal medical data mining...
January 2011: IEEE Transactions on Information Technology in Biomedicine
Jean-Philippe Couderc
The sharing of scientific data reinforces open scientific inquiry; it encourages diversity of analysis and opinion while promoting new research and facilitating the education of next generations of scientists. In this article, we present an initiative for the development of a repository containing continuous electrocardiographic information and their associated clinical information. This information is shared with the worldwide scientific community to improve quantitative electrocardiology and cardiac safety...
November 2010: Journal of Electrocardiology
Raymond R Bond, Dewar D Finlay, Chris D Nugent, George Moore
BACKGROUND: The Body Surface Potential Map (BSPM) is an electrocardiographic method, for recording and displaying the electrical activity of the heart, from a spatial perspective. The BSPM has been deemed more accurate for assessing certain cardiac pathologies when compared to the 12-lead ECG. Nevertheless, the 12-lead ECG remains the most popular ECG acquisition method for non-invasively assessing the electrical activity of the heart. Although data from the 12-lead ECG can be stored and shared using open formats such as SCP-ECG, no open formats currently exist for storing and sharing the BSPM...
2010: BMC Medical Informatics and Decision Making
Edvardas Vaicekavicius, Ramūnas Navickas, Leonas Survila, Vytautas Stuikys, Arnoldas Janavicius, Kestutis Valancius
OBJECTIVES. The aim of this study was to identify the predictors of the postreperfusion mode of death using the distinctions in clinical characteristics of patients who died and survived after reperfusion therapy, treated due to ST-elevation myocardial infarction (STEMI). MATERIAL AND METHODS. This consecutive study has involved 36 patients: 18 patients who died from progressive heart failure (PHF) (group 1, n=13) or from cardiac rupture (CR) (group 2, n=5) after primary coronary intervention. The control group consisted of 18 randomly selected patients who survived in-hospital period (group 3)...
2010: Medicina
Raimon Massanet, Joan-Josep Gallardo-Chacon, Pere Caminal, Alexandre Perera
This work presents a methodology for finding phenotype candidate genes starting from a set of known related genes. This is accomplished by automatically mining and organizing the available scientific literature using Gene Ontology-based semantic similarity. As a case study, Brugada syndrome related genes have been used as input in order to obtain a list of other possible candidate genes related with this disease. Brugada anomaly produces a typical alteration in the Electrocardiogram and carriers of the disease show an increased probability of sudden death...
2009: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Gong Zhang, Witold Kinsner, Bin Huang
This paper presents an electrocardiogram (ECG) data mining scheme based on the ECG frame classification realised by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar non-stationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DTW matching residual either to classify an ECG frame or to add a new class...
December 2009: Computer Methods in Biomechanics and Biomedical Engineering
Qiang Lin, Wenji Wu, Liqin Huang, Yonghua Lin
This paper introduces the omnidirectional M-mode echocardiography (OME), which can detect dynamic information from sequential echocardiography images. The method for detecting dynamic information is based on the rebuilding of their "gray (position)-time" function [Qiang L, Wenjing J, Li Z. A method for detecting dynamic information of sequential images--omnidirectional gray-time waveform and its applications in echocardiography images. In: Proceedings of CISST' 2001. 2001. p. 760-3; Qiang L, Wenjing J, Xiuzhi Y...
September 2006: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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