The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Roger Chou - One of the best experts on this subject based on the ideXlab platform.
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cardiac screening with Electrocardiography stress echocardiography or myocardial perfusion imaging advice for high value care from the american college of physicians
Annals of Internal Medicine, 2015Co-Authors: Roger ChouAbstract:In this article, the High Value Care Task Force of the American College of Physicians recommends against the use of resting or stress Electrocardiography, stress echocardiography, or stress myocard...
Paola Borrelli - One of the best experts on this subject based on the ideXlab platform.
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cardiovascular screening in low income settings using a novel 4 lead smartphone based Electrocardiograph d heart
International Journal of Cardiology, 2017Co-Authors: Niccolo Maurizi, Alessandro Faragli, Jacopo F Imberti, Nicolo Briante, M Targetti, Katia Baldini, Amadou Alpha Sall, Abibou Cisse, Francesca Gigli Berzolari, Paola BorrelliAbstract:Abstract Background MHealth technologies are revolutionizing cardiovascular medicine. However, a low-cost, user-friendly smartphone-based Electrocardiograph is still lacking. D-Heart® is a portable device that enables the acquisition of the ECG on multiple leads which streams via Bluetooth to any smartphone. Because of the potential impact of this technology in low-income settings, we determined the accuracy of D-Heart® tracings in the stratification of ECG morphological abnormalities, compared with 12-lead ECGs. Methods Consecutive African patients referred to the Ziguinchor Regional Hospital (Senegal) were enrolled (n=117; 69 males, age 39±11years). D-Heart® recordings (3 peripheral leads plus V5) were obtained immediately followed by 12 lead ECGs and were assessed blindly by 2 independent observers. Global burden of ECG abnormalities was defined by a semi-quantitative score based on the sum of 9 criteria, identifying four classes of increasing severity. Results D-Heart® and 12-lead ECG tracings were respectively classified as: normal: 72 (61%) vs 69 (59%); mildly abnormal: 42 (36%) vs 45 (38%); moderately abnormal: 3 (3%) vs 3 (3%). None had markedly abnormal tracings. Cohen's weighted kappa (k w ) test demonstrated a concordance of 0,952 (p w =0,893; p Conclusions D-Heart® proved effective and accurate stratification of ECG abnormalities comparable to the 12-lead Electrocardiographs, thereby opening new perspectives for low-cost community cardiovascular screening programs in low-income settings.
N. Maglaveras - One of the best experts on this subject based on the ideXlab platform.
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A rule discovery algorithm appropriate for Electrocardiograph signals
Computers in Cardiology 2004, 2004Co-Authors: S. Konias, N. MaglaverasAbstract:In this paper the problem of discovering rules, which are associations among patterns in the same time series, is considered. A novel algorithm, namely rule discovery algorithm (RDA), appropriate for periodic time series data, like electrocardiograms (ECGs) can be considered, is proposed. The first phase of the algorithm aims to break the sequence (i.e. an ECG) into overlapping, reconfigured length subsequences according to the sampling frequency and the types of ECG abnormalities to be studied. The Pearson correlation coefficient was chosen as the categorization metric, which is independent of the base line shifts and the amplitude scales. At the following phase the categorized sequence was scanned, so the most efficient rules would be mined. The format of those rules is "IF A occurs THEN B occurs WITHIN time T", where A and B are categorized subsequences and T the time duration between A and B. RDA was evaluated on 60 congestive heart failure patients1 ECGs from a home care monitoring database. The mined rules are complementary to the ECGs' plots allowing the physician to test various hypotheses and discover hidden knowledge.
Niccolo Maurizi - One of the best experts on this subject based on the ideXlab platform.
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cardiovascular screening in low income settings using a novel 4 lead smartphone based Electrocardiograph d heart
International Journal of Cardiology, 2017Co-Authors: Niccolo Maurizi, Alessandro Faragli, Jacopo F Imberti, Nicolo Briante, M Targetti, Katia Baldini, Amadou Alpha Sall, Abibou Cisse, Francesca Gigli Berzolari, Paola BorrelliAbstract:Abstract Background MHealth technologies are revolutionizing cardiovascular medicine. However, a low-cost, user-friendly smartphone-based Electrocardiograph is still lacking. D-Heart® is a portable device that enables the acquisition of the ECG on multiple leads which streams via Bluetooth to any smartphone. Because of the potential impact of this technology in low-income settings, we determined the accuracy of D-Heart® tracings in the stratification of ECG morphological abnormalities, compared with 12-lead ECGs. Methods Consecutive African patients referred to the Ziguinchor Regional Hospital (Senegal) were enrolled (n=117; 69 males, age 39±11years). D-Heart® recordings (3 peripheral leads plus V5) were obtained immediately followed by 12 lead ECGs and were assessed blindly by 2 independent observers. Global burden of ECG abnormalities was defined by a semi-quantitative score based on the sum of 9 criteria, identifying four classes of increasing severity. Results D-Heart® and 12-lead ECG tracings were respectively classified as: normal: 72 (61%) vs 69 (59%); mildly abnormal: 42 (36%) vs 45 (38%); moderately abnormal: 3 (3%) vs 3 (3%). None had markedly abnormal tracings. Cohen's weighted kappa (k w ) test demonstrated a concordance of 0,952 (p w =0,893; p Conclusions D-Heart® proved effective and accurate stratification of ECG abnormalities comparable to the 12-lead Electrocardiographs, thereby opening new perspectives for low-cost community cardiovascular screening programs in low-income settings.
S. Konias - One of the best experts on this subject based on the ideXlab platform.
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A rule discovery algorithm appropriate for Electrocardiograph signals
Computers in Cardiology 2004, 2004Co-Authors: S. Konias, N. MaglaverasAbstract:In this paper the problem of discovering rules, which are associations among patterns in the same time series, is considered. A novel algorithm, namely rule discovery algorithm (RDA), appropriate for periodic time series data, like electrocardiograms (ECGs) can be considered, is proposed. The first phase of the algorithm aims to break the sequence (i.e. an ECG) into overlapping, reconfigured length subsequences according to the sampling frequency and the types of ECG abnormalities to be studied. The Pearson correlation coefficient was chosen as the categorization metric, which is independent of the base line shifts and the amplitude scales. At the following phase the categorized sequence was scanned, so the most efficient rules would be mined. The format of those rules is "IF A occurs THEN B occurs WITHIN time T", where A and B are categorized subsequences and T the time duration between A and B. RDA was evaluated on 60 congestive heart failure patients1 ECGs from a home care monitoring database. The mined rules are complementary to the ECGs' plots allowing the physician to test various hypotheses and discover hidden knowledge.