The Experts below are selected from a list of 7392 Experts worldwide ranked by ideXlab platform
Ulf Dahlström - One of the best experts on this subject based on the ideXlab platform.
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Feature Extraction for Systolic Heart Murmur Classification
Annals of Biomedical Engineering, 2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf DahlströmAbstract:Heart Murmurs are often the first signs of pathological changes of the Heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological Murmur from a physiological Murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological Murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of Heart Murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in Heart Murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.
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Feature Extraction for Systolic Heart Murmur Classification
Annals of Biomedical Engineering, 2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf Dahlström, Per AskAbstract:Heart Murmurs are often the first signs of pathological changes of the Heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological Murmur ...
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Using the intelligent stethoscope for extraction of features for systolic Heart Murmur classification
2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf Dahlström, Per AskAbstract:Using the intelligent stethoscope for extraction of features for systolic Heart Murmur classification
Jiannshiou Yang - One of the best experts on this subject based on the ideXlab platform.
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Heart Murmur detection with svm classification
International Conference on Consumer Electronics, 2015Co-Authors: Jiannshiou YangAbstract:This paper presents an approach to detect low frequency vibrations from the human chest and correlate them to cardiac conditions. Our system which includes data acquisition via a TekScan FlexiForce sensor, signal processing, and hardware/software interfacing is developed and tested through clinical trials. A Support Vector Machine (SVM) learning algorithm is used to train and classify signals. Our results show that a SVM is able to separate and distinguish signals between normal and abnormal cardiac conditions.
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a support vector machine svm classification approach to Heart Murmur detection
International Symposium on Neural Networks, 2010Co-Authors: Samuel Rud, Jiannshiou YangAbstract:This paper focuses on the study of detecting low frequency vibrations from the human chest and correlate them to cardiac conditions using new devices and techniques, custom software, and the Support Vector Machine (SVM) classification technique Several new devices and techniques of detecting a human Heart Murmur have been developed through the extraction of vibrations primarily in the range of 10 – 150 Hertz (Hz) on the human chest The devices and techniques have been tested on different types of simulators and through clinical trials Signals were collected using a Kardiac Infrasound Device (KID) and accelerometers integrated with a custom MATLAB software interface and a data acquisition system Using the interface, the data was analyzed and classified by an SVM approach Results show that the SVM was able to classify signals under different testing environments For clinical trials, the SVM distinguished between normal and abnormal cardiac conditions and between pathological and non-pathological cardiac conditions Finally, using the various devices, a correlation between Heart Murmurs and normal Hearts was observed from human chest vibrations.
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non invasive infrasound Heart Murmur detection with a support vector machine svm classification approach
Systems Man and Cybernetics, 2006Co-Authors: Samuel Rud, N St Jacque, A D Vant, Jiannshiou YangAbstract:The goal of this paper is to present new devices and techniques that detect low frequency vibrations from the human chest and correlate them to cardiac conditions. Several new devices and techniques of detecting a human Heart Murmur have been developed through the extraction of vibrations primarily in the range of 10 - 150 Hertz (Hz) on the human chest. The devices and techniques have been tested on different types of simulators and through clinical trials with the consent of the University of Minnesota Institutional Review Board (IRB). Signals were collected using a Kardiac Infrasound Device (KID) and accelerometers integrated with a custom MATLAB software interface and a data acquisition system. Using the interface, the data was analyzed and classifi'ed by a Support Vector Machine (SVM) approach. Results show that the SVM was able to classify signals under different testing environments. For clinical trials, the SVM distinguished between normal and abnormal cardiac conditions and between pathological and non-pathological cardiac conditions. Finally, using the various devices, a correlation between Heart Murmurs and normal Hearts was observed from human chest vibrations.
Christer Ahlström - One of the best experts on this subject based on the ideXlab platform.
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Feature Extraction for Systolic Heart Murmur Classification
Annals of Biomedical Engineering, 2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf DahlströmAbstract:Heart Murmurs are often the first signs of pathological changes of the Heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological Murmur from a physiological Murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological Murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of Heart Murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in Heart Murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.
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Feature Extraction for Systolic Heart Murmur Classification
Annals of Biomedical Engineering, 2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf Dahlström, Per AskAbstract:Heart Murmurs are often the first signs of pathological changes of the Heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological Murmur ...
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Using the intelligent stethoscope for extraction of features for systolic Heart Murmur classification
2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf Dahlström, Per AskAbstract:Using the intelligent stethoscope for extraction of features for systolic Heart Murmur classification
Peter Hult - One of the best experts on this subject based on the ideXlab platform.
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Feature Extraction for Systolic Heart Murmur Classification
Annals of Biomedical Engineering, 2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf DahlströmAbstract:Heart Murmurs are often the first signs of pathological changes of the Heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological Murmur from a physiological Murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological Murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of Heart Murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in Heart Murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.
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Feature Extraction for Systolic Heart Murmur Classification
Annals of Biomedical Engineering, 2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf Dahlström, Per AskAbstract:Heart Murmurs are often the first signs of pathological changes of the Heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological Murmur ...
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Using the intelligent stethoscope for extraction of features for systolic Heart Murmur classification
2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf Dahlström, Per AskAbstract:Using the intelligent stethoscope for extraction of features for systolic Heart Murmur classification
Peter Rask - One of the best experts on this subject based on the ideXlab platform.
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Feature Extraction for Systolic Heart Murmur Classification
Annals of Biomedical Engineering, 2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf DahlströmAbstract:Heart Murmurs are often the first signs of pathological changes of the Heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological Murmur from a physiological Murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological Murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of Heart Murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in Heart Murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.
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Feature Extraction for Systolic Heart Murmur Classification
Annals of Biomedical Engineering, 2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf Dahlström, Per AskAbstract:Heart Murmurs are often the first signs of pathological changes of the Heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological Murmur ...
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Using the intelligent stethoscope for extraction of features for systolic Heart Murmur classification
2006Co-Authors: Christer Ahlström, Peter Hult, Peter Rask, J.-e. Karlsson, Eva Nylander, Ulf Dahlström, Per AskAbstract:Using the intelligent stethoscope for extraction of features for systolic Heart Murmur classification
