Isbn: 9786204202853 - optical fiber sensor in cardiovascular evaluation: development of an automatic algorithm for processing and classification (8 resultados)

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Taschenbuch. Condición: Neu. Optical Fiber Sensor in Cardiovascular Evaluation | Development of an automatic algorithm for processing and classification | Francisco Brito | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204202853 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -According to the World Health Organization (WHO) cardiovascular diseases (CVDs) are the prime cause of death in developed countries. Arterial pulse waveform (APW) analysis provides a non-invasive way for early diagnosis of CVDs. The present work reports the elaboration of an automatic algorithm to extract and classify the acquired data of APW signals based on the risk of CVD related problems. To extract the data several signal pre-processing methods were applied for noise reduction and pulse segmentation. Then, a K-Means clustering algorithm was applied to select the higher quality APWs. The discrepancy between a parallel manual extraction of the waves and the result of K-Means algorithm was practically null. A pool of 32 waveform characterization parameters were extracted including time domain, wavelet transform, root mean square error and frequency domain features. Some parameters were used in the K-Means clustering and the rest were used to train a multi-layer perceptron neural network for APW classification of low risk/high risk of CVDs in each patient. The results obtained are very promising. A portion of this work won the Nascimento Leitão prize. 60 pp. Englisch.…

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Brito FranciscoBiomedical Engineering licentiate in the department of Physics of Aveiro University with an interest in machine learning in clinical decision support.According to the World Health Organization (WHO) cardiovascular .…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - According to the World Health Organization (WHO) cardiovascular diseases (CVDs) are the prime cause of death in developed countries. Arterial pulse waveform (APW) analysis provides a non-invasive way for early diagnosis of CVDs. The present work reports the elaboration of an automatic algorithm to extract and classify the acquired data of APW signals based on the risk of CVD related problems. To extract the data several signal pre-processing methods were applied for noise reduction and pulse segmentation. Then, a K-Means clustering algorithm was applied to select the higher quality APWs. The discrepancy between a parallel manual extraction of the waves and the result of K-Means algorithm was practically null. A pool of 32 waveform characterization parameters were extracted including time domain, wavelet transform, root mean square error and frequency domain features. Some parameters were used in the K-Means clustering and the rest were used to train a multi-layer perceptron neural network for APW classification of low risk/high risk of CVDs in each patient. The results obtained are very promising. A portion of this work won the Nascimento Leitão prize.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -According to the World Health Organization (WHO) cardiovascular diseases (CVDs) are the prime cause of death in developed countries. Arterial pulse waveform (APW) analysis provides a non-invasive way for early diagnosis of CVDs. The present work reports the elaboration of an automatic algorithm to extract and classify the acquired data of APW signals based on the risk of CVD related problems. To extract the data several signal pre-processing methods were applied for noise reduction and pulse segmentation. Then, a K-Means clustering algorithm was applied to select the higher quality APWs. The discrepancy between a parallel manual extraction of the waves and the result of K-Means algorithm was practically null. A pool of 32 waveform characterization parameters were extracted including time domain, wavelet transform, root mean square error and frequency domain features. Some parameters were used in the K-Means clustering and the rest were used to train a multi-layer perceptron neural network for APW classification of low risk/high risk of CVDs in each patient. The results obtained are very promising. A portion of this work won the Nascimento Leitão prize.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch.…