Diss. (sammanfattning) Umeå : Univ., 2007. / Härtill 4 uppsatser.

Thesis (M.S.)--University of Michigan, 2000. / Running title: Measurement of quality-of-life in heart failure. Includes bibliographical references.

Thesis (M.S.)--University of Michigan, 1993.

Thesis (M.S.)--University of Michigan, 1993.

Thesis (M.S.)--University of Michigan, 2000. / Running title: Measurement of quality-of-life in heart failure. Includes bibliographical references.

Chronic heart failure (HF) is a condition that affects more than 900,000 people in the UK. Mortality rates associated with the condition are high, with nearly 20% of patients dying within one year of diagnosis. Continuous monitoring and risk stratification can help identify patients at risk of deterioration and may consequently improve patients' likelihood of survival. Current repeated-measure risk stratification techniques for HF patients often rely on subjective perception of symptoms, such as breathlessness, and markers of fluid retention in the body (e.g. weight). Despite the common use of such markers, studies have shown that they offer limited effectiveness in predicting HF-related events. This thesis set out to identify and evaluate new markers for repeated-measure risk stratification of HF patients. It started with an exploration of traditional HF measurements, including weight, blood pressure, heart rate and symptom scores, and aimed to improve the performance of these measurements using a data-driven approach. A multi-variate model was developed from data acquired during a randomised controlled trial of remotely-monitored HF patients. The rare occurrence of HF-related adverse events during the trial required the developement of a careful methodology. This methodology helped identify the markers with most predictive ability, which achieved moderate performance at identifying patients at risk of HF-related adverse events, clearly outperforming commonly-used thresholds. Subsequently, this thesis explored the potential value of additional, accelerometer-derived physical activity (PA) and sleep markers. For this purpose, the ability of accelerometer-derived markers to differentiate between individuals with and without HF was evaluated. It was found that markers that summarise the frequency and duration of different PA intensities performed best at differentiating between the two groups and may therefore be most suitable for future use in repeated-measure applications. As part of the analysis of accelerometer-derived HF markers, a gap in the methodology of automated accelerometer processing was identified, namely the need for self-reported sleep-onset and wake-up information. As a result, Chapter 5 of this thesis describes the development and evaluation of a data-driven solution for this problem. In summary, this thesis explored both traditional and new, accelerometer-derived markers for the early detection of HF deterioration. It utilised sound methodology to overcome limitations faced by sparse and unbalanced datasets and filled a methodological gap in the processing of signals from wrist-worn accelerometers.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22042号 / 医博第4527号 / 新制||医||1039(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 佐藤 俊哉, 教授 湊谷 謙司, 教授 稲垣 暢也 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Computational ECG (electrocardiogram) analysis enables accurate and faster diagnosis and early prediction of heart failure related symptoms (heart failure worsening). Machine learning, particularly deep learning, has been applied for ECG data successfully. The previous applications, however, either mainly focused on classifying occurrent, known patterns of on-going heart failure or heart failure related diseases such arrhythmia, which have undesirable predictability beforehand, or emphasizing on data from pre-processed public database data. In this dissertation, we developed an approach, however, does not fully capitalize on the potential of deep learning, which directly learns important features from raw input data without relying on a priori knowledge. Here, we present a deep transfer learning pipeline which combines an image-based pretrained deep neural network model with manifold learning to predict the precursors of heart failure (heart failure-worsening and recurrent heart failure related re-hospitalization) using raw ECG time series from wearable devices. In this dissertation, we used the unprocessed real-life ECG data from the SENTINEL-HF study by Dovancescu, et al. to predict the precursors of heart failure worsening. To extract rich features from ECG time series, we took a deep transfer learning approach where 1D time-series of five heartbeats were transformed to 2D images by Gramian Angular Summation Field (GASF) and then the pretrained models, VGG19 were used for feature extraction. Then, we applied UMAP (Uniform Manifold Approximation and Projection) to capture the manifold of the standardized feature space and reduce the dimension, followed by SVM (Support Vector Machine) training. Using our pipeline, we demonstrated that our classifier was able to predict heart failure worsening with 92.1% accuracy, 92.9% precision, 92.6% recall and F1 score of 0.93 bypassing the detection of known abnormal ECG patterns. In conclusion, we demonstrate the feasibility of early alerts of heart failure by predicting the precursor of heart failure worsening based on raw ECG signals. We expected that our approached provided an innovative method to assess the recovery and successfulness for the treatment patient received during the first hospitalization, to predict whether recurrent heart failure is likely to occur, and to evaluate whether the patient should be discharged.

No description available.

No description available.