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Wearable Devices for Ecg and Heart Sound : = Design Considerations to Support Digital Health and Remote Monitoring.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Wearable Devices for Ecg and Heart Sound :/
其他題名:	Design Considerations to Support Digital Health and Remote Monitoring.
作者:	Kumar, Prashanth Shyam.
面頁冊數:	1 online resource (157 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Medical equipment. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29276595click for full text (PQDT)
ISBN:	9798841573388

Wearable Devices for Ecg and Heart Sound : = Design Considerations to Support Digital Health and Remote Monitoring.
Kumar, Prashanth Shyam.

Wearable Devices for Ecg and Heart Sound :Design Considerations to Support Digital Health and Remote Monitoring. - 1 online resource (157 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2022.

Includes bibliographical references

The prevalence of chronic cardiovascular diseases (CVDs) has been rising globally, nearly doubling from 1990 to 2019. The burden of these CVDs is not commensurate with increased physician-to-patient ratios. More research emphasis is needed in developing tools that provide faster and more precise insight into a patient's status so that clinicians can perform risk stratification and clinical management with greater ease and confidence. These tools will enable preventive care rather than treatment after a clinical manifestation. Treatment post-clinical manifestation is undesirable for patients' quality of life, and time- and resource-intensive for clinicians and providers. Wearable form factors for such tools are the least intrusive and most user-friendly for patients. Therefore, this research focuses on approaches to improve two data modalities vital to CVD patients' clinical management, namely, wearable electrocardiograms (ECG) and Vectorcardiography (VCG), and Phonocardiograms (PCGs). Regarding the former, ECG/VCG, a deep learning methodology is proposed to enhance the diagnostic yield of long-term ECG monitoring systems that acquire only a limited number of leads. Regarding the latter, a wearable PCG system is proposed to study the feasibility of acquiring diagnostic-grade PCGs while the wearer performs daily activities. Furthermore, a methodology for patient-specific calibration for a wearable PCG device is described. Currently, the only clinically used PCG tool is an electronic stethoscope used in a hospital or clinical environment, so the collected data and analysis are novel. From an outlook perspective, the studies done as part of this work suggest with quantitative and qualitative evidence that there is a benefit to developing personalized wearable devices using a combined approach of individualized hardware design for improved quality of data and specialized models for more precise diagnostic information while balancing a trade-off between clinical value, and patient's ease of use and quality of life.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841573388Subjects--Topical Terms:

3560831
Medical equipment.
Index Terms--Genre/Form:

542853
Electronic books.

Wearable Devices for Ecg and Heart Sound : = Design Considerations to Support Digital Health and Remote Monitoring.
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520 $a The prevalence of chronic cardiovascular diseases (CVDs) has been rising globally, nearly doubling from 1990 to 2019. The burden of these CVDs is not commensurate with increased physician-to-patient ratios. More research emphasis is needed in developing tools that provide faster and more precise insight into a patient's status so that clinicians can perform risk stratification and clinical management with greater ease and confidence. These tools will enable preventive care rather than treatment after a clinical manifestation. Treatment post-clinical manifestation is undesirable for patients' quality of life, and time- and resource-intensive for clinicians and providers. Wearable form factors for such tools are the least intrusive and most user-friendly for patients. Therefore, this research focuses on approaches to improve two data modalities vital to CVD patients' clinical management, namely, wearable electrocardiograms (ECG) and Vectorcardiography (VCG), and Phonocardiograms (PCGs). Regarding the former, ECG/VCG, a deep learning methodology is proposed to enhance the diagnostic yield of long-term ECG monitoring systems that acquire only a limited number of leads. Regarding the latter, a wearable PCG system is proposed to study the feasibility of acquiring diagnostic-grade PCGs while the wearer performs daily activities. Furthermore, a methodology for patient-specific calibration for a wearable PCG device is described. Currently, the only clinically used PCG tool is an electronic stethoscope used in a hospital or clinical environment, so the collected data and analysis are novel. From an outlook perspective, the studies done as part of this work suggest with quantitative and qualitative evidence that there is a benefit to developing personalized wearable devices using a combined approach of individualized hardware design for improved quality of data and specialized models for more precise diagnostic information while balancing a trade-off between clinical value, and patient's ease of use and quality of life.
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