Monitoring blood pressure is a critical aspect of safeguarding an individual’s health,as early detection of abnormal blood pressure levels facilitates timely medical intervention,ultimately leading to a reduction in ...Monitoring blood pressure is a critical aspect of safeguarding an individual’s health,as early detection of abnormal blood pressure levels facilitates timely medical intervention,ultimately leading to a reduction in mortality rates associated with cardiovascular diseases.Consequently,the development of a robust and continuous blood pressure monitoring system holds paramount significance.In the context of this research paper,we introduce an innovative deep learning regression model that harnesses phonocardiogram(PCG)data to achieve precise blood pressure estimation.Our novel approach incorporates a convolutional neural network(CNN)-based regression model,which not only enhances its adaptability to spatial variations but also empowers it to capture intricate patterns within the PCG signals.These advancements contribute significantly to the overall accuracy of blood pressure estimation.To substantiate the effectiveness of our proposed method,we meticulously gathered PCG signal data from 78 volunteers,adhering to the ethical guidelines of Suranaree University of Technology(Human Research Ethics number EC-65-78).Subsequently,we rigorously preprocessed the dataset to ensure its integrity.We further employed a K-fold cross-validation procedure for data division and alignment,combining the resulting datasets with a CNNfor blood pressure estimation.The experimental results are highly promising,yielding aMeanAbsolute Error(MAE)and standard deviation(STD)of approximately 10.69±7.23 mmHg for systolic pressure and 6.89±5.22 mmHg for diastolic pressure.Our study underscores the potential for precise blood pressure estimation,particularly using PCG signals,paving the way for a practical,non-invasive method with broad applicability in the healthcare domain.Early detection of abnormal blood pressure levels can facilitate timely medical interventions,ultimately reducing cardiovascular disease-related mortality rates.展开更多
Objective of this investigation is to further analyze the cardiac function status change by phonocar-diogram during mixed anesthesia which is conducted by midazolam,skelaxin,fentanyi and propofol.The results show that...Objective of this investigation is to further analyze the cardiac function status change by phonocar-diogram during mixed anesthesia which is conducted by midazolam,skelaxin,fentanyi and propofol.The results show that blood pressure,heart rate,amplitude of R wave and T wave,amplitude of first heart sound（S1）and second heart sound（S2）about 37 subjects after anesthesia decrease compared with baseline,while the ratio of first heart sound and second heart sound（S1/S2）and the ratio of diastole duration and systole duration（D/S）increase.Our study demonstrates that phonocardiogram as a noninvasive,high benefit/cost ratio,objective,repeatable and portable method can be used for the monitoring and evaluation of cardiac function status during anesthesia and operations.展开更多
基金Suranaree University of Technology,Thailand Science Research and Innovation(TSRI)National Science,Research,and Innovation Fund(NSRF)(NRIIS Number 179292).
文摘Monitoring blood pressure is a critical aspect of safeguarding an individual’s health,as early detection of abnormal blood pressure levels facilitates timely medical intervention,ultimately leading to a reduction in mortality rates associated with cardiovascular diseases.Consequently,the development of a robust and continuous blood pressure monitoring system holds paramount significance.In the context of this research paper,we introduce an innovative deep learning regression model that harnesses phonocardiogram(PCG)data to achieve precise blood pressure estimation.Our novel approach incorporates a convolutional neural network(CNN)-based regression model,which not only enhances its adaptability to spatial variations but also empowers it to capture intricate patterns within the PCG signals.These advancements contribute significantly to the overall accuracy of blood pressure estimation.To substantiate the effectiveness of our proposed method,we meticulously gathered PCG signal data from 78 volunteers,adhering to the ethical guidelines of Suranaree University of Technology(Human Research Ethics number EC-65-78).Subsequently,we rigorously preprocessed the dataset to ensure its integrity.We further employed a K-fold cross-validation procedure for data division and alignment,combining the resulting datasets with a CNNfor blood pressure estimation.The experimental results are highly promising,yielding aMeanAbsolute Error(MAE)and standard deviation(STD)of approximately 10.69±7.23 mmHg for systolic pressure and 6.89±5.22 mmHg for diastolic pressure.Our study underscores the potential for precise blood pressure estimation,particularly using PCG signals,paving the way for a practical,non-invasive method with broad applicability in the healthcare domain.Early detection of abnormal blood pressure levels can facilitate timely medical interventions,ultimately reducing cardiovascular disease-related mortality rates.
基金supported by the National Nature Science Foundation of China under Grant No. 30400105973 Project under Grant No. 2003CB716106Outstanding Youth Fund of China under Grant No. 30525030
文摘Objective of this investigation is to further analyze the cardiac function status change by phonocar-diogram during mixed anesthesia which is conducted by midazolam,skelaxin,fentanyi and propofol.The results show that blood pressure,heart rate,amplitude of R wave and T wave,amplitude of first heart sound（S1）and second heart sound（S2）about 37 subjects after anesthesia decrease compared with baseline,while the ratio of first heart sound and second heart sound（S1/S2）and the ratio of diastole duration and systole duration（D/S）increase.Our study demonstrates that phonocardiogram as a noninvasive,high benefit/cost ratio,objective,repeatable and portable method can be used for the monitoring and evaluation of cardiac function status during anesthesia and operations.
