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
Moderate exercise contributes to health, but excessive exercise may lead to physicalinjury or even endanger life. It is pressing for a device that can detect the intensity of exercise.Therefore, in order to enable rea...Moderate exercise contributes to health, but excessive exercise may lead to physicalinjury or even endanger life. It is pressing for a device that can detect the intensity of exercise.Therefore, in order to enable real-time detection of exercise intensity and mitigate the risks of harmfrom excessive exercise, a exercise intensity monitoring system based on the heart rate variability(HRV) from electrocardiogram (ECG) signal and linear features from phonocardiogram (PCG)signal is proposed. The main contributions include: First, accurate analysis of HRV is crucial forsubsequent exercise intensity detection. To enhance HRV analysis, we propose an R-peak detectorbased on encoder-decoder and temporal convolutional network (TCN). Experimental resultsdemonstrate that the proposed R-peak detector achieves an F1 score exceeding 0.99 on real high-intensity exercise ECG datasets. Second, an exercise fatigue monitoring system based on multi-signal feature fusion is proposed. Initially, utilizing the proposed R-peak detector for HRV extractionin exercise intensity detection,which outperforms traditional algorithms, with the system achieving a classification performance of 0.933 sensitivity, 0.802 specificity, and 0.960 accuracy. To further improve the system, we combine HRV with the linear features of PCG. Our exercise intensitydetection system achieves 90.2% specificity, 96.7% recall, and 98.1% accuracy in five-fold cross-validation.展开更多
Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovas...Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovascular information,and their data acquisition devices are quiteconvenient.In this study,a novel hybrid approach of fractional Fourier transform(FRFT)com-bined with linear and discrete wavelet transform(DWT)features extracted from PCG is proposedfor PCG multi-class classification.The proposed system enhances the fatigue detection performanceby combining optimized FRFT features with an effective aggregation of linear features and DWTfeatures.The FRFT technique is employed to convert the 1-D PCG signal into 2-D image which issent to a pre-trained convolutional neural network structure,called VGG-16.The features from theVGG-16 were concatenated with the linear and DWT features to form fused features.The fusedfeatures are sent to support vector machine(SVM)to distinguish six distinct fatigue levels.Experi-mental results demonstrate that the proposed fused features outperform other feature combinationssignificantly.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.
基金the National Natural Science Foundation of China(No.62301056)the Fundamental Research Funds for Central Universities(No.2022QN005).
文摘Moderate exercise contributes to health, but excessive exercise may lead to physicalinjury or even endanger life. It is pressing for a device that can detect the intensity of exercise.Therefore, in order to enable real-time detection of exercise intensity and mitigate the risks of harmfrom excessive exercise, a exercise intensity monitoring system based on the heart rate variability(HRV) from electrocardiogram (ECG) signal and linear features from phonocardiogram (PCG)signal is proposed. The main contributions include: First, accurate analysis of HRV is crucial forsubsequent exercise intensity detection. To enhance HRV analysis, we propose an R-peak detectorbased on encoder-decoder and temporal convolutional network (TCN). Experimental resultsdemonstrate that the proposed R-peak detector achieves an F1 score exceeding 0.99 on real high-intensity exercise ECG datasets. Second, an exercise fatigue monitoring system based on multi-signal feature fusion is proposed. Initially, utilizing the proposed R-peak detector for HRV extractionin exercise intensity detection,which outperforms traditional algorithms, with the system achieving a classification performance of 0.933 sensitivity, 0.802 specificity, and 0.960 accuracy. To further improve the system, we combine HRV with the linear features of PCG. Our exercise intensitydetection system achieves 90.2% specificity, 96.7% recall, and 98.1% accuracy in five-fold cross-validation.
基金the National Natural Sci-ence Foundation of China(No.62301056)the Fundamental Research Funds for Central Universities(No.2022QN005).
文摘Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovascular information,and their data acquisition devices are quiteconvenient.In this study,a novel hybrid approach of fractional Fourier transform(FRFT)com-bined with linear and discrete wavelet transform(DWT)features extracted from PCG is proposedfor PCG multi-class classification.The proposed system enhances the fatigue detection performanceby combining optimized FRFT features with an effective aggregation of linear features and DWTfeatures.The FRFT technique is employed to convert the 1-D PCG signal into 2-D image which issent to a pre-trained convolutional neural network structure,called VGG-16.The features from theVGG-16 were concatenated with the linear and DWT features to form fused features.The fusedfeatures are sent to support vector machine(SVM)to distinguish six distinct fatigue levels.Experi-mental results demonstrate that the proposed fused features outperform other feature combinationssignificantly.
