| 3 | Non-invasive cuffless blood pressure and heart rate monitoring using impedance cardiography显示文摘Background Continuous blood pressure(BP)monitoring provides additional information about how changes in BP may correlate with daily activities and sleep patterns.Recommendations from the American Heart Association and American College of Cardiology strongly suggest confirming a diagnosis of hypertension with continuous BP monitoring.Non-invasive and non-intrusive detection of haemodynamic parameters is emerging as a norm,based on self-monitoring wearable medical devices.Researchers have carried out several studies using non-invasive and continuous BP measurements as an alternative to conventional cuff-based measurements.In this work,we proposed a novel method for cuffless estimation of BP using impedance cardiography(ICG).Methods We conducted a single-centre,cross-sectional study of 104 subjects(of whom 30 were categorized as controls and the remaining 74 as the disease group)at the Medical College and Hospital,Kolkata.The disease group consisted of patients with confirmed coronary artery disease,while the individuals in the control group were deemed to be healthy.All subjects underwent electrocardiogram recording by on-duty doctors in order to determine their health status.A custom-made device based on the principle of impedance plethysmography was designed to record impedance changes due to subjects’peripheral blood flow.The device was used to record ICG signals.In this study,we developed a novel auto-adaptive algorithm based on ICG signals for non-invasive,cuffless,continuous monitoring of BP and heart rate.Separate mathematical models were developed for all the estimated parameters(BP and heart rate)for both the study groups(control and disease).The developed models were auto-adaptive and did not require subject-specific calibration.Performance indicators including,��2,error percentage,standard deviation,and mean difference were used to quantify the performance of the models.Results The ICG signal recorded by the device was used to extract features and compute the augmentation index.The calculated augmentation index values showed strong correlations with systolic BP(��=0.99,��<0.05),diastolic BP(��=0.95,��<0.05),and heart rate(��=0.78,��<0.05).The models were also shown to have a high degree of accuracy for systolic and diastolic BP.Error margins were in the range±2.33 and±1.79 mmHg for systolic BP in disease and control subjects,respectively,and±3.60 and±1.82 mmHg for diastolic BP.However,the accuracy was lower in the prediction of heart rate in disease subjects,with a reported��2 value of 0.72 and an error margin of±2.88 beats per min;for healthy subjects,the results were marginally better,with an error margin of±1.82 beats per min.All statistical analyses were performed using MATLAB(R2017a,MathWorks R○,USA).Conclusion In this study,we developed a non-invasive cuffless approach for estimation of systemic peripheral BP and heart rate using ICG.The proposed methodology eliminated any discomfort to patients caused by inflation of the cuff(in the case of cuff-based BP monitoring)or the need to constantly wear a fingertip photoplethysmography device(in the case of cuffless BP monitoring).The results obtained appeared promising and increased the potential scope of ICG for monitoring other haemodynamic parameters related to heart function. | Sudipta Ghosh Bhabani Prasad Chattopadhyay Ram Mohan Roy Jayanta Mukherjee Manjunatha Mahadevappa | 2022 | Intelligent Medicine2022，2，4： | 0 |
| 4 | Modified distance regularized level set evolution for brain ventricles segmentation显示文摘Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss.Ventricles are one of the prominent structures in the brain;their shape changes,due to their content,the cerebrospinal fluid.Analyzing the morphological changes of ventricles,aids in the diagnosis of atrophy,for which the region of interest needs to be separated from the background.This study presents a modified distance regularized level set evolution segmentation method,incorporating regional intensity information.The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images.Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%,specificity in the range of 98%–99%,and accuracy in the range of 95%–98%.Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy:95%and 0.95,respectively.The parameters of level set formulation vary for different datasets.An optimization procedure was followed to fine tune parameters.The proposed method was found to be efficient and robust against noisy images.The proposed method is adaptive and multimodal. | Thirumagal Jayaraman Sravan Reddy M Manjunatha Mahadevappa Anup Sadhu Pranab Kumar Dutta | 2020 | Visual Computing for Industry,Biomedicine,and Art2020，3，1： | 0 |
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