Publications by authors named "Mozziyar Etemadi"

41 Publications

Use of Ballistocardiography to Monitor Cardiovascular Hemodynamics in Preeclampsia.

Womens Health Rep (New Rochelle) 2021 20;2(1):97-105. Epub 2021 Apr 20.

Division of Cardiology, Department of Internal Medicine, University of California San Francisco, San Francisco, California, USA.

Pregnancy requires a complex physiological adaptation of the maternal cardiovascular system, which is disrupted in women with pregnancies complicated by preeclampsia, putting them at higher risk of future cardiovascular events. The measurement of body movements in response to cardiac ejection ballistocardiogram (BCG) can be used to assess cardiovascular hemodynamics noninvasively in women with preeclampsia. Using a previously validated, modified weighing scale for assessment of cardiovascular hemodynamics through measurement of BCG and electrocardiogram (ECG) signals, we collected serial measurements throughout pregnancy and postpartum and analyzed data in 30 women with preeclampsia and 23 normotensive controls. Using BCG and ECG signals, we extracted measures of cardiac output, J-wave amplitude × heart rate (J-amp × HR). Mixed-effect models with repeated measures were used to compare J-amp × HRs between groups at different time points in pregnancy and postpartum. In normotensive controls, the J-amp × HR was significantly lower early postpartum (E-PP) compared with the second trimester (T2;  = 0.016) and third trimester (T3;  = 0.001). Women with preeclampsia had a significantly lower J-amp × HR compared with normotensive controls during the first trimester (T1;  = 0.026). In the preeclampsia group, there was a trend toward an increase in J-amp × HR from T1 to T2 and then a drop in J-amp × HR at T3 and further drop at E-PP. We observe cardiac hemodynamic changes consistent with those reported using well-validated tools. In pregnancies complicated by preeclampsia, the maximal force of contraction is lower, suggesting lower cardiac output and a trend in hemodynamics consistent with the hyperdynamic disease model of preeclampsia.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1089/whr.2020.0127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080913PMC
April 2021

A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder.

Artif Intell Med 2021 02 5;112:102006. Epub 2021 Jan 5.

Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60201, USA.

High-resolution manometry (HRM) is the primary method for diagnosing esophageal motility disorders and its interpretation and classification are based on variables (features) from data of each swallow. Modeling and learning the semantics directly from raw swallow data could not only help automate the feature extraction, but also alleviate the bias from pre-defined features. With more than 32-thousand raw swallow data, a generative model using the approach of variational auto-encoder (VAE) was developed, which, to our knowledge, is the first deep-learning-based unsupervised model on raw esophageal manometry data. The VAE model was reformulated to include different types of loss motivated by domain knowledge and tuned with different hyper-parameters. Training of the VAE model was found sensitive on the learning rate and hence the evidence lower bound objective (ELBO) was further scaled by the data dimension. Case studies showed that the dimensionality of latent space have a big impact on the learned semantics. In particular, cases with 4-dimensional latent variables were found to encode various physiologically meaningful contraction patterns, including strength, propagation pattern as well as sphincter relaxation. Cases with so-called hybrid L2 loss seemed to better capture the coherence of contraction/relaxation transition. Discriminating capability was further evaluated using simple linear discriminative analysis (LDA) on predicting swallow type and swallow pressurization, which yields clustering patterns consistent with clinical impression. The current work on modeling and understanding swallow-level data will guide the development of study-level models for automatic diagnosis as the next stage.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.102006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901248PMC
February 2021

Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning.

Endosc Int Open 2021 Feb 3;9(2):E233-E238. Epub 2021 Feb 3.

Department of Anesthesiology, Northwestern Medicine, Chicago, Illinois, United States.

Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1055/a-1326-1289DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857968PMC
February 2021

Preventing Intraoperative Hypotension: Artificial Intelligence versus Augmented Intelligence?

Anesthesiology 2020 12;133(6):1170-1172

Department of Anesthesiology Northwestern University Feinberg School of Medicine, Chicago, IL 60611.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/ALN.0000000000003561DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769137PMC
December 2020

Non-Invasive Wearable Patch Utilizing Seismocardiography for Peri-Operative Use in Surgical Patients.

IEEE J Biomed Health Inform 2021 May 11;25(5):1572-1582. Epub 2021 May 11.

Objective: Optimizing peri-operative fluid management has been shown to improve patient outcomes and the use of stroke volume (SV) measurement has become an accepted tool to guide fluid therapy. The Transesophageal Doppler (TED) is a validated, minimally invasive device that allows clinical assessment of SV. Unfortunately, the use of the TED is restricted to the intra-operative setting in anesthetized patients and requires constant supervision and periodic adjustment for accurate signal quality. However, post-operative fluid management is also vital for improved outcomes. Currently, there is no device regularly used in clinics that can track patient's SV continuously and non-invasively both during and after surgery.

Methods: In this paper, we propose the use of a wearable patch mounted on the mid-sternum, which captures the seismocardiogram (SCG) and electrocardiogram (ECG) signals continuously to predict SV in patients undergoing major surgery. In a study of 12 patients, hemodynamic data was recorded simultaneously using the TED and wearable patch. Signal processing and regression techniques were used to derive SV from the signals (SCG and ECG) captured by the wearable patch and compare it to values obtained by the TED.

Results: The results showed that the combination of SCG and ECG contains substantial information regarding SV, resulting in a correlation and median absolute error between the predicted and reference SV values of 0.81 and 7.56 mL, respectively.

Significance: This work shows promise for the proposed wearable-based methodology to be used as an alternative to TED for continuous patient monitoring and guiding peri-operative fluid management.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/JBHI.2020.3032938DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189504PMC
May 2021

Cardiac Function Monitoring for Patients Undergoing Cancer Treatments Using Wearable Seismocardiography: A Proof-of-Concept Study.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:4075-4078

Advances in cancer therapeutics have dramatically improved the survival rate and quality of life in patients affected by various cancers, but have been accompanied by treatment-related cardiotoxicity, e.g. left ventricular (LV) dysfunction and/or overt heart failure (HF). Cardiologists thus need to assess cancer treatment-related cardiotoxic risks and have close followups for cancer survivors and patients undergoing cancer treatments using serial echocardiography exams and cardiovascular biomarkers testing. Unfortunately, the cost-prohibitive nature of echocardiography has made these routine follow-ups difficult and not accessible to the growing number of cancer survivors and patients undergoing cancer treatments. There is thus a need to develop a wearable system that can yield similar information at a minimal cost and can be used for remote monitoring of these patients. In this proof-of-concept study, we have investigated the use of wearable seismocardiography (SCG) to monitor LV function non-invasively for patients undergoing cancer treatment. A total of 12 subjects (six with normal LV relaxation, five with impaired relaxation and one with pseudo-normal relaxation) underwent routine echocardiography followed by a standard six-minute walk test. Wearable SCG and electrocardiogram signals were collected during the six-minute walk test and, later, the signal features were compared between subjects with normal and impaired LV relaxation. Pre-ejection period (PEP) from SCG decreased significantly (p < 0.05) during exercise for the subjects with impaired relaxation compared to the subjects with normal relaxation, and changes in PEP/LV ejection time (LVET) were also significantly different between these two groups (p < 0.05). These results suggest that wearable SCG may enable monitoring of patients undergoing cancer treatments by assessing cardiotoxicity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC44109.2020.9176074DOI Listing
July 2020

Wearable Cuff-Less Blood Pressure Estimation at Home via Pulse Transit Time.

IEEE J Biomed Health Inform 2021 Jun 3;25(6):1926-1937. Epub 2021 Jun 3.

Objective: We developed a wearable watch-based device to provide noninvasive, cuff-less blood pressure (BP) estimation in an at-home setting.

Methods: The watch measures single-lead electrocardiogram (ECG), tri-axial seismocardiogram (SCG), and multi-wavelength photoplethysmogram (PPG) signals to compute the pulse transit time (PTT), allowing for BP estimation. We sent our custom watch device and an oscillometric BP cuff home with 21 healthy subjects, and captured the natural variability in BP over the course of a 24-hour period.

Results: After calibration, our Pearson correlation coefficient (PCC) of 0.69 and root-mean-square-error (RMSE) of 2.72 mmHg suggest that noninvasive PTT measurements correlate with around-the-clock BP. Using a novel two-point calibration method, we achieved a RMSE of 3.86 mmHg. We further demonstrated the potential of a semi-globalized adaptive model to reduce calibration requirements.

Conclusion: This is, to the best of our knowledge, the first time that BP has been comprehensively estimated noninvasively using PTT in an at-home setting. We showed a more convenient method for obtaining ambulatory BP than through the use of the standard oscillometric cuff. We presented new calibration methods for BP estimation using fewer calibration points that are more practical for a real-world scenario.

Significance: A custom watch (SeismoWatch) capable of taking multiple BP measurements enables reliable remote monitoring of daily BP and paves the way towards convenient hypertension screening and management, which can potentially reduce hospitalizations and improve quality of life.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/JBHI.2020.3021532DOI Listing
June 2021

Estimation of Instantaneous Oxygen Uptake During Exercise and Daily Activities Using a Wearable Cardio-Electromechanical and Environmental Sensor.

IEEE J Biomed Health Inform 2021 Mar 5;25(3):634-646. Epub 2021 Mar 5.

Objective: To estimate instantaneous oxygen uptake VO with a small, low-cost wearable sensor during exercise and daily activities in order to enable monitoring of energy expenditure (EE) in uncontrolled settings. We aim to do so using a combination of seismocardiogram (SCG), electrocardiogram (ECG) and atmospheric pressure (AP) signals obtained from a minimally obtrusive wearable device.

Methods: In this study, subjects performed a treadmill protocol in a controlled environment and an outside walking protocol in an uncontrolled environment. During testing, the COSMED K5 metabolic system collected gold standard breath-by-breath (BxB) data and a custom-built wearable patch placed on the mid-sternum collected SCG, ECG and AP signals. We extracted features from these signals to estimate the BxB VO data obtained from the COSMED system.

Results: In estimating instantaneous VO, we achieved our best results on the treadmill protocol using a combination of SCG (frequency) and AP features (RMSE of 3.68 ± 0.98 ml/kg/min and R of 0.77). For the outside protocol, we achieved our best results using a combination of SCG (frequency), ECG and AP features (RMSE of 4.3 ± 1.47 ml/kg/min and R of 0.64). In estimating VO consumed over one minute intervals during the protocols, our median percentage error was 15.8[Formula: see text] for the treadmill protocol and 20.5[Formula: see text] for the outside protocol.

Conclusion: SCG, ECG and AP signals from a small wearable patch can enable accurate estimation of instantaneous VO in both controlled and uncontrolled settings. SCG signals capturing variation in cardio-mechanical processes, AP signals, and state of the art machine learning models contribute significantly to the accurate estimation of instantaneous VO.

Significance: Accurate estimation of VO with a low cost, minimally obtrusive wearable patch can enable the monitoring of VO and EE in everyday settings and make the many applications of these measurements more accessible to the general public.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/JBHI.2020.3009903DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004550PMC
March 2021

Wearable Patch-Based Estimation of Oxygen Uptake and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients With Heart Failure.

J Card Fail 2020 Nov 27;26(11):948-958. Epub 2020 May 27.

Department of ECE, Georgia Institute of Technology, Atlanta, Georgia.

Background: To estimate oxygen uptake (VO) from cardiopulmonary exercise testing (CPX) using simultaneously recorded seismocardiogram (SCG) and electrocardiogram (ECG) signals captured with a small wearable patch. CPX is an important risk stratification tool for patients with heart failure (HF) owing to the prognostic value of the features derived from the gas exchange variables such as VO. However, CPX requires specialized equipment, as well as trained professionals to conduct the study.

Methods And Results: We have conducted a total of 68 CPX tests on 59 patients with HF with reduced ejection fraction (31% women, mean age 55 ± 13 years, ejection fraction 0.27 ± 0.11, 79% stage C). The patients were fitted with a wearable sensing patch and underwent treadmill CPX. We divided the dataset into a training-testing set (n = 44) and a separate validation set (n = 24). We developed globalized (population) regression models to estimate VO from the SCG and ECG signals measured continuously with the patch. We further classified the patients as stage D or C using the SCG and ECG features to assess the ability to detect clinical state from the wearable patch measurements alone. We developed the regression and classification model with cross-validation on the training-testing set and validated the models on the validation set. The regression model to estimate VO from the wearable features yielded a moderate correlation (R of 0.64) with a root mean square error of 2.51 ± 1.12 mL · kg · min on the training-testing set, whereas R and root mean square error on the validation set were 0.76 and 2.28 ± 0.93 mL · kg · min, respectively. Furthermore, the classification of clinical state yielded accuracy, sensitivity, specificity, and an area under the receiver operating characteristic curve values of 0.84, 0.91, 0.64, and 0.74, respectively, for the training-testing set, and 0.83, 0.86, 0.67, and 0.92, respectively, for the validation set.

Conclusions: Wearable SCG and ECG can assess CPX VO and thereby classify clinical status for patients with HF. These methods may provide value in the risk stratification of patients with HF by tracking cardiopulmonary parameters and clinical status outside of specialized settings, potentially allowing for more frequent assessments to be performed during longitudinal monitoring and treatment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cardfail.2020.05.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704799PMC
November 2020

Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI.

Ann Biomed Eng 2020 Jun 16;48(6):1779-1792. Epub 2020 Mar 16.

Department of Biomedical Engineering, Northwestern University, 2145 Sheridan Road, Tech E310, Evanston, IL, 60208, USA.

Cardiac MRI (CMR) techniques offer non-invasive visualizations of cardiac morphology and function. However, imaging can be time-consuming and complex. Seismocardiography (SCG) measures physical vibrations transmitted through the chest from the beating heart and pulsatile blood flow. SCG signals can be acquired quickly and easily, with inexpensive electronics. This study investigates relationships between CMR metrics of function and SCG signal features. Same-day CMR and SCG data were collected from 28 healthy adults and 6 subjects with aortic valve disease history. Correlation testing and statistical median/decile calculations were performed with data from the healthy cohort. MR-quantified flow and function parameters in the healthy cohort correlated with particular SCG energy levels, such as peak aortic velocity with low-frequency SCG (coefficient 0.43, significance 0.02) and peak flow with high-frequency SCG (coefficient 0.40, significance 0.03). Valve disease-induced flow abnormalities in patients were visualized with MRI, and corresponding abnormalities in SCG signals were identified. This investigation found significant cross-modality correlations in cardiac function metrics and SCG signals features from healthy subjects. Additionally, through comparison to normative ranges from healthy subjects, it observed correspondences between pathological flow and abnormal SCG. This may support development of an easy clinical test used to identify potential aortic flow abnormalities.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10439-020-02491-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286773PMC
June 2020

International evaluation of an AI system for breast cancer screening.

Nature 2020 01 1;577(7788):89-94. Epub 2020 Jan 1.

DeepMind, London, UK.

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41586-019-1799-6DOI Listing
January 2020

Seismocardiography and 4D flow MRI reveal impact of aortic valve replacement on chest acceleration and aortic hemodynamics.

J Card Surg 2020 Jan 15;35(1):232-235. Epub 2019 Oct 15.

Radiology, Bioengineering, University of Colorado, Anschutz Medical Campus, Aurora, Colorado.

Aortic valve replacement (AVR) is a common treatment for severe aortic valve disease, which can adversely affect blood flow in the aorta. Seismocardiography (SCG) measures physical vibrations at the exterior of the chest, which can be sensitive to altered cardiac function and flow dynamics. Magnetic resonance imaging (MRI) can image blood movement, and it can provide depiction and quantification of aortic flow. Here we present SCG and MRI measurements from before and after AVR and ascending aorta replacement, in the case of a woman with bicuspid aortic valve disease and a dilated ascending aorta. SCG measurements show elevated energy during systole indicating stenotic flow before surgery and lowered systolic energy levels after replacement with a prosthetic valve. MRI shows jetting, helical flow before surgery, and cohesive flow after.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/jocs.14289DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6948186PMC
January 2020

Classification of Decompensated Heart Failure From Clinical and Home Ballistocardiography.

IEEE Trans Biomed Eng 2020 05 15;67(5):1303-1313. Epub 2019 Aug 15.

Objective: To improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient.

Methods: 1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF.

Results: The best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method.

Conclusion: This work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients.

Significance: In future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TBME.2019.2935619DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7271768PMC
May 2020

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.

Nat Med 2019 06 20;25(6):954-961. Epub 2019 May 20.

Google AI, Mountain View, CA, USA.

With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines. Existing challenges include inter-grader variability and high false-positive and false-negative rates. We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41591-019-0447-xDOI Listing
June 2019

Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients.

Circ Heart Fail 2018 01;11(1):e004313

From the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).

Background: Remote monitoring of patients with heart failure (HF) using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of electrical and mechanical aspects of cardiac function in the context of exercise.

Methods And Results: Patients with compensated (outpatient) and decompensated (hospitalized) HF were fitted with a wearable ECG and seismocardiogram sensing patch. Patients stood at rest for an initial recording, performed a 6-minute walk test, and then stood at rest for 5 minutes of recovery. The protocol was performed at the time of outpatient visit or at 2 time points (admission and discharge) during an HF hospitalization. To assess patient state, we devised a method based on comparing the similarity of the structure of seismocardiogram signals after exercise compared with rest using graph mining (graph similarity score). We found that graph similarity score can assess HF patient state and correlates to clinical improvement in 45 patients (13 decompensated, 32 compensated). A significant difference was found between the groups in the graph similarity score metric (44.4±4.9 [decompensated HF] versus 35.2±10.5 [compensated HF]; <0.001). In the 6 decompensated patients with longitudinal data, we found a significant change in graph similarity score from admission (decompensated) to discharge (compensated; 44±4.1 [admitted] versus 35±3.9 [discharged]; <0.05).

Conclusions: Wearable technologies recording cardiac function and machine learning algorithms can assess compensated and decompensated HF states by analyzing cardiac response to submaximal exercise. These techniques can be tested in the future to track the clinical status of outpatients with HF and their response to pharmacological interventions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/CIRCHEARTFAILURE.117.004313DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5769154PMC
January 2018

Wearable ballistocardiogram and seismocardiogram systems for health and performance.

J Appl Physiol (1985) 2018 02 10;124(2):452-461. Epub 2017 Aug 10.

School of Electrical and Computer Engineering and Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology , Atlanta, Georgia.

Cardiovascular diseases (CVDs) are prevalent in the US, and many forms of CVD primarily affect the mechanical aspects of heart function. Wearable technologies for monitoring the mechanical health of the heart and vasculature could enable proactive management of CVDs through titration of care based on physiological status as well as preventative wellness monitoring to help promote lifestyle choices that reduce the overall risk of developing CVDs. Additionally, such wearable technologies could be used to optimize human performance in austere environments. This review describes our progress in developing wearable ballistocardiogram (BCG)- and seismocardiogram-based systems for monitoring relative changes in cardiac output, contractility, and blood pressure. Our systems use miniature, low-noise accelerometers to measure the movements of the body in response to the heartbeat and novel machine learning algorithms to provide robustness against motion artifacts and sensor misplacement. Moreover, we have mathematically related wearable BCG signals-representing local, cardiogenic movements of a point on the body-to better understood whole body BCG signals, and thereby improved estimation of key health parameters. We validated these systems with experiments in healthy subjects, studies in patients with heart failure, and measurements in austere environments such as water immersion. The systems can be used in future work as a tool for clinicians and physiologists to measure the mechanical aspects of cardiovascular function outside of clinical settings, and to thereby titrate care for patients with CVDs, provide preventative screening, and optimize performance in austere environments by providing real-time in-depth information regarding performance and risk.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/japplphysiol.00298.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867366PMC
February 2018

Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health.

IEEE Trans Biomed Eng 2017 06 16;64(6):1277-1286. Epub 2016 Aug 16.

Goal: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34-1.45 m/s) speeds.

Methods: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking.

Results: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking.

Conclusion: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking.

Significance: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TBME.2016.2600945DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444999PMC
June 2017

MyPectus: First-in-human pilot study of remote compliance monitoring of teens using dynamic compression bracing to correct pectus carinatum.

J Pediatr Surg 2016 Apr 1;51(4):608-11. Epub 2015 Dec 1.

Hospital Privado de Niños Fundación Hospitalaria, Cramer 4601, Capital Federal (C1429AKK), Buenos Aires, Argentina.

Background: Patient compliance is a crucial determinant of outcomes in treatments involving medical braces, such as dynamic compression therapy for pectus carinatum (PC). We performed a pilot study to assess a novel, wireless, real-time monitoring system (MyPectus) to address noncompliance.

Methods: Eight patients (10-16years old) with moderately severe PC deformities underwent bracing. Each patient received a data logger device inserted in the compression brace to sense temperature and pressure. The data were transmitted via Bluetooth 4.0 to an iOS smartphone app, then synced to cloud-based storage, and presented to the clinician on a web-based dashboard. Patients received points for brace usage on the app throughout the 4-week study, and completed a survey to capture patient-reported usage patterns.

Results: In all 8 patients, the data logger sensed and recorded data, which connected through all MyPectus system components. There were occasional lapses in data collection because of technical difficulties, such as limited storage capacity. Patients reported positive feedback regarding points.

Conclusions: The components of the MyPectus system recorded, stored, and provided data to patients and clinicians. The MyPectus system will inform clinicians about issues related to noncompliance: discrepancy between patient-reported and sensor-reported data regarding brace usage; real-time, actionable information; and patient motivation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jpedsurg.2015.11.007DOI Listing
April 2016

A Wearable Patch to Enable Long-Term Monitoring of Environmental, Activity and Hemodynamics Variables.

IEEE Trans Biomed Circuits Syst 2016 Apr 12;10(2):280-8. Epub 2015 May 12.

We present a low power multi-modal patch designed for measuring activity, altitude (based on high-resolution barometric pressure), a single-lead electrocardiogram, and a tri-axial seismocardiogram (SCG). Enabled by a novel embedded systems design methodology, this patch offers a powerful means of monitoring the physiology for both patients with chronic cardiovascular diseases, and the general population interested in personal health and fitness measures. Specifically, to the best of our knowledge, this patch represents the first demonstration of combined activity, environmental context, and hemodynamics monitoring, all on the same hardware, capable of operating for longer than 48 hours at a time with continuous recording. The three-channels of SCG and one-lead ECG are all sampled at 500 Hz with high signal-to-noise ratio, the pressure sensor is sampled at 10 Hz, and all signals are stored to a microSD card with an average current consumption of less than 2 mA from a 3.7 V coin cell (LIR2450) battery. In addition to electronic characterization, proof-of-concept exercise recovery studies were performed with this patch, suggesting the ability to discriminate between hemodynamic and electrophysiology response to light, moderate, and heavy exercise.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TBCAS.2015.2405480DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4643430PMC
April 2016

Tracking clinical status for heart failure patients using ballistocardiography and electrocardiography signal features.

Annu Int Conf IEEE Eng Med Biol Soc 2014 ;2014:5188-91

Heart failure (HF) is an escalating public health problem, with few effective methods for home monitoring. In HF management, the important clinical factors to monitor include symptoms, fluid status, cardiac output, and blood pressure--based on these factors, inotrope and diuretic dosages are adjusted day-by-day to control the disorder and improve the patient's status towards a successful discharge. Previously, the ballistocardiogram (BCG) measured on a weighing scale has been shown to be capable of detecting changes in cardiac output and contractility for healthy subjects. In this study, we investigated whether the BCG and electrocardiogram (ECG) signals measured on a wireless modified scale could accurately track the clinical status of HF patients during their hospital stay. Using logistic regression, we found that the root-mean-square (RMS) power of the BCG provided a good fit for clinical status, as determined based on clinical measurements and symptoms, for the 85 patient days studied from 10 patients (p < 0.01). These results provide a promising foundation for future studies aimed at using the BCG/ECG scale at home to track HF patient status remotely.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC.2014.6944794DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4600348PMC
July 2016

Wearable ballistocardiography: preliminary methods for mapping surface vibration measurements to whole body forces.

Annu Int Conf IEEE Eng Med Biol Soc 2014 ;2014:5172-5

The recent resurgence of ballistocardiogram (BCG) measurement and interpretation technologies has led to a wide range of powerful tools available for unobtrusively assessing mechanical aspects of cardiovascular health at home. Researchers have demonstrated a multitude of modern BCG measurement modalities, including beds, chairs, weighing scales, and wearable approaches. However, many modalities produce significant variations in the morphology of the measured BCG, creating confusion in the analysis and interpretation of the signals. This paper creates a framework for comparing wearable BCG measurements to whole body measurements--such as taken with a weighing scale system--to eventually allow the same analysis and interpretation tools that have been developed for whole body systems to be applied in the future to wearable systems. To the best of our knowledge, it represents the first attempt to morphologically compare vertical acceleration recordings measured on different locations on the torso to whole body displacements measured by BCG instrumentation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC.2014.6944790DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4605430PMC
July 2016

Ballistocardiography and seismocardiography: a review of recent advances.

IEEE J Biomed Health Inform 2015 Jul 7;19(4):1414-27. Epub 2014 Oct 7.

In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/JBHI.2014.2361732DOI Listing
July 2015

Toward continuous, noninvasive assessment of ventricular function and hemodynamics: wearable ballistocardiography.

IEEE J Biomed Health Inform 2015 Jul 23;19(4):1435-42. Epub 2014 Sep 23.

Ballistocardiography, the measurement of the reaction forces of the body to cardiac ejection of blood, is one of the few techniques available for unobtrusively assessing the mechanical aspects of cardiovascular health outside clinical settings. Recently, multiple experimental studies involving healthy subjects and subjects with various cardiovascular diseases have demonstrated that the ballistocardiogram (BCG) signal can be used to trend cardiac output, contractility, and beat-by-beat ventricular function for arrhythmias. The majority of these studies has been performed with "fixed" BCG instrumentation-such as weighing scales or chairs-rather than wearable measurements. Enabling wearable, and thus continuous, recording of BCG signals would greatly expand the capabilities of the technique; however, BCG signals measured using wearable devices are morphologically dissimilar to measurements from "fixed" instruments, precluding the analysis and interpretation techniques from one domain to be applied to the other. In particular, the time intervals between the electrocardiogram (ECG) and BCG-namely, the R-J interval, a surrogate for measuring contractility changes-are significantly different for the accelerometer compared to a "fixed" BCG measurement. This paper addresses this need for quantitatively normalizing wearable BCG measurement to "fixed" measurements with a systematic experimental approach. With these methods, the same analysis and interpretation techniques developed over the past decade for "fixed" BCG measurement can be successfully translated to wearable measurements.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/JBHI.2014.2359937DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4532376PMC
July 2015

Rapid and low-cost prototyping of medical devices using 3D printed molds for liquid injection molding.

J Vis Exp 2014 Jun 27(88):e51745. Epub 2014 Jun 27.

Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco.

Biologically inert elastomers such as silicone are favorable materials for medical device fabrication, but forming and curing these elastomers using traditional liquid injection molding processes can be an expensive process due to tooling and equipment costs. As a result, it has traditionally been impractical to use liquid injection molding for low-cost, rapid prototyping applications. We have devised a method for rapid and low-cost production of liquid elastomer injection molded devices that utilizes fused deposition modeling 3D printers for mold design and a modified desiccator as an injection system. Low costs and rapid turnaround time in this technique lower the barrier to iteratively designing and prototyping complex elastomer devices. Furthermore, CAD models developed in this process can be later adapted for metal mold tooling design, enabling an easy transition to a traditional injection molding process. We have used this technique to manufacture intravaginal probes involving complex geometries, as well as overmolding over metal parts, using tools commonly available within an academic research laboratory. However, this technique can be easily adapted to create liquid injection molded devices for many other applications.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3791/51745DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208739PMC
June 2014

Antenatal maternally-administered phosphodiesterase type 5 inhibitors normalize eNOS expression in the fetal lamb model of congenital diaphragmatic hernia.

J Pediatr Surg 2014 Jan 5;49(1):39-45; discussion 45. Epub 2013 Oct 5.

Department of Surgery, Division of Pediatric Surgery and Fetal Treatment Center, University of California, San Francisco, San Francisco, CA. Electronic address:

Purpose: Pulmonary hypertension (pHTN), a main determinant of survival in congenital diaphragmatic hernia (CDH), results from in utero vascular remodeling. Phosphodiesterase type 5 (PDE5) inhibitors have never been used antenatally to treat pHTN. The purpose of this study is to determine if antenatal PDE5 inhibitors can prevent pHTN in the fetal lamb model of CDH.

Methods: CDH was created in pregnant ewes. Postoperatively, pregnant ewes received oral placebo or tadalafil, a PDE5 inhibitor, until delivery. Near term gestation, lambs underwent resuscitations, and lung tissue was snap frozen for protein analysis.

Results: Mean cGMP levels were 0.53±0.11 in placebo-treated fetal lambs and 1.73±0.21 in tadalafil-treated fetal lambs (p=0.002). Normalized expression of eNOS was 82%±12% in Normal-Placebo, 61%±5% in CDH-Placebo, 116%±6% in Normal-Tadalafil, and 86%±8% in CDH-Tadalafil lambs. Normalized expression of β-sGC was 105%±15% in Normal-Placebo, 82%±3% in CDH-Placebo, 158%±16% in Normal-Tadalafil, and 86%±8% in CDH-Tadalafil lambs. Endothelial NOS and β-sGC were significantly decreased in CDH (p=0.0007 and 0.01 for eNOS and β-sGC, respectively), and tadalafil significantly increased eNOS expression (p=0.0002).

Conclusions: PDE5 inhibitors can cross the placental barrier. β-sGC and eNOS are downregulated in fetal lambs with CDH. Antenatal PDE5 inhibitors normalize eNOS and may prevent in utero vascular remodeling in CDH.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jpedsurg.2013.09.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896891PMC
January 2014

Fabric-based pressure sensor array for decubitus ulcer monitoring.

Annu Int Conf IEEE Eng Med Biol Soc 2013 ;2013:6506-9

Decubitus ulcers occur in an estimated 2.5 million Americans each year at an annual cost of $11 billion to the U.S. health system. Current screening and prevention techniques for assessing risk for decubitus ulcer formation and repositioning patients every 1-2 hours are labor-intensive and can be subjective. We propose use of a Bluetooth-enabled fabric-based pressure sensor array as a simple tool to objectively assess and continuously monitor decubitus ulcer risk.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC.2013.6611045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606918PMC
September 2015

Novel device to trend impedance and fluorescence of the cervix for preterm birth detection.

Annu Int Conf IEEE Eng Med Biol Soc 2013 ;2013:176-9

Preterm birth is the leading cause of worldwide neonatal mortality. It follows a pathologically accelerated form of the normal processes that govern cervical softening and dilation. Softening and dilation occur due to changes in cervical collagen crosslinking, which can be measured non-invasively by changes in tissue fluorescence and impedance. We present a novel device designed specifically to take fluorescence and impedance measurements throughout pregnancy, with the end goal of fusing and trending these measurements to form an early diagnosis of preterm labor.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/EMBC.2013.6609466DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606960PMC
July 2015

Towards BirthAlert--A Clinical Device Intended for Early Preterm Birth Detection.

IEEE Trans Biomed Eng 2013 Dec 23;60(12):3484-93. Epub 2013 Jul 23.

Preterm birth causes 1 million infant deaths worldwide every year, making it the leading cause of infant mortality. Existing diagnostic tests such as transvaginal ultrasound or fetal fibronectin either cannot determine if preterm birth will occur in the future or can only predict the occurrence once cervical shortening has begun, at which point it is too late to reverse the accelerated parturition process. Using iterative and rapid prototyping techniques, we have developed an intravaginal proof-of-concept device that measures both cervical bioimpedance and cervical fluorescence to characterize microstructural changes in a pregnant woman's cervix in hopes of detecting preterm birth before macroscopic changes manifest in the tissue. If successful, such an early alert during this "silent phase" of the preterm birth syndrome may open a new window of opportunity for interventions that may reverse and avoid preterm birth altogether.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TBME.2013.2272601DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4605421PMC
December 2013