Publications by authors named "Md Mobashir Hasan Shandhi"

10 Publications

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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.
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http://dx.doi.org/10.1089/whr.2020.0127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080913PMC
April 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.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176074DOI Listing
July 2020

The Delineation of Fiducial Points for Non-Contact Radar Seismocardiogram Signals Without Concurrent ECG.

IEEE J Biomed Health Inform 2021 Apr 6;25(4):1031-1040. Epub 2021 Apr 6.

Objective: Non-contact sensing of seismocardiogram (SCG) signals through a microwave Doppler radar is promising for biomedical applications. However, the delineation of fiducial points for radar SCG still relies on concurrent ECG which requires a contact sensor and limits the complete non-contact detection of SCG.

Methods: Instead of ECG, a new reference signal, the radar displacement signal of heartbeat (RDH), was derived through the complex Fourier transform and the band pass filtering of the radar signal. The RDH signal was used to locate each cardiac cycle and mask the systolic profile, which was further used to detect an important fiducial point, aortic valve opening (AO). The beat-to-beat interval was estimated from AO-AO interval and compared with the gold standard, ECG R-to-R interval.

Results: For the 22 subjects in the study, the evaluation of the AOs detected by RDH (AO) shows the average detection ratio can reach 90%, indicating a high ratio of the AO that are exactly the same as AO detected using the ECG R-wave (AO). Additionally, the left ventricular ejection time (LVET) values estimated from the ensemble averaged radar waveform through AO segmentation are within 2 ms of those through AO segmentation, for all the detected subjects. Further analysis demonstrates that the beat-to-beat intervals calculated from AO have an average root-mean-square-deviation (RMSD) of 53.73 ms when compared with ECG R-to-R intervals, and have an average RMSD of 23.47 ms after removing the beats in which AO cannot be identified.

Conclusions: Radar signal RDH can be used as a reference signal to delineate fiducial points for non-contact radar SCG signals.

Significance: This study can be applied to develop complete non-contact sensing of SCG and monitoring of vital signs, where contact-based SCG is not feasible.
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http://dx.doi.org/10.1109/JBHI.2020.3009997DOI Listing
April 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.
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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.
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http://dx.doi.org/10.1016/j.cardfail.2020.05.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704799PMC
November 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.
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http://dx.doi.org/10.1109/TBME.2019.2935619DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7271768PMC
May 2020

A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning.

IEEE J Biomed Health Inform 2020 05 29;24(5):1296-1309. Epub 2019 Jul 29.

The ballistocardiography (BCG) signal is a measurement of the vibrations of the center of mass of the body due to the cardiac cycle and can be used for noninvasive hemodynamic monitoring. The seismocardiography (SCG) signals measure the local vibrations of the chest wall due to the cardiac cycle. While BCG is a more well-known modality, it requires the use of a modified bathroom scale or a force plate and cannot be measured in a wearable setting, whereas SCG signals can be measured using wearable accelerometers placed on the sternum. In this paper, we explore the idea of finding a mapping between zero mean and unit l-norm SCG and BCG signal segments such that, the BCG signal can be acquired using wearable accelerometers (without retaining amplitude information). We use neural networks to find such a mapping and make use of the recently introduced UNet architecture. We trained our models on 26 healthy subjects and tested them on ten subjects. Our results show that we can estimate the aforementioned segments of the BCG signal with a median Pearson correlation coefficient of 0.71 and a median absolute deviation (MAD) of 0.17. Furthermore, our model can estimate the R-I, R-J and R-K timing intervals with median absolute errors (and MAD) of 10.00 (8.90), 6.00 (5.93), and 8.00 (5.93), respectively. We show that using all three axis of the SCG accelerometer produces the best results, whereas the head-to-foot SCG signal produces the best results when a single SCG axis is used.
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http://dx.doi.org/10.1109/JBHI.2019.2931872DOI Listing
May 2020

A Unified Framework for Quality Indexing and Classification of Seismocardiogram Signals.

IEEE J Biomed Health Inform 2020 04 26;24(4):1080-1092. Epub 2019 Jul 26.

The seismocardiogram (SCG) is a noninvasively-obtained cardiovascular bio-signal that has gained traction in recent years, however is limited by its susceptibility to noise and motion artifacts. Because of this, signal quality must be assured before data are used to inform clinical care. Common methods of signal quality assurance include signal classification or assignment of a numerical quality index. Such tasks are difficult with SCG because there is no accepted standard for signal morphology. In this paper, we propose a unified method of quality indexing and classification that uses multi-subject-based methods to overcome this challenge. Dynamic-time feature matching is introduced as a novel method of obtaining the distance between a signal and reference template, with this metric, the signal quality index (SQI) is defined as a function of the inverse distance between the SCG and a large set of template signals. We demonstrate that this method is able to stratify SCG signals on held-out subjects based on their level of motion-artifact corruption. This method is extended, using the SQI as a feature for classification by ensembled quadratic discriminant analysis. Classification is validated by demonstrating, for the first time, both detection and localization of SCG sensor misplacement, achieving an F1 score of 0.83 on held-out subjects. This paper may provide a necessary step toward automating the analysis of SCG signals, addressing many of the key limitations and concerns precluding the method from being widely used in clinical and physiological sensing applications.
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http://dx.doi.org/10.1109/JBHI.2019.2931348DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193993PMC
April 2020

Mitigation of Instrument-Dependent Variability in Ballistocardiogram Morphology: Case Study on Force Plate and Customized Weighing Scale.

IEEE J Biomed Health Inform 2020 01 25;24(1):69-78. Epub 2019 Feb 25.

The objective of this study was to investigate the measurement instrument-dependent variability in the morphology of the ballistocardiogram (BCG) waveform in human subjects and computational methods to mitigate the variability. The BCG was measured in 22 young healthy subjects using a high-performance force plate and a customized commercial weighing scale under upright standing posture. The timing and amplitude features associated with the major I, J, K waves in the BCG waveforms were extracted and quantitatively analyzed. The results indicated that 1) the I, J, K waves associated with the weighing scale BCG exhibited delay in the timings within the cardiac cycle relative to the ECG R wave as well as attenuation in the absolute amplitudes than the respective force plate counterparts, whereas 2) the time intervals between the I, J, K waves were comparable. Then, two alternative computational methods were conceived in an attempt to mitigate the discrepancy between force plate versus weighing-scale BCG: a transfer function and an amplitude-phase correction. The results suggested that both methods effectively mitigated the discrepancy in the timings and amplitudes associated with the I, J, K waves between the force plate and weighing-scale BCG. Hence, signal processing may serve as a viable solution to the mitigation of the instrument-induced morphological variability in the BCG, thereby facilitating the standardized analysis and interpretation of the timing and amplitude features in the BCG across wide-ranging measurement platforms.
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http://dx.doi.org/10.1109/JBHI.2019.2901635DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6986214PMC
January 2020

Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation.

IEEE J Biomed Health Inform 2019 11 28;23(6):2365-2374. Epub 2019 Jan 28.

Objective: Systolic time intervals, such as the pre-ejection period (PEP), are important parameters for assessing cardiac contractility that can be measured non-invasively using seismocardiography (SCG). Recent studies have shown that specific points on accelerometer- and gyroscope-based SCG signals can be used for PEP estimation. However, the complex morphology and inter-subject variation of the SCG signal can make this assumption very challenging and increase the root mean squared error (RMSE) when these techniques are used to develop a global model.

Methods: In this study, we compared gyroscope- and accelerometer-based SCG signals, individually and in combination, for estimating PEP to show the efficacy of these sensors in capturing valuable information regarding cardiovascular health. We extracted general time-domain features from all the axes of these sensors and developed global models using various regression techniques.

Results: In single-axis comparison of gyroscope and accelerometer, angular velocity signal around head to foot axis from the gyroscope provided the lowest RMSE of 12.63 ± 0.49 ms across all subjects. The best estimate of PEP, with a RMSE of 11.46 ± 0.32 ms across all subjects, was achieved by combining features from the gyroscope and accelerometer. Our global model showed 30% lower RMSE when compared to algorithms used in recent literature.

Conclusion: Gyroscopes can provide better PEP estimation compared to accelerometers located on the mid-sternum. Global PEP estimation models can be improved by combining general time domain features from both sensors.

Significance: This work can be used to develop a low-cost wearable heart-monitoring device and to generate a universal estimation model for systolic time intervals using a single- or multiple-sensor fusion.
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http://dx.doi.org/10.1109/JBHI.2019.2895775DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874489PMC
November 2019