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Article ; Online: A Physical Model-Based Approach to One-Point Calibration of Pulse Transit Time to Blood Pressure.

Mousavi, Azin / Inan, Omer T / Mukkamala, Ramakrishna / Hahn, Jin-Oh

IEEE transactions on bio-medical engineering

2024 Volume 71, Issue 2, Page(s) 477–483

Abstract: Objective: To develop a novel physical model-based approach to enable 1-point calibration of pulse transit time (PTT) to blood pressure (BP).: Methods: The proposed PTT-BP calibration model is derived by combining the Bramwell-Hill equation and a ... ...

Abstract	Objective: To develop a novel physical model-based approach to enable 1-point calibration of pulse transit time (PTT) to blood pressure (BP). Methods: The proposed PTT-BP calibration model is derived by combining the Bramwell-Hill equation and a phenomenological model of the arterial compliance (AC) curve. By imposing a physiologically plausible constraint on the skewness of AC at positive and negative transmural pressures, the number of tunable parameters in the PTT-BP calibration model reduces to 1. Hence, as opposed to most existing PTT-BP calibration models requiring multiple (≥2) PTT-BP measurements to personalize, the PTT-BP calibration model can be personalized to an individual subject using a single PTT-BP measurement pair. Equipped with the physically relevant PTT-AC and AC-BP relationships, the proposed approach may serve as a universal means to calibrate PTT to BP over a wide BP range. The validity and proof-of-concept of the proposed approach were evaluated using PTT and BP measurements collected from 22 healthy young volunteers undergoing large BP changes. Results: The proposed approach modestly yet significantly outperformed an empiric linear PTT-BP calibration with a group-average slope and subject-specific intercept in terms of bias (5.5 mmHg vs 6.4 mmHg), precision (8.4 mmHg vs 9.4 mmHg), mean absolute error (7.8 mmHg vs 8.8 mmHg), and root-mean-squared error (8.7 mmHg vs 10.3 mmHg, all in the case of diastolic BP). Conclusion: We demonstrated the preliminary proof-of-concept of an innovative physical model-based approach to one-point PTT-BP calibration. Significance: The proposed physical model-based approach has the potential to enable more accurate and convenient calibration of PTT to BP.
MeSH term(s)	Humans ; Blood Pressure/physiology ; Calibration ; Blood Pressure Determination ; Arteries ; Pulse Wave Analysis
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2023.3307658
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Article ; Online: A Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity.

Nikbakht, Mohammad / Chan, Michael / Lin, David J / Gazi, Asim H / Inan, Omer T

IEEE journal of biomedical and health informatics

2024 Volume PP

Abstract: Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact ... ...

Abstract	Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact accuracy and robustness of the measurements. A deep learning model based on the U-Net architecture is proposed to recover SCG signals contaminated by motion noise induced by walking. The model performance was evaluated through qualitative visualization, as well as quantitative analyses. Quantitative analyses included distance-based comparisons before and after applying our model. Analyses also included assessments of the model's efficacy in improving the performance of downstream tasks related to health parameter estimation during walking. Experimental findings revealed that the denoising model improved similarity to clean signals by approximately 90%. The performance of the model in enhancing heart rate estimation demonstrated a mean absolute error of 1.21 BPM and a root-mean-squared error (RMSE) of 1.97 BPM during walking after denoising with 9.16 BPM and 10.38 BPM improvements, respectively, compared to without denoising. Furthermore, the RMSEs of aortic opening and aortic closing time estimation after denoising for one dataset with catheter ground truth were 7.29 ms and 19.71 ms during walking, respectively, with 50.33 ms and 51.91 ms RMSE improvements compared to without denoising. And for another dataset with ICG-derived PEP ground truth, the RMSE of aortic opening time estimation after denoising was 10.21 ms during walking, with 38.74 ms RMSE improvement compared to without denoising. The proposed model attenuates motion noise from corrupted SCG signals while preserving cardiac information. This development paves the way for improved ambulatory cardiac health monitoring using wearable accelerometers during daily activities.
Language	English
Publishing date	2024-04-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2024.3392532
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Article ; Online: Improving Biological Joint Moment Estimation During Real-World Tasks with EMG and Instrumented Insoles.

Scherpereel, Keaton L / Molinaro, Dean D / Shepherd, Max K / Inan, Omer T / Young, Aaron J

IEEE transactions on bio-medical engineering

2024 Volume PP

Abstract: Objective: Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for ...

Abstract	Objective: Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique. Methods: We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities during both activity types. Results: Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p<0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p<0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p<0.05) which was significantly higher than either modality individually (p<0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p<0.05). Conclusion: These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. Significance: Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.
Language	English
Publishing date	2024-04-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2024.3388874
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Article ; Online: Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals.

Chan, Michael / Ganti, Venu G / Inan, Omer T

IEEE journal of biomedical and health informatics

2022 Volume 26, Issue 6, Page(s) 2481–2492

Abstract: Objective: At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the ... ...

Abstract	Objective: At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. Methods: In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. Major results: We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R Conclusion: ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. Significance: We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.
MeSH term(s)	Artifacts ; COVID-19 ; Electrocardiography ; Humans ; Respiration ; Respiratory Rate ; Signal Processing, Computer-Assisted
Language	English
Publishing date	2022-06-03
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3144990
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ab Jg. 2022: Lesesaal (EG)

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Article: Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals.

Soliman, Moamen M / Ganti, Venu G / Inan, Omer T

IEEE sensors journal

2022 Volume 22, Issue 18, Page(s) 18093–18103

Abstract: The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air ...

Abstract	The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.
Language	English
Publishing date	2022-08-10
Publishing country	United States
Document type	Journal Article
ISSN	1530-437X
ISSN	1530-437X
DOI	10.1109/jsen.2022.3196601
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Article ; Online: Improving Respiratory Timing Estimation Using Quality Indexing and Electrocardiogram-Derived Respiration.

Gazi, Asim H / Jung, Hewon / Kimball, Jacob P / Inan, Omer T

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 3249–3252

Abstract: Numerous applications require accurate estimation of respiratory timings. Respiratory effort (RSP) measurement is a popular approach to accomplish this, especially when the tightness of the sensing belt around the chest can be ensured. In less controlled ...

Abstract	Numerous applications require accurate estimation of respiratory timings. Respiratory effort (RSP) measurement is a popular approach to accomplish this, especially when the tightness of the sensing belt around the chest can be ensured. In less controlled settings, however, belt looseness and artifacts from movement of the belt on the chest can corrupt the signal. This paper demonstrates that respiration quality indexing and outlier removal can help mitigate these issues, improving estimates of respiration rate (RR), inspiration time (Ti), and expiration time (T
MeSH term(s)	Algorithms ; Electrocardiography/methods ; Humans ; Respiration ; Respiratory Rate ; Signal Processing, Computer-Assisted
Language	English
Publishing date	2022-09-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871873
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Editorial: Cardiac Vibration Signals: Old Techniques, New Tricks, and Applications.

Tavakolian, Kouhyar / Inan, Omer T / Hahn, Jin-Oh / Di Rienzo, Marco

Frontiers in physiology

2022 Volume 13, Page(s) 931362

Language	English
Publishing date	2022-06-17
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2564217-0
ISSN	1664-042X
ISSN	1664-042X
DOI	10.3389/fphys.2022.931362
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Article ; Online: Novel Noninvasive Biosensors and Artificial Intelligence for Optimized Heart Failure Management.

Neill, Luke / Etemadi, Mozziyar / Klein, Liviu / Inan, Omer T

JACC. Basic to translational science

2022 Volume 7, Issue 3, Page(s) 316–318

Language	English
Publishing date	2022-04-04
Publishing country	United States
Document type	Journal Article
ISSN	2452-302X
ISSN (online)	2452-302X
DOI	10.1016/j.jacbts.2022.02.014
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Article ; Online: Fitts' Law Based Performance Metrics to Quantify Tremor in Individuals With Essential Tremor.

Kim, Jeonghee / Wichmann, Thomas / Inan, Omer T / DeWeerth, Stephen P

IEEE journal of biomedical and health informatics

2022 Volume 26, Issue 5, Page(s) 2169–2179

Abstract: Current methods of evaluating essential tremor (ET) either rely on subjective ratings or use limited tremor metrics (i.e., severity/amplitude and frequency). In this study, we explored performance metrics from Fitts' law tasks that replicate and expand ... ...

Abstract	Current methods of evaluating essential tremor (ET) either rely on subjective ratings or use limited tremor metrics (i.e., severity/amplitude and frequency). In this study, we explored performance metrics from Fitts' law tasks that replicate and expand existing tremor metrics, to enable low-cost, home-based tremor quantification and analyze the cursor movements of individuals using a 3D mouse while performing a collection of drawing tasks. We analyzed the 3D mouse cursor movements of 11 patients with ET and three controls, on three computer-based tasks-a spiral navigation (SPN) task, a rectangular track navigation (RTN) task, and multi-directional tapping/clicking (MDT)-with several performance metrics (i.e., outside area (OA), throughput (TP in Fitts' law), path efficiency (PE), and completion time (CT). Using an accelerometer and scores from the Essential Tremor Rating Assessment Scale (TETRAS), we correlated the proposed performance metrics with the baseline tremor metrics and found that the OA of the SPN and RTN tasks were strongly correlated with baseline tremor severity (R
MeSH term(s)	Benchmarking ; Essential Tremor/diagnosis ; Humans ; Movement ; Pilot Projects ; Psychomotor Performance ; Tremor
Language	English
Publishing date	2022-05-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2021.3129989
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Article ; Online: A Feasibility Study on Tribological Origins of Knee Acoustic Emissions.

Gharehbaghi, Sevda / Jeong, Hyeon Ki / Safaei, Mohsen / Inan, Omer T

IEEE transactions on bio-medical engineering

2022 Volume 69, Issue 5, Page(s) 1685–1695

Abstract: Objective: Considering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs). The goal of this study is to compare knee biomechanical signals against synchronously ... ...

Abstract	Objective: Considering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs). The goal of this study is to compare knee biomechanical signals against synchronously recorded joint sounds and assess the hypothesis that JAEs are attributed to tribological origins. Methods: JAE, electromyography, ground reaction force signals, and motion capture markers were synchronously recorded from ten healthy subjects while performing two-leg and one-leg squat exercises. The biomechanical signals were processed to calculate a tribological parameter, lubrication coefficient, and JAEs were divided into short windows and processed to extract 64-time-frequency features. The lubrication coefficients and JAE features of two-leg squats were used to label the windows and train a classifier that discriminates the knee lubrication modes only based on JAE features. Results: The classifier was used to predict the label of one-leg squat JAE windows and it achieved a high test-accuracy of 84%. The Pearson correlation coefficient between the estimated friction coefficient and predicted JAE scores was 0.83 ± 0.08. Furthermore, the lubrication coefficient threshold, separating two lubrication modes, decreased by half from two-leg to one-leg squats. This result was consistent with tribological changes in the knee load as it was inversely doubled in one-leg squats. Significance: This study supports the potential use of JAEs as a quantitative biomarker to extract tribological information. Since arthritis and similar disease impact the roughness of the joint cartilage, the use of JAEs could have broad implications for studying joint frictions and monitoring joint structural changes with wearable devices.
MeSH term(s)	Acoustics ; Biomechanical Phenomena ; Feasibility Studies ; Friction ; Humans ; Knee Joint ; Posture
Language	English
Publishing date	2022-04-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2021.3127030
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