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Linschmann O, Horstmann T, Leonhardt S, Lueken M. Sensor Fusion of Cardiorespiratory Signals Using an Adaptive Kalman Filter . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082963 DOI: 10.1109/embc40787.2023.10340942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
For unobtrusive monitoring of vital signs, redundant sensors are beneficial to fuse several sensor measurements which can improve the estimation of, e.g. heart rate and respiratory rate. In this paper, an adaptive unscented Kalman filter is used to estimate respiratory rate and heart rate on a new simplified model for cardiorespiratory coupling. Additionally, the Kalman filter is tuned to incorporate the non-white system noise of the model. The Kalman filter is tested on synthesised data with variations regarding SNR, model mismatch and amount of sensors. For respiratory rate, a median squared error of as low as 0.02BPM2 and, for heart rate, a median squared error of as low as 0.2BPM2 for ideal assumptions is achieved.
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Lueken M, Gramlich M, Leonhardt S, Marx N, Zink MD. Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation. SENSORS (BASEL, SWITZERLAND) 2023; 23:5618. [PMID: 37420786 DOI: 10.3390/s23125618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/30/2023] [Accepted: 06/09/2023] [Indexed: 07/09/2023]
Abstract
Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.
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Affiliation(s)
- Markus Lueken
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
| | - Michael Gramlich
- Department of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, Germany
| | - Steffen Leonhardt
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany
| | - Nikolaus Marx
- Department of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, Germany
| | - Matthias D Zink
- Department of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, Germany
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Nazari E, Biviji R, Roshandel D, Pour R, Shahriari MH, Mehrabian A, Tabesh H. Decision fusion in healthcare and medicine: a narrative review. Mhealth 2022; 8:8. [PMID: 35178439 PMCID: PMC8800206 DOI: 10.21037/mhealth-21-15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/02/2021] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVE To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. BACKGROUND The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information. METHODS We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review. CONCLUSIONS Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.
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Affiliation(s)
- Elham Nazari
- Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Rizwana Biviji
- Science of Healthcare Delivery, College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - Danial Roshandel
- Centre for Ophthalmology and Visual Science (affiliated with the Lions Eye Institute), The University of Western Australia, Perth, Western Australia, Australia
| | - Reza Pour
- Department of Computer Engineering, Azad University, Mashhad, Iran
| | - Mohammad Hasan Shahriari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amin Mehrabian
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Hamed Tabesh
- Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran
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Silva D, Leonhardt S, Antink CH. Copula-Based Data Augmentation on a Deep Learning Architecture for Cardiac Sensor Fusion. IEEE J Biomed Health Inform 2021; 25:2521-2532. [PMID: 33237869 DOI: 10.1109/jbhi.2020.3040551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In the wake of Big Data, traditional Machine Learning techniques are now often integrated in the clinical workflow. Despite more capable, Deep Learning methods are not equally accepted given their unsatiated need for great amounts of training data and transversal use of the same architectures in fundamentally different areas with weakly-substantiated adaptations. To address the former, a cardiorespiratory signal synthesizer was designed by conditional sampling from a multimodally trained stochastic system of Gaussian copulas integrated in a Markov chain. With respect to the latter, a multi-branch convolutional neural network architecture was conceived to learn the best cardiac sensor-fusion strategy at every abstraction layer. The network was tailored to the tasks of cycle detection and classification for different cardiac modality combinations by a synthesizer-based data augmentation training framework and Bayesian hyperparameter optimization. The synthesizer yielded highly realistic signals in the time, frequency and phase domains for both healthy and pathological heart cycles as well as artifacts of different modalities. Benchmarking suggested that the network is able to surpass previous architectures and data augmentation provided a performance boost in realistic data availability scenarios. These included insufficient training data volume, as low as 150 cycles long, artifact contamination and absence of a classification data type in training.
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Paul M, Mota AF, Antink CH, Blazek V, Leonhardt S. Modeling photoplethysmographic signals in camera-based perfusion measurements: optoelectronic skin phantom. BIOMEDICAL OPTICS EXPRESS 2019; 10:4353-4368. [PMID: 31565494 PMCID: PMC6757484 DOI: 10.1364/boe.10.004353] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 06/26/2019] [Accepted: 06/28/2019] [Indexed: 06/10/2023]
Abstract
The remote acquisition of photoplethysmographic (PPG) signals via a video camera, also known as photoplethysmography imaging (PPGI), is not yet standardized. In general, PPGI is investigated with test persons in a laboratory setting. While these in-vivo tests have the advantage of generating real-life data, they suffer from the lack of repeatability and are comparatively effort-intensive because human subjects are required. Consequently, studying changes in signal morphology, for example, due to aging or pathological effects, is practically impossible. As a tool to study these effects, a hardware PPG simulator has been developed: this is a phantom which simulates and generates both 1D and locally resolved 2D optical PPG signals. Here, we demonstrate that it is possible to generate PPG-like signals with various signal morphologies by means of a purely optoelectronic setup, namely an LED array, and to analyze them by means of PPGI. Signals extracted via a camera show good agreement with simulated generated signals. In fact, the first phantom design is suitable to demonstrate this qualitatively.
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Affiliation(s)
- Michael Paul
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Ana Filipa Mota
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Christoph Hoog Antink
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Vladimir Blazek
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
- The Czech Institute of Informatics, Robotics and Cybernetics (CIIRC), Czech Technical University, Prague, Czech Republic
| | - Steffen Leonhardt
- Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
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Berief F, Leonhardt S, Antink CH. Modelling and Synthesizing Motion Artifacts in Unobtrusive Multimodal Sensing using Copulas. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:6006-6009. [PMID: 30441705 DOI: 10.1109/embc.2018.8513690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The use of non-contact sensing modalities to estimate apatient's vital signs is a promising approach to improve remote monitoring. One of the main challenges in non-contact sensing are motion artifacts, which can cause severe problems and must not be disregarded when designing non-contact systems. Combining multiple sensors and using intelligent sensor-fusion algorithms can reduce the influence of motion artifacts and improve the robustness of the vital sign estimation. Training and validating algorithms are important parts of the development process, but acquiring real data is usually a time-consuming task. Therefore a method to generate a large number of multi-sensor motion artifacts is needed. In this paper we investigate motion artifacts and their inter-dependence in a multi-sensor system. From these analyses, a multivariate mathematical artifact model is derived. Further-more, we propose a general synthesizing algorithm for artificial motion artifacts that allows creating an arbitrary number of multi-sensor motion artifacts. Finally, we compare the artificially created artifacts with real artifacts and evaluate our algorithm. Both qualitative indicators, e.g. signal morphology, and quantitative analyses, e.g. statistical distance measures, show a good accuracy of our model.
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Hoog Antink C, Schulz F, Leonhardt S, Walter M. Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring. SENSORS 2017; 18:s18010038. [PMID: 29295594 PMCID: PMC5795602 DOI: 10.3390/s18010038] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 12/19/2017] [Accepted: 12/20/2017] [Indexed: 11/17/2022]
Abstract
Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNRS is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.
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Affiliation(s)
- Christoph Hoog Antink
- Philips Chair for Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany.
| | - Florian Schulz
- Philips Chair for Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany.
| | - Steffen Leonhardt
- Philips Chair for Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany.
| | - Marian Walter
- Philips Chair for Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany.
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