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Trybek P, Sobotnicka E, Wawrzkiewicz-Jałowiecka A, Machura Ł, Feige D, Sobotnicki A, Richter-Laskowska M. A New Method of Identifying Characteristic Points in the Impedance Cardiography Signal Based on Empirical Mode Decomposition. SENSORS (BASEL, SWITZERLAND) 2023; 23:675. [PMID: 36679466 PMCID: PMC9861967 DOI: 10.3390/s23020675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/22/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
The accurate detection of fiducial points in the impedance cardiography signal (ICG) has a decisive impact on the proper estimation of diagnostic parameters such as stroke volume or cardiac output. It is, therefore, necessary to find an algorithm that is able to assess their positions with great precision. The solution to this problem is, however, quite challenging with regard to the high sensitivity of the ICG technique to the noise and varying morphology of the acquired signals. The aim of this study is to propose a novel method that allows us to overcome these limitations. The developed algorithm is based on Empirical Mode Decomposition (EMD)-an effective technique for processing and analyzing various types of non-stationary signals. We find high correlations between the results obtained from the algorithm and annotated by an expert. This, in turn, implies that the difference in estimation of the diagnostic-relevant parameters is small, which suggests that the method can automatically provide precise clinical information.
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Affiliation(s)
- Paulina Trybek
- Institute of Physics, Faculty of Science and Technology, University of Silesia in Katowice, 41-500 Chorzow, Poland
| | - Ewelina Sobotnicka
- Łukasiewicz Research Network—Krakow Institute of Technology, The Centre for Biomedical Engineering, Zakopianska Str. 73, 30-418 Krakow, Poland
| | - Agata Wawrzkiewicz-Jałowiecka
- Department of Physical Chemistry and Technology of Polymers, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Łukasz Machura
- Institute of Physics, Faculty of Science and Technology, University of Silesia in Katowice, 41-500 Chorzow, Poland
| | - Daniel Feige
- Łukasiewicz Research Network—Krakow Institute of Technology, The Centre for Biomedical Engineering, Zakopianska Str. 73, 30-418 Krakow, Poland
- PhD School, Silesian University of Technology, 2A Akademicka, 44-100 Gliwice, Poland
| | - Aleksander Sobotnicki
- Łukasiewicz Research Network—Krakow Institute of Technology, The Centre for Biomedical Engineering, Zakopianska Str. 73, 30-418 Krakow, Poland
| | - Monika Richter-Laskowska
- Institute of Physics, Faculty of Science and Technology, University of Silesia in Katowice, 41-500 Chorzow, Poland
- Łukasiewicz Research Network—Krakow Institute of Technology, The Centre for Biomedical Engineering, Zakopianska Str. 73, 30-418 Krakow, Poland
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Chabchoub S, Mansouri S, Ben Salah R. Signal processing techniques applied to impedance cardiography ICG signals - a review. J Med Eng Technol 2022; 46:243-260. [PMID: 35040738 DOI: 10.1080/03091902.2022.2026508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Over the last decade, Computer-Aided Diagnosis (CAD) systems have been provided significant research focus by researchers. CAD systems have been developed in order to minimise visual errors, to compensate manual interpretation, and to help medical staff to take decisions swiftly. These systems have been considered as powerful tools for a reliable, automatic, and low-cost monitoring and diagnosis. CAD systems are based on analysis and classification of several physiological signals for detecting and assessing different diseases related to the corresponding organ. The implementation of these systems requires the application of several advanced signal processing techniques. Specifically, in cardiology, CAD systems have achieved promising results in providing an accurate and rapid detection of cardiovascular diseases (CVDs). Particularly, the number of works on signal processing field for impedance cardiography (ICG) signals starts to grow slowly in recent years. This paper presents a review study of signal processing techniques applied to the ICG signal for the denoising, the analysis, the classification and the characterisation purposes. This review is intended to provide researchers with a broad overview of the currently used signal processing techniques for ICG signal analysis, as well as to improve future research by applying other recent advanced methods.
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Affiliation(s)
- Souhir Chabchoub
- Laboratory of Biophysics and Medical Technologies, University of Tunis El-Manar, ISTMT, Tunis, Tunisia
| | - Sofienne Mansouri
- Laboratory of Biophysics and Medical Technologies, University of Tunis El-Manar, ISTMT, Tunis, Tunisia.,Department of Medical Equipment Technology, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
| | - Ridha Ben Salah
- Laboratory of Biophysics and Medical Technologies, University of Tunis El-Manar, ISTMT, Tunis, Tunisia
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Mansouri S, Alharbi Y, Alshrouf A, Alqahtani A. Cardiovascular Diseases Diagnosis by Impedance Cardiography. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2022; 13:88-95. [PMID: 36694881 PMCID: PMC9837870 DOI: 10.2478/joeb-2022-0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Indexed: 06/17/2023]
Abstract
Cardiovascular disease (CVD) represents the leading cause of mortality worldwide. In order to diagnose CVDs, there are a range of detection methods, among them, the impedance cardiography technique (ICG). It is a non-invasive and low-cost method. In this paper, we highlight recent advances and developments of the CDVs diagnosis mainly by the ICG method. We considered papers published during the last five years (from 2017 until 2022). Based on this study, we expressed the need for an ICG database for the different CDVs.
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Affiliation(s)
- Sofiene Mansouri
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, TunisTunisia
| | - Yousef Alharbi
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Anwar Alshrouf
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Abdulrahman Alqahtani
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Majmaah City, Saudi Arabia
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Sel K, Osman D, Jafari R. Non-Invasive Cardiac and Respiratory Activity Assessment From Various Human Body Locations Using Bioimpedance. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2021; 2:210-217. [PMID: 34458855 PMCID: PMC8388562 DOI: 10.1109/ojemb.2021.3085482] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Objective: Bioimpedance sensing is a powerful technique that measures the tissue impedance and captures important physiological parameters including blood flow, lung movements, muscle contractions, body fluid shifts, and other cardiovascular parameters. This paper presents a comprehensive analysis of the modality at different arterial (ulnar, radial, tibial, and carotid arteries) and thoracic (side-rib cage and top thoracolumbar fascia) body regions and offers insights into the effectiveness of capturing various cardiac and respiratory activities. Methods: We assess the bioimpedance performance in estimating inter-beat (IBI) and inter -breath intervals (IBrI) on six-hours of data acquired in a pilot-study from five healthy participants at rest. Results: Overall, we achieve mean errors as low as 0.003 ± 0.002 and 0.67 ± 0.28 seconds for IBI and IBrI estimations, respectively. Conclusions: The results show that bioimpedance can be effectively used to monitor cardiac and respiratory activities both at limbs and upper body and demonstrate a strong potential to be adopted by wearables that aim to provide high-fidelity physiological sensing to address precision medicine needs.
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Affiliation(s)
- Kaan Sel
- Texas A&M University, College Station, TX 77843 USA
| | - Deen Osman
- Texas A&M University, College Station, TX 77843 USA
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Rath A, Mishra D, Panda G, Satapathy SC. Heart disease detection using deep learning methods from imbalanced ECG samples. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102820] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Yang C, Ojha BD, Aranoff ND, Green P, Tavassolian N. Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals. Sci Rep 2020; 10:17521. [PMID: 33067495 PMCID: PMC7568576 DOI: 10.1038/s41598-020-74519-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 10/05/2020] [Indexed: 12/31/2022] Open
Abstract
This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.
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Affiliation(s)
- Chenxi Yang
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | - Banish D Ojha
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | | | - Philip Green
- Columbia University Medical Center, New York, NY, 10032, USA
| | - Negar Tavassolian
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.
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Salah IB, De la Rosa R, Ouni K, Salah RB. Automatic diagnosis of valvular heart diseases by impedance cardiography signal processing. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Yang C, Aranoff ND, Green P, Tavassolian N. Classification of Aortic Stenosis Using Time-Frequency Features From Chest Cardio-Mechanical Signals. IEEE Trans Biomed Eng 2019; 67:1672-1683. [PMID: 31545706 DOI: 10.1109/tbme.2019.2942741] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVES This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the clinic. METHODS Experimental measurements were collected from twenty patients with AS and twenty healthy subjects. Firstly, a digital signal processing framework is proposed to extract time-frequency features. The features are then selected via the analysis of variance test. Different combinations of features are evaluated using the decision tree, random forest, and artificial neural network methods. Two classification tasks are conducted. The first task is a binary classification between normal subjects and AS patients. The second task is a multi-class classification of AS patients with co-existing valvular heart diseases. RESULTS In the binary classification task, the average accuracies achieved are 96.25% from decision tree, 97.43% from random forest, and 95.56% from neural network. The best performance is from combined SCG and GCG features with random forest classifier. In the multi-class classification, the best performance is 92.99% using the random forest classifier and SCG features. CONCLUSION The results suggest that the solution could be a feasible method for classifying aortic stenosis, both in the binary and multi-class tasks. It also indicates that most of the important time-frequency features are below 11 Hz. SIGNIFICANCE The proposed method shows great potential to provide continuous monitoring of valvular heart diseases to prevent patients from sudden critical cardiac situations.
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Nabian M, Yin Y, Wormwood J, Quigley KS, Barrett LF, Ostadabbas S. An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2018; 6:2800711. [PMID: 30443441 PMCID: PMC6231905 DOI: 10.1109/jtehm.2018.2878000] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 09/05/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena. This paper presents the development of a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction of these physiological signals according to the state-of-the-art studies reported in the scientific literature and feedback received from the field experts. Our open-source Bio-SP tool is intended to assist researchers in affective computing, digital and mobile health, and telemedicine to extract relevant physiological patterns (i.e., features) from these biosignals semi-automatically and reliably. In this paper, we describe the successful algorithms used for signal-specific quality checking, artifact/noise filtering, and segmentation along with introducing features shown to be highly relevant to category discrimination in several healthcare applications (e.g., discriminating patterns associated with disease versus non-disease). Further, the Bio-SP tool is a publicly-available software written in MATLAB with a user-friendly graphical user interface (GUI), enabling future crowd-sourced modification to these tools. The GUI is compatible with MathWorks Classification Learner app for inference model development, such as model training, cross-validation scheme farming, and classification result computation.
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Affiliation(s)
- Mohsen Nabian
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
- Harvard Medical SchoolBostonMA02115USA
| | - Yu Yin
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
| | | | | | - Lisa F. Barrett
- Department of PsychologyNortheastern UniversityBostonMA02115USA
| | - Sarah Ostadabbas
- Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
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