| 1. |
- Benouar, Sara, et al.
(författare)
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Classification of impedance cardiography dZ/dt complex subtypes using pattern recognition artificial neural networks
- 2021
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Ingår i: Biomedizinische Technik (Berlin. Zeitschrift). - : Walter de Gruyter. - 1862-278X .- 0013-5585. ; 66:5, s. 515-527
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Tidskriftsartikel (refereegranskat)abstract
- In impedance cardiography (ICG), the detection of dZ/dt signal (ICG) characteristic points, especially the X point, is a crucial step for the calculation of hemodynamic parameters such as stroke volume (SV) and cardiac output (CO). Unfortunately, for beat-to-beat calculations, the accuracy of the detection is affected by the variability of the ICG complex subtypes. Thus, in this work, automated classification of ICG complexes is proposed to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. A novel pattern recognition artificial neural network (PRANN) approach was implemented, and a divide-and-conquer strategy was used to identify the five different waveforms of the ICG complex waveform with output nodes no greater than 3. The PRANN was trained, tested and validated using a dataset from four volunteers from a measurement of eight electrodes. Once the training was satisfactory, the deployed network was validated on two other datasets that were completely different from the training dataset. As an additional performance validation of the PRANN, each dataset included four volunteers for a total of eight volunteers. The results show an average accuracy of 96% in classifying ICG complex subtypes with only a decrease in the accuracy to 83 and 80% on the validation datasets. This work indicates that the PRANN is a promising method for automated classification of ICG subtypes, facilitating the investigation of the extraction of hemodynamic parameters from beat-to-beat dZ/dt complexes.
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| 2. |
- Benouar, Sara, et al.
(författare)
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Systematic variability in ICG recordings results in ICG complex subtypes : steps towards the enhancement of ICG characterization
- 2018
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Ingår i: Journal of Electrical Bioimpedance. - : Walter de Gruyter. - 1891-5469. ; 9:1, s. 72-82
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Tidskriftsartikel (refereegranskat)abstract
- The quality of an impedance cardiography (ICG) signal critically impacts the calculation of hemodynamic parameters. These calculations depend solely on the identification of ICG characteristic points on the ABEXYOZ complex. Unfortunately, contrary to the relatively constant morphology of the PQRST complex in electrocardiography, the waveform morphology of ICG data is far from stationary, which causes difficulties in the accuracy of the automated detection of characteristic ICG points. This study evaluated ICG recordings obtained from 10 volunteers. The results indicate that there are several different waveforms for the ABEXYOZ complex; there are up to five clearly distinct waveforms for the ABEXYOZ complex in addition to those that are typically reported. The differences between waveform types increased the difficulty of detecting ICG points. To accurately detect all ICG points, the ABEXYOZ complex should be analyzed according to the corresponding waveform type.
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| 3. |
- Benouar, Sara, et al.
(författare)
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Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points : a predictive model
- 2023
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Ingår i: Frontiers in Physiology. - : Frontiers Media S.A.. - 1664-042X. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- One of the crucial steps in assessing hemodynamic parameters using impedance cardiography (ICG) is the detection of the characteristic points in the dZ/dt ICG complex, especially the X point. The most often estimated parameters from the ICG complex are stroke volume and cardiac output, for which is required the left ventricular pre-ejection time. Unfortunately, for beat-to-beat calculations, the accuracy of detection is affected by the variability of the ICG complex subtypes. Thus, in this work, we aim to create a predictive model that can predict the missing points and decrease the previous work percentages of missing points to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. Thus, a time-series non-linear autoregressive model with exogenous inputs (NARX) feedback neural network approach was implemented to forecast the missing ICG points according to the different existing subtypes. The NARX was trained on two different datasets with an open-loop mode to ensure that the network is fed with correct feedback inputs. Once the training is satisfactory, the loop can be closed for multi-step prediction tests and simulation. The results show that we can predict the missing characteristic points in all the complexes with a success rate ranging between 75% and 88% in the evaluated datasets. Previously, without the NARX predictive model, the successful detection rate was 21%–30% for the same datasets. Thus, this work indicates a promising method and an accuracy increase in the detection of X, Y, O, and Z points for both datasets.
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| 4. |
- Benouar, Sara, et al.
(författare)
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Where to be from the typical ICG waveform
- 2018
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Ingår i: Proceedings of IC2EM-2018. - 9789931954804 ; , s. 227-232
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Konferensbidrag (refereegranskat)abstract
- The ICG characteristic points are crucialfor the calculation of hemodynamic parameters. However, in opposite of the relatively constant morphology of the PQRST waves in electrocardiography. The waveform morphology of the ABEXYOZ complex of the ICG is far from stationary, which yields to difficulties in the automated detection of characteristic ICG points. This study evaluates ICG recordings obtained from 4volunteers. The results indicate that there are atypical ABEXYOZ complex in addition to the known typical complex. These atypical waveforms increase the difficulty of ICG signal analysis. To accurately and automatically detect the characteristic ICGpoints, and as a perspective of a future work, the targeted waveform must be known. In apurpose of classifying the atypical waveform into several types of the ABEXYOZ complex. This waveform types should be identified before the analysis of the dZ/dt ICG signal.
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| 5. |
- Hafid, Abdelakram, et al.
(författare)
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EMG & EIMG measurement for Arm & Hand motions using custom made instrumentation based on Raspberry PI
- 2021
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Ingår i: 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH). - : IEEE. - 9781665440844 - 9781665431255 ; , s. 54-58
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Konferensbidrag (refereegranskat)abstract
- Recording and processing physiological signal that give intrinsic characteristics information is one of scientific community needs. Electromyography (EMG) and Electrical Impedance Myography (EIMG) are both non-invasive approaches to measure and evaluate the muscle conditions and activity. Z-RPI device is a custom-made measurement device developed basically for ECG and ICG records. This paper presents the feasibility of acquiring surface EMG and EIMG signal of biceps and forearm muscle contractions using the Z-RPI device. The results obtained are acceptable, encouraging and converge to literature result. Thus, it shows that the Z-RPI device can be used for relatively several biomedical applications other than the ECG and ICG measurement. For supporting developers in research and engineering education.
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| 6. |
- Hafid, Abdelakram, et al.
(författare)
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Full Impedance Cardiography Measurement Device Using Raspberry PI3 and System-on-Chip Biomedical Instrumentation Solutions
- 2018
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2168-2194 .- 2168-2208. ; 22:6, s. 1883-1894
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Tidskriftsartikel (refereegranskat)abstract
- Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-I t-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPl.com, for open access under a Non Commercial-Share A like 4.0 International License.
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| 7. |
- Hafid, Abdelakram, et al.
(författare)
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Full impedance cardiography measurement device using raspberry PI3 and system-on-chip biomedical instrumentation solutions
- 2018
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 22:6, s. 1883-1894
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Tidskriftsartikel (refereegranskat)abstract
- Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-It-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPI.com, for open access under a Non Commercial-Share A like 4.0 International License.
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| 8. |
- Hafid, Abdelakram, et al.
(författare)
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Simultaneous Recording of ICG and ECG Using Z-RPI Device with Minimum Number of Electrodes
- 2018
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Ingår i: Journal of Sensors. - : Hindawi Publishing Corporation. - 1687-725X .- 1687-7268. ; 2018
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Tidskriftsartikel (refereegranskat)abstract
- Impedance cardiography (ICG) is a noninvasive method for monitoring mechanical function of the heart with the use of electrical bioimpedance measurements. This paper presents the feasibility of recording an ICG signal simultaneously with electrocardiogram signal (ECG) using the same electrodes for both measurements, for a total of five electrodes rather than eight electrodes. The device used is the Z-RPI. The results present good performance and show waveforms presenting high similarity with the different signals reported using different electrodes for acquisition; the heart rate values were calculated and they present accurate evaluation between the ECG and ICG heart rates. The hemodynamics and cardiac parameter results present similitude with the physiological parameters for healthy people reported in the literature. The possibility of reducing number of electrodes used for ICG measurement is an encouraging step to enabling wearable and personal health monitoring solutions.
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| 9. |
- Zairi, Hadjer, et al.
(författare)
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Intelligent system for detecting cardiac arrhythmia on FPGA
- 2014
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Ingår i: 2014 5th International Conference on Information and Communication Systems (ICICS). - : IEEE. - 9781479930234 ; , s. 1-5
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Konferensbidrag (refereegranskat)abstract
- This paper presents a hardware implementation of the detection system of cardiac arrhythmias on FPGA. Thus we are interested in the software and hardware detection of the QRS complex. Our algorithm is based around a recursive digital filters (FIR) and non-recursive (IIR). The hardware implementation of our algorithm was done in HDL (hardware description language). For this our generated source has been simulated, synthesized and tested on Xilinx FPGA (Field Programmable Gate Array) card using the MIT BIH data base. The results shows the contribution of this implementation for embedded systems to better track patients by minimizing the size of the screen and increasing the computational efficiency while reducing the execution time.
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