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Sheikh SAA, Gurel NZ, Gupta S, Chukwu IV, Levantsevych O, Alkhalaf M, Soudan M, Abdulbaki R, Haffar A, Clifford GD, Inan OT, Shah AJ. Validation of a new impedance cardiography analysis algorithm for clinical classification of stress states. Psychophysiology 2022; 59:e14013. [PMID: 35150459 PMCID: PMC9177512 DOI: 10.1111/psyp.14013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 01/01/2023]
Abstract
Pre-ejection period (PEP) is an index of sympathetic nervous system activity that can be computed from electrocardiogram (ECG) and impedance cardiogram (ICG) signals, but sensitive to speech/motion artifact. We sought to validate an ICG noise removal method, three-stage ensemble-average algorithm (TEA), in data acquired from a clinical trial comparing active versus sham non-invasive vagal nerve stimulation (tcVNS) after standardized speech stress. We first compared TEA's performance versus the standard conventional ensemble-average algorithm (CEA) approach to classify noisy ICG segments. We then analyzed ECG and ICG data to measure PEP and compared group-level differences in stress states with each approach. We evaluated 45 individuals, of whom 23 had post-traumatic stress disorder (PTSD). We found that the TEA approach identified artifact-corrupted beats with intraclass correlation coefficient > 0.99 compared to expert adjudication. TEA also resulted in higher group-level differences in PEP between stress states than CEA. PEP values were lower in the speech stress (vs. baseline rest) group using both techniques, but the differences were greater using TEA (12.1 ms) than CEA (8.0 ms). PEP differences in groups divided by PTSD status and tcVNS (active vs. sham) were also greater when using the TEA versus CEA method, although the magnitude of the differences was lower. In conclusion, TEA helps to accurately identify noisy ICG beats during speaking stress, and this increased accuracy improves sensitivity to group-level differences in stress states compared to CEA, suggesting greater clinical utility.
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Affiliation(s)
- Shafa-at Ali Sheikh
- Department of Biomedical Informatics, Emory University, Atlanta, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
| | - Nil Z. Gurel
- Neurocardiology Research Center of Excellence and Cardiac Arrhythmia Center, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Shishir Gupta
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA
| | - Ikenna V. Chukwu
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA
| | - Oleksiy Levantsevych
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA
| | - Mhmtjamil Alkhalaf
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA
| | - Majd Soudan
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA
| | - Rami Abdulbaki
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA
| | - Ammer Haffar
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, USA
| | - Omer T. Inan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
| | - Amit J. Shah
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA
- Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, USA
- Atlanta Veterans Affairs Health Care System, Atlanta, USA
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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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Czarnek G, Richter M, Strojny P. Cardiac sympathetic activity during recovery as an indicator of sympathetic activity during task performance. Psychophysiology 2020; 58:e13724. [PMID: 33205516 DOI: 10.1111/psyp.13724] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Revised: 10/20/2020] [Accepted: 10/23/2020] [Indexed: 11/28/2022]
Abstract
The goals of this research were to analyze cardiac sympathetic recovery patterns and evaluate whether sympathetic cardiac responses to a task challenge can be predicted using residual cardiac activity measured directly after the task (that is, during the recovery period). In two studies (total N = 181), we measured cardiac sympathetic activity, quantified as pre-ejection period and RB interval, during both task performance and the 2-min recovery period following the task. Additional analyses examined effects on the RZ interval. We found that sympathetic recovery from a task was rather quick: Cardiovascular recovery occurred within the first 30 s of the recovery period. Nevertheless, residual cardiac activity during the recovery period had predictive power for task-related cardiac activity. This suggests that sympathetic cardiac activity during recovery may serve as a useful indicator of task-related cardiac sympathetic activity. We discuss the implications of these findings for practical applications and the design of future studies.
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Affiliation(s)
- Gabriela Czarnek
- Nano Games, Cracow, Poland.,Institute of Psychology, Jagiellonian University, Krakow, Poland
| | - Michael Richter
- School of Psychology, Liverpool John Moores University, Liverpool, UK
| | - Paweł Strojny
- Nano Games, Cracow, Poland.,Faculty of Management and Social Communication, Jagiellonian University, Krakow, Poland
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Ali Sheikh SA, Shah A, Levantsevych O, Soudan M, Alkhalaf J, Bahrami Rad A, Inan OT, Clifford GD. An open-source automated algorithm for removal of noisy beats for accurate impedance cardiogram analysis. Physiol Meas 2020; 41:075002. [PMID: 32784269 DOI: 10.1088/1361-6579/ab9b71] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The impedance cardiogram (ICG) is a non-invasive sensing modality for assessing the mechanical aspects of cardiac function, but is sensitive to artifacts from respiration, speaking, motion, and electrode displacement. Electrocardiogram (ECG)-synchronized ensemble averaging of ICG (conventional ensemble averaging method) partially mitigates these disturbances, as artifacts from intra-subject variability (ISVar) of ICG morphology and event latency remain. This paper describes an automated algorithm for removing noisy beats for improved artifact suppression in ensemble-averaged (EA) ICG beats. APPROACH Synchronized ECG and ICG signals from 144 male subjects at rest in different psychological conditions were recorded. A 'three-stage EA ICG beat' was formed by passing 60-seconds non-overlapping ECG-synchronized ICG signals through three filtering stages. The amplitude filtering stage removed spikes/noisy beats with amplitudes outside of normal physiological ranges. Cross-correlation was applied to remove noisy beats in coarse and fine filtering stages. The accuracy of the algorithm-detected artifacts was measured with expert-identified artifacts. Agreement between the expert and the algorithm was assessed using intraclass correlation coefficients (ICC) and Bland-Altman plots. The ISVar of the cardiac parameters was evaluated to quantify improvement in these estimates provided by the proposed method. MAIN RESULTS The proposed algorithm yielded an accuracy of 96.3% and high inter-rater reliability (ICC > 0.997). Bland-Altman plots showed consistently accurate results across values. The ISVar of the cardiac parameters derived using the proposed method was significantly lower than those derived via conventional ensemble averaging method (p < 0.0001). Enhancement in resolution of fiducial points and smoothing of higher-order time derivatives of the EA ICG beats were observed. SIGNIFICANCE The proposed algorithm provides a robust framework for removal of noisy beats and accurate estimation of ICG-based parameters. Importantly, the methodology reduced the ISVar of cardiac parameters. An open-source toolbox has been provided to enable other researchers to readily reproduce and improve upon this work.
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Affiliation(s)
- Shafa-At Ali Sheikh
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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