Physiological measurement最新文献

{"title":"Phase angle and impedance ratio as meta-inflammation biomarkers after a colon cleansing protocol in a group of overweight young women.","authors":"L O Tapasco-Tapasco, C A Gonzalez-Correa, A Letourneur","doi":"10.1088/1361-6579/ad46df","DOIUrl":"10.1088/1361-6579/ad46df","url":null,"abstract":"<p><p><i>Objective</i>. Blood C-reactive protein (CRP) and the electrical bioimpedance spectroscopy (EBIS) variables phase angle (PhA) and impedance ratio (IR) have been proposed as biomarkers of metainflammation in overweight/obesity. CRP involves taking blood samples, while PhA and IR imply a less-than-2-minute-non-invasive procedure. In this study, values for these variables and percent body fat mass (PBFM) were obtained and compared before and immediately after a colon cleansing protocol (CCP), aimed at modulating intestinal microbiota and reducing metainflammation, as dysbiosis and the latter are intrinsically related, as well as along a period of 8 weeks after it.<i>Approach</i>. 20 female volunteers (20.9-24.9 years old) participated: 12 in an overweight group (<b>OG</b>), and 8 in a lean group (<b>LG</b>). The<b>OG</b>was divided in two subgroups (<i>n</i>= 6, each): control (<b>CSG</b>) and experimental (<b>ESG</b>). The<b>ESG</b>underwent a 6-day CCP at week 2, while 5 volunteers in the<b>CSG</b>underwent it at week 9.<i>Main results.</i>Pre/post-CCP mean values for the variables in the<b>OG</b>were: PBFM (34.3/31.3%), CRP (3.7/0.6 mg dl^-1), PhA (6.9/7.5°) and IR*10 (0.78/0.77). Calculated<i>R</i>²correlation factors among these variables are all above 0.89. The favourable changes first seen in the<b>ESG</b>were still present 8 weeks after the CCP.<i>Significance.</i>(a) the CCP drastically lowers meta-inflammation, (b) EBIS can be used to measure metainflammation, before and after treatment, (c) for microbiota modulation, CCP could be a good alternative to more drastic procedures like faecal microbiota transplantation; (d) reestablishing eubiosis by CCP could be an effective coadjutant in the treatment of overweight young adult women.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140860107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative validation of the suprasternal pressure signal to assess respiratory effort during sleep.","authors":"Luca Cerina, Gabriele B Papini, Pedro Fonseca, Sebastiaan Overeem, Johannes P van Dijk, Fokke van Meulen, Rik Vullings","doi":"10.1088/1361-6579/ad4c35","DOIUrl":"10.1088/1361-6579/ad4c35","url":null,"abstract":"<p><p><i>Objective.</i>Intra-esophageal pressure (Pes) measurement is the recommended gold standard to quantify respiratory effort during sleep, but used to limited extent in clinical practice due to multiple practical drawbacks. Respiratory inductance plethysmography belts (RIP) in conjunction with oronasal airflow are the accepted substitute in polysomnographic systems (PSG) thanks to a better usability, although they are partial views on tidal volume and flow rather than true respiratory effort and are often used without calibration. In their place, the pressure variations measured non-invasively at the suprasternal notch (SSP) may provide a better measure of effort. However, this type of sensor has been validated only for respiratory events in the context of obstructive sleep apnea syndrome (OSA). We aim to provide an extensive verification of the suprasternal pressure signal against RIP belts and Pes, covering both normal breathing and respiratory events.<i>Approach.</i>We simultaneously acquired suprasternal (207) and esophageal pressure (20) signals along with RIP belts during a clinical PSG of 207 participants. In each signal, we detected breaths with a custom algorithm, and evaluated the SSP in terms of detection quality, breathing rate estimation, and similarity of breathing patterns against RIP and Pes. Additionally, we examined how the SSP signal may diverge from RIP and Pes in presence of respiratory events scored by a sleep technician.<i>Main results.</i>The SSP signal proved to be a reliable substitute for both esophageal pressure (Pes) and respiratory inductance plethysmography (RIP) in terms of breath detection, with sensitivity and positive predictive value exceeding 75%, and low error in breathing rate estimation. The SSP was also consistent with Pes (correlation of 0.72, similarity 80.8%) in patterns of increasing pressure amplitude that are common in OSA.<i>Significance.</i>This work provides a quantitative analysis of suprasternal pressure sensors for respiratory effort measurements.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140945596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finger photopletysmography detects early acute blood loss in compensated blood donors: a pilot study.","authors":"Gerardo Speroni, Patricia Antedoro, Silvia Marturet, Gabriela Martino, Celia Chavez, Cristian Hidalgo, María V Villacorta, Ivo Ahrtz, Manuel Casadei, Nora Fuentes, Peter Kremeier, Stephan H Böhm, Gerardo Tusman","doi":"10.1088/1361-6579/ad4c54","DOIUrl":"10.1088/1361-6579/ad4c54","url":null,"abstract":"<p><p><i>Objective.</i>Diagnosis of incipient acute hypovolemia is challenging as vital signs are typically normal and patients remain asymptomatic at early stages. The early identification of this entity would affect patients' outcome if physicians were able to treat it precociously. Thus, the development of a noninvasive, continuous bedside monitoring tool to detect occult hypovolemia before patients become hemodynamically unstable is clinically relevant. We hypothesize that pulse oximeter's alternant (AC) and continuous (DC) components of the infrared light are sensitive to acute and small changes in patient's volemia. We aimed to test this hypothesis in a cohort of healthy blood donors as a model of slight hypovolemia.<i>Approach.</i>We planned to prospectively study blood donor volunteers removing 450 ml of blood in supine position. Noninvasive arterial blood pressure, heart rate, and finger pulse oximetry were recorded. Data was analyzed before donation, after donation and during blood auto-transfusion generated by the passive leg-rising (PLR) maneuver.<i>Main results.</i>Sixty-six volunteers (44% women) accomplished the protocol successfully. No clinical symptoms of hypovolemia, arterial hypotension (systolic pressure < 90 mmHg), brady-tachycardia (heart rate <60 and >100 beats-per-minute) or hypoxemia (SpO₂< 90%) were observed during donation. The AC signal before donation (median 0.21 and interquartile range 0.17 a.u.) increased after donation [0.26(0.19) a.u;<i>p</i>< 0.001]. The DC signal before donation [94.05(3.63) a.u] increased after blood extraction [94.65(3.49) a.u;<i>p</i>< 0.001]. When the legs' blood was auto-transfused during the PLR, the AC [0.21(0.13) a.u.;<i>p</i>= 0.54] and the DC [94.25(3.94) a.u.;<i>p</i>= 0.19] returned to pre-donation levels.<i>Significance.</i>The AC and DC components of finger pulse oximetry changed during blood donation in asymptomatic volunteers. The continuous monitoring of these signals could be helpful in detecting occult acute hypovolemia. New pulse oximeters should be developed combining the AC/DC signals with a functional hemodynamic monitoring of fluid responsiveness to define which patient needs fluid administration.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140945616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization","authors":"Kshama Kodthalu Shivashankara, Deepanshi, Afagh Mehri Shervedani, Gari D Clifford, Matthew A Reyna and Reza Sameni","doi":"10.1088/1361-6579/ad4954","DOIUrl":"https://doi.org/10.1088/1361-6579/ad4954","url":null,"abstract":"Objective. Cardiovascular diseases are a major cause of mortality globally, and electrocardiograms (ECGs) are crucial for diagnosing them. Traditionally, ECGs are stored in printed formats. However, these printouts, even when scanned, are incompatible with advanced ECG diagnosis software that require time-series data. Digitizing ECG images is vital for training machine learning models in ECG diagnosis, leveraging the extensive global archives collected over decades. Deep learning models for image processing are promising in this regard, although the lack of clinical ECG archives with reference time-series data is challenging. Data augmentation techniques using realistic generative data models provide a solution. Approach. We introduce ECG-Image-Kit, an open-source toolbox for generating synthetic multi-lead ECG images with realistic artifacts from time-series data, aimed at automating the conversion of scanned ECG images to ECG data points. The tool synthesizes ECG images from real time-series data, applying distortions like text artifacts, wrinkles, and creases on a standard ECG paper background. Main results. As a case study, we used ECG-Image-Kit to create a dataset of 21 801 ECG images from the PhysioNet QT database. We developed and trained a combination of a traditional computer vision and deep neural network model on this dataset to convert synthetic images into time-series data for evaluation. We assessed digitization quality by calculating the signal-to-noise ratio and compared clinical parameters like QRS width, RR, and QT intervals recovered from this pipeline, with the ground truth extracted from ECG time-series. The results show that this deep learning pipeline accurately digitizes paper ECGs, maintaining clinical parameters, and highlights a generative approach to digitization. Significance. The toolbox has broad applications, including model development for ECG image digitization and classification. The toolbox currently supports data augmentation for the 2024 PhysioNet Challenge, focusing on digitizing and classifying paper ECG images.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141169224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features.","authors":"Mingfeng Jiang, Feibiao Bian, Jucheng Zhang, Tianhai Huang, Ling Xia, Yonghua Chu, Zhikang Wang, Jun Jiang","doi":"10.1088/1361-6579/ad46e1","DOIUrl":"10.1088/1361-6579/ad46e1","url":null,"abstract":"<p><p><i>Objective.</i>Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).<i>Approach.</i>A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.<i>Main results.</i>According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.<i>Significance.</i>This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140858460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention.","authors":"Bernhard Hametner, Severin Maurer, Alina Sehnert, Martin Bachler, Stefan Orter, Olivia Zechner, Markus Müllner-Rieder, Michael Penkler, Siegfried Wassertheurer, Walter Sehnert, Thomas Mengden, Christopher C Mayer","doi":"10.1088/1361-6579/ad45ab","DOIUrl":"10.1088/1361-6579/ad45ab","url":null,"abstract":"<p><p><i>Background.</i>Non-invasive continuous blood pressure (BP) monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic BP (change), became very popular recently, especially in the context of cuffless BP measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings.<i>Objective.</i>The aim of this work is therefore the investigation whether PAT follows oscillometric systolic BP readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment.<i>Approach.</i>A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric BP and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between BP and PAT.<i>Main</i><i>results.</i>The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in BP, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic BP increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic BP at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic BP and changes of PAT from baseline to the physical task (-0.33 [-0.63, 0.01],<i>p</i>< 0.048), and from physical to mental task (-0.51 [-0.77, -0.14],<i>p</i>= 0.001), but not for baseline to mental task (-0.12 [-0,43,0,20],<i>p</i>= 0.50) in the experimental group.<i>Significance.</i>PAT and the used digital, handheld device proved to register changes in BP and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support BP management by tracking relative, not absolute, BP changes during non-pharmacological interventions.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140870309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of admittance and cardiac magnetic resonance generated pressure-volume loops in a porcine model.","authors":"Stine Andersen, Pernille Holmberg Laursen, Gregory John Wood, Mads Dam Lyhne, Tobias Lynge Madsen, Esben Søvsø Szocska Hansen, Peter Johansen, Won Yong Kim, Mads Jønsson Andersen","doi":"10.1088/1361-6579/ad4a03","DOIUrl":"10.1088/1361-6579/ad4a03","url":null,"abstract":"<p><p><i>Objective</i>. Pressure-volume loop analysis, traditionally performed by invasive pressure and volume measurements, is the optimal method for assessing ventricular function, while cardiac magnetic resonance (CMR) imaging is the gold standard for ventricular volume estimation. The aim of this study was to investigate the agreement between the assessment of end-systolic elastance (Ees) assessed with combined CMR and simultaneous pressure catheter measurements compared with admittance catheters in a porcine model.<i>Approach</i>. Seven healthy pigs underwent admittance-based pressure-volume loop evaluation followed by a second assessment with CMR during simultaneous pressure measurements.<i>Main results</i>. Admittance overestimated end-diastolic volume for both the left ventricle (LV) and the right ventricle (RV) compared with CMR. Further, there was an underestimation of RV end-systolic volume with admittance. For the RV, however, Ees was systematically higher when assessed with CMR plus simultaneous pressure measurements compared with admittance whereas there was no systematic difference in Ees but large differences between admittance and CMR-based methods for the LV.<i>Significance</i>. LV and RV Ees can be obtained from both admittance and CMR based techniques. There were discrepancies in volume estimates between admittance and CMR based methods, especially for the RV. RV Ees was higher when estimated by CMR with simultaneous pressure measurements compared with admittance.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140903885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position and self-reported sleep.","authors":"Marlene Rietz, Jesper Schmidt-Persson, Martin Gillies Banke Rasmussen, Sarah Overgaard Sørensen, Sofie Rath Mortensen, Søren Brage, Peter Lund Kristensen, Anders Grøntved, Jan Christian Brønd","doi":"10.1088/1361-6579/ad450d","DOIUrl":"10.1088/1361-6579/ad450d","url":null,"abstract":"<p><p><i>Objective.</i>This study aimed to examine differences in heart rate variability (HRV) across accelerometer-derived position, self-reported sleep, and different summary measures (sleep, 24 h HRV) in free-living settings using open-source methodology.<i>Approach.</i>HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate (HR) and accelerometry data were collected using single-lead electrocardiography and trunk- and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files were processed and analysed in the RHRV R package. Start time and duration spent in physical behaviours were extracted, and time and frequency analysis for each episode was performed. Differences in HRV estimates across activities were compared using linear mixed models adjusted for age and sex with subject ID as random effect. Next, repeated-measures Bland-Altman analysis was used to compare 24 h RMSSD estimates to HRV during self-reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the approach of employing accelerometer-determined episodes to examine activity-independent HRV was described.<i>Main results.</i>HRV was estimated for 31 289 episodes in 160 individuals (53.1% female) at a mean age of 41.4 years. Significant differences in HR and most markers of HRV were found across positions [Mean differences RMSSD: Sitting (Reference) - Standing (-2.63 ms) or Lying (4.53 ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor agreement between 24 h estimates compared to sleep HRV was detected. Sensitivity analyses confirmed that removing the first and last 30 s of accelerometry-determined HR episodes was an accurate strategy to account for orthostatic effects.<i>Significance.</i>Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep. The proposed approach for free-living HRV analysis may be an effective strategy to remove confounding by physical activity when the aim is to monitor general autonomic stress.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140864991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatically detecting OSAHS patients based on transfer learning and model fusion.","authors":"Li Ding, Jianxin Peng, Lijuan Song, Xiaowen Zhang","doi":"10.1088/1361-6579/ad4953","DOIUrl":"10.1088/1361-6579/ad4953","url":null,"abstract":"<p><p><i>Objective</i>. Snoring is the most typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) that can be used to develop a non-invasive approach for automatically detecting OSAHS patients.<i>Approach</i>. In this work, a model based on transfer learning and model fusion was applied to classify simple snorers and OSAHS patients. Three kinds of basic models were constructed based on pretrained Visual Geometry Group-16 (VGG16), pretrained audio neural networks (PANN), and Mel-frequency cepstral coefficient (MFCC). The XGBoost was used to select features based on feature importance, the majority voting strategy was applied to fuse these basic models and leave-one-subject-out cross validation was used to evaluate the proposed model.<i>Main results</i>. The results show that the fused model embedded with top-5 VGG16 features, top-5 PANN features, and MFCC feature can correctly identify OSAHS patients (AHI > 5) with 100% accuracy.<i>Significance</i>. The proposed fused model provides a good classification performance with lower computational cost and higher robustness that makes detecting OSAHS patients at home possible.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings.","authors":"Jantine J Wisse, Peter Somhorst, Joris Behr, Arthur R van Nieuw Amerongen, Diederik Gommers, Annemijn H Jonkman","doi":"10.1088/1361-6579/ad46e3","DOIUrl":"10.1088/1361-6579/ad46e3","url":null,"abstract":"<p><p><i>Objective.</i>Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters.<i>Approach.</i>Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients.<i>Main result.</i>Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data.<i>Significance.</i>Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140852321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}