Regional ventilation distribution in healthy lungs: can reference values be established for electrical impedance tomography parameters?

Physiological definition for region of interest selection in electrical impedance tomography data: description and validation of a novel method

Objective. Geometrical region of interest (ROI) selection in electrical impedance tomography (EIT) monitoring may lack sensitivity to subtle changes in ventilation distribution. Therefore, we demonstrate a new physiological method for ROI definition. This is relevant when using ROIs to compute subsequent EIT-parameters, such as the ventral-to-dorsal ratio during a positive end-expiratory pressure (PEEP) trial.Approach.Our physiological approach divides an EIT image to ensure exactly 50% tidal impedance variation in the ventral and dorsal region. To demonstrate the effects of our new method, EIT measurements during a decremental PEEP trial in 49 mechanically ventilated ICU-patients were used. We compared the center of ventilation (CoV), a robust parameter for changes in ventro-dorsal ventilation distribution, to our physiological ROI selection method and different commonly used ROI selection methods. Moreover, we determined the impact of different ROI selection methods on the PEEP level corresponding to a ventral-to-dorsal ratio closest to 1.Main results.The division line separating the ventral and dorsal ROI was closer to the CoV for our new physiological method for ROI selection compared to geometrical ROI definition. Moreover, the PEEP level corresponding to a ventral-to-dorsal ratio of 1 is strongly influenced by the chosen ROI selection method, which could have a profound clinical impact; the within-subject range of PEEP level was 6.2 cmH2O depending on the chosen ROI selection method.Significance.Our novel physiological method for ROI definition is sensitive to subtle ventilation-induced changes in regional impedance (i.e. due to (de)recruitment) during mechanical ventilation, similar to the CoV.

The influence of different spontaneous breathing trials on regional ventilation distribution in patients with prolonged mechanical ventilation

OBJECTIVE

This study aimed to explore the influence of different spontaneous breathing trials (SBTs) on regional ventilation distribution in patients with prolonged mechanical ventilation (PMV).

METHODS

A total of 24 patients with PMV were analyzed retrospectively. They received three different SBT modes which are automatic tube compensation (ATC), continuous positive airway pressure (CPAP), and T-piece (TP), over three days, and every SBT lasted two hours. Electrical impedance tomography (EIT) was used to monitor the SBT process and five-minute EIT data from five periods (pre-SBT which is t0, at the beginning and the end of the first hour SBT are t1 and t2, at the beginning and the end of the second hour SBT are t3 and t4) were analyzed.

RESULTS

In all PMV patients, the temporal skew of aeration (TSA) values at t3 were significantly different in three SBTs (ATC: 18.18±22.97; CPAP: 20.42±17.01; TP:11.26±11.79; p=0.05). In the weaning success group, TSA (t1) values were significantly different too (ATC: 11.11±13.88; CPAP: 19.09±15.77; TP: 9.09±12.74; p=0.04). In the weaning failure group, TSA (t4) values were significantly different in three SBTs (ATC: 36.67±18.46; CPAP: 15.38±11.69; TP: 17.65±17.93; p=0.04). The patient's inspiratory effort (Global flow index at t1) in patients with weaning failure under CPAP (3.51±4.31) was significantly higher than that in the ATC (1.15±1.47) and TP (0.89±1.28). The SBT mode with the best ventilation uniformity may be the one that activates the respiratory muscles the most which may be the optimal SBT. The SBT mode of most uniform ventilation distribution settings varies from patient to patient.

CONCLUSION

The regional ventilation distribution was different for each individual, making the SBT with the best ventilation distribution of patients need to be personalized. EIT is a tool that can be considered for real-time assessment.

Sex differences in chest electrical impedance tomography findings

Objective.Electrical impedance tomography (EIT) has been used to determine regional lung ventilation distribution in humans for decades, however, the effect of biological sex on the findings has hardly ever been examined. The aim of our study was to determine if the spatial distribution of ventilation assessed by EIT during quiet breathing was influenced by biological sex.Approach.219 adults with no known acute or chronic lung disease were examined in sitting position with the EIT electrodes placed around the lower chest (6th intercostal space). EIT data were recorded at 33 images/s during quiet breathing for 60 s. Regional tidal impedance variation was calculated in all EIT image pixels and the spatial distribution of the values was determined using the established EIT measures of centre of ventilation in ventrodorsal (CoVvd) and right-to-left direction (CoVrl), the dorsal and right fraction of ventilation, and ventilation defect score.Main results.After exclusion of one subject due to insufficient electrode contact, 218 data sets were analysed (120 men, 98 women) (age: 53 ± 18 vs 50 ± 16 yr (p= 0.2607), body mass index: 26.4 ± 4.0 vs 26.4 ± 6.6 kg m-2(p= 0.9158), mean ± SD). Highly significant differences in ventilation distribution were identified between men and women between the right and left chest sides (CoVrl: 47.0 ± 2.9 vs 48.8 ± 3.3% of chest diameter (p< 0.0001), right fraction of ventilation: 0.573 ± 0.067 vs 0.539 ± 0.071 (p= 0.0004)) and less significant in the ventrodorsal direction (CoVvd: 55.6 ± 4.2 vs 54.5 ± 3.6% of chest diameter (p= 0.0364), dorsal fraction of ventilation: 0.650 ± 0.121 vs 0.625 ± 0.104 (p= 0.1155)). Ventilation defect score higher than one was found in 42.5% of men but only in 16.6% of women.Significance.Biological sex needs to be considered when EIT findings acquired in upright subjects in a rather caudal examination plane are interpreted. Sex differences in chest anatomy and thoracoabdominal mechanics may explain the results.

Electrical Impedance Tomography, Artificial Intelligence, and Variable Ventilation: Transforming Respiratory Monitoring and Treatment in Critical Care

BACKGROUND

Electrical Impedance Tomography (EIT), combined with variable ventilation strategies and Artificial Intelligence (AI), is poised to revolutionize critical care by transitioning from reactive to predictive approaches. This integration aims to enhance patient outcomes through personalized interventions and real-time monitoring.

METHODS

this narrative review explores the principles and applications of EIT, variable ventilation, and AI in critical care. EIT impedance sensing creates dynamic images of internal physiology, aiding the management of conditions like Acute Respiratory Distress Syndrome (ARDS). Variable ventilation mimics natural breathing variability to improve lung function and minimize ventilator-induced lung injury. AI enhances EIT through advanced image reconstruction techniques, neural networks, and digital twin technology, offering more accurate diagnostics and tailored therapeutic interventions.

CONCLUSIONS

the confluence of EIT, variable ventilation, and AI represents a significant advancement in critical care, enabling a predictive, personalized approach. EIT provides real-time insights into lung function, guiding precise ventilation adjustments and therapeutic interventions. AI integration enhances EIT diagnostic capabilities, facilitating the development of personalized treatment plans. This synergy fosters interdisciplinary collaborations and sets the stage for innovative research, ultimately improving patient outcomes and advancing the future of critical care.

Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system

Objective.Wearable electrical impedance tomography (EIT) can be used to monitor regional lung ventilation and perfusion at the bedside. Due to its special system architecture, the amplitude of the injected current is usually limited compared to stationary EIT system. This study aims to evaluate the performance of current injection patterns with various low-amplitude currents in healthy volunteers.Approach.A total of 96 test sets of EIT measurement was recorded in 12 healthy subjects by employing adjacent and opposite current injection patterns with four amplitudes of small current (i.e. 1 mA, 500 uA, 250 uA and 125 uA). The performance of the two injection patterns with various currents was evaluated in terms of signal-to-noise ratio (SNR) of thorax impedance, EIT image metrics and EIT-based clinical parameters.Main results.Compared with adjacent injection, opposite injection had higher SNR (p< 0.01), less inverse artifacts (p< 0.01), and less boundary artifacts (p< 0.01) with the same current amplitude. In addition, opposite injection exhibited more stable EIT-based clinical parameters (p< 0.01) across the current range. For adjacent injection, significant differences were found for three EIT image metrics (p< 0.05) and four EIT-based clinical parameters (p< 0.01) between the group of 125 uA and the other groups.Significance.For better performance of wearable pulmonary EIT, currents greater than 250 uA should be used in opposite injection, 500 uA in adjacent one, to ensure a high level of SNR, a high quality of reconstructed image as well as a high reliability of clinical parameters.

Monitoring of spontaneous pneumothorax using electrical impedance tomography: A case report

Pneumothorax is an emergency in thoracic surgeries and respiratory medicine. A technique is warranted for real-time monitoring of pneumothorax at the bedside so that rapid diagnosis and timely intervention can be achieved. We report herein a case in which electrical impedance tomography (EIT) was employed at the bedside to monitor lung ventilation of a patient with spontaneous pneumothorax during treatment. It was found that the affected side/healthy side ventilation ratio and global inhomogeneity were strongly correlated with the severity of pneumothorax. The use of EIT allowed intuitive observation of the effect of pneumothorax on ventilation, which helped the doctors make immediate diagnosis and intervention. After timely and successful treatment, the patient was discharged. This is the first case reporting a complete therapeutic course of spontaneous pneumothorax assessed with EIT. Our case demonstrated that EIT could monitor regional ventilation loss of the affected side of pneumothorax patients at the bedside, and dynamically assess the treatment effect of pneumothorax, which provides an important imaging basis for clinical pneumothorax treatment.

Regional ventilation distribution before and after laparoscopic lung parenchymal resection

Objective.The aim of the present study was to evaluate the influence of one-sided pulmonary nodule and tumour on ventilation distribution pre- and post- partial lung resection.Approach.A total of 40 consecutive patients scheduled for laparoscopic lung parenchymal resection were included. Ventilation distribution was measured with electrical impedance tomography (EIT) in supine and surgery lateral positions 72 h before surgery (T1) and 48 h after extubation (T2). Left lung to global ventilation ratio (Fl), the global inhomogeneity index (GI), standard deviation of regional ventilation delay (RVDSD) and pendelluft amplitude (Apendelluft) were calculated to assess the spatial and temporal ventilation distribution.Main results.After surgery (T2), ventilation at the operated chest sides generally deteriorated compared to T1 as expected. For right-side resection, the differences were significant at both supine and left lateral positions (p< 0.001). The change of RVDSDwas in general more heterogeneous. For left-side resection, RVDSDwas worse at T2 compared to T1 at left lateral position (p= 0.002). The other EIT-based parameters showed no significant differences between the two time points. No significant differences were observed between supine and lateral positions for the same time points respectively.Significance.In the present study, we found that the surgery side influenced the ventilation distribution. When the resection was performed on the right lung, the postoperative ipsilateral ventilation was reduced and the right lung ratio fell significantly. When the resection was on the left lung, the ventilation delay was significantly increased.

Monitoring the Efficacy of High-Flow Nasal Cannula Oxygen Therapy in Patients with Acute Hypoxemic Respiratory Failure in the General Respiratory Ward: A Prospective Observational Study

High-flow nasal cannula (HFNC) is widely used to treat hypoxemic respiratory failure. The effectiveness of HFNC treatment and the methods for monitoring its efficacy in the general ward remain unclear. This prospective observational study enrolled 42 patients who had acute hypoxemic respiratory failure requiring HFNC oxygen therapy in the general adult respiratory ward. The primary outcome was the all-cause in-hospital mortality. Secondary outcomes included the association between initial blood test results and HFNC outcomes. Regional ventilation distributions were monitored in 24 patients using electrical impedance tomography (EIT) after HFNC initiation. Patients with successful HFNC treatment had better in-hospital survival (94%) compared to those with failed HFNC treatment (0%, p < 0.001). Neutrophil-to-lymphocyte ratios of ≥9 were more common in patients with failed HFNC (70%) compared to those with successful HFNC (52%, p = 0.070), and these patients had shorter hospital survival rates after HFNC treatment (p = 0.046, Tarone-Ware test). Patients with successful HFNC treatment had a more central ventilation distribution compared to those with failed HFNC treatment (p < 0.05). Similarly, patients who survived HFNC treatment had a more central distribution compared to those who did not survive (p < 0.001). We concluded that HFNC in the general respiratory ward may be a potential rescue therapy for patients with respiratory failure. EIT can potentially monitor patients receiving HFNC therapy.

Investigation of high flow nasal cannule efficiency with electric impedance tomography based parameters in COVID-19 adults patients: a retrospective study

Background/Aim

This study aimed to investigate the effects of oxygen therapy using a high flow nasal cannula (HFNC) on patients diagnosed with COVID-19 Acute Respiratory Distress Syndrome (C-ARDS) by utilizing electrical impedance tomography (EIT)-based parameters.

Materials and Methods

Oxygen therapy was administered to the patients at two different flow rates and two different positions: T0-baseline measurements were taken in the supine position before any therapy was initiated. T1-HFNC was administered in the supine position with a flow rate of 30 L/min. T2-HFNC was administered in the supine position with a flow rate of 50 L/min. T3-HFNC was administered in the prone position with a flow rate of 30 L/min. T4-HFNC was administered in the prone position with a flow rate of 50 L/min. EIT-based parameters (global inhomogeneity index (GI index), center of ventilation (CoV), regional ventilation delay index (RVD index), region of interest ratio (ROI ratio)), as well as respiratory and hemodynamic parameters of the patients, were recorded from the database.

Results

A total of twenty patients were included in this retrospective observational study. The mean age of the included patients was 64.3 ± 10.6 years. Statistically significant differences were observed in the measurements of GI index, CoV, RVD index, ROI ratio, PaO2/FiO2 ratio, respiratory rate, and mean arterial pressure parameters across different time intervals (p < 0.005). Pairwise comparisons of EIT parameters and measurements of respiratory and hemodynamic parameters at five different time points revealed statistically significant differences. For the GI index, significant differences were observed between the mean measurements taken at T0-T1, T0-T2, T0-T3, T0-T4, T1-T3, T1-T4, T2-T3, T2-T4, and T3-T4 time intervals (p < 0.05). Regarding CoV, significant differences were found between the mean measurements taken at T0-T3, T1-T3, T2-T3, and T3-T4 time intervals (p < 0.05). Additionally, for the ROI ratio, significant differences were observed between the measurement averages taken at each time interval (p < 0.05).

Conclusion

Our findings suggest that prone positioning during the management of C-ARDS patients leads to improved lung homogeneity, as indicated by EIT parameters. However, further research is required to enhance the visualization of ventilation using EIT.

Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography

BACKGROUND

High-flow nasal cannula (HNFC) is able to provide ventilation support for patients with hypoxic respiratory failure. Early prediction of HFNC outcome is warranted, since failure of HFNC might delay intubation and increase mortality rate. Existing methods require a relatively long period to identify the failure (approximately 12 h) and electrical impedance tomography (EIT) may help identify the patient's respiratory drive during HFNC.

OBJECTIVES

This study aimed to investigate a proper machine-learning model to predict HFNC outcomes promptly by EIT image features.

METHODS

The Z-score standardization method was adopted to normalize the samples from 43 patients who underwent HFNC and six EIT features were selected as model input variables through the random forest feature selection method. Machine-learning methods including discriminant, ensembles, k-nearest neighbour (KNN), artificial neural network (ANN), support vector machine (SVM), AdaBoost, xgboost, logistic, random forest, bernoulli bayes, gaussian bayes and gradient-boosted decision trees (GBDT) were used to build prediction models with the original data and balanced data proceeded by the synthetic minority oversampling technique.

RESULTS

Prior to data balancing, an extremely low specificity (less than 33.33%) as well as a high accuracy in the validation data set were observed in all the methods. After data balancing, the specificity of KNN, xgboost, random forest, GBDT, bernoulli bayes and AdaBoost significantly reduced (p<0.05) while the area under curve did not improve considerably (p>0.05); and the accuracy and recall decreased significantly (p<0.05).

CONCLUSIONS

The xgboost method showed better overall performance for balanced EIT image features, which may be considered as the ideal machine learning method for early prediction of HFNC outcomes.

Regional ventilation in spontaneously breathing COVID-19 patients during postural maneuvers assessed by electrical impedance tomography

BACKGROUND

Gravity-dependent positioning therapy is an established concept in the treatment of severe acute respiratory distress syndrome and improves oxygenation in spontaneously breathing patients with hypoxemic acute respiratory failure. In patients with coronavirus disease 2019, this therapy seems to be less effective. Electrical impedance tomography as a point-of-care functional imaging modality for visualizing regional ventilation can possibly help identify patients who might benefit from positioning therapy and guide those maneuvers in real-time. Therefore, in this prospective observational study, we aimed to discover typical patterns in response to positioning maneuvers.

METHODS

Distribution of ventilation in 10 healthy volunteers and in 12 patients with hypoxemic respiratory failure due to coronavirus disease 2019 was measured in supine, left, and right lateral positions using electrical impedance tomography.

RESULTS

In this study, patients with coronavirus disease 2019 showed a variety of ventilation patterns, which were not predictable, whereas all but one healthy volunteer showed a typical and expected gravity-dependent distribution of ventilation with the body positions.

CONCLUSION

Distribution of ventilation and response to lateral positioning is variable and thus unpredictable in spontaneously breathing patients with coronavirus disease 2019. Electrical impedance tomography might add useful information on the immediate reaction to postural maneuvers and should be elucidated further in clinical studies. Therefore, we suggest a customized individualized positioning therapy guided by electrical impedance tomography.

The influence of reference electrode in electrical impedance tomography

Background

Some electrical impedance tomography (EIT) devices equip reference electrodes. In practice, it is not uncommon to observe high contact impedance for the reference electrode. The influence of bad contact reference electrode on data quality is unknown. The study aimed to investigate the influence of reference electrode on EIT image reconstruction.

Methods

Thirty lung healthy volunteers were prospectively examined with EIT. The subjects were spontaneously breathing in supine position. Three scenarios were constructed: 1. Normal measurement; 2. Reference electrode disconnected without recalibration; 3. Reference electrode disconnected, and the measurement restarted after recalibration of the system. EIT-based parameters measuring spatial and temporal ventilation distributions were calculated and compared. A so-call deviation score was calculated to assess the differences in EIT parameters between scenarios 2 and 1, between 3 and 1.

Results

The absolute differences for all parameters were significantly higher than zero (p < 0.01 for all parameters and scenarios). Deviation score for scenario 2 was 4.5 ± 3.5. Four subjects had a deviation score of 0 in scenario 2 and five subjects had a score of 1. The deviation in scenario 3 was higher (6.1 ± 3.1). No subjects had a score of 0 and only two subjects had a score of 1.

Conclusions

For EIT systems that equips with reference electrode, it is important to ensure the proper contact and functionality of the reference electrode. The EIT data quality would remain unchanged in only a small portion of subjects.

The global inhomogeneity index assessed by electrical impedance tomography overestimates PEEP requirement in patients with ARDS: an observational study

Background

Electrical impedance tomography (EIT) visualises alveolar overdistension and alveolar collapse and enables optimisation of ventilator settings by using the best balance between alveolar overdistension and collapse (ODCL). Besides, the global inhomogeneity index (GI), measured by EIT, may also be of added value in determining PEEP. Optimal PEEP is often determined based on the best dynamic compliance without EIT at the bedside. This study aimed to assess the effect of a PEEP trial on ODCL, GI and dynamic compliance in patients with and without ARDS. Secondly, PEEP levels from “optimal PEEP” approaches by ODCL, GI and dynamic compliance are compared.

Methods

In 2015–2016, we included patients with ARDS using postoperative cardiothoracic surgery patients as a reference group. A PEEP trial was performed with four consecutive incremental followed by four decremental PEEP steps of 2 cmH₂O. Primary outcomes at each step were GI, ODCL and best dynamic compliance. In addition, the agreement between ODCL, GI, and dynamic compliance was determined for the individual patient.

Results

Twenty-eight ARDS and 17 postoperative cardiothoracic surgery patients were included. The mean optimal PEEP, according to best compliance, was 10.3 (±2.9) cmH₂O in ARDS compared to 9.8 (±2.5) cmH₂O in cardiothoracic surgery patients. Optimal PEEP according to ODCL was 10.9 (±2.5) in ARDS and 9.6 (±1.6) in cardiothoracic surgery patients. Optimal PEEP according to GI was 17.1 (±3.9) in ARDS compared to 14.2 (±3.4) in cardiothoracic surgery patients.

Conclusions

Currently, no golden standard to titrate PEEP is available. We showed that when using the GI, PEEP requirements are higher compared to ODCL and best dynamic compliance during a PEEP trial in patients with and without ARDS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01801-7.

Electrical Impedance Tomography in Acute Respiratory Distress Syndrome Management

OBJECTIVE

To describe, through a narrative review, the physiologic principles underlying electrical impedance tomography, and its potential applications in managing acute respiratory distress syndrome (ARDS). To address the current evidence supporting its use in different clinical scenarios along the ARDS management continuum.

DATA SOURCES

We performed an online search in Pubmed to review articles. We searched MEDLINE, Cochrane Central Register, and clinicaltrials.gov for controlled trials databases.

STUDY SELECTION

Selected publications included case series, pilot-physiologic studies, observational cohorts, and randomized controlled trials. To describe the rationale underlying physiologic principles, we included experimental studies.

DATA EXTRACTION

Data from relevant publications were reviewed, analyzed, and its content summarized.

DATA SYNTHESIS

Electrical impedance tomography is an imaging technique that has aided in understanding the mechanisms underlying multiple interventions used in ARDS management. It has the potential to monitor and predict the response to prone positioning, aid in the dosage of flow rate in high-flow nasal cannula, and guide the titration of positive-end expiratory pressure during invasive mechanical ventilation. The latter has been demonstrated to improve physiologic and mechanical parameters correlating with lung recruitment. Similarly, its use in detecting pneumothorax and harmful patient-ventilator interactions such as pendelluft has been proven effective. Nonetheless, its impact on clinically meaningful outcomes remains to be determined.

CONCLUSIONS

Electrical impedance tomography is a potential tool for the individualized management of ARDS throughout its different stages. Clinical trials should aim to determine whether a specific approach can improve clinical outcomes in ARDS management.

First Attempt at Using Electrical Impedance Tomography to Predict High Flow Nasal Cannula Therapy Outcomes at an Early Phase

Objective: Spatial and temporal ventilation distributions in patients with acute respiratory failure during high flow nasal cannula (HFNC) therapy were previously studied with electrical impedance tomography (EIT). The aim of the study was to explore the possibility of predicting HFNC failure based on various EIT-derived parameters.

Methods: High flow nasal cannula failure was defined reintubation within 48 h after HFNC. EIT was performed with the patients spontaneously breathing in the supine position at the start of HFNC. EIT-based indices (comprising the global inhomogeneity index, center of ventilation, ventilation delay, rapid shallow breathing index, minute volume, and inspiration to expiration time) were explored and evaluated at three time points (prior to HFNC, T1; 30 min after HFNC started, T2; and 1 h after, T3).

Results: A total of 46 subjects were included in the final analysis. Eleven subjects had failed HFNC. The time to failure was 27.8 ± 12.4 h. The ROX index (defined as SpO₂/FiO₂/respiratory rate) for HFNC success patients was 8.3 ± 2.7 and for HFNC failure patients, 6.2 ± 1.8 (p = 0.23). None of the investigated EIT-based parameters showed significant differences between subjects with HFNC failure and success. Further subgroup analysis indicated that a significant difference in ventilation inhomogeneity was found between ARDS and non-ARDS [0.54 (0.37) vs. 0.46 (0.28) as evaluated with GI, p < 0.01]. Ventilation homogeneity significantly improved in ARDS after 60-min HFNC treatment [0.59 (0.20) vs 0.57 (0.19), T1 vs. T3, p < 0.05].

Conclusion: Spatial and temporal ventilation distributions were slightly but insignificantly different between the HFNC success and failure groups. HFNC failure could not be predicted by changes in EIT temporal and spatial indexes of ventilation distribution within the first hour. Further studies are required to predict the outcomes of HFNC.

Identification and analysis of stable breathing periods in electrical impedance tomography recordings

Objective. In this paper, an automated stable tidal breathing period (STBP) identification method based on processing electrical impedance tomography (EIT) waveforms is proposed and the possibility of detecting and identifying such periods using EIT waveforms is analyzed. In wearable chest EIT, patients breathe spontaneously, and therefore, their breathing pattern might not be stable. Since most of the EIT feature extraction methods are applied to STBPs, this renders their automatic identification of central importance.Approach. The EIT frame sequence is reconstructed from the raw EIT recordings and the raw global impedance waveform (GIW) is computed. Next, the respiratory component of the raw GIW is extracted and processed for the automatic respiratory cycle (breath) extraction and their subsequent grouping into STBPs.Main results. We suggest three criteria for the identification of STBPs, namely, the coefficient of variation of (i) breath tidal volume, (ii) breath duration and (iii) end-expiratory impedance. The total number of true STBPs identified by the proposed method was 294 out of 318 identified by the expert corresponding to accuracy over 90%. Specific activities such as speaking, eating and arm elevation are identified as sources of false positives and their discrimination is discussed.Significance. Simple and computationally efficient STBP detection and identification is a highly desirable component in the EIT processing pipeline. Our study implies that it is feasible, however, the determination of its limits is necessary in order to consider the implementation of more advanced and computationally demanding approaches such as deep learning and fusion with data from other wearable sensors such as accelerometers and microphones.