Fabian Braun

Functional chest imaging using electrical impedance tomography (EIT) has experienced an impressive technological development since its invention in the early eighties of the last century. The number of experimental and clinical studies using this technology is continuously rising, and the increasing availability of devices approved for clinical use accelerates and diversifies its applications in patients. EIT is predominantly used in intensive care units but its utilisation in operating theatres, delivery rooms, pulmonary function laboratories and even remote outpatient settings is growing. Chest EIT is mainly applied to determine the regional distribution of pulmonary ventilation, aeration changes, and respiratory system mechanics both during mechanical ventilation and spontaneous breathing, but an increase in the use of chest EIT for imaging lung perfusion and cardiac action has recently been noted. The ongoing innovation of both EIT hardware and software, the new application fields, and the rising number of users of this technology require consensus on EIT terminology and definitions. This secures a common framework for conducting EIT studies, patient examinations and guarantees unified analysis of EIT data, documentation, reporting and comparability of findings. Our article provides a comprehensive consensus document on EIT terminology and definitions generated by EIT experts of the international TRanslational EIT development stuDy group in cooperation with the producers of EIT technology. It not only updates and extends the first consensus EIT terminology published in 2017, but also offers a new taxonomy of EIT measures, systematically based on the quantification of ventilation-related, heartbeat-related, and contrast-enhanced EIT signals. Thanks to its clear structure with tabulated recommended EIT terms, abbreviations, comprehensible explanations, notes, extensive literature sources and parameter calculations, EIT researchers, clinical users and manufacturers may use this document as a reference source of information relevant for chest EIT.

Study objectives: Sleep staging is usually performed by manual scoring of polysomnography (PSG), which is expensive, laborious, and poorly scalable. We propose an alternative to PSG for ambulatory sleep staging using wearable photoplethysmography (PPG) recorded by a smartwatch and automated scoring. Methods: We previously trained a deep learning model on public datasets, with the specific purpose of performance generalizability to unseen datasets. In the present work, the model was assessed on two datasets of reflective PPG collected from wrist-worn devices: (1) 68 overnight recordings and (2) for the first time, 493 long-term recordings each lasting for 24 hours (170 subjects). Findings were compared either to (1) expert scored sleep stages from PSG for the night recordings or (2) actigraphy for the long-term recordings. Results: For the overnight recordings, the PPG-based model achieved 78.7% accuracy and a Cohen’s κ of 0.68 on reflective PPG collected using wrist-worn devices compared to PSG using a 4-class setup (wake, N1, and N2 combined, N3 and REM), and a sleep/wake accuracy of 94.1%, with a Cohen’s κ of 0.71. For the long-term recordings, a sleep/wake accuracy of 92.5% with a Cohen’s κ of 0.80 was achieved when compared to a state-of-the-art actigraphy-based deep learning model. Conclusions: This state-of-the-art accuracy achieved on wrist-worn devices represents a significant advancement for home sleep monitoring and a valuable alternative to PSG-based sleep staging. Additionally, our model demonstrated promising results on long-term ambulatory recordings, paving the way towards continuous ambulatory monitoring of sleep stages and sleep–wake cycles. Statement of Significance Sleep staging is crucial to diagnose sleep disorders, but traditional methods are laborious and costly. We developed a sleep staging model that demonstrates high performance and exceptional generalization to unseen datasets, including those from wrist-worn devices, thereby possibly enabling accurate sleep staging from wearable technology. Furthermore, we evaluated the model's performance on 24-hour recordings of subjects of various health conditions, offering valuable insights for clinical applications and future research. These advancements significantly enhance the feasibility of continuous sleep monitoring at home, a low-cost, scalable, and comfortable alternative to current methods.

Cardiac arrhythmias (CAs) are associated with critical heart-related complications such as stroke or heart failure. Because of the intermittent and asymptomatic presentation of some CAs in their early stages, the screening remains limited using traditional methods based on ECG. Recent advances in photoplethysmography (PPG) have revealed substantial potential for wearable devices to detect CAs within large populations. Although PPG has demonstrated efficiency in discriminating atrial fibrillation (AF) from normal sinus rhythm, it remains unclear whether AF detection methods remains effective in the presence of CAs other than AF. In this study, we applied a recurrent neural network on a dataset containing eight different types of CAs (Table 1). The classifier processes sequences of interbeat intervals (IBIs) as input and discriminates between normal and abnormal rhythms. The algorithm was evaluated on 64 patients (45 males and 19 females , with a mean age of 55.9 ± 16.0) undergoing a diagnostic or therapeutic electrophysiological procedure. This dataset includes simultaneous recordings of PPG signals from a wrist-bracelet and 12-lead ECG, with the latter used as the gold-standard for annotating cardiac rhythms. The classifier achieved 84% accuracy, 77% sensitivity and 88% specificity in detecting abnormal rhythms. Table 1 shows that AF (99.6%), atrial tachycardia (100%) and AVRT (96.4%) were well classified as abnormal rhythm, whereas the reliability of the detection decreased for atrial flutter (65.4%), atrial or ventricular bigeminy (72.4%) and ventricular tachycardia (80.2%). Undetected abnormal rhythms were often characterized by a rather regular rhythm (illustrated by VT in Figure 1) and CAs for which PPG heartbeat detection was initially not optimal (Bigeminy in Figure 1). In conclusion, this study shows the capability of PPG-based technology to detect various CAs extending beyond AF. It highlights the merits and limitations of IBI-based detection of abnormal rhythms, highlighting the need for a better comprehension of the peripheral hemodynamic signature of CAs.

Objective. Current wearable respiratory monitoring devices provide a basic assessment of the breathing pattern of the examined subjects. More complex monitoring is needed for healthcare applications in patients with lung diseases. A multi-sensor vest allowing continuous lung imaging by electrical impedance tomography (EIT) and auscultation at six chest locations was developed for such advanced application. The aims of our study were to determine the vest’s capacity to record the intended bio-signals, its safety and the comfort of wearing in a first clinical investigation in healthy adult subjects. Approach. Twenty subjects (age range: 23–65 years) were studied while wearing the vests during a 14-step study protocol comprising phases of quiet and deep breathing, slow and forced full expiration manoeuvres, coughing, breath-holding in seated and three horizontal postures. EIT, chest sound and accelerometer signals were streamed to a tablet using a dedicated application and uploaded to a back-end server. The subjects filled in a questionnaire on the vest properties using a Likert scale. Main results. All subjects completed the full protocol. Good to excellent EIT waveforms and functional EIT images were obtained in 89% of the subjects. Breathing pattern and posture dependent changes in ventilation distribution were properly detected by EIT. Chest sounds were recorded in all subjects. Detection of audible heart sounds was feasible in 44%–67% of the subjects, depending on the sensor location. Accelerometry correctly identified the posture in all subjects. The vests were safe and their properties positively rated, thermal and tactile properties achieved the highest scores. Significance. The functionality and safety of the studied wearable multi-sensor vest and the high level of its acceptance by the study participants were confirmed. Availability of personalized vests might further advance its performance by improving the sensor-skin contact.

Current oxygen delivery modes lack monitoring and can be cumbersome for patients with chronic respiratory diseases. Integrating a pulse oximeter and nasal oxygen cannulas into eyeglasses would reduce the burden of current solutions. An ear pulse oximeter (OxyFrame) was evaluated on 16 healthy volunteers and 20 hypoxemic patients with chronic respiratory diseases undergoing a prespecified protocol simulating daily activities. Correlation, error, and accuracy root mean square error (A_RMS) were calculated to compare S_pO₂ measured by OxyFrame, a standard pulse oximeter (MASIMO), and arterial blood gas analysis (aBGA). S_pO₂ measured by OxyFrame and MASIMO correlated strongly in volunteers, with low error and high accuracy (r = 0.85, error = 0.2 ± 2.9%, A_RMS = 2.88%). Performances were similar in patients (r = 0.87, error 0 ± 2.5%, A_RMS = 2.49% compared with MASIMO; and r = 0.93, error = 0.4 ± 1.9%, A_RMS = 1.94% compared with aBGA). However, the percentage of rejected measurements was high (volunteers 77.2%, patients 46.9%). The OxyFrame cavum conchae pulse oximeter was successfully evaluated, and demonstrated accurate S_pO₂ measurements, compliant with ISO 80601-2-61:2017. Several reasons for the high rejection rate were identified, and potential solutions were proposed, which might be valuable for optimization of the sensor hardware.