This paper evaluates the effectiveness of the peer evaluation in a first-year Bachelor of Science (BSc) Electrical Engineering project involving 17 groups of 6-9 students. Students evaluated each other and themselves on five peer evaluation dimensions, namely job performance, attitude, leadership/initiative, communication, and teamwork, using a 1-5 scale (5 being the highest). The academic performance metrics (first-year BSc overall grade point average (GPA) and project final grade) were collected for our analysis. A dedicated measure, the “Factor” (a student's average peer rating divided by the overall group average), was used to measure the peer evaluation results. Overall, though the correlation between peer evaluations and academic performance was low (r = 0.04), we found a strong correlation (r = 0.71) among students with lower peer evaluation scores. In general, in groups, self-assessments and peer evaluations were highly correlated (r = 0.82). We performed further statistical analyses such as multiple linear regression, clustering, mediation analysis and random forest regression in this study. While peer evaluations capture important aspects of teamwork and interpersonal skills, for most of the students, they seem likely more reflective of project-related competencies than necessarily only the overall GPA. Our findings suggest that the insights from BuddyCheck data can serve as an early indicator for targeted future interventions, enhancing collaborative learning outcomes in our projects. Note that, to further preserve anonymity, neither the project name nor the academic year/cohort is disclosed in this paper.

Background: Atrial electrograms recorded from the epicardium provide an important tool for studying the initiation, perpetuation, and treatment of AF. However, the properties of these electrograms depend largely on the properties of the electrode arrays that are used for recording these signals. Method: In this study, we use the electrode's transfer function to model and analyze the effect of electrode size on the properties of measured electrograms. To do so, we use both simulated as well as clinical data. To simulate electrogram arrays we use a two-dimensional (2D) electrogram model as well as an action propagation model. For clinical data, however, we first estimate the trans-membrane current for a higher resolution 2D modeled cell grid and later use these values to interpolate and model electrograms with different electrode sizes. Results: We simulate electrogram arrays for 2D tissues with 3 different levels of heterogeneity in the conduction and stimulation pattern to model the inhomogeneous wave propagation observed during atrial fibrillation. Four measures are used to characterize the properties of the simulated electrogram arrays of different electrode sizes. The results show that increasing the electrode size increases the error in LAT estimation and decreases the length of conduction block lines. Moreover, visual inspection also shows that the activation maps generated by larger electrodes are more homogeneous with a lower number of observed wavelets. The increase in electrode size also increases the low voltage areas in the tissue while decreasing the slopes and the number of detected deflections. The effect is more pronounced for a tissue with a higher level of heterogeneity in the conduction pattern. Similar conclusions hold for the measurements performed on clinical data. Conclusion: The electrode size affects the properties of recorded electrogram arrays which can respectively complicate our understanding of atrial fibrillation. This needs to be considered while performing any analysis on the electrograms or comparing the results of different electrogram arrays.

Background: Local activation time (LAT) annotation in unipolar electrograms is complicated by interference from nonlocal atrial activities of neighboring tissue. This happens due to the spatial blurring that is inherent to electrogram recordings. In this study, we aim to exploit multi-electrode electrogram recordings to amplify the local activity in each electrogram and subsequently improve the annotation of LATs. Methods: An electrogram array can be modeled as a spatial convolution of per cell transmembrane currents with an appropriate distance kernel, which depends on the cells’ distances to the electrodes. By deconvolving the effect of the distance kernel from the electrogram array, we undo the blurring and estimate the underlying transmembrane currents as our desired local activities. However, deconvolution problems are typically highly ill-posed and result in unstable solutions. To overcome this issue, we propose to use a regularization term that exploits the sparsity of the first-order time derivative of the transmembrane currents. Results: We perform experiments on simulated two-dimensional tissues, as well as clinically recorded electrograms during paroxysmal atrial fibrillation. The results show that the proposed approach for deconvolution can improve the annotation of the true LAT in the electrograms. We also discuss, in summary, the required electrode array specifications for an appropriate recording and subsequent deconvolution. Conclusion: By ignoring small but local deflections, algorithms based on steepest descent are prone to generate smoother activation maps. However, by exploiting multi-electrode recordings, we can efficiently amplify small but local deflections and reveal new details in the activation maps that were previously missed.

An electrogram array can be modeled as a spatial convolution of transmembrane currents generated by each activated atrial cell, with an appropriate measurement function that depends on the cells' distance to the electrodes. Compared to electrograms, transmembrane currents suffer less from the superposition of far-field atrial activities as they are not affected by the spatial convolution. As a result, transmembrane currents represent more local information and estimation of the local activation time using the steepest deflection will be more accurate than when using fractionated electrograms. However, transmembrane current estimation from electrogram array recordings is an under-determined problem, having infinite solutions, among which the desired solution. To constrain the solution space, additional prior information can be used. As the temporal derivative of transmembrane currents are typically sparse, we use this as prior information. We use a Split Bregman method with a quick convergence rate to iteratively solve the problem. Our implementation of circulant operations in the formulation of the solution of each step, makes it possible to perform them very fast using FFTs. Using simulated electrograms, we show that the proposed approach outperforms the conventional approach of annotating the steepest deflection of electrograms as the activation time.

Diagnosis and treatment of atrial fibrillation can benefit from various signal processing approaches employed on atrial electrograms. However, the performance and interpretation of these approaches get highly degraded by far-field ventricular activities (VAs) that distort the morphology of the pure atrial activities (AAs). In this study, we aim to remove VAs from the recorded unipolar electrogram while preserving the AA components. To do so, we have developed a framework which first removes the VA-containing segments and interpolates the remaining samples. This will also partly remove the atrial components that overlap with VA signals, e.g., during atrial fibrillation. To reconstruct the AA components, we estimate them from the removed VA-containing segments based on a low-rank and sparse matrix decomposition and add them back to the electrograms. The presented framework is of rather low complexity, preserves AA components, and requires only a single EGM recording. Instrumental comparison to template matching and subtraction and independent component analysis shows that the proposed approach leads to smoother results with better similarity to the true atrial signal.

Finding the hidden parameters of the cardiac electrophysiological model would help to gain more insight on the mechanisms underlying atrial fibrillation, and subsequently, facilitate the diagnosis and treatment of the disease in later stages. In this work, we aim to estimate tissue conductivity from recorded electrograms as an indication of tissue (mal)functioning. To do so, we first develop a simple but effective forward model to replace the computationally intensive reaction-diffusion equations governing the electrical propagation in tissue. Using the simplified model, we present a compact matrix model for electrograms based on conductivity. Subsequently, we exploit the simplicity of the compact model to solve the ill-posed inverse problem of estimating tissue conductivity. The algorithm is demonstrated on simulated data as well as on clinically recorded data. The results show that the model allows to efficiently estimate the conductivity map. In addition, based on the estimated conductivity, realistic electrograms can be regenerated demonstrating the validity of the model.