Biomedical data generation and collection have become faster and more ubiquitous. Consequently, datasets are increasingly spread across hospitals, research institutions, or other entities. Exploiting such distributed datasets simultaneously can be beneficial; in particular, classification using machine learning models such as decision trees is becoming increasingly common and important. However, given that biomedical data is highly sensitive, sharing data records across entities or centralizing them in one location are often prohibited due to privacy concerns or regulations. We design PrivaTree, an efficient and privacy-preserving protocol for collaborative training of decision tree models on distributed, horizontally partitioned, biomedical datasets. Although decision tree models may not always be as accurate as neural networks, they have better interpretability and are helpful in decision-making processes, which are crucial for biomedical applications. PrivaTree follows a federated learning approach, where raw data is not shared, and where every data provider computes updates to a global decision tree model being trained, on their private dataset. This is followed by privacy-preserving aggregation of these updates using additive secret-sharing, in order to collaboratively update the model. We implement PrivaTree, and evaluate its computational and communication efficiency on three different biomedical datasets, as well as the accuracy of the resulting models. Compared to the model centrally trained on all data records, the obtained collaborative model presents a modest loss of accuracy, while consistently outperforming the accuracy of the local models, trained separately by each data provider. Moreover, PrivaTree is more efficient than existing solutions, which makes it usable for training decision trees with numerous nodes, on large complex datasets, with both continuous and categorical attributes, as often found in the biomedical field.

Alternans of cardiac action potential duration (APD) is a well-known arrhythmogenic mechanism which results from dynamical instabilities. The propensity to alternans is classically investigated by examining APD restitution and by deriving APD restitution slopes as predictive markers. However, experiments have shown that such markers are not always accurate for the prediction of alternans. Using a mathematical ventricular cell model known to exhibit unstable dynamics of both membrane potential and Ca²⁺ cycling, we demonstrate that an accurate marker can be obtained by pacing at cycle lengths (CLs) varying randomly around a basic CL (BCL) and by evaluating the transfer function between the time series of CLs and APDs using an autoregressive-moving-average (ARMA) model. The first pole of this transfer function corresponds to the eigenvalue (λ(alt)) of the dominant eigenmode of the cardiac system, which predicts that alternans occurs when λ(alt) ≤ -1. For different BCLs, control values of λ(alt) were obtained using eigenmode analysis and compared to the first pole of the transfer function estimated using ARMA model fitting in simulations of random pacing protocols. In all versions of the cell model, this pole provided an accurate estimation of λ(alt). Furthermore, during slow ramp decreases of BCL or simulated drug application, this approach predicted the onset of alternans by extrapolating the time course of the estimated λ(alt). In conclusion, stochastic pacing and ARMA model identification represents a novel approach to predict alternans without making any assumptions about its ionic mechanisms. It should therefore be applicable experimentally for any type of myocardial cell.

We propose a new method for ventricular cancellation and atrial modelling in the ECG of patients suffering from atrial fibrillation. Our method is based on dictionary learning. It extends both the average beat subtraction and the sparse source separation approaches. Experiments on synthetic data show that this method can almost completely suppress the ventricular activity, but it generates some artifacts. Contrary to other ventricular cancellations methods, our approach also learns a model for the atrial activity.

This paper explores a novel approach for ventricular and atrial activities estimation in electrocardiogram (ECG) signals, based on sparse source separation. Sparse decompositions of ECG over signal-adapted multi-component dictionaries can lead to natural separation of its components. In this work, dictionaries of functions adapted to ventricular and atrial activities are respectively defined. Then, the weighted orthogonal matching pursuit algorithm is used to unmix the two components of ECG signals. Despite the simplicity of the approach, results are very promising, showing the capacity of the algorithm to generate realistic estimations of atrial and ventricular activities.

Real life deployment of robot formation cannot assume that robots are going to be correctly positioned to move in a particular configuration. To do so, we propose an approach that allows the group to determine autonomously the most appropriate assignment of positions in the formation. Our approach is distributed and uses directional visual perception to localize robots. Inter-robot communication allows them to share information on which robots are nearby, so that each can evaluate it ability to be the conductor of the group and assign formation positions to the other robots by minimizing repositioning. The assignment search is done using a distributed bounded depth-first with pruning search. The robot with the best score is selected as the conductor, and the other robots receive from the conductor their assignment in the formation. Validation of our work is done in simulation and with Pioneer 2 robots.

This paper describes two initiatives aiming at improving code reusability for programming mobile robots: robotflow/flowdesigner, a data-flow programming environment; MARIE (mobile and autonomous robotics integration environment), a programming environment allowing multiple applications, programs and tools, to operate on one or multiple machines/OS and work together on a mobile robot implementation. Robotflow/flowdesigner's objective is to provide a modular, graphical programming environment that would help visualize and understand what is really happening in the robot's control loops, sensors, actuators, by using graphical probes. MARIE aims at avoiding making an exclusive choice on particular programming tools, making it possible to reuse code and applications.