A diagnostic room for lower limb amputee based on virtual reality and an intelligent space.

This article proposes a virtual reality (VR) system for diagnosing and rehabilitating lower limb amputees. A virtual environment and an intelligent space are the basis of the proposed solution. The target audiences are physiotherapists and doctors, and the aim is to provide a VR-based system to allow visualization and analysis of gait parameters and conformity. The multi-camera system from the intelligent space acquires images from patients during gait. This way, it is possible to generate tridimensional information for the VR-based system. Among the provided functionalities, the user can explore the virtual environment and manage several features, such as gait reproduction and parameters displayed, using a head-mounted display and hand controllers. Besides, the system presents an automatic classifier that can assist physiotherapists and doctors in assessing abnormalities from conventional human gait. We evaluate the system through two quantitative experiments. The first one addresses the performance evaluation of the automatic classifier. The second analysis is through a Likert scale questionnaire submitted to a group of physiotherapists. In this case, the specialists evaluate the existing features of the proposed framework. The results from the questionnaire showed that the virtual environment is suitable for helping track patients' rehabilitation. Also, the neural network-based classifier results are promising, averaging higher than 91% for all evaluation metrics. Finally, a comparison with related works in the literature highlights the contributions of the proposed solution to the field.

Data Augmentation for Automatic Identification of Spatiotemporal Dispersion Electrograms in Persistent Atrial Fibrillation Ablation Using Machine Learning.

Catheter ablation is increasingly used to treat atrial fibrillation (AF), the most common sustained cardiac arrhythmia encountered in clinical practice. A recent breakthrough finding in AF ablation consists in identifying ablation sites based on their spatiotemporal dispersion (STD). STD stands for a delay of the cardiac activation observed in intracardiac electrograms (EGMs) across contiguous leads. In practice, interventional cardiologists localize STD sites visually using the PentaRay multipolar mapping catheter. This work aims at automatically characterizing STD by classifying EGM data into STD vs. non STD groups using machine learning (ML) techniques. A dataset of 23082 multichannel EGM recordings acquired by the PentaRay coming from 16 persistent AF patients is included in this study. A major problem hampering the classification performance lies in the highly imbalanced dataset ratio. We suggest to tackle data imbalance using adapted data augmentation techniques including 1) undersampling 2) oversampling 3) lead shift 4) time reversing and 5) time shift. These tools are designed to preserve the integrity of the cardiac data and are validated by a partner cardiologist. They provide enhancement in classification performance in terms of sensitivity, which increases from 50% to 80% while maintaining accuracy and AUC around 90% with oversampling. Bootstrapping is applied to check the variability of the trained classifiers.Clinical relevance-The machine learning techniques developed in this contribution are expected to aid cardiologists in performing patient-tailored catheter ablation procedures for treating persistent AF.