Validation of a New Ankle Brachial Index Measurement System Using Pulse Wave Velocity.

Peripheral artery disease (PAD) is a common circulatory disorder characterized by the accumulation of fats, cholesterol, and other substances in the arteries that restrict blood flow to the extremities, especially the legs. The ankle brachial index (ABI) is a highly reliable and valid non-invasive test for diagnosing PAD. However, the traditional method has limitations. These include the time required, the need for Doppler equipment, the training of clinical staff, and patient discomfort. PWV refers to the speed at which an arterial pressure wave propagates along the arteries, and this speed is conditioned by arterial elasticity and stiffness. To address these limitations, we have developed a system that uses electrocardiogram (ECG) and photoplethysmography (PPG) signals to calculate pulse wave velocity (PWV). We propose determining the ABI based on this calculation. Validation was performed on 22 diabetic patients, and the results demonstrate the accuracy of the system, maintaining a margin of ±0.1 compared with the traditional method. This confirms the correlation between PWV and ABI and positions this technique as a promising alternative to overcome some of the limitations of the conventional method.

Enhancing surgeons' gaze strategies in endoscopic surgery during simulated surgical emergency situations thanks to computer vision.

Deficient visualization in minimally invasive surgery often causes misperceptions, which can lead to an increase of iatrogenic lesions and complications. This is especially critical for novice surgeons, who are prone to adopt inadequate switching gaze strategies, thereby increasing the chance of unforeseen complications. In this paper the use of an additional computer-aided vision system was tested for improvement of the reaction of the surgeons to unforeseen complications. Gaze patterns were analyzed using a gaze tracker, as well as other metrics such as task completion time or reaction time to sudden bleeding. While completion time did not show significant difference between tested modalities (p<0.1), the reaction time showed a downward trend as more auxiliary computer-aided vision systems were added (p<0.005). These results support the benefits of including additional vision systems for minimally invasive surgery processes.Clinical Relevance- This work assesses the advantages of including an additional computer vision system to prevent unforeseen complications during minimally invasive surgeries.

Augmented Reality Holographic Visualization System for Surgery Auxiliary Visualization: Proof of Concept for Surgical Training.

An Augmented Reality (AR) system based on the holographic projection of the relevant anatomic structures is proposed for auxiliary visualization during surgeries. The current two-dimensional visualization systems require the surgeons to mentally extract the associated three-dimensional information during the interventions, which entails risks and complications. This work shows an AR holographic projection system for real-time three-dimensional representation of the relevant surgical information, thus overcoming this problem. As an initial proof of concept, the system is experimentally assessed as potential surgery training tool.Clinical Relevance- This work explores the potential of AR holographic projection systems for intraoperative assistance to the surgical team, starting from its possible use as surgery training and planning tool.

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals.

Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients' health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.