Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world which shows rising prevalence leading to increased comorbidities, such as, Ischemic heart disease and Stroke that the main cause of deaths in the world. Since AF and most of the arrhythmias are generated due to electrical problems at the heart, electrocardiography provides the best noninvasive method to diagnose and QRS complex play an important role as a benchmark. In this paper, a novel methodology for QRS complex detection is presented. The algorithm introduces a modification of the well known Pan Tompkins approach, performing a multi channel detection, based on the signal to noise ratio of every channel. After application of the squaring operation in the channels with the highest signal to noise ratio a new single channel is created with improved quality, allowing the accurate detection of the QRS complexes in signals with atrial arrhythmias. The approach was tested in electrocardiography records from the Hospital Universitario de Valencia in Spain, showing an average positive predictive value of 99.6% and an average sensitivity of 99.9%.
In this article, we show the development of a low-cost hardware/software system based on close range photogrammetry to track the movement of a person performing weightlifting. The goal is to reduce the costs to the trainers and athletes dedicated to this sport when it comes to analyze the performance of the sportsman and avoid injuries or accidents. We used a web-cam as the data acquisition hardware and develop the software stack in Processing using the OpenCV library. Our algorithm extracts size, position, velocity, and acceleration measurements of the bar along the course of the exercise. We present detailed characteristics of the system with their results in a controlled setting. The current work improves the detection and tracking capabilities from a previous version of this system by using HSV color model instead of RGB. Preliminary results show that the system is able to profile the movement of the bar as well as determine the size, position, velocity, and acceleration values of a marker/target in scene. The average error finding the size of object at four meters of distance is less than 4%, and the error of the acceleration value is 1.01% in average.
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