Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia.

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with "S.E.S Hospital Universitario de Caldas" ( https://hospitaldecaldas.com/ ) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

Estimation of PQ distance dispersion for atrial fibrillation detection.

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. It is associated with significantly increased morbidity and mortality. Diagnosis of the disease can be based on the analysis of the electrical atrial activity, on quantification of the heart rate irregularity or on a mixture of the both approaches. Since the amplitude of the atrial waves is small, their analysis can lead to false results. On the other hand, the heart rate based analysis usually leads to many unnecessary warnings. Therefore, our goal is to develop a new method for effective AF detection based on the analysis of the electrical atrial waves. METHODS: The proposed method employs the fact that there is a lack of repeatable P waves preceding QRS complexes during AF. We apply the operation of spatio-temporal filtering (STF) to magnify and detect the prominent spatio-temporal patterns (STP) within the P waves in multi-channel ECG recordings. Later we measure their distances (PQ) to the succeeding QRS complexes, and we estimate dispersion of the obtained PQ series. For signals with normal sinus rhythm, this dispersion is usually very low, and contrary, for AF it is much raised. This allows for effective discrimination of this cardiologic disorder. RESULTS: Tested on an ECG database consisting of AF cases, normal rhythm cases and cases with normal rhythm restored by the use of cardioversion, the method proposed allowed for AF detection with the accuracy of 98.75% on the basis of both 8-channel and 2-channel signals of 12 s length. When the signals length was decreased to 6 s, the accuracy varied in the range of 95%-97.5% depending on the number of channels and the dispersion measure applied. CONCLUSIONS: Our approach allows for high accuracy of atrial fibrillation detection using the analysis of electrical atrial activity. The method can be applied to an early detection of the desease and can advantageously be used to decrease the number of false warnings in systems based on the analysis of the heart rate.

Automated Atrial Fibrillation Detection by ECG Signal Processing: A Review.

Cardiovascular diseases are the main cause of death in the world, according to the World Health Organization. Among them, ischemic heart disease is at the top, followed by a stroke. Several studies have revealed that atrial fibrillation (AF), which is the most common cardiac arrhythmia, increases up to five fold the overall risk of stroke. As AF can be asymptomatic, approximately 20% of the AF cases remain undiagnosed. AF can be detected by analyzing electrocardiography records. Many studies have been conducted to develop automatic methods for AF detection. This paper reviews some of the most relevant methods, classified into three groups: analysis of heart rate variability, analysis of the atrial activity, and hybrid methods. Their benefits and limitations are analyzed and compared, and our beliefs about where AF automatic detection research could be addressed are presented to improve its effectiveness and performance.