Area Triangulation Method for Automatic Detection of Venous Emptying Maneuvers

Abstract Venous refilling time (VRT) can diagnose the presence of venous diseases in lower limbs. In order to calculate VRT it is necessary to determine the End of the Emptying Maneuvers (EEM). First Derivative Method (FDM) can be employed for automatic detection of the EEM, but its sensitivity to artifacts and noise can degrade its performance. In contrast, studies report that Area Triangulation Method (ATM) evinces effectiveness in biosignals point finding. This work compares the exactness of ATM and FDM for recognition of the EEM. The annotations made by 3 trained human observers on 37 photoplethysmography records were used as a reference. Bland-Altman graphics supported the analysis of agreement among human observers and methods, which was complemented with Analysis of variance and Multiple Comparisons statistical tests. Results showed that ATM is more accurate than FDM for automatic detection of the EEM, with statistically significant differences (p-value < 0.01)

Detección de arritmias a partir de la determinación de la frecuencia cardiaca con fotopletismografía

Cardiovascular diseases (CVD) kill about 18 million people each year which constitute the leading cause of death and disability worldwide. Among cardiovascular diseases, cardiac arrhythmias are the most common. For several years, new studies have highlighted the potential of the photoplethysmographic wave to detect arrhythmias, surpassing in simplicity and cost reduction to electrocardiography (ECG). This study proposes a method of detecting systolic peaks of the photoplethysmographic wave to determine the heart rate and establish the presence of tachycardia, bradycardia, or asystole. The systolic peak detection method calculates the first derivative of the previously filtered signal. It then applies a thresholding process. Finally, in a clustering stage, the DBSCAN algorithm is used. The peak detection algorithm was evaluated on 42 signals from an international multiparametric database for RR estimation. The evaluation of the method showed high accuracy and precision (0 ± 2 ms). The sensitivity and positive predictive value were 99%. These results allow determining the heart rate with accuracy and precision of 0 ± 1 beats per minute. The algorithm was evaluated in arrhythmia classification using 155 signals from the PhysioNet/Computing in Cardiology Challenge 2015 database. For this evaluation, the algorithm showed acceptable results for detecting asystole, bradycardia, and tachycardia. The sensitivity and positive predictive values were 79% and 88% for asystole, 74% and 64% for bradycardia, and 80% and 99% for tachycardia, respectively. The method's effectiveness may be affected in signals with significant variations in amplitude or low signal-to-noise ratios (SNR). However, the results under these conditions are still acceptable and are very good at high SNR signals.Las enfermedades cardiovasculares (ECV) cobran la vida de cerca de 18 millones de personas cada año, constituyendo la principal causa de muerte e incapacidad en el mundo. Entre las enfermedades cardiovasculares, las arritmias cardiacas son las más comunes. Desde hace varios años, nuevos estudios han destacado las potencialidades de la onda fotopletismográfica para detectar arritmias, superando en sencillez y reducción de costos a la electrocardiografía (ECG). En este estudio se propone un método de detección de picos sistólicos de la onda fotopletismográfica para determinar la frecuencia cardiaca y con ello establecer la presencia de taquicardia, bradicardia o asístole. El método de detección de picos sistólicos calcula la primera derivada de la señal previamente filtrada. A continuación aplica un proceso de umbralización. Finalmente, en una etapa de agrupamiento se emplea el algoritmo DBSCAN. El algoritmo de detección de picos fue evaluado en 42 señales de una base de datos internacional multiparamétrica para la estimación del RR. La evaluación del método mostró alta exactitud y precisión (0±2 ms) y una sensibilidad y valor predictivo positivo del 99 %. Estos resultados permiten determinar la frecuencia cardiaca con una exactitud y precisión de 0±1 latido por minuto. Además, este algoritmo es evaluado en clasificación de arritmias utilizando 155 señales de la base de datos del PhysioNet/Computing in Cardiology Challenge del 2015. Para esta evaluación el algoritmo mostró resultados aceptables en la detección de asístole, bradicardia y taquicardia. La sensibilidad y el valor predictivo positivo fue del 79% y 88% para asístole, 74% y 64% para bradicardia y, 80% y 99% para taquicardia respectivamente. La efectividad del método puede afectarse en registros de señales con grandes variaciones de amplitud y/o con relaciones señal-ruido (SNR) bajas. No obstante, los resultados en estas condiciones son aceptables y son muy buenos en señales de alto SNR

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Contains fulltext : 179531.pdf (Publisher’s version ) (Closed access)Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.13 p