Abstract
The mitral valve plays a vital role in our circulatory system. To study its functionality, it is important to measure clinically relevant parameters, such as its thickness, mobility and shape. Since manual segmentation is impractical, time consuming and requires expert knowledge, an automatic segmentation tool can have a significant clinical impact, providing objective measures to clinicians for understanding the morphology and behaviour of the mitral valve. In this work, a real time tracking method has been proposed for ultrasound videos obtained with the Parasternal Long Axis view. The algorithm is semi-automatic, assumes manual Anterior Mitral Leaflet segmentation in the first frame and then it uses mathematical morphology algorithms to obtain tracking results, further refined by localized active contours during the whole cardiac cycle. Finally, the medial axis is extracted for a quantitative analysis. Results show that the algorithm can segment 1137 frames extracted from 9 fully annotated sequences of the real clinical video data in only 0.89 sec/frame, with an average error of 5 pixels. Furthermore, the algorithms exhibited robust tracking performance in the most difficult situations, which are large frame-to-frame displacements.

Abstract
Heart sounds are difficult to interpret due to events with very short temporal onset between them (tens of milliseconds) and dominant frequencies that are out of the human audible spectrum. Computer-assisted decision systems may help but they require robust signal processing algorithms. In this paper, we propose a new algorithm for heart sound segmentation using a hidden semi-Markov model. The proposed algorithm infers more suitable sojourn time parameters than those currently suggested by the state of the art, through a maximum likelihood approach. We test our approach over three different datasets, including the publicly available PhysioNet and Pascal datasets. We also release a pediatric dataset composed of 29 heart sounds. In contrast with any other dataset available online, the annotations of the heart sounds in the released dataset contain information about the beginning and the ending of each heart sound event. Annotations were made by two cardiopulmonologists. The proposed algorithm is compared with the current state of the art. The results show a significant increase in segmentation performance, regardless the dataset or the methodology presented. For example, when using the PhysioNet dataset to train and to evaluate the HSMMs, our algorithm achieved average an F-score of 92% compared to 89% achieved by the algorithm described in [D.B. Springer, L. Tarassenko, and G. D. Clifford, "Logistic regressionHSMM-based heart sound segmentation," IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 822-832, 2016]. In this sense, the proposed approach to adapt sojourn time parameters represents an effective solution for heart sound segmentation problems, even when the training data does not perfectly express the variability of the testing data.

Abstract
In this paper, a novel subject-adaptable heartbeat classificationmodel is presented, in order to address the significant interperson variations in ECG signals. A multiview learning approach is proposed to automate subject adaptation using a small amount of unlabeled personal data, without requiring manual labeling. The designed subject-customized models consist of two models, namely, general classification model and specific classification model. The general model is trained using similar subjects out of a population dataset, where a pattern matching based algorithm is developed to select the subjects that are "similar" to the particular test subject for model training. In contrast, the specific model is trained mainly on a small amount of high-confidence personal dataset, resulting from multiview-based learning. The learned general model represents the population knowledge, providing an interperson perspective for classification, while the specific model corresponds to the specific knowledge of the subject, offering an intraperson perspective for classification. The two models supplement each other and are combined to achieve improved personalized ECG analysis. The proposed methods have been validated on the MIT-BIH Arrhythmia Database, yielding an average classification accuracy of 99.4% for ventricular ectopic beat class and 98.3% for supraventricular ectopic beat class, which corresponds to a significant improvement over other published results.

Abstract
In this exploratory study we propose to analyze, in healthy adult volunteers, the heart electrical (electrocardiogram, ECG) and mechanical (phonocardiogram, PCG) activity during exercise. Heart sounds amplitude, frequency content, and RS2, may be important features in the non-invasive assessment of heart activity, such as for the estimation of cardiac output and blood pressure. Nine healthy volunteers were monitored with ECG and PCG simultaneously, under a stress test. After each workload level a 10 s window of signal was collected. PCG first (S1) and second (S2) heart sounds were manually annotated, based on time of QRS complex occurrence. A QRS detector was implemented to detect the QRS complex, and time intervals between electrical and mechanical events. Extracted features were analyzed in relation to heart rate (HR), including RS2, S1 and S2 amplitudes, and high frequency content of S1 and S2. Spearman correlation was used. Changes between baseline and maximum workload stage/HR for each volunteer were analyzed. Significant correlation was observed between HR, and all characteristics extracted (P<0.01). There was a clear difference between all variables from baseline to maximum workload level: with increasing workload/HR heart sounds amplitude increased (more pronounced in S1), RS2 decreased, and high frequency content of S2 decreased in relation to the high frequency content of S1, demonstrating that dynamic cardiovascular relations are individualized during cardiac stress and that assumptions for resting conditions may not be assumed.

Abstract
Stress is a major factor for the degradation of cardiac health in first responder professionals such as firefighters. Monitoring stress during real events might be the key for controlling this problem. In this paper we inspect how standard heart rate variability (HRV) measures are associated with the self-perception of stress of firefighters in action, supported by an advanced technological solution to acquire this data. Results obtained from more than 94 hours of annotated ECG recordings of firefighters in action are promising, showing positive association with various standard HRV measures. Given the richness of the gathered data, we have also measured the association of the HRV measures with the stage of a firefighting event (pre, during, post), obtaining some interesting results that hint that the psychological impact of the post-event may be one of the most concerning situations for a firefighter, motivating further studies on this in the future.

Abstract
Leishmania is a unicellular parasite that infects mammals. Biologists are interested in determining the effect of drugs in Leishmania infections. This requires the manual annotation of the number of macrophages and parasites in images, in order to obtain the percentage of infection (PI), the average number of parasites per infected cell (NPI) and the infection index (IX). Considering that manual annotation is tedious, time-consuming and often erroneous, in this paper we propose an automatic method for automatic annotation of Leishmania infections using fluorescence microscopy. Moreover, when compared to related works, the proposed method is able to get superior performance under most perspectives.