Effects of Juglans regia Root Bark Extract on Platelet Aggregation, Bleeding Time, and Plasmatic Coagulation: In Vitro and Ex Vivo Experiments.

Platelets have an important role in thrombosis and haemostasis. Hyperactivity of the platelets has been associated with thromboembolic diseases and represents the main cause of complications of cardiovascular diseases. Crude aqueous extract (CAE) of Juglans regia root bark was evaluated for bleeding time, antiaggregant activity by using agonists, thrombin, ADP, collagen, or arachidonic acid (in vitro and ex vivo), and anticoagulant activity by measuring the clotting parameters: activated partial thromboplastin time, prothrombin time, thrombin time, and fibrinogen dosage (in vitro and ex vivo). The result of this study reported that the strongest antiaggregant effect of CAE in vitro was observed on the ADP-induced aggregation with inhibitions up to 90 %, while, in ex vivo experiments, the inhibition (more than 80 %) was observed with all agonists. Anticoagulant effect of CAE significantly prolonged the TT and decreased the fibrinogen level in vitro and ex vivo without interfering with APTT and PT. The bleeding time in mice and rats was significantly increased by CAE. The antiplatelet and anticoagulant effect observed in this study suggest that Juglans regia could have antithrombotic and/or thrombolytic activities and provide an alternative therapy against thrombotic complications related to cardiovascular diseases.

QRS detection using S-Transform and Shannon energy.

This paper presents a novel method for QRS detection in electrocardiograms (ECG). It is based on the S-Transform, a new time frequency representation (TFR). The S-Transform provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum. We exploit the advantages of the S-Transform to isolate the QRS complexes in the time-frequency domain. Shannon energy of each obtained local spectrum is then computed in order to localize the R waves in the time domain. Significant performance enhancement is confirmed when the proposed approach is tested with the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivity of 99.84%, a positive predictivity of 99.91% and an error rate of 0.25%. Furthermore, to be more convincing, the authors illustrated the detection parameters in the case of certain ECG segments with complicated patterns.

ECG beat classification using a cost sensitive classifier.

In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat. An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost.