Characterization of Clinical Outcomes in Patients with Cutis Marmorata Telangiectatica Congenita

Cutis marmorata telangiectatica congenita (CMTC) is an uncommon, congenital, cutaneous vascular disease with an unknown pathogenesis. Although considered as a benign condition, anomalies such as body asymmetry are frequently associated. Herein we present a series of patient with diagnosis of CMTC with a focus on clinical outcomes. In our series, limb length abnormalities were the most common associate anomaly, occurring in 24% of the subjects, similar to other series with rates of 33% and 27% but higher than the general population (6.7%). Importantly, dermatologist should be aware of the frequently associated anomalies in CMTC, such as leg length discrepancy, which may have serious consequences if not recognized and treated.

Detection and Classification of ECG Signal through Machine learning

The electrical activity which might be acquired by inserting the probes on the body exterior that is originated within the individual muscle cells of the heart and is summed to indicate an indication wave form referred to as the EKG (ECG). Cardiac Arrhythmia is an associate anomaly within the heart which may be diagnosed with the usage of signals generated by Electrocardiogram (ECG). For the classification of ECG signals a software application model was developed and has been investigated with the usage of the MIT-BIH database. The version is based on some existing algorithms from literature, entails the extraction of a few temporal features of an ECG signal and simulating it with a trained FFNN. The software version may be employed for the detection of coronary heart illnesses in patients. The neural network’s structure and weights are optimized using Particle Swarm Optimization (PSO). The FFNN trained with set of rules by PSO increase its accuracy. The overall accuracy and sensitivity of the algorithm is about 93.687 % and 92%.