In this paper, a novel method for classifying electrocardiogram signals in mobile devices is proposed, which classifies different arrhythmias according to the Association for the Advancement of Medical Instrumentation standard EC57. A convolutional neural network has been constructed, trained and validated with the MIT-BIH Arrhythmia Dataset, which has 5 different classes: normal beat, supraventricular premature beat, premature ventricular contraction, fusion of ventricular and normal beat, unclassifiable beat. Once trained and validated, the model is subjected to a post-training quantization stage using the TensorFlow Lite conversion method. The obtained results were satisfactory, before and after the quantization, the convolutional neural network obtained an accuracy of 98.5%. With the quantization technique it was possible to obtain a significant reduction in model size, thus enabling the development of the mobile application, this reduction was approximately 90% compared to the original model size.

This paper describes a comparison between three pre-trained neural networks for the classification of chest X-ray images: Xception, Inception V3, and NasNetLarge. Networks were implemented using learning transfer; The database used was the chest x-ray data set, which contains a total of 5856 chest x-ray images of pediatric patients aged one to five years, with three classes: Normal Viral Pneumonia and Bacterial Pneumonia. Data were divided into three groups: validation, testing and training. A comparison was made with the work of kermany who implemented the Inception V3 network in two ways: (Pneumonia X Normal) and (Bacterial Pneumonia X Viral Pneumonia). The nets used had good accuracy, being the NasNetLarge network the best precision, which was 95.35 \% (Pneumonia X Normal) and 91.79 \% (Viral Pneumonia X Bacterial Pneumonia) against 92.80 \% in (Pneumonia X Normal) and 90.70 \% (Viral Pneumonia X Bacterial Pneumonia) from kermany's work, the Xception network also achieved an improvement in accuracy compared to kermany's work, with 93.59 \% at (Normal X Pneumonia) and 91.03 \% in (Viral Pneumonia X Bacterial Pneumonia).

This paper presents an approach for the classification of child chest X-ray images into two classes: pneumonia and normal. We employ Convolutional Neural Networks, from pre-trained networks together with a quantization process, using the platform TensorFlow Lite method. This reduces the processing requirement and computational cost. Results have shown accuracy up to 95.4% and 94.2% for MobileNetV1 and MobileNetV2, respectively. The resulting mobile app also presents a simple and intuitive user interface.

This paper aims to compare the behave of different signals when applied to different compression techniques, to test and find the best compression techniques to each different signal, also proving that different signals behave differently in distinct types of compression, the results of this work was satisfactory to prove that different types of compression can be used on signals to achieve better results.