This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast, the lowest is the prediction for normal sinus ECG waveforms, i.e., 74 out of 80 samples are correct (92.5% accuracy). This is due to the image features of normal sinus ECG waveforms being almost similar to the image features of supraventricular arrhythmia ECG waveforms. However, the model has been tuned to achieve an optimal prediction. In the second part, we presented the hardware implementation with the predictive model embedded in an NVIDIA Jetson Nanoprocessor for the online and real-time classification of ECG waveforms.
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Introduction: Restrictive pulmonary disorder is reducing VO2 max values. It can be caused by lung can’t take oxygen from outside air freely. Pulmonary rehabilitation is known to increase the VO2 max. One of the pulmonary rehabilitation is deep breathing exercise. In this study aimed to know the improvement VO2 max after deep breathing exercise.
Methods: This was an experimental without control pre and post-experimental study. The Six Minutes Walking Test (6MWT) was measured in patients with restrictive pulmonary disorder, after deep breathing exercise two times a day, for four weeks in May 2018.
Results: Fifteen subjects were recruited, with the mean age was 70,76 ± 5,33 years old, 6MWT was 375,13 ± 44,19 m and VO2 Max 31,61±0,86 ml/kg/minute. After four weeks intervention, 6MWT value was 401±44,57 m (p=0.000) and VO2 Max score was 32,11±0,87 ml/kg/minute (p=0.000).
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