This is because the models tend to learn the particularities of the patient's heartbeat during the training, obtaining expressive numbers during the test (very close to 100%). As previously mentioned, this heartbeat division protocol is called in the literature intra-patient scheme or paradigm [...
Each classifier can show the confusion matrix, which summarizes the accuracy for each true label class, as the ComplexTrees results below: With the evaluation test (split instances and confusion matrix) results for each WEKA and MATLAB, the ECG arrhythmia extraction and analysis were well evaluated...
2.3 Results The conducted simulations resulted in distributions of the body surface potential as depicted in Fig. 2.4. When stimulated, both hearts produced regions on the torso of increased potential in the basal direction and decreased potential in the apical 2.4 Discussion 19 direction. The ...
The numbers in parentheses correspond, respectively, to specificity and sensitivity (spec/sens). Numbers in bold mark the best result in each row (each ground-truth arrhythmia type). 6.2. Embedding Applied to our 10 s segmented and combined test and training data set, the pipeline described ...
Applying the same CUDA cores to a Jetson Nano and a GeForce-capable GPU results in a very potent software development ecosystem. Furthermore, Jetson Nano features a hybrid architecture, meaning that the CPU can start the operating system and configure it to use the GPU’s CUDA characteristics ...
4. Results and Discussion This section presents the results of 40 possible scenarios employed in the proposed driving fatigue detection framework. These scenarios, described in Table 7, combine five resampling scenarios, two feature extraction method scenarios, and four ensemble learning model scenarios....
These results show that the proposed CWT model had higher robustness in classifying ECG data into the AF, normal, and other rhythms classes compared to the STFT models. After comparing all the results, the selected network and its parameters are as follows: Figure 10. Test results from the ...
Most deep neural network architectures are prone to over-fitting, meaning that if the accuracy of the network on the training set is increased, the accuracy of the network on the actual test set may not improve. This situation indicates that the model is overfitting the training set and cannot...
The results demonstrate that our method is lightweight and practical, qualifying it for continuous monitoring applications in clinical settings for automated ECG interpretation to support cardiologists. Keywords: cardiac health; heart disease detection; convolutional neural; deep learning; electrocardiogram (...
The longitudinal analysis aimed to identify and analyze the most commonly used processing blocks in this field, and the results derived from this part of the study enabled the design of an efficient detection system with the most suitable processing blocks. The transversal analysis was carried out ...