Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- ECG-based heartbeat classification for arrhythmia detection: a step-by-step AI exploratory process(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) Silva Mendez, Adrian; TAMEZ PEÑA, JOSE GERARDO; 67337; Tamez Peña, José Gerardo; emipsanchez; Gutiérrez Ruiz, Dania; Santos Díaz, Alejandro; Martínez Ledesma, Juan Emmanuel; School of Engineering and Sciences; Campus MonterreyThis document presents the thesis of “ECG-based heartbeat classification for arrhythmia detection: A step-by-step AI Exploratory Process” for the degree of Master in Computer Science at Tecnológico de Monterrey. One of the biggest causes of death around the world (including third and first world countries) are Cardiovascular Diseases. Arrhythmia is one of those diseases in which the heart beats at an inconsistent and abnormal rhythm due to a malfunction in the electrical system of the heart. The detection, diagnosis, and classification are very challenging tasks for doctors as time is a crucial factor on the table. If it is not done in time, the patient’s life can be at risk. This proposal explores different Data Pre-processing and Feature Generation techniques to create an efficient and accurate binary classification model capable of distinguishing normal from abnormal heartbeats with an Accuracy and Sensitivity ranging in the 80-90% with a 10% increase when compared to a RAW feature vector. One of the most important ideas discussed throughout this thesis includes decomposing the ECG signal in Frequency and Time domains usingDual Tree Complex Wavelet Transform to create a Feature Vector. Another important highlight of this thesis is database manipulation, including the exclusion and the correct distribution of subjects across the training and testing sets. The approach aims to test the feature vectors by training different Supervised Learning Models including K Nearest Neighbours, Random Forest, and X-Gradient Boosting. We will be using the MIT-BIH Arrhythmia Database for the experimentation process.
- Front-End modeling for emotional state recognition(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-02) Velázquez Flores, Oliver Alejandro; Nolazco Flores, Juan Arturo; puemcuervo; de la Cueva Hernández, Víctor Manuel; Gutiérrez Rodríguez, Andrés Eduardo; School of Engineering and Sciences; Campus MonterreyMorphological biometrics have proved to be important contributors to e-security and e-health alike. One of its subdivisions, behavioral biometrics, focuses on understanding an individual based on several activities, in this case, a user with drawing and handwritten tasks. Following up the EMOTHAW methodology, this work focuses on extracting and generating new features from the original raw attributes of the aforementioned database. These techniques range from signal processing, physics, and statistics from which important feature vectors and models are created. The signal processing techniques focuses on calculating the logarithmic energy of a signal and then generate both the spectral and cepstral domains to create new numerical features for the resulting vector. In the physics department, traditional kinematic variables are calculated from the position of each task, and lastly the statistical features includes momentum, descriptive statistics, and the different means available in the literature (arithmetic, harmonic, etc.). Furthermore, the implementation of a combined dimensionality reduction (PCA) and feature selection (a novel correlation-based filtering algorithm) pipeline methodology is key for the continuous improvement of the results presented in this research thesis project, going from 60.5 - 71.6 % to 100% accuracy on a binary problem, and reaching accuracy of 82.45% in a ternary problem. This was accomplished by generating synthetic observations to compensate the imbalance distribution of classes intrinsic to health databases with added Gaussian White Noise to a sample number of real observations. Finally, by implementing a Machine Learning frame of several classification algorithms with the library H2O, a considerable testing efficiency was reached, guarantying the best performance.