Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Classification of EEG signals: an assistance approach for remote rehabilitation for the upper limb(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Lazcano Herrera, Alicia Guadalupe; Alfaro Ponce, Mariel; emipsanchez; Chairez Oria, Jorge Isaac; González Mendoza, Miguel; Guzmán Zavaleta, Zobeida Jezabel; School of Engineering and Sciences; Campus Estado de México; Fuentes Aguilar, Rita QuetziquelRehabilitation technologies help disabled people face the many challenges in their daily lives. As a consequence, there has been an increase in the interest in developing technologies such as Human-Computer Interfaces (HCI) and Brain-Computer Interfaces (BCI). These technologies can be triggered by many biosignals and their related studies or extraction techniques, being one of these biosignals the ones related to information on brain activity. Electroencephalography represents electrical brain activity as a form of brain signal; the records produced by this technique are called electroencephalograms (EEG).This technique involves the pickup of the biopotential, the signal conditioning, the signal recording, and the signal analysis, being one of their main goals the observation and analysis of brain responses to sensor stimuli.Despite the many advantages of the use of EEG signals and other technologies for BCI composition, one of the challenges we face is the complexity of interpreting and classifying EEG signals. This is where the use of Artificial Intelligence (AI) and Machine Learning(ML) algorithms becomes crucial. The development of ML algorithms for EEG signal analysis is not just a trend but a necessity in our quest to understand and harness the power of brain signals.Nowadays, to analyze brain signals, algorithms such as Neural networks have been used, and among all the architectures available, Recurrent Neural Networks become popular because they can provide context in their predictions. In this category can be found the Long- Short Term Memory (LSTM) networks, which are NN’s with a memory block that can ”store”information. Using this ML algorithm for the analysis of EEG signals could help develop new technologies that could assist impaired people aided with technologies like remote assistance or remote rehabilitation. The present dissertation aims to apply different techniques which involve Machine Learning (ML) techniques, to analyze, process, and classify EEG signals to integrate the information derived into an application that can be used to apply remote rehabilitation aid. This dissertation is divided into two major axes: one focuses on the EEG signals and analysis and the second axis is focused on the application of ML algorithms for classifying Motor/Imagery(MI) information that could be integrated into a remote rehabilitation application. It will discuss the results obtained in the use of Time-Domain and Frequency-Domain techniques for extraction features of EEG signals in publicly available datasets (Physionet Motor/Imagery dataset) and an acquired dataset that could replicate the information found in the literature, the application of ML algorithms for feature selection, the advantages of the normalization process, the application of Neural Networks (two types, recurrent neural networks, and convolutional neural networks) to classify EEG MI information and how can this be integrated into a platform for remote rehabilitation that helps to avoid the abandonment of therapy and that offers supports to take rehabilitation measures in remote places. These results remark the use of the BiLSTM NNs for EEG MI information classification with an accuracy of 91.25% and the use of the Convolutional Neural Network SquezeenNet with a maximum accuracy reported of 92.23%
- Automatic multi-target clinical classification and biomarker discovery in cancer(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-10) Ayton, Sarah Gabrielle; JOSE GERARDO TAMEZ PENA; 3059469; Treviño Alvarado, Víctor; puemcuervo, emipsanchez; Tamez Peña, José Gerardo; Martínez Ledesma, Juan Emmanuel; Pavlicova, Martina; Maley, Carlo C.; Fuentes Aguilar, Rita Q; Robles Espinoza, C. Daniela; School of Engineering and Sciences; Campus MonterreyPrecision medicine relies on accurate and interpretable biomarker and subtype discovery. Many multi-omics subtyping algorithms have been developed to manage subtype identification across platforms but have yet to be evaluated with respect to identification of clinically prognostic subtypes. Further, many comprehensive characterization studies of cancer, which have identified multi-omics subtypes or molecular subtype signatures, have done so through the use of manually-derived expert-designed trees. Despite interpretability, current decision tree approaches are unable to explainably reproduce subtyping findings, owing to the complex nature of molecular and clinical factors driving the disease. Current machine learning (ML) approaches do not achieve interpretability (explainability) across disease endpoints, and models constructed manually by trained experts can be subjective. We develop a multi-objective decision tree (MuTATE) framework which performs automated, explainable, and multi-outcome segmentation to construct interpretable trees, simultaneously identifying biomarkers and subtypes of clinical relevance across disease endpoints. Molecular, clinical, and survey data may be input to identify prognostic biomarkers with either preventive or therapeutic implications. We provide a proof-of-concept for multi-objective, quantitative, explainable trees, enabling interpretable, automated molecular insights for precision medicine. This comprehensive approach can improve therapeutic decisions and has applications across complex diseases, and the availability of our method as an R package enables improved access to comprehensive and quantitaive disease modeling to identify those who may benefit from different treatment plans.
- Robust unsupervised statistical learning for the identification and prediction of the risk profiles(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-11-15) Nezhadmoghadam, Fahimeh; TAMEZ PEÑA, JOSE GERARDO; 67337; Tamez Peña, José Gerardo; puemcuervo, emipsanchez; Treviño Alvarado, Víctor Manuel; Martínez Ledesma, Juan Emmanuel; Santos Díaz, Alejandro; Martínez Torteya, Antonio; School of Engineering and Sciences; Campus MonterreyThe discovery of disease subtypes substantially impacts the selection of patient-specific treatment with implications for long-term survival and disease-related outcomes. Given the heterogeneity of disease phenotypes and the demand for a clear understanding of the features associated with the onset of the disease, this discovery of clinically relevant disease subtypes is not straightforward. Consequently, it is essential for clinical researchers that techniques of disease subtyping be robust and reproducible in clinical settings. This dissertation aims to provide a simple clinical tool that predicts the specific disease subtype of a patient. Therefore a robust unsupervised statistical learning method is presented, developed, and validated that analyzes multidimensional datasets and returns reproducible, robust unsupervised clustering Models of the identified patient subtypes. Unsupervised clustering techniques could realistically model disease heterogeneity. Each cluster represents a distinct homogenous disease subtype discovered through the analysis of the predicted Class-Co-Association Matrix (PCCAM) created by randomly resampling research data. Primarily, there is a PCCAM resulting from the test results of replicated random-crossvalidation of unsupervised clustering that depicts the joint probability of subjects-pairs belonging to the same cluster; thus, PCCAM can result in the discovery of all the reproducible clusters present in the studied data. We applied the proposed methodology to various diseases to discover subtypes such as Alzheimer's disease, Covid-19, and acute myeloid leukemia cancer with different data types. Our findings showed the proposed unsupervised approach could discover the subtypes of disease with statistical differences. Also, the characterization of discovered subgroups indicated other substantial differences in some features we considered studying amongst subgroups.
- Analysis and use of textual definitions through a transformer neural network model and natural language processing(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-02) Baltazar Reyes, Germán Eduardo; BALTAZAR REYES, GERMAN EDUARDO; 852898; Ponce Cruz, Pedro; puemcuervo; McDaniel, Troy; Balderas Silva, David Christopher; Rojas Hernández, Mario; School of Engineering and Sciences; Campus Ciudad de México; López Caudana, Edgar OmarThere is currently an information overload problem, where data is excessive, disorganized, and presented statically. These three problems are deeply related to the vocabulary used in each document since the usefulness of a document is directly related to the number of understood vocabulary. At the same time, there are multiple Machine Learning algorithms and applications that analyze the structure of written information. However, most implementations are focused on the bigger picture of text analysis, which is to understand the structure and use of complete sentences and how to create new documents as long as the originals. This problem directly affects the static presentation of data. For these past reasons, this proposal intends to evaluate the semantical similitude between a complete phrase or sentence and a single keyword, following the structure of a regular dictionary, where a descriptive sentence explains and shares the exact meaning of a single word. This model uses a GPT-2 Transformer neural network to interpret a descriptive input phrase and generate a new phrase that intends to speak about the same abstract concept, similar to a particular keyword. The validation of the generated text is in charge of a Universal Sentence Encoder network, which was finetuned for properly relating the semantical similitude between the total sum of words of a sentence and its corresponding keyword. The results demonstrated that the proposal could generate new phrases that resemble the general context of the descriptive input sentence and the ground truth keyword. At the same time, the validation of the generated text was able to assign a higher similarity score between these phrase-word pairs. Nevertheless, this process also showed that it is still needed deeper analysis to ponderate and separate the context of different pairs of textual inputs. In general, this proposal marks a new area of study for analyzing the abstract relationship of meaning between sentences and particular words and how a series of ordered vocables can be detected as similar to a single term, marking a different direction of text analysis than the one currently proposed and researched in most of the Natural Language Processing community.
- Unsupervised Deep Learning Recurrent Model for Audio Fingerprinting(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-04-16) Báez Suárez, Abraham; BAEZ SUAREZ, ABRAHAM; 328083; Nolazco Flores, Juan Arturo; Vargas Rosales, César Vargas; Gutiérrez Rodríguez, Andrés Eduardo; Rodríguez Dagnino, Ramón Martín; Loyola González, Octavio; Escuela de Ingeniería y Ciencias; Campus MonterreyAudio fingerprinting techniques were developed to index and retrieve audio samples by comparing a content-based compact signature of the audio instead of the entire audio sample, thereby reducing memory and computational expense. Different techniques have been applied to create audio fingerprints, however, with the introduction of deep learning, new data-driven unsupervised approaches are available. This doctoral dissertation presents a Sequence-to-Sequence Autoencoder Model for Audio Fingerprinting (SAMAF) which improved hash generation through a novel loss function composed of terms: Mean Square Error, minimizing the reconstruction error; Hash Loss, minimizing the distance between similar hashes and encouraging clustering; and Bitwise Entropy Loss, minimizing the variation inside the clusters. The performance of the model was assessed with a subset of VoxCeleb1 dataset, a "speech in-the-wild" dataset. Furthermore, the model was compared against three baselines: Dejavu, a Shazam-like algorithm; Robust Audio Fingerprinting System (RAFS), a Bit Error Rate (BER) methodology robust to time-frequency distortions and coding/decoding transformations; and Panako, a constellation algorithm-based adding time-frequency distortion resilience. Extensive empirical evidence showed that our approach outperformed all the baselines in the audio identification task and other classification tasks related to the attributes of the audio signal with an economical hash size of either 128 or 256 bits for one second of audio. Additionally, the developed technology was deployed into two 9-1-1 Emergency Operation Centers (EOCs), located in Palm Beach County (PBC) and Greater Harris County (GH), allowing us to evaluate the performance in real-time in an industrial environment.