Real time electrocardiogram identification with multi-modal machine learning algorithms

Weaknesses in conventional identification technologies such as identification cards, badges and RFID tags prompts attention to biometric form of identification. Biometrics like voice, brain signal and finger print are unique human traits that can be used for identification. In this paper we present an identification system based on Electrocardiogram (heart signal). There is a considerable number of research in the past with high accuracy for identification, however, most ignore the practical time required to identify an individual. In this study, we explored a more practical approach in identification by reducing the number of time required for identification. We explore ways to identity a person within 3–4 s using just 5 heart beats. We extracted few reliable features from each QRS complexes, combined effort of three algorithms to achieve 96% accuracy. This approach is more suitable and practical in real time applications where time for identification is important

Improving the accessibility of digital content via mobile technology. A case study of Mount Kenya University

Globally, Higher Education Institutions (HEI) have embraced the use of mobile technology in the delivery of instructional resources which has promised multiple benefits in digital or blended learning, HEIs are facing the challenge of high internet tariffs. The current study sought to improve the accessibility of digital content via mobile technology within limited Internet connectivity contexts. The study used a quantitative research approach within which a descriptive survey research design was adopted. The case study was Mount Kenya University in Kenya. The study was guided by the Technology Acceptance Model (TAM). The target population was 15123 individuals comprising of 15,000 students and 123 were educators/ ICT staff who accessed digital content in the academic year 2018/2019. The mobile-based model used a WIFI router device which is not internet supported as an alternative to a wired internet connection where students and educators access digital content from the mobile sub-server which was not connected to the internet through their mobile technology. The findings showed that there is a statistically significant relationship between internet connectivity, type of mobile technology, user literacy, data caching, and eLearning policy had a significant effect on the accessibility of digital content. The variables were statistically significant. The adjusted R squared was 0.862 indicating that 86.2 percent of the total variation of accessibility of digital content can be explained by Internet connectivity, e-learning policy, type of mobile technology, data caching, and user literacy. The study then went ahead to develop a mobile-based e-learning model. The findings showed that the use of mobile-based e-learning (m-learning) in universities will greatly improve access to digital content and hence e-learning. The study recommends the use of m-learning as it will provide alternative means of optimizing Internet connectivity

A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson’s Disease

Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7 % in 70 % of the patients.Centro de Investigación Biomédica en RedDepto. de Psicología Experimental, Procesos Cognitivos y LogopediaDepto. de Radiología, Rehabilitación y FisioterapiaFac. de PsicologíaFac. de MedicinaTRUEpu

Estudio de técnicas avanzadas de análisis de datos para el proceso de auditoría informática en el módulo del sistema académico de la Universidad Técnica del Norte (UTN)

Estudiar técnicas avanzadas de análisis de datos para el proceso de auditoría informática en el módulo del sistema académico de la Universidad Técnica del Norte (UTN).La implementación de sistemas académicos que facilita la gestión de datos e información relacionada con el personal docente, administrativo y estudiantil de las instituciones de educación superior, provoca una creciente cantidad de información, dificultando el análisis de la información dentro del proceso de auditoría informática. El objetivo de este estudio es ayudar a los auditores a realizar procesos de auditoría informática más eficientes utilizando técnicas de procesamiento y análisis de datos avanzadas. Se efectuó una revisión exhaustiva de la literatura para establecer una base teórica sólida. Se seleccionaron tres técnicas de minería de datos: agrupación, asociación y correlación, para identificar patrones significativos en grandes conjuntos de datos. En el caso de estudio se empleó la técnica de agrupación, se centró en el módulo de notas y calificaciones del sistema integrado universitario (SIIU) de la Universidad Técnica del Norte. Para este propósito, se utilizó la metodología de minería de datos CRISP-DM. El enfoque de la auditoría informática se centró en el historial de modificación de notas para evaluar la calidad de los datos del sistema académico. Se verificó la confidencialidad, precisión, trazabilidad y comprensibilidad de acuerdo con la norma ISO/IEC 25012:2008. Se obtuvieron resultados que revelaron niveles variables de imprecisión en diferentes facultades, con un rango del 5% al 30% de datos imprecisos. Este estudio demostró cómo la aplicación de técnicas avanzadas de procesamiento y análisis de datos puede mejorar los procesos de auditoría informática y ayudar a detectar errores en los datos del sistema académico.Ingenierí