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Visar resultat 1 - 5 av 9 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Unsupervised Detection of Interictal Epileptiform Discharges in Routine Scalp EEG : Machine Learning Assisted Epilepsy Diagnosis

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen Vi3

    Författare :Shuai Shao; [2023]
    Nyckelord :EEG; electroencephalography; IED; interictal epileptiform discharges; spike detection; epilepsy; unsupervised; Fourier transform; STFT; short-time Fourier transform; CWT; continuous wavelet transform; DWT; discrete wavelet transform; ML; machine learning; ANN; artificial neural network; CNN; convolutional neural network; autoencoder; HMM; hidden Markov model; ECS; Euclidean distance of cumulative spectrum;

    Sammanfattning : Epilepsy affects more than 50 million people and is one of the most prevalent neurological disorders and has a high impact on the quality of life of those suffering from it. However, 70% of epilepsy patients can live seizure free with proper diagnosis and treatment. Patients are evaluated using scalp EEG recordings which is cheap and non-invasive. LÄS MER

  3. 2. Frequency analysis of whisker movement in mouse models of Parkinson's disease

    Kandidat-uppsats, KTH/Datavetenskap

    Författare :Vilhelm Hellmér; André Fredriksen; [2022]
    Nyckelord :;

    Sammanfattning : As the life expectancy in the world increases, so does the prevalence of many age-related diseases. One such disease is Parkinson’s disease (PD) which is a neurological disease that affects patients through tremors, muscle stiffness and has an inhibitory effect on movement. LÄS MER

  4. 3. Transfer learning techniques in time series analysis

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Robinson Sablons de Gélis; [2021]
    Nyckelord :Deep learning; Time series; Transfer learning; Self-supervised learning; Domain adaptation; Djupinlärning; tidsserier; överföringsinlärning; självövervakad inlärning; domänanpassning;

    Sammanfattning : Deep learning works best with vast andd well-distributed data collections. However, collecting and annotating large data sets can be very time-consuming and expensive. Moreover, deep learning is specific to domain knowledge, even with data and computation. E. LÄS MER

  5. 4. Digital Signal Characterization for Seizure Detection Using Frequency Domain Analysis

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Jing Li; [2021]
    Nyckelord :Fourier Transform; Wavelet Transform; EEG and ECG Anomaly Detection; Approximate Entropy; Hellinger Distance; Long Short- Term Memory; Fourier Transform; Wavelet Transform; EEG och ECG Anomalidetektion; Approximativ Entropi; Hellinger Distans; Lång Korttidsminne;

    Sammanfattning : Nowadays, a significant proportion of the population in the world is affected by cerebral diseases like epilepsy. In this study, frequency domain features of electroencephalography (EEG) signals were studied and analyzed, with a view being able to detect epileptic seizures more easily. LÄS MER

  7. 5. An Application of the Continuous Wavelet Transform to Financial Time Series

    Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

    Författare :Klas Eliasson; [2018]
    Nyckelord :Wavelets; Continuous Wavelet Transform; CWT; Financial Time Series; Currency Trading; Technology and Engineering;

    Sammanfattning : Wavelet theory, which shares fundamental concepts with windowed Fourier analysis, introduces the notion of scale in an effort to aid in joint time-frequency analysis. Having century-old roots, much of the essential research on the subject of wavelets was conducted during the 1970s and 1980s. LÄS MER