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    Wyświetlanie 1-3 z 3
    Tytuł:
    A Low Power and High Performance Hardware Design for Automatic Epilepsy Seizure Detection
    Autorzy:
    Rafiammal, S. Syed
    Najumnissa, D.
    Anuradha, G.
    Mohideen, S. Kaja
    Jawahar, P. K.
    Mutalib, Syed Abdul
    Powiązania:
    https://bibliotekanauki.pl/articles/963923.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    epilepsy detection
    system on chip
    implementation
    Quadrature Linear Discriminant Analysis
    hardware design
    seizure detection
    Opis:
    An application specific integrated design using Quadrature Linear Discriminant Analysis is proposed for automatic detection of normal and epilepsy seizure signals from EEG recordings in epilepsy patients. Five statistical parameters are extracted to form the feature vector for training of the classifier. The statistical parameters are Standardised Moment, Co-efficient of Variance, Range, Root Mean Square Value and Energy. The Intellectual Property Core performs the process of filtering, segmentation, extraction of statistical features and classification of epilepsy seizure and normal signals. The design is implemented in Zynq 7000 Zc706 SoC with average accuracy of 99%, Specificity of 100%, F1 score of 0.99, Sensitivity of 98% and Precision of 100 % with error rate of 0.0013/hr., which is approximately zero false detection.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 4; 707-712
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An identification source of variation on the water quality pattern in the Malacca River basin using chemometric approach
    Autorzy:
    Hua, A. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/204612.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    hierarchical cluster analysis
    discriminant analysis
    principal component analysis
    multiple linear
    regression analysis
    Opis:
    The Malacca River basin experienced river water pollution which caused a major deterioration to the ecosystems and environmental health. This study is carried out to assess the water quality data and identify the pattern of water pollution sources in the study area, and also to develop a predictive performance of water quality in the Malacca River basin. A chemometric approach using a combination of HCA, DA, PCA, and MLR, was applied into twenty water quality variables from nine sampling stations that were collected from January until December of 2015 in the river basin. HCA pointed out three clusters, namely Cluster 1 (C1) with low pollution source, Cluster 2 (C2) with moderate pollution source, and Cluster 3 (C3) with high pollution source. In the DA analysis, the results showed 21 variables, 12 variables, and 9 variables for standard mode, forward stepwise mode, and backward stepwise mode, respectively. Meanwhile, the PCA indicated that the main source of pollutants is detected from residential, industrial, commercial, agricultural, animal livestock, as well as forest land. Among the three models developed from MLR analysis, C3 with a high pollution source is detected to be the most suitable model to be used for the prediction of Water Quality Index in the Malacca River basin. This study proposed for an effective river water quality management by having new water quality monitoring network to be designed for more practical use in order to reduce time and effort, as well as cost saving purposes.
    Źródło:
    Archives of Environmental Protection; 2018, 44, 4; 111-122
    2083-4772
    2083-4810
    Pojawia się w:
    Archives of Environmental Protection
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of Statistical Features and Multilayer Neural Network to Automatic Diagnosis of Arrhythmia by ECG Signals
    Autorzy:
    Slama, A. B.
    Lentka, Ł.
    Mouelhi, A.
    Diouani, M. F.
    Sayadi, M.
    Smulko, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/221289.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Multilayer Neural Network
    arrhythmia diagnosis
    ECG signal processing
    Principal Component Analysis
    Fisher’s Linear Discriminant
    Opis:
    Abnormal electrical activity of heart can produce a cardiac arrhythmia. The electrocardiogram (ECG) is a non-invasive technique which is used as a diagnostic tool for cardiac diseases. Non-stationarity and irregularity of heartbeat signal imposes many difficulties to clinicians (e.g., in the case of myocardial infarction arrhythmia). Fortunately, signal processing algorithms can expose hidden information within ECG signal contaminated by additive noise components. This paper explores a method of de-noising ECG signal by the discrete wavelet transform (DWT) and further detecting arrhythmia by estimated statistical parameters. Parameters of the de-noised ECG signals were used to form an input data vector determining whether the examined patient suffers from a cardiac arrhythmia or not. Input data were transformed using selected linear methods in order to reduce dimension of the input vector. A neural network was used to detect illness. Compared with the results of recent studies, the proposed method provides more accurate diagnosis based on the examined ECG signal data.
    Źródło:
    Metrology and Measurement Systems; 2018, 25, 1; 87-101
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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