As they are the smallest functional parts of the muscle, motor units (MUs) are considered as the basic building blocks of the neuromuscular system. Monitoring MU recruitment, de-recruitment, and firing rate (by either invasive or surface techniques) leads to the understanding of motor control strategies and of their pathological alterations. EMG signal decomposition is the process of identification and classification of individual motor unit action potentials (MUAPs) in the interference pattern detected with either intramuscular or surface electrodes. Signal processing techniques were used in EMG signal decomposition to understand fundamental and physiological issues. Many techniques have been developed to decompose intramuscularly detected signals with various degrees of automation. This paper investigates the application of autocorrelation function (ACF) method to decompose EMG signals to their frequency components. It was found that using the proposed method gives a quite good frequency resolution as compared to that resulting from using short time fast Fourier transform (STFFT); thus more MU’s can be distinguished.
Dengue fever is a mosquito-borne viral infection that produces characteristic abnormalities in routine blood tests, yet these hematologic changes are typically analysed separately for each parameter rather than as a combined multivariate profile. This study investigated whether the joint hematologic profile of adult dengue patients in Bangladesh is systematically displaced from healthy adult reference values. We analysed a cohort of laboratory-confirmed adult dengue cases from a Bangladeshi hospital and focused on four core hematologic indices: haemoglobin, white blood cell count, platelet count, and platelet distribution width (PDW). External adult reference means were used to define a healthy location vector, and robust multivariate infer
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