A note on fractal dimensions of biomedical waveforms
ABSTRACT In this paper, we study performance of Katz method of computing fractal dimension of waveforms, and its estimation accuracy is compared with Higuchi's method. The study is performed on four synthetic parametric fractal waveforms for which true fractal dimensions can be calculated, and real sleep electroencephalogram. The dependence of Katz's fractal dimension on amplitude, frequency and sampling frequency of waveforms is noted. Even though the Higuchi's method has given more accurate estimation of fractal dimensions, the study suggests that the results of Katz's based fractal dimension analysis of biomedical waveforms have to be carefully interpreted.
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- "The value of this index is usually a non-integer, fractional number; hence, the designation of a fractal dimension. There are many notions of FD, and various algorithms have been proposed to compute them (Raghavendra and Dutt, 2009). None of these methods, however, should be considered as universal, which justifies an empirical comparison of their abilities as feature extractors from EMG signals. "
ABSTRACT: Abstract The study of electromyographic (EMG) signals has gained increased attention in the last decades since the proper analysis and processing of these signals can be instrumental for the diagnosis of neuromuscular diseases and the adaptive control of prosthetic devices. As a consequence, various pattern recognition approaches, consisting of different modules for feature extraction and classification of EMG signals, have been proposed. In this paper, we conduct a systematic empirical study on the use of Fractal Dimension (FD) estimation methods as feature extractors from EMG signals. The usage of FD as feature extraction mechanism is justified by the fact that EMG signals usually show traces of self-similarity and by the ability of FD to characterize and measure the complexity inherent to different types of muscle contraction. In total, eight different methods for calculating the FD of an EMG waveform are considered here, and their performance as feature extractors is comparatively assessed taking into account nine well-known classifiers of different types and complexities. Results of experiments conducted on a dataset involving seven distinct types of limb motions are reported whereby we could observe that the normalized version of the Katz׳s estimation method and the Hurst exponent significantly outperform the others according to a class separability measure and five well-known accuracy measures calculated over the induced classifiers.Engineering Applications of Artificial Intelligence 11/2014; 36:81 - 98. DOI:10.1016/j.engappai.2014.07.009 · 1.96 Impact Factor
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ABSTRACT: In this study, the in vitro antimicrobial and antiviral activities of the lysozyme from marine strain S-12-86 (LS) were investigated. The antimicrobial activity of LS was tested by minimum inhibition concentration (MIC) and minimum bactericidal concentration (MBC) method. The inhibiting effects of LS on pseudo rabies virus (PRV) in swine kidney cells (PK-15 cells) were judged by cytopathogenic effect test (CPE). The results showed LS had a broad antimicrobial spectrum against several standard strains including gram-positive bacteria, gram-negative bacteria, fungi, etc. The MIC of LS was 0.25–4.00 mg mL−1 and its MBC was 0.25–8.00 mg mL−1, respectively. Observation under the transmission electron microscope revealed that the cell wall of Candida albicans was distorted seriously, and the cytoplasm with many cavities was asymmetrical after being hydrolyzed by LS. The median cytotoxicity concentration (TC50) of LS was 100.0 μg mL−1, the median effective concentration (EC50) was 0.46 μg mL−1, and the selectivity index (TI = TC50/EC50) was 217. LS could inhibit PRV in PK-15 cells when it was added to cell culture medium at 0, 2, 4, 6, and 8 h after PK-15 cells had been infected by PRV. From the results, we concluded that LS had broad antimicrobial spectrum and good inhibiting effects on PRV.Agricultural Sciences in China 01/2008; DOI:10.1016/S1671-2927(08)60029-2 · 0.82 Impact Factor
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ABSTRACT: Deep Brain Stimulation (DBS) is a treatment routinely used to alleviate the symptoms of Parkinson's disease (PD). In this type of treatment, electrical pulses are applied through electrodes implanted into the basal ganglia of the patient. As the symptoms are not permanent in most patients, it is desirable to develop an on-demand stimulator, applying pulses only when onset of the symptoms is detected. This study evaluates a feature set created for the detection of tremor - a cardinal symptom of PD. The designed feature set was based on standard signal features and researched properties of the electrical signals recorded from subthalamic nucleus (STN) within the basal ganglia, which together included temporal, spectral, statistical, autocorrelation and fractal properties. The most characterized tremor related features were selected using statistical testing and backward algorithms then used for classification on unseen patient signals. The spectral features were among the most efficient at detecting tremor, notably spectral bands 3.5-5.5 Hz and 0-1 Hz proved to be highly significant. The classification results for determination of tremor achieved 94% sensitivity with specificity equaling one.