Electroencephalography signals are typically used for analyzing epileptic seizures. These signals are highly nonlinear and nonstationary, and some specific patterns exist for certain disease types that are hard to develop an automatic epileptic seizure detection system. This paper discussed statistical mechanics of complex networks, which inherit the characteristic properties of electroencephalography signals, for feature extraction via a horizontal visibility algorithm in order to reduce processing time and complexity. The algorithm transforms a time series signal into a complex network, which some features are abbreviated. The statistical mechanics are calculated to capture distinctions pertaining to certain diseases to form a feature vector. The feature vector is classified by multiclass classification via a k‐nearest neighbor classifier, a multilayer perceptron neural network, and a support vector machine with a 10‐fold cross‐validation criterion. In performance evaluation of proposed method with healthy, seizure‐free interval, and seizure signals, firstly, input data length is regarded among some practical signal samples by optimizing between accuracy‐processing time, and the proposed method yields outstanding performance on the average classification accuracy for 3‐class problems mainly for detection of seizure‐free interval and seizure signals and acceptable results for 2‐class and 5‐class problems comparing with conventional methods. The proposed method is another tool that can be used for classifying signal patterns, as an alternative to time/frequency analyses.
BackgroundElectromyography (EMG) signals recorded from healthy, myopathic, and amyotrophic lateral sclerosis (ALS) subjects are nonlinear, non-stationary, and similar in the time domain and the frequency domain. Therefore, it is difficult to classify these various statuses.MethodsThis study proposes an EMG-based feature extraction method based on a normalized weight vertical visibility algorithm (NWVVA) for myopathy and ALS detection. In this method, sampling points or nodes based on sampling theory are extracted, and features are derived based on relations among the vertical visibility nodes with their amplitude differences as weights. The features are calculated via selective statistical mechanics and measurements, and the obtained features are assembled into a feature matrix as classifier input. Finally, powerful classifiers, such as k-nearest neighbor, multilayer perceptron neural network, and support vector machine classifiers, are utilized to differentiate signals of healthy, myopathy, and ALS cases.ResultsPerformance evaluation experiments are carried out, and the results revealed 98.36% accuracy, which corresponds to approximately a 2% improvement compared with conventional methods.ConclusionsAn EMG-based feature extraction method using a NWVVA is proposed and implemented to detect healthy, ALS, and myopathy statuses.
This paper presents a method of digital image watermarking on digital audio signal that is robust against MPEG compression based on the principle of the human hearing called Psychoacoustic model. Based on the Psychoacoustic model, human hearing depends on both frequency and the power of the signal. Human ears can not separate the difference if the signal above the hearing threshold of the Psychoacoustic is varied by small amount. The Wavelet Packet Transform is chosen to transform the signal. The audio signal was decomposed using wavelet with 4 levels. A binary image is embedded into significant coefficients node (4,2) to node (4,10) selective from detail coefficient. The frequency domain and the chosen frequencies within the range of 4kHz-15kHz because it is hard for human ear to detect the difference in this range. For security, the watermark bits are randomly permuted before being embedded to the signal. Only the owner knew the key randomization which is implemented by Pseudorandom. In this experiment, 10 different audio song and 2 types of binary watermarking images of size 64x64 pixels and 32x32 pixels were tested. The results show that the normalized correlation (NC) is improved by 4.98 percent and signal-to-noise ratio (SNR) is comparable to the previous method.
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