EMG-Based Essential Tremor Detection Using PSD Features With Recurrent Feedforward Back Propogation Neural Network

Essential tremors (ET) are slow progressive neurological disorder that reduces muscular movements and involuntary muscular contractions. The further complications of ET may lead to Parkinson’s disease and therefore it is very crucial to identify at the early onset. This research study deals with the identification of the presence of ET from the EMG of the patient by using power spectral density (PSD) features. Several PSD estimation methods such as Welch, Yule Walker, covariance, modified covariance, Eigen Vector based on Eigen value and MUSIC, and Thompson Multitaper are employed and are then classified using a recurrent feedback Elman neural network (RFBEN). It is observed from the experimental results that the MUSIC method of estimating the PSD of the EMG along with RFBEN classifier yields a classification accuracy of 99.81%. It can be concluded that the proposed approach demonstrates the possibility of developing automated computer aided diagnostic tool for early detection of Essential tremors.

EMG BASED ESSENTIAL TREMOR DETECTION USING PSD FEATURES WITH RECURRENT FEEDFORWARD BACKPROPOGATION NEURAL NETWORK

Essential tremors (ET) are slow progressive neurological disorder that reduces muscular movements and involuntary muscular contractions. The further complications of ET may lead to Parkinson’s disease and therefore it is very crucial to identify at the early onset. This research study deals with the identification of the presence of ET from the EMG of the patient by using power spectral density (PSD) features. Several PSD estimation methods such as Welch, Yule Walker, covariance, modified covariance, Eigen Vector based on Eigen value and MUSIC, and Thompson Multitaper are employed and are then classified using a recurrent feedback Elman neural network (RFBEN). It is observed from the experimental results that the MUSIC method of estimating the PSD of the EMG along with RFBEN classifier yields a classification accuracy of 99.81%. It can be concluded that the proposed approach demonstrates the possibility of developing automated computer aided diagnostic tool for early detection of Essential tremors.

A noise PSD estimation algorithm using derivative-based high-pass filter in non-stationary noise conditions

The minimum mean-square error (MMSE)-based noise PSD estimators have been used widely for speech enhancement. However, the MMSE noise PSD estimators assume that the noise signal changes at a slower rate than the speech signal— which lacks the ability to track the highly non-stationary noise sources. Moreover, the performance of the MMSE-based noise PSD estimator largely depends upon the accuracy of the a priori SNR estimation in practice. In this paper, we introduce a noise PSD estimation algorithm using a derivative-based high-pass filter in non-stationary noise conditions. The proposed method processes the silent and speech frames of the noisy speech differently to estimate the noise PSD. It is due to the non-stationary noise that can be mixed with silent and speech-dominated frames non-uniformly. We first introduce a spectral-flatness-based adaptive thresholding technique to detect the speech activity of the noisy speech frames. Since the silent frame of the noisy speech is completely filled with noise, the noise periodogram is directly computed from it without applying any filtering. Conversely, a 4th order derivative-based high-pass filter is applied during speech activity of the noisy speech frame to filter out the clean speech components while leaving behind mostly the noise. The noise periodogram is computed from the filtered signal—which counteracts the leaking of clean speech power. The noise PSD estimate is obtained by recursively averaging the previously estimated noise PSD and the current estimate of the noise periodogram. The proposed method is found to be effective in tracking the rapidly changing as well as the slowly varying noise PSD than the competing methods in non-stationary noise conditions for a wide range of signal-to-noise ratio (SNR) levels. Extensive objective and subjective scores on the NOIZEUS corpus demonstrate that the application of the proposed noise PSD with MMSE-based speech enhancement methods produce higher quality and intelligible enhanced speech than the competing methods.

Hardware-Efficient Bartlett Spectral Density Estimator Based on Optimized R22FFT Processor Using CCSSI Method

This paper offers a novel, low-power, hardware-efficient, yet high-frequency architecture for a power spectral density (PSD) estimator, based on the Bartlett method, for low-power biomedical applications. The Bartlett method is a nonparametric method for PSD estimation. The proposed architecture operates based on a modified multiplierless 64-point optimized radix-22 single-path delay feedback (R22SDF) FFT processor. To obtain the final result, it also uses modified safe-scaling in a way that removes the need to use several extra hardware units. It takes advantage of combined coefficient selection and shift-and-add implementation (CCSSI) for computing twiddle factors which is a new algorithm based on digital computer coordinate rotation (CORDIC) for generating trigonometric values. The proposed method has the capability of operating on short word lengths (WLs). Artix-7 is the FPGA used in this research and Verilog is the language used for hardware design. For 8-bit WL and 244-mW power, a frequency of 286 MHz has been achieved. Several vital signals are used for performance comparison of the proposed technique with state-of-the-art designs.