Journal of Mechanics in Medicine and Biology 

About: Journal of Mechanics in Medicine and Biology is an academic journal published by World Scientific. The journal publishes majorly in the area(s): Medicine & Computer science. It has an ISSN identifier of 0219-5194. Over the lifetime, 1948 publications have been published receiving 12353 citations.

The tensegrity-truss as a model for spine mechanics: biotensegrity

Abstract: The commonly accepted "tower of blocks" model for vertebrate spine mechanics is only useful when modeling a perfectly balanced, upright, immobile spine. Using that model, in any other position than perfectly upright, the forces generated will tear muscle, crush bone and exhaust energy. A new model of the spine uses a tensegrity-truss system that will model the spine right side up, upside-down or in any position, static or dynamic. In a tensegrity-truss model, the loads distribute through the system only in tension or compression. As in all truss systems, there are no levers and no moments at the joints. The model behaves non-linearly and is energy efficient. Unlike a tower of blocks, it is independent of gravity and functions equally well on land, at sea, in the air or in space and models the spines of fish and fowl, bird and beast.

Automatic identification of epileptic eeg signals using nonlinear parameters

Abstract: Epilepsy is a brain disorder causing people to have recurring seizures. Electroencephalogram (EEG) is the electrical activity of the brain signals that can be used to diagnose the epilepsy. The EEG signal is highly nonlinear and nonstationary in nature and may contain indicators of current disease, or warnings about impending diseases. The chaotic measures like correlation dimension (CD), Hurst exponent (H), and approximate entropy (ApEn) can be used to characterize the signal. These features extracted can be used for automatic diagnosis of seizure onsets which would help the patients to take appropriate precautions. These nonlinear features have been reported to be a promising approach to differentiate among normal, pre-ictal (background), and epileptic EEG signals. In this work, these features were used to train both Gaussian mixture model (GMM) and support vector machine (SVM) classifiers. The performance of the two classifiers were evaluated using the receiver operating characteristics (ROC) curves. Our results show that the GMM classifier performed better with average classification efficiency of 95%, sensitivity and specificity of 92.22% and 100%, respectively.

Depression diagnosis support system based on eeg signal entropies

Abstract: Electroencephalography (EEG) is a measure which represents the functional activity of the brain. We show that a detailed analysis of EEG measurements provides highly discriminant features which indicate the mental state of patients with clinical depression. Our feature extraction method revolves around a novel processing structure that combines wavelet packet decomposition (WPD) and non-linear algorithms. WPD was used to select appropriate EEG frequency bands. The resulting signals were processed with the non-linear measures of approximate entropy (ApEn), sample entropy (SampEn), renyi entropy (REN) and bispectral phase entropy (Ph). The features were selected using t-test and only discriminative features were fed to various classifiers, namely probabilistic neural network (PNN), support vector machine (SVM), decision tree (DT), k-nearest neighbor algorithm (k-NN), naive bayes classification (NBC), Gaussian mixture model (GMM) and Fuzzy Sugeno Classifier (FSC). Our classification results show that, with a classification accuracy of 99.5%, the PNN classifier performed better than the rest of classifiers in discriminating between normal and depression EEG signals. Hence, the proposed decision support system can be used to diagnose, and monitor the treatment of patients suffering from depression.

A novel approach to detect epileptic seizures using a combination of tunable-q wavelet transform and fractal dimension

Abstract: The detection and quantification of seizures can be achieved through the analysis of nonstationary electroencephalogram (EEG) signals. The detection of these intractable seizures involving human beings is a challenging and difficult task. The analysis of EEG through human inspection is prone to errors and may lead to false conclusions. The computer-aided systems have been developed to assist neurophysiologists in the identification of seizure activities accurately. We propose a new machine learning and signal processing-based automated system that can detect epileptic episodes accurately. The proposed algorithm employs a promising time-frequency tool called tunable-Q wavelet transform (TQWT) to decompose EEG signals into various sub-bands (SBs). The fractal dimensions (FDs) of the SBs have been used as the discriminating features. The TQWT has many attractive features, such as tunable oscillatory attribute and time-invariance property, which are favorable for the analysis of nonstationary and transient signals. Fractal dimension is a nonlinear chaotic trait that has been proven to be very useful in the analysis and classifications of nonstationary signals including EEG. First, we decompose EEG signals into the desired SBs. Then, we compute FD for each SB. These FDs of the SBs have been applied to the least-squares support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel. We have used 10-fold cross-validation to ensure reliable performance and avoid the possible over-fitting of the model. In the proposed study, we investigate the following four popular classification tasks (CTs) related to different classes of EEG signals: (i) normal versus seizure (ii) seizure-free versus seizure (iii) nonseizure versus Seizure (iv) normal versus seizure-free. The proposed model surpassed existing models in the area under the receiver operating characteristics (ROC) curve, Matthew’s correlation coefficient (MCC), average classification accuracy (ACA), and average classification sensitivity (ACS). The proposed system attained perfect 100% ACS for all CTs considered in this study. The method achieved the highest classification accuracy as well as the largest area under ROC curve (AUC) for all classes. The salient feature of our proposed model is that, though many models exist in the literature, which gave high ACA, however, their performance has not been evaluated using MCC and AUC along with ACA simultaneously. The evaluation of the performance in terms of only ACA which may be misleading. Hence, the performance of the proposed model has been assessed not only in terms of ACA but also in terms AUC and MCC. Moreover, the performance of the model has been found to be almost equivalent to a perfect model, and the performance of the proposed model surpasses the existing models for the CTs investigated by us. Therefore, the proposed model is expected to assist clinicians in analyzing seizures accurately in less time without any error.

Classification of eeg signals in normal and depression conditions by ann using rwe and signal entropy

Abstract: EEG is useful for the analysis of the functional activity of the brain and a detailed assessment of this non-stationary waveform can provide crucial parameters indicative of the mental state of patients. The complex nature of EEG signals calls for automated analysis using various signal processing methods. This paper attempts to classify the EEG signals of normal and depression patients using well-established signal processing techniques involving relative wavelet energy (RWE) and artificial feedForward neural network. High frequency noise present in the recorded signal is removed using total variation filtering (TVF). Classification of the frequency bands of EEG signals into appropriate detail levels and approximation level is carried out using an eight-level multiresolution decomposition method of discrete wavelet transform (DWT). Parseval's theorem is used for calculating the energy at different resolution levels. RWE analysis gives information about the signal energy distribution at different decomposition levels. Both RWE and feedforward Network are used to classify the signals from normal controls and depression patients. The performance of the artificial neural network was evaluated using the classification accuracy and its value of 98.11% indicates a great potential for classifying normal and depression signals.