Coupled artificial neural networks to estimate 3D whole-body posture, lumbosacral moments, and spinal loads during load-handling activities.

Biomechanical modeling approaches require body posture to evaluate the risk of spine injury during manual material handling. The procedure to measure body posture via motion-analysis techniques as well as the subsequent calculations of lumbosacral moments and spine loads by, respectively, inverse-dynamic and musculoskeletal models are complex and time-consuming. We aim to develop easy-to-use yet accurate artificial neural networks (ANNs) that predict 3D whole-body posture (ANNposture), segmental orientations (ANNangle), and lumbosacral moments (ANNmoment) based on our measurements during load-handling activities. Fifteen individuals each performed 135 load-handling activities by reaching (0 kg) or handling (5 and 10 kg) weights located at nine different horizontal and five vertical (0, 30, 60, 90, and 120 cm from the floor) locations. Whole-body posture was measured via a motion capture system and lumbosacral moments were calculated via a 3D top-down eight link-segment inverse-dynamic model. ANNposture, ANNangle, and ANNmoment were trained (RMSEs = 6.7 cm, 29.8°, and 16.2 Nm, respectively) and their generalization capability was tested (RMSE = 7.0 cm and R2 = 0.97, RMSE = 29.9° and R2 = 0.85, and RMSE = 16.5 Nm and R2 = 0.97, respectively). These ANNs were subsequently coupled to our previously-developed/validated ANNload, which predicts spinal loads during 3D load-handling activities. The results showed outputs of the coupled ANNs for L4-L5 intradiscal pressure (IDPs) during a number of activities were in agreement with measured IDPs (RMSE = 0.37 MPa and R2 = 0.89). Hence, coupled ANNs were found to be robust tools to evaluate posture, lumbosacral moments, spinal loads, and thus risk of injury during load-handling activities.

Reprint of "A new approach to analyze data from EEG-based concealed face recognition system".

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

A new approach to analyze data from EEG-based concealed face recognition system.

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

EEG phase synchronization during hypnosis induction.

Hypnosis is a mental state or set of attitudes usually induced by a procedure known as hypnotic induction. In order to provide the basic physiological conditions for potentially successful hypnosis treatment of medical and psychological problems, the determination of a subject's hypnotizability level is important. Currently, the hypnotizability level is determined using different standard subjective tests. To avoid the different drawbacks of these subjective clinical tests, a practical objective method based on the correlation between electroencephalograph (EEG) phase synchronization and hypnosis susceptibility levels is presented in this study. This method can be used by clinicians instead of the traditional subjective methods to classify hypnotizability level. Thirty-two subjects with different hypnosis susceptibility levels contributed to this research. Using statistical analyses, it was concluded that, in highly hypnotizable people, the EEG phase synchronization between different paired channels, located on the frontal lobe, is significantly different from that in subjects with medium or low hypnotizability.

Phase space analysis of Event Related Potential during episodic memory retrieval.

In this study we have used recurrence analysis (RA) to classify ERPs that appear from episodic memory retrieval in old/new recognition task. Since RA is based on embedding phase space, we have used correlation dimension and autocorrelation function in order to estimate embedding dimension and the lag time between successive components of each of embedding space vectors alternatively. According to RA the rates of classification have been improved in comparison to previous study. We have obtained 98.9% accuracy for train data and 97.7% for test data. Furthermore we could classify ERPs with line interference noise using RA.

An investigation on chaotic approach to detect functional gastrointestinal disorders.

Recently, interest has turned to the mathematical concept of chaos as an explanation for a variety of complex processes in nature. Because of this property, it is hypothesized that biomedical signal may be an example of chaos. In this review, some of our basic concepts of nonlinear dynamics and chaos are illustrated. Mathematical techniques developed to study the properties of nonlinear dynamical systems are outlined. Finally, the results of applying these techniques to the study of stomach disorders are discussed. The application of these powerful and novel mathematical techniques to analysis of the Gastric Electrical Activity (GEA) has provided now insights into the Functional Gastrointestinal Disorders (FGD) and may have considerable utility in the diagnosis.