Junzhong Zou

A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection

Abstract Background Many investigations based on nonlinear methods have been carried out for the research of seizure detection. However, some of these nonlinear measures cannot achieve satisfying performance without considering the basic rhythms of epileptic EEGs. New method To overcome the defects, this paper proposed a framework on wavelet-based nonlinear features and extreme learning machine (ELM) for the seizure detection. Three nonlinear methods, i.e., approximate entropy (ApEn), sample entropy (SampEn) and recurrence quantification analysis (RQA) were computed from orignal EEG signals and corresponding wavelet decomposed sub-bands separately. The wavelet-based energy was measured as the comparative. Then the combination of sub-band features was fed to ELM and SVM classifier respectively. Results The decomposed sub-band signals show significant discrimination between interictal and ictal states and the union of sub-band features helps to achieve better detection. All the three nonlinear methods show higher sensitivity than the wavelet-based energy analysis using the proposed framework. The wavelet-based SampEn-ELM detector reaches the best performance with a sensitivity of 92.6% and a false detection rate (FDR) of 0.078. Compared with SVM, the ELM detector is better in terms of detection accuracy and learning efficiency. Comparison with existing method(s) The decomposition of original signals into sub-bands leads to better identification of seizure events compared with that of the existing nonlinear methods without considering the time–frequency decomposition. Conclusions The proposed framework achieves not only a high detection accuracy but also a very fast learning speed, which makes it feasible for the further development of the automatic seizure detection system.

Effectiveness research of deep brain stimulation operation for patients with Parkinson's disease based on polar coordination system with varied origin

Parkinsons disease (PD) is a common disease of central nervous system among the elderly, and its complex symptoms bring up some difficulties for the clinical diagnosis. And Deep Brain Stimulation (DBS) is an effective treatment method for patients with Parkinsons disease. In this study, a method based on polar coordination system with varied origin was proposed to quantitatively evaluate the availability of DBS operation for patients with Parkinsons disease. Totally, seven patients with Parkinsons disease were participated in the spiral drawing experiment and their scores under Unified Parkinsons disease Rating Scale (UPDRS) were also recorded. According to the characteristics of hand movement in spiral drawing, a set of parameters were defined and calculated before and after DBS operation. Compared with the UPDRS scores, experimental results showed that this method of evaluation was consistent with the clinical diagnosis. The proposed method based on polar coordinate system with varied origin is effective in clinical diagnosis, and it provides a basis for quantitative evaluating the effectiveness of surgery and treatment.

A new quantitative evaluation method of Parkinson's disease based on free spiral drawing

Parkinsons disease is a common disease of central nervous system among the elderly, and its complex symptoms bring some difficulties for the clinical diagnosis. In this study, a new method based on free spiral drawing was proposed to for quantitative evaluation of hand movement of patients with Parkinsons disease. According to the characteristics of hand movement in free spiral drawing, a set of parameters were defined and calculated. Experimental results showed that this method of evaluation was consistent with the clinical diagnosis. The proposed method based on free spiral drawing is effective in clinical diagnosis, and it provides a basis for quantitative evaluating the effectiveness of surgery and treatment.

Dynamic feature extraction of epileptic EEG using recurrence quantification analysis

Detecting the reliable transition point embedded in the electroencephalograms (EEGs) is a challenge in the field of epileptic research. In this research, a recurrence quantification analysis (RQA) is proposed to help medical doctors to reveal dynamical characteristics in EEGs of patients suffering from epilepsy. In contrast with traditional chaos methods, the merits of RQA method is that it can measure the complexity of a short and non-stationary signal without any assumptions such as linear, stationary and noiseless noise. In this study, EEGs with generalized epilepsy were collected in Epilepsy Center of Renji Hospital. The test results show that three RQA measurements, i.e. recurrence rate, determinism and entropy can track the complexity changes of brain electrical activity. RQA variables show a large fluctuation in pre-ictal stage, which reflects a transitional state leading to seizure activity. On the contrary, RQA variables fluctuate in relatively small bounds in ictal stage, which is due to organized and self-sustained rhythmic discharge. Therefore, RQA could be a promising approach in prediction and diagnosis for epileptic seizures.

Quantitative evaluation of hand movement in spiral drawing for patients with Parkinson's disease based on modeling in polar coordinate system with varied origin

Parkinsons disease is one of the most common neurodegenerative disorders, and its complex symptoms bring some difficulties for the clinical diagnosis. In this study, a method based on mathematical modeling of hand movement in spiral drawing was proposed to quantitatively evaluate the performance of patients with Parkinsons disease. Unlike the previous works, the hand movement data in spiral drawing were analyzed and modeled under the polar coordinate system with varied origin. Experimental results gained from the model showed that this method of evaluation was consistent with the clinical diagnosis before and after deep brain stimulation operation. The proposed method based on mathematical modeling in polar coordinate system with varied origin is effective in clinical diagnosis, and it provides a basis for quantitative evaluating the effectiveness of surgery and treatment.

Systematic Evaluation of Relaxation Circumstances Based on Bio-neurological Signals

The widespread use of relaxation technique amongst medicine community and sustained work environments makes a more complete understanding of its physiological effects. This research proposes a systematic evaluation for relaxation circumstances, which consists of subjective evidence and objective evidence. Innovative feature of this evaluation system is adding bio-neurological signals to support previous findings about relaxation circumstances. A work-rest schedule containing mental calculation and music relaxation is specially designed to reflect effect of prolonged cognitive work and relaxation in mental work environments. The results indicate that short period of music relaxation in sustained mental work is effective to counteract the accumulation of mental fatigue and improve work efficiency. This systematic evaluation method can be widely applicable, in medical fields, working environments and daily life for the purpose of prediction, detection and evaluation of human states.

Comfortable environments for mental work by suitable work-rest schedule: Mental fatigue and relaxation

Sustained mental work easily causes mental fatigue. Comfortable environments are necessary to be introduced into mental work. It is very practical research to explore the characteristics under progressive mental fatigue and make a suitable work-rest schedule. In this research, a work-rest schedule containing mental calculation task and music relaxation is specially designed to reflect effect of prolonged cognitive work and relaxation factors in mental work environments. An integrated design and evaluation system for human fatigue-relaxation research is proposed, which consisted of subjective evidence (visual analogue scale) and objective evidence (calculation performance and bio-neurological signals especially EEG signals). The results from subjective evidence and objective evidence indicate that a short break of music relaxation (e.g. 15 minutes) in sustained mental work can successfully counteract the accumulation of mental fatigue and improve work efficiency. EEG analysis supports the music function from bio-neurological point of view. Suitable work-rest schedule has widespread application in boarder population and various situations to alleviate the impact of every life and improve life satisfaction.

Automatic determination of sleep stage through bio-neurological signals contaminated with artifacts by a conditional probability of the knowledge base

In this study, an automatic sleep-stage determination system with the capacity for artifact detection was developed. The methodology was based on the conditional probability of the knowledge base of an expert visual inspection. Expert visual inspection was the manual scoring of sleep stages and artifacts by a qualified clinician. The knowledge base consisted of probability density functions of characteristic parameters for stages and artifacts. Automatic sleep-stage determination and artifact detection were carried out based on a value of conditional probability. The total overnight bioneurological signals under the usual recording conditions with the artifacts of four subjects were analyzed. The results of automatic sleep-stage determination showed a close agreement with the expert visual inspections. In addition, an artifact can be detected at the same time by using the same method. With the capacity for artifact detection, the proposed automatic sleep-stage determination system can be adapted for real clinical applications.

Hand movement compensation for patients with Parkinson's disease based on polar coordination system with varied origin

Parkinsons disease (PD) is a common movement disorder caused from the neurological dysfunction in basal ganglia. Patients with Parkinsons disease have serious movement problems, such as movement disorders, tremor and rigidity. In this study, a new technique of hand movement compensation for patients with Parkinsons disease based on spiral drawing tasks was proposed. In order to extract the hand movement feature of patients with Parkinsons disease, the polar coordination system with varied origin was used to gain characteristic parameters more accurately. The compensation model was constructed by using the parameters obtained in spiral drawing tasks, and we also do simulation under this model to verify the effectiveness. The simulation results proved that the proposed compensation technique will be effective for assisting the movement of patients with Parkinsons disease.

EEG Feature Extraction During Mental Fatigue and Relaxation by Principal Component Analysis

EEG is one of the most predictive and reliable measurements for mental fatigue and relaxation evaluation. The aim of this study is to transform a number of EEG spectrum variables into few principal components using PCA method. After transformation, EEG multivariate dataset can be visualized in a lower-dimensional space where different mental states are clearly discriminated from each other.