The foot drop functional electrical stimulation (FES) system consisting of various sensors has been widely applied to the disease of the foot drop. However, the current system is limited to the research on walking on the ground and ignores other important actions of foot in one's daily life, such as walking up and down the stairs, squatting and lying down, etc. In this work, we applied the dual axis angle sensor to the system of the foot drop FES for the first time. Such a system can not only stimulate the foot drop during normal walking, but also identify squatting, sitting, and lying down etc. and furthermore, the system can switch off automatically. In the meanwhile, it can also detect falls and other dangerous actions. The accuracy of our system can achieve 100%, 81.9%, 95.8%, 99.0% and 66.9% for normal walking, sitting-standing, walking up the stairs, walking down the stairs and squatting-standing respectively.
Adult ; Biosensing Techniques ; instrumentation ; methods ; Electric Stimulation ; instrumentation ; methods ; Equipment Design ; Female ; Foot Deformities, Acquired ; therapy ; Humans ; Male ; Middle Aged ; Young Adult

A falling is a risky incident of safety and health of human. It may cause serious injuries, such as bone fracture, and even death. A falling detection method based on inclinometer is described. At first, we collect angle data recorded by a wearable inclinometer placed at subject's waist. The angular data are transmitted to PC through a wireless data transmission device. Then, the falling duration is divided into three phases: the state of fall, the impact phase, and the posture phase. We make threshold-based fall-detection decisions in every phase after feature extraction and analysis of the short-time angle data. Finally, a robust falling detection result is given by comprehensive considerations of the three phases decisions. The experiment results proved that the accuracy of our falling detection method was up to 97.23% without undetected falls.
Accidental Falls ; prevention & control ; Aged ; Algorithms ; Equipment Design ; Female ; Humans ; Male ; Monitoring, Ambulatory ; instrumentation ; methods ; Posture

The signal analysis of heart rate variability (HRV) has been very significant for heart disease of aided diagnosis, monitoring and evaluation. We proposed a new method of HRV signal analysis based on the Hilbert spectrum entropy dividing frequency range. According to Hilbert spectrum characteristics of the multi-resolution and the characteristic of HRV signal frequency spectrum, the Hilbert time-frequency spectrum entropy of HRV signal in different frequency range and the full frequency Hilbert time-frequency spectrum entropy with weighting factor were calculated. This approach was analyzed after the appropriate separation for various physiological factors based on the frequency range and it is more conducive to reflect the physiological and the pathological characteristics. Applying the new approach to the actual HRV signal of the MIT-BIH standard database, we obtained the results which showed that this method could effectively differentiate from the sample group for the young, the elder and the patients with atrial fibrillation, and for the sample group for the healthy persons and CHF patients, the performance in statistical analysis was superior to those of the general time-frequency entropy method. The approach could provide an effective analysis method for clinical HRV signal.
Algorithms ; Electrocardiography ; methods ; Entropy ; Heart Rate ; physiology ; Humans ; Signal Processing, Computer-Assisted

Traditional EP analysis is developed under the condition that the background noises in EP are Gaussian distributed. Alpha stable distribution, a generalization of Gaussian, is better for modeling impulsive noises than Gaussian distribution in biomedical signal processing. Conventional blind separation and estimation method of evoked potentials is based on second order statistics (SOS). In this paper, we propose a new algorithm based on minimum dispersion criterion and Givens matrix. The simulation experiments show that the proposed new algorithm is more robust than the conventional algorithm.
Algorithms ; Artifacts ; Brain ; physiology ; Electroencephalography ; methods ; Evoked Potentials ; physiology ; Humans ; Normal Distribution ; Signal Processing, Computer-Assisted

Objective To analyze the texture features of SPIO-enhanced MR imaging in rat models of hepatocellular carcinoma (HCC) and hepatocirrhosis with gray level co-occurrence matrix (GLCM). Methods HCC and hepatocirrhosis models were established in rats. SPIO-enhanced MR images were obtained. A total of 161 regions of interests (ROIs, 81 of HCC and 80 of hepatocirrhosis) were selected manually. Feature values as angular second moment, contrast, correlation, inverse difference moment, entropy, variance were extracted based on GLCM. The differences of feature values between two groups were statistically analyzed. Results In SPIO-enhanced MR images, hypointense signal changes were found in hepatocirrhosis, as well as hyperintensity in HCC nodules and intermixed intensity in larger HCC nodules. Correlation and entropy values of HCC group were higher than that of hepatocirrhosis group, while the angular second moment, contrast, inverse difference moment, and variance values were lower than hepatocirrhosis group. Conclusion The feature values based on GLCM could be used for the further computer aided diagnosis of SPIO-enhanced MR images in rat models of HCC and hepatocirrhosis.

Medical ultrasonic imaging is frequently used for diagnosing the fatty liver disease. In order to help doctors diagnose fatty liver disease more precisely, we need to construct a quantitative assessment system, and in this paper, we propose a method to construct such system with the use of multiresolution fractal Brownian motion model and the genetic algorithm. In such a way, a set of standards can help doctors diagnose the degree of the fatty liver disease more precisely.
Algorithms ; Fatty Liver ; diagnostic imaging ; genetics ; Humans ; Image Interpretation, Computer-Assisted ; Image Processing, Computer-Assisted ; methods ; Models, Statistical ; Models, Theoretical ; Motion ; Ultrasonography

Lung nodules are one of the most common pathological changes, thus early detection of lung nodule is very important for the diagnosis medical treatment of lung eancer. In recent years, as the application of multi-slice spiral CT(MSCT), high-resolution CT(HRCT) and low-dose chest CTCLDCT), computer-aided diagnosis (CAD) system will be more essential and more important. Since CAD system can improve the working efficiency of doctors and provide service to more patients, has become the research hotspot and achievement has been made in relevant area internationally recently. This review summarizes the basic methods and applieations of computer-aided detection and diagnosis of lung nodule based on CT image.

The automatic spike detection in EEG is significant in both diagnosing illness and alleviating the heavy labour force of the doctor. This paper proposes a new EMD based method to complete spike detection. It decomposes a signal into a few intrinsic mode functions (IMF), and then applies the nonlinear energy operator (NEO) to the first IMF to complete the automatic detection. Sufficient results are obtained by applying this method to the spike detection of the simulation signal and the real epileptic EEG signal.
Algorithms ; Artifacts ; Electroencephalography ; methods ; Epilepsy ; physiopathology ; Humans ; Nonlinear Dynamics ; Principal Component Analysis ; methods ; Signal Processing, Computer-Assisted

It is of great importance for the detection of epilepsy in clinical applications. Based on the limitations of the common used approximate entropy (ApEn) in the epilepsy detection, this paper analyzes epileptic EEG signals with the sample entropy (SampEn) approach, a new method for signal analysis with much higher precision than that of the ApEn. Data analysis results show that the values from both ApEn and SampEn decrease significantly when the epilepsy is burst. Furthermore, the SampEn is more sensitive to EEG changes caused by the epilepsy, about 15%-20% higher than the results of the ApEn.
Algorithms ; Data Interpretation, Statistical ; Electroencephalography ; methods ; Entropy ; Epilepsy ; diagnosis ; physiopathology ; Humans ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted

Hilbert-Huang transform (HHT) is a new time-frequency analytic method to analyze the nonlinear and the non-stationary signals. The key step of this method is the empirical mode decomposition (EMD), with which any complicated signal can be decomposed into a finite and small number of intrinsic mode functions (IMF). In this paper, a new EMD based method for suppressing the cross-term of Wigner-Ville distribution (WVD) is developed and is applied to analyze the epileptic EEG signals. The simulation data and analysis results show that the new method suppresses the cross-term of the WVD effectively with an excellent resolution.
Algorithms ; Electroencephalography ; methods ; Epilepsy ; physiopathology ; Humans ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted