Biomedical Engineering: Applications, Basis and Communications 

About: Biomedical Engineering: Applications, Basis and Communications is an academic journal published by World Scientific. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 1016-2372. Over the lifetime, 1082 publications have been published receiving 5230 citations. The journal is also known as: Biomedical engineering. Applications, basis, communications.

Classification of eeg signals for detection of epileptic seizures based on wavelets and statistical pattern recognition

Abstract: The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for the detection of epileptic seizures using wavelet transform and statistical pattern recognition. The decision making process is comprised of three main stages: (a) feature extraction based on wavelet transform, (b) feature space dimension reduction using scatter matrices and (c) classification by quadratic classifiers. The proposed methodology was applied on EEG data sets that belong to three subject groups: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval and (c) epileptic subjects during a seizure. An overall classification accuracy of 99% was achieved. The results confirmed that the proposed algorithm has a potential in the classification of EEG signals and detection of epileptic seizures, and could thus further improve the diagnosis of epilepsy.

Using correlation coefficient in ecg waveform for arrhythmia detection

Abstract: Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on the morphology characteristics of arrhythmias using correlation coefficient in ECG signal. Subjects for experiments included normal subjects, patients with atrial premature contraction (APC), and patients with ventricular premature contraction (PVC). So and Chan's algorithm was used to find the locations of QRS complexes. When the QRS complexes were detected, the correlation coefficient and RR-interval were utilized to calculate the similarity of arrhythmias. The algorithm was tested using MIT-BIH arrhythmia database and every QRS complex was classified in the database. The total number of test data was 538, 9 and 24 for normal beats, APCs and PVCs, respectively. The results are presented in terms of, performance, positive predication and sensitivity. High overall performance (99.3%) for the classification of the different categories of arrhythmic beats was achieved. The positive prediction results of the system reach 99.44%, 100% and 95.35% for normal beats, APCs and PVCs, respectively. The sensitivity results of the system are 99.81%, 81.82% and 95.83% for normal beats, APCs and PVCs, respectively. Results revealed that the system is accurate and efficient to classify arrhythmias resulted from APC or PVC. The proposed arrhythmia detection algorithm is therefore helpful to the clinical diagnosis.

Study of plaque vulnerability in coronary artery using mooney–rivlin model: a combination of finite element and experimental method

Abstract: Atherosclerosis is a disease in which plaque builds up inside arteries. It is also considered as one of the most serious and common forms of cardiovascular disease which can lead to heart attack and stroke. In the current research, finite element method is used to anticipate plaque vulnerability based on peak plaque stress using human samples. A total of 23 healthy and atherosclerotic human coronary arteries, including 14 healthy and 9 atherosclerotic are removed within 5 h postmortem. The samples are mounted on a uniaxial tensile test machine and the obtained mechanical properties are used in finite element models. The results, including the Mooney–Rivlin hyperelastic constants of the samples as well as peak plaque stresses, are computed. It is demonstrated that the atherosclerotic human coronary arteries have significantly (p < 0.05) higher stiffness compared to healthy ones. The hypocellular plaque, in addition, has the highest stress values compared to the cellular and calcified ones and, consequently, is so prone to rupture. The calcified plaque type, nevertheless, has the lowest stress values and, remains stable. The results of this study can be used in the plaque vulnerability prediction and could have clinical implications for interventions and surgeries, such as balloon angioplasty, bypass and stenting.

A new approach for identifying sleep apnea syndrome using wavelet transform and neural networks

Abstract: This paper describes a new technique to classify and analyze the electroencephalogram (EEG) signal and recognize the EEG signal characteristics of Sleep Apnea Syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN). The EEG signals are separated into Delta, Theta, Alpha, and Beta spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. We treated the wavelet coefficient as the kind of the training input of artificial neural network, might result in 6 groups of wavelet coefficients per second signal by way of characteristic part processing technique of the artificial neural network designed by our group, we carried out the task of training and recognition of SAS symptoms. Then the neural network was configured to give three outputs to signify the SAS situation of the patient. The recognition threshold for all test signals turned out to have a sensitivity level of approximately 69.64% and a specificity value of approximately 44.44%. In neurology clinics, this study offers a clinical reference value for identifying SAS, and could reduce diagnosis time and improve medical service efficiency.

Toxicity assessment of montmorillonite as a drug carrier for pharmaceutical applications: yeast and rats model

Abstract: Our previous study indicated that montmorillonite (MMT for short) was pharmaceutically feasible to be used as a carrier of an anticancer drug 5-FU. Emphasis of this study is thus placed on the toxicity of MMT to address whether it is clinically safe for practical use. Hematological data of rats model showed that the rats administered with oral MMT had significant increases in hemoglobin (Hb) concentration, Hamatocrit and RBC count than those of oral PBS buffer (p 0.05). Hematological analysis for intravenous injection also showed no statistically significant differences between experimental and control group (p>0.05). Biochemical analysis pointed out that compared to oral PBS there was not only a significant decrease of sodium (Na+) and chloride (Cl−) ion (p MMTK-SA ≫ MMT-SA > MMT according to EC0 or EC50 line. These all suggested that MMT alone is much less toxic than Lannate and potassium hexacyanoferrate(III). Apparently, it was concluded that MMT alone could be considered non-toxic to S. cerevisiae and Wistar rat for myriads of applications.