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Quantitative classification of cardiac arrhythmia is an important tool in ICU and CCU that enables on line monitoring of the cardiac activities. Among fatal arrhythmias are atrial fibrillation (AF), ventricular tachycardia (VT), and ventricular fibrillation that require special algorithms for detection and so for direct medical actions. In this paper, a combined bispectrum and bicoherency classification...
The quality of our life is tied to the quality of our sleep. People with sleep deficits may experience impaired performance, irritability, lack of concentration, and daytime drowsiness. Increased mobility in bed can be a sign of disrupted sleep. Therefore, body movements in bed represent an important behavioral aspect of sleep. In this paper, we propose a method for detection and classification of...
The accurate segmentation of the teeth from the triangle mesh is an important step in computer-aided orthodontic. Because teeth come in different shapes and their arrangements vary substantially from one individual to another, tooth segmentation is difficult. This paper proposes a new method to accurately segment the teeth interactively. Based on curvature values of the triangle mesh, feature points...
The identification of protein-protein interactions along with their spatial and temporal localization is vital data for assigning functional information to proteins. Historically, these data sets obtained from fluorescence microscopy, have been analyzed manually, a process that is both time consuming and tedious. The development of an automated system that can measure the location dynamics of the...
Time/frequency analysis has been extensively used in biomedical signal processing. By extracting some essential features from the electro-physiological signals, these methods are able to determine the clinical pathology mechanisms of some diseases. Fourier spectrum analysis provides a common framework for examining the distribution of global energy in the frequency domain. However, this method assumes...
In this paper, we proposed a heartbeat classification algorithm based on linear discriminant analysis and artificial neural network. For the input of classifier, we extracted 275 input features from the first derivative signal of ECG signal and RR interval information and it was reduced to be 6 by LDA. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm...
In patients suffering from various sleep disorders and some elderly patients, sleep is disturbed with frequent but brief arousal. These events do not cause behavioral awakening, but can lead to excessive day time sleepiness. These brief arousals or microarousals (MAs) can be identified on a standard polysomnogram as a transient abrupt change of frequency, typically in the alpha and extended beta (16-40...
We have studied the electrocardiogram (ECG) as a potential biometric for human identity verification. This research investigates the relationship between ECG biometric features and body mass index (BMI) using correlation analysis and linear regression methods. Using our ECG database of 168 normal healthy people (113 females and 55 males), we studied normalized features extracted from a one-lead, resting,...
Sleep is a natural periodic state of rest for the body, in which the eyes usually close and consciousness is completely or partially lost. Consequently, there is a decrease in bodily movements and responsiveness to external stimuli. Slow wave sleep is of immense interest as it is the most restorative sleep stage during which the body recovers from weariness. During this sleep stage, electroencephalographic...
The automated extraction of brain vessels from magnetic resonance angiography (MRA) has found its applications in vascular disease diagnosis, endovascular operation and neurosurgical planning. In this paper we first present a concise technical review on cerebral vasculature extraction from MRA. It reveals the latest development in the area of vessel extraction. Then we detail the main challenges to...
We proposed an efficient method for classification of diffused liver diseases based on Gabor wavelet. It is well known that Gabor wavelets attain maximum joint space-frequency resolution which is highly significant in the process of texture extraction and presentation. This property has been explored here as the proposed method outperforms the classification rate obtained by using dyadic wavelets...
This paper describes a method to extract the vascular centerlines and contours in coronary angiography. The proposed approach associates geometric moments for the estimation of a "cylinder-like model" and relies on a tracking process. The orientation of the cylinder axis and its local diameter are computed from the analytical expressions of the geometric moments of up to order 2. Experimental...
In this paper we present an algorithm as the combination of a low level morphological operation and model based global circular shortest path scheme to explore the segmentation of the right ventricle. Traditional morphological operations were employed to obtain the region of interest, and adjust it to generate a mask. The image cropped by the mask is then partitioned into a few overlapping regions...
The article presents a concept of balance among image points based on the correlation of points and then develops a new binarization method for gray scale image, which can be used in vessel extraction of DSA images. A point is in balance only when its resistance counteracts the influence from the neighborhood, while noise breaks down such balance. By restoring the balance of every single point, the...
Breast thermography is considered particularly valuable for early breast tumors detection. The fast growing tumor has a higher metabolic rate and associated increase in local vascularization. It will cause the occurrence of some asymmetric heat patterns. Clinical interpretation of a breast thermogram is primarily based on the asymmetry analysis of these heat patterns visually and subjectively. In...
The classification of the uterine myoma and adenomyosis from their ultrasound images mainly depends on doctors' experience and lacks objective criterions. Here a novel classification method is proposed using the multiresolution analysis and the orientational fractal analysis. Firstly, texture features under various resolutions and orientational fractal features are obtained from ultrasound images...
Textural features have been shown to be valuable in tumor diagnosis. This study combines three practical textural features in ultrasound (US) images, i.e. spatial gray-level dependence matrices (SGLDMs), gray-level difference matrix (GLDM) and auto-covariance matrix, to identify breast tumor as benign or malignant. The textural features were extracted from 147 3-D ultrasound cases and each case composes...
Ultrasound is an important way of physical examination nowadays. Many literatures tried to find a quantified standard for diagnosing ultrasonic images; however, they neglected the effect of scan-conversion on image textures. This paper provides a preprocessing method, which practically conforms to the ultrasound image formats, and then compares the effect of the preprocessing method. Several images...
Electroencephalogram (EEG) might be the most predictive and reliable physiological indicator of mental fatigue. However, the extraction of key features from massive EEG data for mental fatigue identification remains a challenge. The objective of this study is to identify the key EEG features in relationship to mental fatigue, from a broad pool of EEG features generated by quantitative EEG (qEEG) techniques,...
The treatment and therapy to be administered on breast cancer patients are dependent on the stage of the disease at time of diagnosis. It is therefore crucial to determine the stage at the earliest time possible. Tumor dissemination to axillary lymph nodes has been regarded as an indication of tumor aggression, thus the stage of the disease. Neural networks have been employed in many applications...
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