Journal of Medical Signals and Sensors
Volume:1 Issue: 2, May-Aug 2011

Electrocardiogram (ECG) is one of the most common biological signals which plays a significant role in diagnosis of heart diseases. One of the most important parts of ECG signal processing is interpretation of QRS complex and obtaining its characteristics. R wave is one of the most important sections of this complex which has an essential role in diagnosis of heart rhythm irregularities and also in determining heart rate variability (HRV). This paper employs Hilbert and wavelet transforms as well as adaptive thresholding method to investigate an optimal combination of these signal processing techniques for detection of R peak. In the experimental sections of this paper the proposed algorithms are evaluated using both ECG signals from MIT-BIH database and synthetic data simulated in MATLAB environment with different arrhythmias, artifacts and noise levels. Finally, by using wavelet and Hilbert transforms also employing adaptive thresholding technique, an optimal combinational method for R peak detection namely WHAT is obtained that outperforms other techniques quantitatively and qualitatively.

As the T-wave section in electrocardiogram (ECG) illustrates the repolarisation phase of heart activity, the information which is accumulated in this section is so significant that can explain the proper operation of electrical activities in heart. Long QT syndrome (LQT) and T-Wave Alternans (TWA) have imperceptible effects on time and amplitude of T-wave interval. Therefore, T-wave shapes of these diseases are similar to normal beats. Consequently, several T-wave features can be used to classify LQT and TWA diseases from normal ECGs. Totally 22 features including 17 morphological and 5 wavelet features have been extracted from T-wave to show the ability of this section to recognize normal and abnormal records. This recognition can be implemented by pre-processing, T-wave feature extraction and artificial neural network (ANN) classifier using Multi Layer Perceptron (MLP). The ECG signals obtained from 142 patients (40 normal, 47 LQT and 55 TWA) are processed and classified from MIT-BIH database. The specificity factor for normal, LQT and TWA classifications are 99.89%, 99.90% and 99.43%, respectively. T-wave features are one of the most important descriptors for LQT syndrome, Normal and TWA of ECG classification. The morphological features of T-wave have also more effect on classification performance in LQT, TWA and normal samples compared with wavelet features.

Increasing frame rate is a challenging issue for better interpretation of medical images and diagnosis based on tracking the small transient motions of myocardium and valves in real time visualization. In this paper, Manifold learning algorithm is applied to extract the nonlinear embedded information about echocardiography images from the consecutive images in two dimensional manifold space. In this method we presume that the dimensionality of echocardiography images obtained from a patient is artificially high and the images can be described as functions of only a few underlying parameters such as periodic motion due to heartbeat. By this approach, each image is projected as a point on the reconstructed manifold; hence the relationship between images in the new domain can be obtained according to periodicity of the heart cycle. To have a better tracking of the echocardiography images during the fast motions of heart we have rearranged the similar frames of consecutive heart cycles in a sequence. This provides a full view slow motion of heart movement through increasing the frame rate to three times the traditional ultrasound systems.

This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize the risks for the patients. This method consists of four steps: preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features but also increases the classification accuracy by selecting most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively.

Electric wheelchairs (EW) experience various terrains surfaces and slopesas well asoccupants with diverse weights. This, in turn, imparts a substantial amount of perturbation to the EWdynamics. In this paper we make use of a two-degree-of-freedom control architecture called disturbance observer (DOB) which reduces sensitivity to model uncertainties while enhancing rejection of disturbances caused due to entering slopes. The feedback loop which is designed via characteristic loci method (CLM) is then augmented with a DOB with a parametrized low-pass filter. According to disturbance rejection, sensitivity reduction, and noise rejection of the whole controller, three performance indices are definedwhich enable us to pick thefilter’s optimal parameters using a multi-objective optimization (MOO) approach callednon-dominated sorting genetic algorithm-II (NSGA-II). Finally, experimental results show desirable improvement in stiffness and disturbance rejection of the proposed controller as well as its robust stability.

Electro Encephalography is one of the reliable sources to detect sleep onset while driving. In this study we have tried to demonstrate that sleepiness and alertness signals are separable with an appropriate margin by extracting suitable features. So first of all we have recorded EEG signals from 10 volunteers. They were obliged to be sleep deprived about 20 hours before the test. We recorded the signals when subjects did a virtual driving game. They tried to pass some barriers that were shown on monitor. Process of recording was ended After 45 minutes. Then after preprocessing of recorded signals, we labeled them by drowsiness and alertness, by using times associated with pass times of the barriers or crash times to them. Then we extracted some chaotic features (include Higuchi's fractal dimension and Petrosian's fractal dimension) and logarithm of energy of signal. By applying two tailed t-test, we have shown that these features can create 95% significance level of difference between drowsiness and alertness in each EEG channels.

Breast lesion segmentation in MR Images is one of the most important parts of clinical diagnostic tools. Pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. On the other hand, unsupervised segmentation methods need no prior knowledge and lead to low performance. However, semi-supervised learning (SSL) which uses not only a few labeled data but also a large amount of unlabeled data promises higher accuracy with less effort. In this paper, we propose a new interactive semi-supervised approach to segmentation of suspicious lesions in breast MRI. Since using a suitable classifier in this approach has an important role in its performance, in this paper, we present a semi-supervised algorithm IMPST (Improved Self_Training) which is improved version of Self-Training method and increase segmentation accuracy. Experimental results show that performance of segmentation in this approach is higher than supervised and unsupervised methods such as K.N.N, Bayesian, SVM and Fuzzy c-Means.