In this paper, we propose an encryption scheme for the medical image encryption based on combination of scrambling and confusion. Chaotic cat map is used for the scrambling the addresses of the medical image pixels. In order to provide security for the scheme, a modified form of Simplified version of Advance Encryption Standard (S-AES) is introduced and applied. The modification is that we make use of chaos for S-box design and replace it with that of S-AES. The so called Chaotic S-AES has all cryptographic characteristics and requirements of S-AES. Hence, the main contribution of this work is that we make use of chaos in both image diffusion and confusion parts. In order to check the performance of the method, experimental implementation has been done. It worth be noting that the resistance of the scheme against differential and linear cryptanalysis is at least as of S-AES.

Background: Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.Objective: In this study, we aimed to evaluate the efficiency of discrete wavelet transform (DWT) based features extracted from HRV which were further selected by genetic algorithm (GA), and were deployed by support vector machine to HRV classification.Materials and Methods: In this paper, 53 ECGs including 3 different beat types (ventricular fibrillation (VF), atrial fibrillation (AF) and also normal sinus rhythm (NSR)), were selected from the MIT/BIH arrhythmia database. The approach contains 4 stages including HRV signal extraction from each ECG signal, feature extraction using DWT (entropy, mean, variance, kurtosis and spectral component β), best features selection by GA and classification of normal and abnormal ECGs using the selected features by support vector machine (SVM).Results: The performance of the classification procedure employing the combination of selected features were evaluated using several measures including accuracy, sensitivity, specificity and precision which resulted in 97.14%, 97.54%, 96.9% and 97.64%, respectively.Conclusion: A comparative analysis with the related existing methods illustrates  the proposed method has a higher potential in the classification of AF and VF. The attempt to classify the ECG signal has been successfully achieved. The proposed method has shown a promising sensitivity of 97.54% which indicates that this technique is an excellent model for computer-aided diagnosis of cardiac arrhythmias.

We aimed to study the effects of an anti-gravity treadmill (AlterG) training on balance and postural stability in children with cerebral palsy (CP). AlterG training was performed 3 days/week for 8 weeks, with up to 45 minutes of training per session. The subject was evaluated before and after the 8-week training. The effects of training on the balance and postural stability was evaluated based on the Romberg test that was performed by using a posturography device. The parameters quantifying Center-of-Pressure (CoP) were calculated using different analytical approaches including power spectral density and principal components analyses. All of the key parameters including the Stabilogram, the Fast Fourier Transform (FFT) Energy, the Eigenvectors, and the Eigenvalues of CoP were modified between 14%-84%. The results indicated that the balance features were improved substantially after training. The clinical implication is that the AlterG has the potential to effectively improve postural stability in children with cerebral palsy.

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