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For continuous monitoring of cardiovascular diseases, this article presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts, and the results well demonstrate the advantages of our proposal, especially at high compression ratios. [ABSTRACT FROM AUTHOR]

This paper presents an improved reconstruction approach for compressed sensing based ECG acquisition in Internet of Medical Things. The proposed method exploits the concepts of adaptive overcomplete dictionary and QRS detection in CS domain. Based on whether there is a QRS complex, the ECG frames to be reconstructed are divided into several categories and corresponding overcomplete dictionaries are trained to fit these different kinds of ECG frames. Specifically, QRS detection is first performed directly on the compressed measurements to determine the QRS morphology without actually reconstructing the signal in advance, and then a suitable overcomplete dictionary is chosen for the signal reconstruction. Because the selected dictionaries well fit the ECG frames, the reconstruction quality is consequently improved. Comparative experiments have been conducted, and the results well demonstrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a solution to satisfy the increasing requirements for secure medical image transmission and storage over public networks. The proposed scheme can simultaneously encrypt and compress the medical image using compressive sensing (CS) and pixel swapping based permutation approach. In the CS phase, the plain image is compressed and encrypted by chaos-based Bernoulli measurement matrix, which is generated under the control of the introduced Chebyshev map. The quantized measurements are then encrypted by permutation-diffusion type chaotic cipher for the second level protection. Simulations and extensive security analyses have been performed. The results demonstrate that at a large scale of compression ratio the proposed cryptosystem can provide satisfactory security level and reconstruction quality. [ABSTRACT FROM AUTHOR]

This paper presents an information authentication scheme using sparse representation of double random phase encoding in fractional Fourier transform domain. Different from traditional cryptographic applications, only sparse version of the ciphertext is available at the receiver end. The decrypted image is unrecognizable and of no meaningful information of the plaintext, which can enhance the resistance against various attacks. The decrypted image can be authenticated using nonlinear optical correlation approach. Numerical simulations have been carried out, and the results prove the effectiveness and noise resistance of the proposed scheme. [ABSTRACT FROM AUTHOR]