Percentage Residual Difference Research Articles

Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG.

Hybrid pixel based method for multimodal image fusion based on Integration of Pulse Coupled Neural Network (PCNN) and Genetic Algorithm (GA) using Empirical Mode Decomposition (EMD)

Medical diagnosis using images is critical for defining the patient’s condition and assisting the decision process for treatment by the physician. Certain infections and conditions like Colorectal Cancer, lung lesion, brain tumor, Metastasis can be ambiguous and the diagnosis cannot be accurately determined using single image. Image fusion is phenomena that helps to alleviate this underlying problem. Image fusion is the process of integrating complementary information from two or more distinct images into a single image without loss of information. It has the capability to process the information from each pixel in the input image and endure the information from source images thereby improving the readability of information, thus aiding the physicians to diagnose the health conditions in a much efficient manner. In this paper, we propose a novel image fusion strategy that is the amalgamation of Pulse Coupled Neural Network (PCNN) and Genetic Algorithm (GA) using Empirical mode Decomposition (EMD). We compared our technique to some of the classic and novel image fusion strategies in our performance evaluation.Hybridization of PCNN with GA using EMD improves the fusion effect, image detail clarity, and time efficiency considerably than existing state of art strategies. Our quantitative analysis illustrates the effectiveness of the proposed strategy in terms of entropy, Percentage Residual Difference (PRD), Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR).

A New Approach for Secure Cloud-Based Electronic Health Record and its Experimental Testbed

The tremendous development of the Internet of Things (IoT) technology in the last decades has fostered advancement in automatic medical assistive devices to support the existing Electronic Health Record (EHR) system. As an integral part of the EHR electronic model, public cloud servers store medical data. Unfortunately, public cloud servers are prone to security and privacy breach. This paper introduces a novel non-cryptographic approach to preserve electrocardiograph (ECG) data confidentiality and integrity in the EHR environment. The main objective of the proposed anonymization algorithm is to obscure the patient’s cardiac information during transmission and to protect information stored in the cloud database. Although we focus on ECG data, generalization to other types of clinical data can be derived using the proposed method. Performance evaluation of the proposed scheme showed that the algorithm conceals both fiducial and non-fiducial features of the data. Therefore, confidentiality feature is preserved. This paper examined confidentiality of the anonymized data using the Percentage Residual Difference (PRD) and investigated the integrity of the reconstructed data in terms of cross-correlation. Security analysis carried out using the PRD, brute force attack, and performance comparison between the proposed algorithm and existing methods. Evaluation showed that the proposed scheme offers a secure non-cryptographic model for transmission and storing clinical data in the cloud. Moreover, in terms of processing time, the proposed algorithm is ten times faster than the existing wavelet packet method when processing long ECG data, 65,536 sample points. In a real-time experimental testbed, the implemented proposed system was successful.

Lattice-Based Minimum-Distortion Data Hiding

Lattices have been conceived as a powerful tool for data hiding. While conventional studies and applications focus on achieving the optimal robustness versus distortion tradeoff, in some applications such as data hiding in medical/physiological signals, the primary concern is to achieve a minimum amount of distortion to the cover signal. In this paper, we revisit the celebrated quantization index modulation (QIM) scheme and propose a minimum-distortion version of it, referred to as MD-QIM. The crux of MD-QIM is to move the data point to only the boundary of the Voronoi region of the lattice point indexed by a message, which suffices for subsequent correct decoding. At any fixed code rate, the scheme achieves the minimum amount of distortion by sacrificing the robustness to the additive white Gaussian noise (AWGN) attacks. Simulation results confirm that our scheme significantly outperforms QIM in terms of mean square error (MSE), peak signal to noise ratio (PSNR) and percentage residual difference (PRD).

Towards using Single EEG Channel for Human Identity Verification

Biometrics is an interesting area of research as a result of tremendous technological advances, especially in security. It is considered as an automated technology used for identification based on biological or behavioral human traits. An electroencephalogram (EEG) is the brain electrical activity signals considered as biological traits used in biometrics systems. The primary goal of this work is trying to find a single EEG channel to be used for human identification purposes. A single EEG channel recording is used for personal identity-based verification mode, which is preferred for many subjects with instant real-time system decisions. Percent residual difference (PRD) is a common quantitative measurement used to determine the human identity-based measures the distance between two signals. The proposed system sensitivity gives 100% using some single channels placed in the parietal and occipital lobes. The proposed system takes a short time in the enrolment process with an instant decision using verification mode, which is preferred with a large number of subjects. Also, using imaginary tasks is preferred for human identity verification.

ECG steganography based on tunable Q‐factor wavelet transform and singular value decomposition

AbstractThe article presents a novel ECG steganography scheme based on the tunable Q‐factor wavelet transformation (TQWT) and also singular value decomposition (SVD) techniques that ensure better safety and confidentiality of patient information. Initial parameters such as Q, r, and J are used to decompose the cover signal into individual frequency sub‐bands with the tunable Q‐factor wavelet transform (TQWT). The singular value decomposition (SVD) technique is used to further decompose high‐frequency sub‐band coefficients into singular values. The watermark information is then embedded with high‐frequency sub‐band coefficients by involving the quantization process. The performance of this proposed system is successfully evaluated by considering various metrics, such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), percentage residual difference (PRD), and bit error rate (BER). The simulation results of the proposed scheme are observed to be better than other traditional algorithms.

Improved ECG Watermarking Technique Using Curvelet Transform.

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient’s data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient’s data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance—even when the hidden messages were large size.

High Performance Fractal Compression for EEG Health Network Traffic

Intelligent healthcare systems need recording, sending and treating large amounts of medical data produced from various kinds of medical sensors nodes. This is a challenging task and may lead to increase the network traffic, latency, and energy consumption. Therefore, it is important to apply a health data reduction mechanism for eliminating the data redundancy and saving the energy thus enhance the smart healthcare system performance. In this paper, a potential EEG Fractals compression model is proposed for reducing the transmitted EEG traffic from Patient Data Aggregator (PDA) to the destination (doctor, smart hospitals, emergency response, etc.). The proposed model supports EEG patient data communication and improve Wireless Body Sensor Network by reducing network traffic. Main model metrics are inspected as the determined Fractals Block Size found to be the vital playing role on producing higher Compression Ratio (CR) and drive the required Percentage Residual Difference (PRD). Both results and performance are compared with other techniques as the proposed model has outperformed them completely. The resultant CR can reach up to 160 and keep PRD less than 1.

Chaos-based steganography technique to secure information and integrity preservation of smart grid readings using wavelet

In recent years, smart grids have attracted much attention due to their digital properties. Smart grids play a vital role in transmitting the collected readings of meter and other sensitive information of customers through the public network. Therefore, the confidentiality of consumers’ information and integrity protection of periodically collected readings are required for better assessment. This paper presents a novel technique of steganography based on wavelet and Hénon chaotic map to achieve integrity and authentication. Periodically collected readings are used as a cover object for hiding information of smart meter and consumer. The discrete wavelet transform is used to decompose readings into sub-band coefficients and used as a cover object. Sensitive information is converted into cipher using Hénon chaotic map. Stego readings are constructed with negligible distortion in original readings after concealing bits into a cover object by applying double-precision floating-point format. Percentage residual difference (PRD) test has been performed between stego and original readings as well as reconstructed readings. PRD test values have been observed to be very less than other existing algorithms. Experimental results illustrate that the proposed algorithm is reliable, robust and highly sensitive to its initial key, and retrieval of information at receiver’s end is not labyrinthine.

A reversible and multipurpose ECG data hiding technique for telemedicine applications

Biomedical signals serves as a backbone of telemedicine industry and its analysis help the remote diagnosis possible. Security of biomedical data is one of the critical issues during its transmission from one storage device to another storage device. In this work, a completely reversible data hiding method is proposed for the ECG (Electrocardiogram) data. The proposed scheme is designed in such a way that it can find out false ownership claims as well as detect the tampered region of ECG data. In addition, original ECG signal is perfectly reconstructed from watermarked signal. The reversibility is tested for a large set of 460 different ECG signals generated from MIT-BIH arrhythmia database and complete reversibility (100%) is obtained each time. In proposed approach, deep neural network is used for an improved error prediction and prediction error expansion (PEE) is combined with it to ensure reversibility. Proposed method is a high capacity method and data hiding of 0.99 bps (bits per sample) is achieved with very little distortion in watermarked signal. The performance of proposed work is evaluated through percentage residual difference (PRD), signal to noise ratio (SNR) and normalized cross correlation (NCC). The most important contribution of proposed work is its multipurpose nature: ownership detection, tamper localization and 100% reversibility. Tamper detection and localization performance of the proposed method is also tested against different attacks and it is found to be quite good.

MULTI-CHANNEL ECG-BASED STEGANOGRAPHY

There is a growing tendency for the concealment of secure information into electrocardiogram (ECG) signals in a way that the embedded ECGs still remain diagnosable. The average length of ECG recording for a primary diagnosis takes no longer than 1[Formula: see text]min yielding to limit its concealment capacity. To overcome this drawback, we enhanced both concealment capacity and embedding quality by: (I) using 12-lead ECGs to span more embedding space, (II) shuffling input message bits via nonlinear feedback shift register (NLFSR) method, (III) inserting the selected bits of each channel into the high-frequency wavelet coefficients of non-QRS parts. Inserting the message bits into high frequency coefficients of less important ECG parts leads to preserve the quality watermarked ECGs. To assess the proposed method, a text containing different letters (changes with size of both non-QRS segments and high-frequency sub-band) was hidden through 12-lead ECG signals of 56 randomly selected subjects of PTB database, where each signal length is 10[Formula: see text]s. The performance was compared to state-of-the-art ECG-based steganography schemes in terms of the following criteria: percentage residual difference (PRD), peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and bit error rate (BER). Our results showed that the proposed scheme has benefits of fast computing along with secure embedding, providing high capacity of data hiding.

QR code based patient data protection in ECG steganography.

Connected health enables patient centric interventions resulting in better healthcare and hence better living. In order to accomplish this, bio-signals, medical and diagnosis information are shared and accessed by multiple actors and it is important to protect the privacy of patient data. Steganography is widely used to protect patient data by hiding it in the medical information. Current work investigates ECG steganography using Discrete Wavelet Transform (DWT) and Quick Response (QR) code. Steganography deteriorates the ECG signal and it is important to minimize this deterioration to preserve diagnosability. 1D ECG signal is converted to 2D ECG image and decomposed into sub-bands by subjecting it to DWT. The novelty of the proposed approach lies in converting the patient data into QR code and using it as watermark in ECG steganography. The QR code is embedded in the 2D image using additive quantization scheme. The performance of proposed method is measured using Peak Signal to Noise Ratio, Percentage Residual Difference and Kullback-Leibler distance. These metrics are used as a measure of imperceptibility while the data loss during retrieval is measured by Bit Retrieval Rate. The proposed method is demonstrated on normal ECG signals obtained from MIT-BIH database for different QR code versions. Metrics reveal that imperceptibility decreased for increasing patient data size and increasing scaling factors. Metrics were independent of the sub-band and the proposed method allows reliable patient data protection with full retrieval ability.

A Novel Multistandard Compliant Hand Function Assessment Method Using an Infrared Imaging Device.

Many post-stroke patients suffer varying degrees of hand function and fine motor skills impairment. Both passive and active hand rehabilitation training are beneficial in improving the strength and dexterity of the hands. However, hand rehabilitation programs should be prescribed based on an accurate assessment of hand function. In this paper, we propose a novel method for hand function assessment, which can accurately measure multiple joint angles of a hand simultaneously using a portable infrared based imaging device. Different from traditional assessment methods that are often based on a clinician's subjective observations and ordinal charts, this method provides an accurate, fast, and objective evaluation using infrared imaging sensors. Performance evaluation and benchmarking for the proposed measurement system were carried out using the correlation coefficient (CC) method, the root mean squared error, and the percentage residual difference method (PRD). A clinical trial involving 25 participants resulted in a higher correlation with CC of 0.9672 and PRD of 8.8%, which indicated that the developed assessment framework is compliant with multiple assessment standards such as Swanson impairment evaluation and Fugl-Meyer assessment. The new hand function assessment method can be used to replace traditional methods for fine hand function modeling and assessment in rehabilitation medicine and can also play an important role in precision post-stroke function analysis.

Compressive sensing of phased array ultrasonic signal in defect detection: Simulation study and experimental verification

Ultrasonic phased array techniques are widely used for defect detection in structural health monitoring field. The increase in the element number, however, leads to larger amounts of data acquired and processed. Recently developed compressive sensing states that sparse signals may be accurately recovered from far fewer measurements, suggesting the possibility of breaking through the sampling limit of the Nyquist theorem. In light of this significant advantage, the novel use of the compressive sensing methodology for ultrasonic phased array in defect detection is proposed in this work. Based on CIVA software, we first present a simulated study on the effectiveness of the compressive sensing applied in ultrasonic phased array in defect detection through the average mean percent residual difference at varying compression rates. The results particularly show that the compressive sensing yields a breakthrough of the sampling limitation. We then experimentally demonstrate comparative analyses on the signals extracted from three types of artificial flaws (through-hole, flat-bottom hole, and electrical discharge machining notches) on two different specimens (made of aluminum and 20# steel). To find the optimal algorithm combination, the best sparse representation basis is chosen among fast Fourier transform, discrete cosine transform, and 34 wavelet kernels; the reconstruction performance is compared between five greedy algorithms; and the recovery accuracy is further improved via four sensing matrices selection. We also evaluate the influence of the sampling rate, and our results are comparable with the gold standard of signal compression, namely, the discrete wavelet transform.

A generalised wavelet packet‐based anonymisation approach for ECG security application

AbstractAn electrocardiogram (ECG) signal contains cardiovascular information, which is private in nature. Therefore, during the distribution and storage of an ECG in a public repository, it is necessary to anonymise the ECG data. An eavesdropper could record an unsecured ECG and use it as recognition data to gain access via an ECG biometric system. Anonymised ECG data also hide cardiovascular details of a patient upholding Health Information Protection and Privacy Act (1996) regulations. In this paper, a generalised wavelet packet‐based ECG anonymisation framework is proposed. The proposed method uses wavelet packet transform and reversible function and/or operation to conceal fiducial and non‐fiducial features from normal and abnormal ECG signals. At the receiver end, only authorised personnel possessing a secret key and know the reversible function will be able to reconstruct the original ECG from the anonymised ECG. Performance analysis using cross correlation, power spectral density and percentage residual difference methods show that the proposed anonymisation algorithm conceals fiducial and non‐fiducial features in normal and abnormal ECG signals. Moreover, the reconstructed ECG is highly correlated with the original ECG, which achieves a lossless reconstruction of the ECG data and proves the robustness of the proposed method. It is found from the performance analysis results that the proposed anonymisation scheme provides high‐security protection to ECG data and patient privacy. Copyright © 2017 John Wiley & Sons, Ltd.

Imperceptibility—Robustness tradeoff studies for ECG steganography using Continuous Ant Colony Optimization

ECG Steganography ensures protection of patient data when ECG signals embedded with patient data are transmitted over the internet. Steganography algorithms strive to recover the embedded patient data entirely and to minimize the deterioration in the cover signal caused by the embedding. This paper presents a Continuous Ant Colony Optimization (CACO) based ECG Steganography scheme using Discrete Wavelet Transform and Singular Value Decomposition. Quantization techniques allow embedding the patient data into the ECG signal. The scaling factor in the quantization techniques governs the tradeoff between imperceptibility and robustness. The novelty of the proposed approach is to use CACO in ECG Steganography, to identify Multiple Scaling Factors (MSFs) that will provide a better tradeoff compared to uniform Single Scaling Factor (SSF). The optimal MSFs significantly improve the performance of ECG steganography which is measured by metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback–Leibler distance and Bit Error Rate. Performance of the proposed approach is demonstrated on the MIT-BIH database and the results validate that the tradeoff curve obtained through MSFs is better than the tradeoff curve obtained for any SSF. The results also advocate appropriate SSFs for target imperceptibility or robustness.

ECG steganography using curvelet transform

Abstract ECG steganography allows secured transmission of patient data that are tagged to the ECG signals. Signal deterioration leading to loss of diagnosis information and inability to retrieve patient data fully are the major challenges with ECG steganography. In this work, an attempt has been made to use curvelet transforms which permit identifying the coefficients that store the crucial information about diagnosis. The novelty of the proposed approach is the usage of curvelet transform for ECG steganography, adaptive selection of watermark location and a new threshold selection algorithm. It is observed that when coefficients around zero are modified to embed the watermark, the signal deterioration is the least. In order to avoid overlap of watermark, an n × n sequence is used to embed the watermark. The imperceptibility of the watermark is measured using metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference and Kullback-Leibler distance. The ability to extract the patient data is measured by the Bit Error Rate. Performance of the proposed approach is demonstrated on the MIT-BIH database and the results validate that coefficients around zero are ideal for watermarking to minimize deterioration and there is no loss in the data retrieved. For an increased patient data size, the cover signal deteriorates but the Bit Error Rate is zero. Therefore the proposed approach does not affect diagnosability and allows reliable steganography.

Steganography in arrhythmic electrocardiogram signal.

Security and privacy of patient data is a vital requirement during exchange/storage of medical information over communication network. Steganography method hides patient data into a cover signal to prevent unauthenticated accesses during data transfer. This study evaluates the performance of ECG steganography to ensure secured transmission of patient data where an abnormal ECG signal is used as cover signal. The novelty of this work is to hide patient data into two dimensional matrix of an abnormal ECG signal using Discrete Wavelet Transform and Singular Value Decomposition based steganography method. A 2D ECG is constructed according to Tompkins QRS detection algorithm. The missed R peaks are computed using RR interval during 2D conversion. The abnormal ECG signals are obtained from the MIT-BIH arrhythmia database. Metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback-Leibler distance and Bit Error Rate are used to evaluate the performance of the proposed approach.

Robust privacy preservation and authenticity of the collected data in cognitive radio network—Walsh–Hadamard based steganographic approach

Cognitive Radio Networks have recently attracted attention because of high efficiency and throughput performance. They transmit (1) repetitively collected readings (e.g. monitoring) and (2) highly confidential data (e.g. geometric location). However, the privacy and the authenticity of the transmitted data are major challenges. This paper proposes a novel steganographic technique that guarantees (1) strong end-to-end protection of the confidential information by hiding them randomly inside the normal readings using a generated key, and (2) robust evidence of authenticity for the transported readings. To expand hiding, the Walsh–Hadamard Transformation (WHT) is used to decompose normal readings into a set of coefficients. To achieve minimum distortion, only the least featured coefficients are used. To achieve high security, a key is used to reshape the coefficients into a random 2D M-by-N matrix and to generate a randomly selected order used in the hiding process. To accurately measure the distortion after hiding and extracting the confidential data, Percentage Residual Difference (PRD) has been used. It is obvious from experiments that our technique has little effect on the original readings (<1%). Also, our security evaluation proves that unauthorized retrieval of the intended confidential information within a reasonable time is highly improbable.

Effective low-power wearable wireless surface EMG sensor design based on analog-compressed sensing.

Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process.