Seedahmed S. Mahmoud

Novel methods of faster cardiovascular diagnosis in wireless telecardiology

With the rapid development wireless technologies, mobile phones are gaining acceptance to become an effective tool for cardiovascular monitoring. However, existing technologies have limitations in terms of efficient transmission of compressed ECG over text messaging communications like SMS and MMS. In this paper, we first propose an ECG compression algorithm which allows lossless transmission of compressed ECG over bandwidth constrained wireless link. Then, we propose several algorithms for cardiovascular abnormality detection directly from the compressed ECG maintaining end to end security, patient privacy while offering the benefits of faster diagnosis. Next, we show that our mobile phone based cardiovascular monitoring solution is capable of harnessing up to 6.72 times faster diagnosis compared to existing technologies. As the decompression time on a doctors mobile phone could be significant, our method will be highly advantageous in patient wellness monitoring system where a doctor has to read and diagnose from compressed ECGs of several patients assigned to him. Finally, we successfully implemented the prototype system by establishing mobile phone based cardiovascular patient monitoring.

A Mobile Phone Based Intelligent Telemonitoring Platform

In this paper, we propose a generic smart telemonitoring platform in which the computation power of the mobile phone is highly utilized. In this approach, compression of ECG is done in real-time by the mobile phone for the very first time. The fast and effective compression scheme, designed for the proposed telemonitoring system, outperforms most of the real-time lossless ECG compression algorithms. This mobile phone based computation platform is a promising solution for privacy issues in telemonitoring through encryptions. Moreover, the mobile phones used in this platform performs preliminary detection of abnormal biosignal in realtime. Apart from the usage of mobile phones, this platform supports background biosignal abnormality surveillance using data mining agent.

Time-frequency analysis of normal and abnormal biological signals

Abstract Due to the non-stationary, multicomponent nature of biomedical signals, the use of time-frequency analysis can be inevitable for these signals. The choice of the proper time-frequency distribution (TFD) that can reveal the exact multicomponent structure of biological signals is vital in many applications, including the diagnosis of medical abnormalities. In this paper, the instantaneous frequency (IF) estimation using four well-known TFDs is applied for analyzing biological signals. These TFDs are: the Wigner–Ville distribution (WVD), the Choi–Williams distribution (CWD), the Exponential T-distribution (ETD) and the Hyperbolic T-distribution (HTD). Their performance over normal and abnormal biological signals as well as over multicomponent frequency modulation (FM) signals in additive Gaussian noise was compared. Moreover, the feasibility of utilizing the wavelet transform (WT) in IF estimation is also studied. The biological signals considered in this work are the surface electromyogram (SEMG) with the presence of ECG noise and abnormal cardiac signals. The abnormal cardiac signals were taken from a patient with malignant ventricular arrhythmia, and a patient with supraventricular arrhythmia. Simulation results showed that the HTD has a superior performance, in terms of resolution and cross-terms reduction, as compared to other time-frequency distributions.

Geometrical model for mobile radio channel with hyperbolically distributed scatterers

A geometrical channel model with hyperbolically distributed scatterers for a macrocell mobile environment is presented. This model provides the statistics of direction-of-arrival (DOA) information, as well as amplitude, time delay and phase data for wireless communication channel. The model is useful for designing a forward link beamformer. Simulation results of the channel impulse response and the probability density function for some channel parameters are presented. The data produced from the model is consistent with real world wireless channel characteristics.

Spatial and temporal statistics for the geometrical-based hyperbolic macrocell channel model

A geometric scattering model that predicts the actual measured probability density functions (pdfs) for the direction-of-arrival (DOA) and time-of-arrival (TOA) is highly desirable. Recently we proposed geometrical-based hyperbolic distributed scatterers (GBHDS) model for a macrocell mobile environment. In this paper we investigate the temporal and spatial behavior of this model. The GBHDS model provides the statistics of the DOA and the TOA for multipath components, which are required to test adaptive array algorithms for cellular applications. The joint TOA/DOA pdf at the base station (BS), marginal TOA pdf, marginal DOA pdf, power azimuth spectrum (PAS), and power delay spectrum (PDS) are derived and simulated. Simulation results for the GBHDS model are compared to those of the geometrical-based single bounce macrocell (GBSBM) channel model, the Gaussian scatterer density (GSD) channel model, the geometrical-based exponential (GBE) channel model, as well as to experimental data. It is shown that there is a good match between the GBHDS channel model results and the measurement data, especially for the DOA and TOA pdfs.

A wavelet based secured ECG distribution technique for patient centric cpproach

Electrocardiogram (ECG) provides detail condition of the heart of a cardiac patient. ECG also contains some features, which can serve as a biometric entity for identification of a particular patient. Therefore, when ECG is transmitted for remote telehealth application, it is susceptible to patientpsilas privacy. Interception of ECG data may release patients overall condition to the wrong hand. To protect patientspsila privacy HIPAA regulations are in place. However, according to the literature, research related to the securing (encryption) is scarce. In this paper, we proposed two methods of ECG encryption and distribution for a patient centric telehealth application. Using wavelet decomposition techniques only important portion of the ECG signal is selected for encryption. The remaining ECG coefficients (wavelet decomposition) are uploaded to a public ECG repository. The doctor downloads the publicly available coefficients and uses the encrypted coefficients, which has already been distributed to him from the patient, to retrieve the original ECG. Apart from providing complete security, this architecture provides faster ECG transmission by achieving a high compression ratio of up to 2.81.

A novel wavelet packet-based anti-spoofing technique to secure ECG data

Research related to ECG based biometric authentication is recently gaining popularity. However, there is substantial lack of research in anti-spoofing measurement to protect ECG recognition data from being captured in the wrong hands, where it might be subject to replay attack. Anonymisation of ECG data not only protects this valuable biometric data from being utilised for unauthorised access to restricted facility, but also hides major cardiovascular details of a particular person upholding HIPAA regulations. This paper proposes a novel ECG anonymisation technique based on wavelet packets. It was proven to provide 100% anonymisation, showing robustness against replay attack by the spoofer. Even with the most recent available technology, the anonymised ECG remained totally unidentified. A key, which is only 5.8% of the original ECG, is securely distributed to the authorised personnel for reconstruction of the original ECG.

A blind equalization algorithm for biological signals transmission

Direct transmission of biological signals such as electrocardiogram (ECG) and electroencephalogram (EEG) through mobile network provides practically unlimited movement of the patients and unlimited coverage area. However, transmission of such signals over a bandlimited channel or through a multipath propagation is subject to inter symbol interference (ISI), whereby adjacent symbols on the output of the channel smear and overlap each other causing degradation of error performance. Mitigation of such kind of distortion can be achieved through equalization filter. Recently an adaptive blind channel equalization using sinusoidally-distributed dithered signed-error constant modulus algorithm (DSE-CMA) has been proposed. In this paper we investigate the performance and the feasibility of this scheme for wireless ECG and EEG transmission. Also, this paper discusses the importance of adaptive blind equalizer for biological signals transmission over existing wireless networks such as Global System for Mobile Communications (GSM) and the Enhanced Data rates for GSM Evolution (EDGE). The geometrical-based hyperbolically distributed scatterers (GBHDS) channel model for macrocell environments was simulated with angular spreads (AS) taken from measurement data. Simulation results show that the low complexity of implementation and the fast convergence rate are the major advantages of deploying this scheme for telemedicine applications. It is also shown that the equalizer output signal is highly correlated with the original transmitted signal in time and joint time-frequency domains.

A Time-Frequency Approach for the Analysis of Normal and Arrhythmia Cardiac Signals

Previously, electrocardiogram (ECG) signals have been analyzed in either a time-indexed or spectral form. The reality, is that the ECG and all other biological signals belong to the family of multicomponent nonstationary signals. Due to this reason, the use of time-frequency analysis can be unavoidable for these signals. The Husimi and Wigner distributions are normally used in quantum mechanics for phase space representations of the wavefunction. In this paper, we introduce the Husimi distribution (HD) to analyze the normal and abnormal ECG signals in time-frequency domain. The abnormal cardiac signal was taken from a patient with supraventricular arrhythmia. Simulation results show that the HD has a good performance in the analysis of the ECG signals comparing with the Wigner-Ville distribution (WVD).

A new ECG obfuscation method: A joint feature extraction & corruption approach

With cardiovascular disease as the number one killer of modern era, ECG is collected, stored and transmitted in greater frequency than ever before. However, lack of existing research in secured ECG distribution forces patient privacy under serious threat. Moreover, without adopting secured ECG transmission and storage, HIPAA recommendations for patient privacy are being compromised. This paper presents a new ECG obfuscation method, which uses cross correlation based template matching approach to detect all ECG features followed by corruption of those features with added noises. Without the knowledge of the templates used for feature matching and the noise, the obfuscated features can not be reconstructed. Therefore, three templates and three noises for P wave, QRS Complex and T wave comprise the key, which is only 0.4%-0.9% of the original ECG file size. Only authored doctors possessing this key can reconstruct the original signal. Key distribution is extremely efficient and fast due to small size. Moreover, if the obfuscated ECG reaches to the wrong hand (hacker), it would appear as regular ECG without encryption. Therefore, traditional decryption techniques including powerful brute force attack are useless against this obfuscation. Finally, with unimaginably high number of noise combinations the security strength of the presented method is very high.