ECG reduction for wearable sensor

The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for real-time computation and incremental data arrival. In this paper, we describe a computationally naive, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10: 1 while still maintaining an error rate below 10%

Compression of ECG signals

V této práci je obecně popsáno několik základních kompresních algoritmů využívaných jak při ztrátové, tak při neztrátové kompresi. Jmenovitě se jedná o proudové kódování, Lempel-Ziv, Huffmanovo kódování, Move-to-Front, vlnkovou transformaci a Burrows-Wheelerovu transformaci. Cílem práce je vytvořit kompresní algoritmus EKG s implementací proudového kódování a BWT. Dalším postupem použitým v algoritmu je vlnková transformace. Algoritmus je napsán v programovém prostředí MATLAB.This thesis contains general description of several basic compression algorithms used either for lossy and lossless compression. These algorithms are runlength encoding, Lempel-Ziv, Huffman encoding, Move-to-Front, wavelet transform and Burrows-Wheeler transform. Aim of this thesis is creation of ECG compression algorithm with implementation of runlength encoding and BWT. Another technique used in algorithm is wavelet transform. The algorithm is written in programming environment MATLAB.

Secure steganography, compression and diagnoses of electrocardiograms in wireless body sensor networks

Submission of this completed form results in your thesis/project being lodged online at the RMIT Research Repository. Further information about the RMIT Research Repository is available at http://researchbank.rmit.edu.au Please complete abstract and keywords below for cataloguing and indexing your thesis/project. Abstract (Minimum 200 words, maximum 500 words) The usage of e-health applications is increasing in the modern era. Remote cardiac patients monitoring application is an important example of these e-health applications. Diagnosing cardiac disease in time is of crucial importance to save many patients lives. More than 3.5 million Australians suffer from long-term cardiac diseases. Therefore, in an ideal situation, a continuous cardiac monitoring system should be provided for this large number of patients. However, health-care providers lack the technology required to achieve this objective. Cloud services can be utilized to fill the technology gap for health-care providers. However, three main problems prevent health-care providers from using cloud services. Privacy, performance and accuracy of diagnoses. In this thesis we are addressing these three problems. To provide strong privacy protection services, two steganography techniques are proposed. Both techniques could achieve promising results in terms of security and distortion measurement. The differences between original and resultant watermarked ECG signals were less then 1%. Accordingly, the resultant ECG signal can be still used for diagnoses purposes, and only authorized persons who have the required security information, can extract the hidden secret data in the ECG signal. Consequently, to solve the performance problem of storing huge amount of data concerning ECG into the cloud, two types of compression techniques are introduced: Fractal based lossy compression technique and Gaussian based lossless compression technique. This thesis proves that, fractal models can be efficiently used in ECG lossy compression. Moreover, the proposed fractal technique is a multi-processing ready technique that is suitable to be implemented inside a cloud to make use of its multi processing capability. A high compression ratio could be achieved with low distortion effects. The Gaussian lossless compression technique is proposed to provide a high compression ratio. Moreover, because the compressed files are stored in the cloud, its services should be able to provide automatic diagnosis capability. Therefore, cloud services should be able to diagnose compressed ECG files without undergoing a decompression stage to reduce additional processing overhead. Accordingly, the proposed Gaussian compression provides the ability to diagnose the resultant compressed file. Subsequently, to make use of this homomorphic feature of the proposed Gaussian compression algorithm, in this thesis we have introduced a new diagnoses technique that can be used to detect life-threatening cardiac diseases such as Ventricular Tachycardia and Ventricular Fibrillation. The proposed technique is applied directly to the compressed ECG files without going through the decompression stage. The proposed technique could achieve high accuracy results near to 100% for detecting Ventricular Arrhythmia and 96% for detecting Left Bundle Branch Block. Finally, we believe that in this thesis, the first steps towards encouraging health-care providers to use cloud services have been taken. However, this journey is still long

Cardiovascular data analytics for real time patient monitoring

Improvements in wearable sensor devices make it possible to constantly monitor physiological parameters such as electrocardiograph (ECG) signals for long periods. Remote patient monitoring with wearable sensors has an important role to play in health care, particularly given the prevalence of chronic conditions such as cardiovascular disease (CVD)—one of the prominent causes of morbidity and mortality worldwide. Approximately 4.2 million Australians suffer from long-term CVD with approximately one death every 12 minutes. The assessment of ECG features, especially heart rate variability (HRV), represents a non-invasive technique which provides an indication of the autonomic nervous system (ANS) function. Conditions such as sudden cardiac death, hypertension, heart failure, myocardial infarction, ischaemia, and coronary heart disease can be detected from HRV analysis. In addition, the analysis of ECG features can also be used to diagnose many types of life-threatening arrhythmias, including ventricular fibrillation and ventricular tachycardia. Non-cardiac conditions, such as diabetes, obesity, metabolic syndrome, insulin resistance, irritable bowel syndrome, dyspepsia, anorexia nervosa, anxiety, and major depressive disorder have also been shown to be associated with HRV. The analysis of ECG features from real time ECG signals generated from wearable sensors provides distinctive challenges. The sensors that receive and process the signals have limited power, storage and processing capacity. Consequently, algorithms that process ECG signals need to be lightweight, use minimal storage resources and accurately detect abnormalities so that alarms can be raised. The existing literature details only a few algorithms which operate within the constraints of wearable sensor networks. This research presents four novel techniques that enable ECG signals to be processed within the limitations of resource constraints on devices to detect some key abnormalities in heart function. - The first technique is a novel real-time ECG data reduction algorithm, which detects and transmits only those key points that are critical for the generation of ECG features for diagnoses. - The second technique accurately predicts the five-minute HRV measure using only three minutes of data with an algorithm that executes in real-time using minimal computational resources. - The third technique introduces a real-time ECG feature recognition system that can be applied to diagnose life threatening conditions such as premature ventricular contractions (PVCs). - The fourth technique advances a classification algorithm to enhance the performance of automated ECG classification to determine arrhythmic heart beats based on noisy ECG signals. The four novel techniques are evaluated in comparison with benchmark algorithms for each task on the standard MIT-BIH Arrhythmia Database and with data generated from patients in a major hospital using Shimmer3 wearable ECG sensors. The four techniques are integrated to demonstrate that remote patient monitoring of ECG using HRV and ECG features is feasible in real time using minimal computational resources. The evaluation show that the ECG reduction algorithm is significantly better than existing algorithms that can be applied within sensor nodes, such as time-domain methods, transformation methods and compressed sensing methods. Furthermore, the proposed ECG reduction is found to be computationally less complex for resource constrained sensors and achieves higher compression ratios than existing algorithms. The prediction of a common HRV measure, the five-minute standard deviation of inter-beat variations (SDNN) and the accurate detection of PVC beats was achieved using a Count Data Model, combined with a Poisson-generated function from three-minute ECG recordings. This was achieved with minimal computational resources and was well suited to remote patient monitoring with wearable sensors. The PVC beats detection was implemented using the same count data model together with knowledge-based rules derived from clinical knowledge. A real-time cardiac patient monitoring system was implemented using an ECG sensor and smartphone to detect PVC beats within a few seconds using artificial neural networks (ANN), and it was proven to provide highly accurate results. The automated detection and classification were implemented using a new wrapper-based hybrid approach that utilized t-distributed stochastic neighbour embedding (t-SNE) in combination with self-organizing maps (SOM) to improve classification performance. The t-SNE-SOM hybrid resulted in improved sensitivity, specificity and accuracy compared to most common hybrid methods in the presence of noise. It also provided a better, more accurate identification for the presence of many types of arrhythmias from the ECG recordings, leading to a more timely diagnosis and treatment outcome.Doctor of Philosoph

Efficient and secured wireless monitoring systems for detection of cardiovascular diseases

Cardiovascular Disease (CVD) is the number one killer for modern era. Majority of the deaths associated with CVD can entirely be prevented if the CVD struck person is treated with urgency. This thesis is our effort in minimizing the delay associated with existing tele-cardiology application. We harnessed the computational power of modern day mobile phones to detect abnormality in Electrocardiogram (ECG). If abnormality is detected, our innovative ECG compression algorithm running on the patient's mobile phone compresses and encrypts the ECG signal and then performs efficient transmission towards the doctors or hospital services. According to the literature, we have achieved the highest possible compression ratio of 20.06 (95% compression) on ECG signal, without any loss of information. Our 3 layer permutation cipher based ECG encoding mechanism can raise the security strength substantially higher than conventional AES or DES algorithms. If in near future, a grid of supercomputers can compare a trillion trillion trillion (1036) combinations of one ECG segment (comprising 500 ECG samples) per second for ECG morphology matching, it will take approximately 9.333 X 10970 years to enumerate all the combinations. After receiving the compressed ECG packets the doctor's mobile phone or the hospital server authenticates the patient using our proposed set of ECG biometric based authentication mechanisms. Once authenticated, the patients are diagnosed with our faster ECG diagnosis algorithms. In a nutshell, this thesis contains a set of algorithms that can save a CVD affected patient's life by harnessing the power of mobile computation and wireless communication