A FPGA system for QRS complex detection based on Integer Wavelet Transform

Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a real-time hardware based solution for this task. To filter ECG signal and to extract QRS complex it employs the Integer Wavelet Transform. The system includes several components and is incorporated in a single FPGA chip what makes it suitable for direct embedding in medical instruments or wearable health care devices. It has sufficient accuracy (about 95%), showing remarkable noise immunity and low cost. Additionally, each system component is composed of several identical blocks/cells what makes the design highly generic. The capacity of today existing FPGAs allows even dozens of detectors to be placed in a single chip. After the theoretical introduction of wavelets and the review of their application in QRS detection, it will be shown how some basic wavelets can be optimized for easy hardware implementation. For this purpose the migration to the integer arithmetic and additional simplifications in calculations has to be done. Further, the system architecture will be presented with the demonstrations in both, software simulation and real testing. At the end, the working performances and preliminary results will be outlined and discussed. The same principle can be applied with other signals where the hardware implementation of wavelet transform can be of benefit

Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

DELINEATION OF ECG FEATURE EXTRACTION USING MULTIRESOLUTION ANALYSIS FRAMEWORK

ECG signals have very features time-varying morphology, distinguished as P wave, QRS complex, and T wave. Delineation in ECG signal processing is an important step used to identify critical points that mark the interval and amplitude locations in the features of each wave morphology. The results of ECG signal delineation can be used by clinicians to associate the pattern of delineation point results with morphological classes, besides delineation also produces temporal parameter values of ECG signals. The delineation process includes detecting the onset and offset of QRS complex, P and T waves that represented as pulse width, and also the detection of the peak from each wave feature. The previous study had applied bandpass filters to reduce amplitude of P and T waves, then the signal was passed through non-linear transformations such as derivatives or square to enhance QRS complex. However, the spectrum bandwidth of QRS complex from different patients or same patient may be different, so the previous method was less effective for the morphological variations in ECG signals. This study developed delineation from the ECG feature extraction based on multiresolution analysis with discrete wavelet transform. The mother wavelet used was a quadratic spline function with compact support. Finally, determination of R, T, and P wave peaks were shown by zero crossing of the wavelet transform signals, while the onset and offset were generated from modulus maxima and modulus minima. Results show the proposed method was able to detect QRS complex with sensitivity of 97.05% and precision of 95.92%, T wave detection with sensitivity of 99.79% and precision of 96.46%, P wave detection with sensitivity of 56.69% and precision of 57.78%. The implementation in real time analysis of time-varying ECG morphology will be addressed in the future research

A wavelet-based ECG delineation algorithm for 32-bit integer online processing

<p>Abstract</p> <p>Background</p> <p>Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li <it>et al. </it>in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as <it>root mean square </it>(RMS) or floating point algebra, which are computationally demanding.</p> <p>Methods</p> <p>This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT.</p> <p>Results</p> <p>The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods.</p> <p>Conclusions</p> <p>The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.</p

Tele-cardiology sensor networks for remote ECG monitoring

One of today’s most pressing matters in medical care is the response time to patients in need. The scope of this thesis is to suggest a solution that would help reduce response time in emergency situations utilizing wireless sensor networks technology. Wireless sensor network researches have recently gained unprecedented momentum in both industries and academia, especially its potential applications in Emergency Medical Services and Intensive Care Units. The enhanced power efficiency, minimized production cost, condensed physical layout, as well as reduced wired connections, presents a much more proficient and simplified approach to the continuous monitoring of patients’ physiological status. This thesis focuses on the areas of remote ECG feature extraction utilizing wavelet transformation concepts and sensor networks technology. The proposed sensor network system provides the following contributions. The low-cost, low-power wearable platforms are to be distributed to patients of concern and will provide continuous ECG monitoring by measuring electrical potentials between various points of the body using a galvanometer. The system is enabled with integrated RF communication capability that will relay the signals wirelessly to a workstation monitor. The workstation is equipped with ECG signal processing software that performs ECG characteristic extractions via wavelet transformation. Lastly, a low-complex, end-to-end security scheme is also incorporated into this system to ensure patient privacy. Other notable features include location tracking algorithms for patient tracking, and MATLAB Server environment for internal communication