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

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

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased

Mathematical tools for identifying the fetal response to physical exercise during pregnancy

In the applied mathematics literature there exist a significant number of tools that can reveal the interaction between mother and fetus during rest and also during and after exercise. These tools are based on techniques from a number of areas such as signal processing, time series analysis, neural networks, heart rate variability as well as dynamical systems and chaos. We will briefly review here some of these methods, concentrating on a method of extracting the fetal heart rate from the mixed maternal-fetal heart rate signal, that is based on phase space reconstructio

Computationally Efficient QRS Detection Analysis In Electrocardiogram Based On Dual-Slope Method

A dramatic growth of interest for wearable technology has been fostered by recent technological advances in sensors, low-power integrated circuits and wireless communications. This interest originates from the need of monitoring a patient over extensive period of time. For cardiac patients, wearable heart monitoring sensors have already become a life-saving intervention ensuring continuous monitoring during daily life. Therefore, it is essential for an accurate monitoring and diagnosis of heart patients. Patients can be equipped with wireless, miniature and lightweight sensors. The sensors temporarily store physiological data and then periodically upload the data to a database server. These recorded data sets are then analyzed to predict any possibility of worsening patient\u27s situation or explored to assess the effect of clinical intervention. To obtain accurate response with less computational complexity as well as long battery life time, there is a demand of developing fast and accurate algorithm and prototypes for wearable heart monitoring sensors. A computationally efficient QRS detection algorithm is indispensable for low power operation on electrocardiogram (ECG) signal. In need of detecting QRS complex, most of the early works were proposed based on derivatives of ECG signal. They can be easily implemented with high computational speed. But owing to the inherent variability in ECG, these methods are highly affected by large derivatives of baseline noises. Algorithms based on neural network (NN) showed relatively robust performance against noise but requires exhaustive training and estimation of model parameter. On the other hand, wavelet based methods have the choice problem of mother wavelet. Hence, none of these methods is suitable for giving a long battery performance in wearable devices with high accuracy. Recently, Wang et al. proposed a novel dual slope QRS detection algorithm which has less computational complexity as well as high accuracy. Considering that the width of the QRS complex is relatively fixed, this algorithm is based on the fact that the largest change of slope usually happens at the peak of QRS complex. The hardware requirement is also low. However, the method has a set of time consuming slope calculations on both sides of each sample. To avoid such time consuming slope calculation, only one sample on each side can be highlighted. In addition, the multiplication of the left and right hand side slope should give us a very high value in QRS complex. The goal of this thesis is to develop a new computationally efficient method to detect QRS complexes and compare with the other renowned QRS detection algorithms. MIT-BIH arrhythmia database based on patients of different heart diseases and database containing ECG from healthy subjects are used. To analyze the performance, false negative (FN) and false positive (FP) are evaluated. A false negative (FN) occurs when algorithm fails to detect an actual QRS complex quoted in the corresponding annotation file of the database record and a false positive (FP) means a false beat detection. Error rate (ER) , Sensitivity (Se) and Specificity (Sp) are calculated using FP and FN

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