Body Sensor Network Based Context-Aware QRS Detection

In this paper, a body sensor network (BSN) based context-aware QRS detection scheme is proposed. The algorithm uses the context information provided by the body sensor network to improve the QRS detection performance by dynamically selecting those leads with the best SNR and taking advantage of the best features of two complementary detection algorithms. The accelerometer data from the BSN are used to classify the daily activities of patients and provide context information. The classification results indicate the types of activities that were engaged in. They also indicate their corresponding intensity, which is related to the signal-to-noise ratio (SNR) of the ECG recordings. Activity intensity is first fed to the lead selector to eliminate those leads with low SNR, and then is fed to a selector to select a proper QRS detector according to the noise level. An MIT-BIH noise stress test database is used to evaluate the algorithms.     

Denoising and R-Peak Detection of Electrocardiogram Signal Based on EMD and Improved Approximate Envelope

The electrocardiogram (ECG ) signal is prone to various high and low frequency noises, including baseline wandering and power-line interference, which become the source of errors in QRS and in other extracted features. This paper presents a new ECG signal-processing approach based on empirical mode decomposition (EMD) and an improved approximate envelope method. To reduce the number of the initial intrinsic mode functions (IMFs), a Butterworth lowpass filter is used to eliminate high frequency noises before the EMD. To correct baseline wandering and to eliminate low frequency noises, the two last-order IMFs are abandoned. An improved approximate envelope is proposed and applied after the Hilbert transform to enhance the energy of QRS complexes and to suppress unwanted P/T waves and noises. Then, an algorithm based on the slope threshold is used for R-peak detection. The proposed denoising and R-peak detection algorithm are validated using the MIT-BIH Arrhythmia Database. The simulation results show that the proposed method can effectively eliminate the Gaussian noise, baseline wander, and power-line interference added to the ECG signal. The method can also function reliably even under poor signal quality and with long P and T peaks. The QRS detector has an average sensitivity of Se=99.94 % and a positive predictivity of +P=99.87 % over the first lead of the MIT-BIH Arrhythmia Database.     

The energy-efficient implementation of an adaptive-filtering-based QRS complex detection method for wearable devices

Electrocardiogram (ECG) can be used as a valid way for diagnosing heart disease. To fulfill ECG processing in wearable devices by reducing computation complexity and hardware cost, two kinds of adaptive filters are designed to perform QRS complex detection and motion artifacts removal, respectively. The proposed design achieves a sensitivity of 99.49% and a positive predictivity of 99.72%, tested under the MIT-BIH ECG database. The proposed design is synthesized under the SMIC 65-nm CMOS technology and verified by post-synthesis simulation. Experimental results show that the power consumption and area cost of this design are of 160 μW and 1.09×10⁵ μm², respectively.     

基于小波和扩展卡尔曼的心电检测

文中将小波变换和扩展卡尔曼滤波器相结合,利用小波变换多尺度多分辨的特点,将心电信号进行分解。然后对心电信号在各尺度上进行扩展卡尔曼滤波。最后在扩展卡尔曼滤波的输出结果上进行QRS波形检测。文中方法经MIT-BIH心电数据库检验,QRS波Se（探测灵敏度）在99.40%以上,同时,QRS＋P（正探测率）在99.39%以上,提高了心电信号检测的正确率。     

自适应CA-CFAR的心电信号R波检测技术

针对心电信号R波的突变特性,利用雷达信号的检测方法,本文提出一种自适应单元平均恒虚警率(cell averaging-constant false alarm rate, CA-CFAR)的R波检测方法。首先利用滤波器组对心电信号进行预处理;然后将预处理后的信号利用自适应CA-CFAR检测判决;最后由心电信号R波的间隔特性做一个不应期剔除规则的处理,得到R波的定位。对美国麻省理工学院提供的MIT-BIH数据库中心电图(Electrocardiograph, ECG)信号仿真,实验证明,自适应参考单元的CA-CFAR对MIT-BIH的ECG信号R波检测的精准率为99.842%,检测误差为0.354%。实测数据表明了算法的有效性和适用性。      

Wearable Organic Electrochemical Transistor Array for Skin-Surface Electrocardiogram Mapping Above a Human Heart

Electrocardiogram (ECG) mapping can provide vital information in sports training and cardiac disease diagnosis. However, most electronic devices for monitoring ECG signals need to use multiple long wires, which limit their wearability and conformability in practical applications, while wearable ECG mapping based on integrated sensor arrays has been rarely reported. Herein, ultra-flexible organic electrochemical transistor (OECT) arrays used for wearable ECG mapping on the skin surface above a human heart are presented. QRS complexes of ECG signals at different recording distances and directions relative to the heart are obtained. Furthermore, the ECG signals are successfully analyzed by the devices before and after exercise, indicating potential applications in some sports training and fitness scenarios. The OECT arrays that can conveniently monitor spacial ECG signals in the heart region may find niche applications in wearable electronics and healthcare products in the future.     

Neural-network-based adaptive matched filtering for QRS detection

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.     

Clustering ECG complexes using hermite functions and self-organizing maps

An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NN's are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.     

Genetic design of optimum linear and nonlinear QRS detectors

Describes an approach to the design of optimum QRS detectors. The authors report on detectors including a linear or nonlinear polynomial filter, which enhances and rectifies the QRS complex, and a simple, adaptive maxima detector. The parameters of the filter and the detector, and the samples to be processed are selected by a genetic algorithm which minimizes the detection errors made on a set of reference ECG signals. Three different architectures and the experimental results achieved on the MIT-BIH Arrhythmia Database are described     

Low‐cost wireless sensor networks for remote cardiac patients monitoring applications

One of today's most pressing matters in medical care is response time to patients in need. Scope of this research is to suggest a solution that would help reduce response time in emergency situations utilizing technologies of wireless sensor networks. The enhanced power efficiency, minimized production cost, condensed physical layout, and reduced wired connections present a much more proficient and simplified approach to the continuous monitoring of patients' physiological status. The proposed sensor network system is composed of wearable vital sign sensors and a workstation monitor. The wearable platforms are to be distributed to patients of concern. The wearable platforms can provide continuous electrocardiogram (ECG) monitoring by measuring electrical potentials between various points of the body using a galvanometer. They will then relay the ECG signals wirelessly to the workstation monitor. In addition to displaying the data, the workstation will also perform signal wavelet transformation for ECG characteristic extractions. Copyright © 2007 John Wiley & Sons, Ltd.     

A novel method for detection of the transition between atrial fibrillation and sinus rhythm

Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.     

A Real-Time QRS Detection Algorithm

We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.     

Atrial activity enhancement by Wiener filtering using an artificial neural network

This paper describes a novel technique for the cancellation of the ventricular activity for applications such as P-wave or atrial fibrillation detection. The procedure was thoroughly tested and compared with a previously published method, using quantitative measures of performance. The novel approach estimates, by means of a dynamic time delay neural network (TDNN), a time-varying, nonlinear transfer function between two ECG leads. Best results were obtained using an Elman TDNN with nine input samples and 20 neurons, employing a sigmoidal tangencial activation in the hidden layer and one linear neuron in the output stage. The method does not require a previous stage of QRS detection. The technique was quantitatively evaluated using the MIT-BIH arrhythmia database and compared with an adaptive cancellation scheme proposed in the literature. Results show the advantages of the proposed approach, and its robustness during noisy episodes and QRS morphology variations.     

A real-time microprocessor QRS detector system with a 1-ms timingaccuracy for the measurement of ambulatory HRV

The design, test methods, and results of an ambulatory QRS detector are presented. The device is intended for the accurate measurement of heart rate variability (HRV) and reliable QRS detection in both ambulatory and clinical use. The aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device. The complete monitor system consists of a host computer and the detector unit. The detector device is constructed of a commonly available digital signal processing (DSP) microprocessor and other components. The QRS detection algorithm uses optimized prefiltering in conjunction with a matched filter and dual edge threshold detection. The purpose of the prefiltering is to attenuate various noise components in order to achieve improved detection reliability. The matched filter further improves signal-to-noise ratio (SNR) and symmetries the QRS complex for the threshold detection, which is essential in order to achieve the desired performance. The decision for detection is made in real-time and no search-back method is employed. The host computer is used to configure the detector unit, which includes the setting of the matched filter impulse response, and in the retrieval and postprocessing of the measurement results. The QRS detection timing accuracy and detection reliability of the detector system was tested with an artificially generated electrocardiogram (EGG) signal corrupted with various noise types and a timing standard deviation of less than 1 ms was achieved with most noise types and levels similar to those encountered in real measurements. A QRS detection error rate (ER) of 0.1 and 2.2% was achieved with records 103 and 105 from the MIT-BIH Arrhythmia database, respectively     

Digital fractional order operators for R-wave detection in electrocardiogram signal

In this study, we present an effective R-wave detection method in the QRS complex of the electrocardiogram (ECG) based on digital differentiation and integration of fractional order. The detection algorithm is performed in two steps. The pre-processing step is based on a fractional order digital band-pass filter whose fractional order is obtained by maximising the signal to noise ratio of the ECG signal, followed by a five points differentiator of fractional order 1.5 then the squaring transformation and the smoothing are used to generate peaks corresponding to the ECG parts with high slopes. The detection step is a new and simple strategy which is also based on fractional order operators for the localisation of the R waves. The MIT/BIH arrhythmia database is used to test the effectiveness of the proposed method. The algorithm has provided very good performance and has achieved about 99.86% of the detection rate for the standard database. The results obtained are presented, discussed and compared to the most recent and efficient R-wave detection algorithms.     

基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

A micropower analog-digital heart rate detector chip

A micropower detector chip integrated with CMOS technology has been developed for a hand-held heart rate monitoring instrument mainly used by professional athletes and others who exercise seriously to increase their endurance and overall performance. The chip uses linear bandpass filtering and threshold detection and is implemented with analog switched-capacitor (SC) and digital techniques. The bandpass filter transfer function is derived from the spectral contents of noisy ECG signals. The layout was designed in full custom style in order to minimize the silicon area. The chip achieves a detection rate of 90% with a simulated ECG signal corrupted with Gaussian noise (signal-to-noise ratio = 5) in the heart rate range 60-200 1/min with a total current consumption of less than 30µA from a 3-V supply. The detection rate is 96.9% for a noisy signal obtained from the MIT-BIH arrhythmia database.     

基于WLS估计双导联提取QRS波群时间-电压面积

 提出一种提取QRS波群时间-电压面积的新方法,以QRS波群内的一点为基点,向前和向后逐段求出线段参数的WLS估计和线性度,根据线段参数的WLS估计和线性度确定基线,在基线上利用假设检验的方法得出该导联上的QRS波群的起点和终点,在另一导联上用同样的方法求出该QRS波群的起点和终点并最终确定该QRS波群的起点和终点.从而提取了QRS波群时间—电压面积这一特征参数.该方法成功应用于MIT-BIH数据库的QT数据库的所有105个数据文件,在第一组专家标记的3623个QRS波群上获得97.5%的精确度;在第二组专家标记的404个QRS波群上获得98.0%的精确度.为解决非人工交互的实时的心电图计算机自动诊断打下了基础.     

Beat-based ECG compression using gain-shape vector quantization

An electrocardiogram (ECG) data compression scheme is presented using the gain-shape vector quantization. The proposed approach utilizes the fact that ECG signals generally show redundancy among adjacent heartbeats and adjacent samples. An ECG signal is QRS detected and segmented according to the detected fiducial points. The segmented heartbeats are vector quantized, and the residual signals are calculated and encoded using the AREA algorithm. The experimental results show that with the proposed method both visual quality and the objective quality are excellent even in low bit rates. An average PRD of 5.97% at 127 b/s is obtained for the entire 48 records in the MIT-BIH database. The proposed method also outperforms others for the same test dataset.     

一种便携式心电监测仪的设计

介绍一种能实时处理、处理速度快的心电检测仪,它以基于ARM Cortex-M3内核的单片机为核心,以大容量SD卡作为存储媒介,并实现人机交互、波形回放、心律失常分析及病情报警功能。系统采取实时的QRS波检测算法,并嵌入文件系统将心电数据以文本的形式存储,提高了数据的可读性和移植性。检测仪经MIT-BIH数据库和实际人体测试检验,达到了实际要求。