Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems

Bradycardia can be modulated using the cardiac pacemaker, an implantable medical device which sets and balances the patient’s cardiac health. The device has been widely used to detect and monitor the patient’s heart rate. The data collected hence has the highest authenticity assurance and is convenient for further electric stimulation. In the pacemaker, ECG detector is one of the most important element. The device is available in its new digital form, which is more efficient and accurate in performance with the added advantage of economical power consumption platform. In this work, a joint algorithm based on biorthogonal wavelet transform and run-length encoding (RLE) is proposed for QRS complex detection of the ECG signal and compressing the detected ECG data. Biorthogonal wavelet transform of the input ECG signal is first calculated using a modified demand based filter bank architecture which consists of a series combination of three lowpass filters with a highpass filter. Lowpass and highpass filters are realized using a linear phase structure which reduces the hardware cost of the proposed design approximately by 50%. Then, the location of the R-peak is found by comparing the denoised ECG signal with the threshold value. The proposed R-peak detector achieves the highest sensitivity and positive predictivity of 99.75 and 99.98 respectively with the MIT-BIH arrhythmia database. Also, the proposed R-peak detector achieves a comparatively low data error rate (DER) of 0.002. The use of RLE for the compression of detected ECG data achieves a higher compression ratio (CR) of 17.1. To justify the effectiveness of the proposed algorithm, the results have been compared with the existing methods, like Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.     

可穿戴式心电信号R峰检测的心拍感知卷积网络

目的 实现可穿戴式心电信号的R峰检测,为准确估计心率、心率变异性等生理参数提供基础。方法 采用全卷积网络预测R峰热图,对热图进行峰值定位获得R峰位置。引入心拍感知模块,联合心拍数量预测任务和R峰热图预测任务进行学习,提高卷积网络对全局上下文信息的提取能力。心拍感知模块预测的心拍数量还可估计R-R间期,用作峰值定位的峰间最小水平距离。为满足移动端的实时应用,采用深度可分离卷积减小模型的参数量和计算量。结果 实验仅使用可穿戴式心电数据训练模型。测试中定位误差容忍度设置为150 ms时,本文方法在可穿戴式心电数据集和公开数据集LUDB上的R峰检测灵敏度均高达100%,真阳率均超过99.9%;对于时长10 s的ECG信号,R峰检测CPU耗时约为23.2 ms。结论 本文方法对可穿戴式和常规心电信号的R峰检测均可达到良好效果,且满足R峰检测的实时性需求。     

Wavelet-based Encoding Scheme for Controlling Size of Compressed ECG Segments in Telecardiology Systems

One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption.     

Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features

The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.     

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.     

胎儿心电信号的无创提取：基于时间卷积编解码网络

目的 实现从孕妇腹壁混合心电信号中提取微弱的胎儿心电信号,为准确估计胎儿心率、分析胎儿心电波形等提供基础。方法 利用深度卷积网络（deep CNN）优越的非线性映射能力,本文提出了一种基于时间卷积编解码网络的非线性自适应噪声消除（nonlinear ANC）提取框架,以实现胎儿心电信号的有效提取。首先构建适用于处理胎儿心电信号的深度时间卷积网络（TCED-Net）模型作为非线性映射工具;然后以孕妇胸部心电信号为参考,利用该模型估计孕妇腹壁混合心电信号中的母体心电成分;最后从腹壁混合信号中减去所估计的母体心电成分,以得到完整的胎儿心电信号。实验利用合成心电数据（FECGSYNDB）和临床心电数据（NIFECGDB、PCDB）对方法性能进行测试与对比。结果 本文方法在FECGSYNDB上的胎儿R峰检测精度（[F1]值）、均方误差（MSE）和质量信噪比（qSNR）分别达到98.89 %,0.20和7.84;在NIFECGDB上的[F1]值达到99.1%;在 PCDB 上的[F1]值达到 98.61%。在不同数据集中较之 EKF（[F1=]93.84%）、ES-RNN([F1] =97.20% )和 AECG-DecompNet（[F1]=95.43%）等现有性能最佳的算法,本文方法的R峰检测精度指标分别高出5.05%、1.9%和3.18%,均优于现有最佳方法。结论 与现有算法相比,本文方法可以提取出更为清晰的胎儿心电信号,对孕期进行有效的胎儿健康监护具有一定的应用价值。     

An Integrated Approach Using Chaotic Map & Sample Value Difference Method for Electrocardiogram Steganography and OFDM Based Secured Patient Information Transmission

This paper presents a patient’s confidential data hiding scheme in electrocardiogram (ECG) signal and its subsequent wireless transmission. Patient’s confidential data is embedded in ECG (called stego-ECG) using chaotic map and the sample value difference approach. The sample value difference approach effectually hides the patient’s confidential data in ECG sample pairs at the predefined locations. The chaotic map generates these predefined locations through the use of selective control parameters. Subsequently, the wireless transmission of the stego-ECG is analyzed using the Orthogonal Frequency Division Multiplexing (OFDM) system in a Rayleigh fading scenario for telemedicine applications. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through the statistical and clinical performance measures. Statistical measures comprise of Percentage Root-mean-square Difference (PRD), Peak Signal to Noise Ratio (PSNR), and Kulback-Leibler Divergence (KL-Div), etc. while clinical metrics includes wavelet Energy Based Diagnostic Distortion (WEDD) and Wavelet based Weighted PRD (WWPRD). The various channel Signal-to-Noise Ratio scenarios are simulated for wireless communication of stego-ECG in OFDM system. The proposed method over all the 48 records of MIT-BIH arrhythmia database resulted in average, PRD = 0.26, PSNR = 55.49, KL-Div = 3.34 × 10^?6, WEDD = 0.02, and WWPRD = 0.10 with secret data size of 21Kb. Further, a comparative analysis of proposed method and recent existing works was also performed. The results clearly, demonstrated the superiority of proposed method.     

Automatic Classification of Heartbeats Using Wavelet Neural Network

The electrocardiogram (ECG) signal is widely employed as one of the most important tools in clinical practice in order to assess the cardiac status of patients. The classification of the ECG into different pathologic disease categories is a complex pattern recognition task. In this paper, we propose a method for ECG heartbeat pattern recognition using wavelet neural network (WNN). To achieve this objective, an algorithm for QRS detection is first implemented, then a WNN Classifier is developed. The experimental results obtained by testing the proposed approach on ECG data from the MIT-BIH arrhythmia database demonstrate the efficiency of such an approach when compared with other methods existing in the literature.     

Towards Improved Healthcare Performance: Examining Technological Possibilities and Patient Satisfaction with Wireless Body Area Networks

This paper investigates the benefits of using less intrusive wireless technologies for heart monitoring. By replacing well established heart monitoring devices (i.e. Holter) with wireless ECG based Body Area Networks (BAN), improved healthcare performance can be achieved, reflected in (1) high quality ECG recordings during physical activities and (2) increased patient satisfaction. A small scale clinical trial was conducted to compare both technologies and the results illustrate that the wireless ECG monitor was able to detect ECG signals intended for arrhythmia diagnostics. Furthermore, from a patient’s perspective, both technologies were evaluated using three dimensions, namely; hygienic aspects, physical activity, and skin reactions. Results demonstrate that the wireless ECG BAN showed better performance, especially regarding the hygienic aspects. It was also favourable for use during physical activities, and the signal quality of the wireless sensor system demonstrated good performance regarding signal noise and artefact disturbances. This paper concludes that wireless cardiac monitoring systems have significant benefits from a patient’s perspective, and further clinical trials should be conducted to further evaluate the new ECG based BAN system, to identify the possibility of widespread adoption and utilisation of wireless technology for arrhythmia diagnostics.     

Analysis of Myocardial Infarction Using Discrete Wavelet Transform

Myocardial infarction (MI), is commonly known as a heart attack, occurs when the blood supply to the portion of the heart is blocked causing some heart cells to die. This information is depicted in the elevated ST wave, increased Q wave amplitude and inverted T wave of the electrocardiogram (ECG) signal. ECG signals are prone to noise during acquisition due to electrode movement, muscle tremor, power line interference and baseline wander. Hence, it becomes difficult to decipher the information about the cardiac state from the morphological changes in the ECG signal. These signals can be analyzed using different signal processing techniques. In this work, we have used multiresolution properties of wavelet transformation because it is suitable tool for interpretation of subtle changes in the ECG signal. We have analyzed the normal and MI ECG signals. ECG signal is decomposed into various resolution levels using the discrete wavelet transform (DWT) method. The entropy in the wavelet domain is computed and the energy–entropy characteristics are compared for 2282 normal and 718 MI beats. Our proposed method is able to detect the normal and MI ECG beat with more than 95% accuracy.     

基于无线的心电采集与传送系统设计

利用nRF401无线收发芯片设计一个心电信号的前端采集与传送系统。该系统可以嵌入心电监护系统中，实现信号采集系统与信号处理系统分离，增强设备应用的灵活性。     

From Pacemaker to Wearable: Techniques for ECG Detection Systems

With the alarming rise in the deaths due to cardiovascular diseases (CVD), present medical research scenario places notable importance on techniques and methods to detect CVDs. As adduced by world health organization, technological proceeds in the field of cardiac function assessment have become the nucleus and heart of all leading research studies in CVDs in which electrocardiogram (ECG) analysis is the most functional and convenient tool used to test the range of heart-related irregularities. Most of the approaches present in the literature of ECG signal analysis consider noise removal, rhythm-based analysis, and heartbeat detection to improve the performance of a cardiac pacemaker. Advancements achieved in the field of ECG segments detection and beat classification have a limited evaluation and still require clinical approvals. In this paper, approaches on techniques to implement on-chip ECG detector for a cardiac pacemaker system are discussed. Moreover, different challenges regarding the ECG signal morphology analysis deriving from medical literature is extensively reviewed. It is found that robustness to noise, wavelet parameter choice, numerical efficiency, and detection performance are essential performance indicators required by a state-of-the-art ECG detector. Furthermore, many algorithms described in the existing literature are not verified using ECG data from the standard databases. Some ECG detection algorithms show very high detection performance with the total number of detected QRS complexes. However, the high detection performance of the algorithm is verified using only a few datasets. Finally, gaps in current advancements and testing are identified, and the primary challenge remains to be implementing bullseye test for morphology analysis evaluation.     

Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals

In this paper, MODWT is used to decompose the Electrocardiography (ECG) signals and to identify the changes of R waves in the noisy input ECG signal. The MODWT is used to handle the arbitrary changes in the input signal. The R wave’s detctected by the proposed framework is used by the doctors and careholders to take necessary action for the patients. MATLAB simulink model is used to develop the simulation model for the MODWT method. The performance of the MODWT based remote health monitoring system method is comparatively analyzed with other ECG monitoring approaches such as Haar Wavelet Transformation (HWT) and Discrete Wavelet Transform (DWT). Sensitivity, specificity, and Receiver Operating Characteristic (ROC) curve are calculated to evaluate the proposed Internet of Things with MODWT based ECG monitoring system. We have used MIT-BIH Arrythmia Database to perform the experiments.     

Wireless Electrocardiogram Transmission in ISM Band: An Approach Towards Telecardiology

Remote monitoring of biomedical signals provides an opportunity to extend health care service to a distant patient. In this paper, a short range wireless telecardiology system is described with the objective to transmit electrocardiogram signal for remote end acquisition. The acquired signal was compressed using a combination of modified delta encoding and run length encoding technique and transmitted using a wireless transceiver operating in 2.4 GHz industrial, scientific and medical band to a distance of 400 ft. In the receiving end, error check principle was used to find any data loss before the data is reconstructed for feature extraction. With Physionet data using 8-bit quantization an average compression ratio (CR) of 12.23, percentage root mean squared difference (PRD) of 4.342 and PRD normalized (PRDN) of 9.271 were obtained. With ECG data collected from healthy volunteers, these figures came out to be 14.64, 12.92 and 13.46 respectively. An improvement of performance was observed with 10 bit quantization of ECG data. Computational simplicity of the proposed algorithm provides an opportunity to use a low end microcontroller to implement the compression in standalone hardware.     

Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis

The non-stationary and multi-frequency nature of biomedical signal activities makes the use of time-frequency distributions (TFDs) for analysis inevitable. Time-frequency analysis provides simultaneous interpretations in both time and frequency domain enabling comprehensive explanation, presentation and interpretation of electrocardiogram (ECG) signals. The diversity of TFDs and specific properties for each type show the need to determine the best TFD for ECG analysis. In this study, a performance evaluation of five TFDs in term of ECG abnormality detection is presented. The detection criteria based on extracted features from most important ECG signal components (QRS) to detect normal and abnormal cases. This is achieved by estimating its energy concentration magnitude using the TFDs. The TFDs analyse ECG signals in one-minute interval instead of conventional time domain approach that analyses based on beat or frame containing several beats. The MIT-BIH normal sinus rhythm ECG database total records of 18 long-term ECG sampled at 128 Hz have been analysed. The tested TFDs include Dual-Tree Wavelet Transform, Spectrogram, Pseudo Wigner-Ville, Choi-Williams, and Born-Jordan. Each record is divided into one-minute slots, which is not considered previously, and analysed. The sample periods (slots) are randomly selected ten minutes interval for each record. This result with 99.44% detection accuracy for 15,735 ECG beats shows that Choi-Williams distribution is most reliable to be used for heart problem detection especially in automated systems that provide continuous monitoring for long time duration.     

A New Approach to Detection of ECG Arrhythmias: Complex Discrete Wavelet Transform Based Complex Valued Artificial Neural Network

This paper presents the new automated detection method for electrocardiogram (ECG) arrhythmias. The detection system is implemented with integration of complex valued feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using complex discrete wavelet transform (CWT). The aim of using CWT is to compress data and to reduce training time of network without decreasing accuracy rate. Obtained complex valued features are used as input to the complex valued artificial neural network (CVANN) for classification of ECG arrhythmias. Ten types of the ECG arrhythmias used in this study were selected from MIT-BIH ECG Arrhythmias Database. Two different classification tasks were performed by the proposed method. In first classification task (CT-1), whether CWT-CVANN can distinguish ECG arrhythmia from normal sinus rhythm was examined one by one. For this purpose, nine classifiers were improved and executed in CT-1. Second classification task (CT-2) was to recognize ten different ECG arrhythmias by one complex valued classifier with ten outputs. Training and test sets were formed by mixing the arrhythmias in a certain order. Accuracy rates were obtained as 99.8% (averaged) and 99.2% for the first and second classification tasks, respectively. All arrhythmias in training and test phases were classified correctly for both of the classification tasks.     

紧张状态下累积性疲劳的短时心电特征分析研究

目的 现阶段大部分对人体疲劳进行检测与分类的研究都是通过模拟驾驶或者睡眠剥夺的方法来诱发快速疲劳,在进行长期疲劳检测时是否存在一种短时心电特征可作为累积性疲劳的生物标记物尚不明确。 方法 本文采用了一种新的实验方案,具体实施方法是动态跟踪14名被试在参加大学生电子设计大赛期间的心电信号,并收集被试的主观疲劳量表与睡眠时长,首先计算心率变异性、心电熵值、高低频值、平均RR间期等13项特征,接着对清醒与疲劳时的心电特征差值使用威尔科克森符号秩检验进行比较。 结果 实验结果表明,被试在缺少睡眠的情况下主观疲劳程度会加深,短时心电特征的高频值、高低频之比和庞加莱图SD2具有统计学意义。 结论 通过累积性疲劳实验在常见的心电特征中进行探索性分析后发现了具有统计学意义的检测疲劳的生物标记物,短时心电特征未来可用于累积性疲劳的检测。     

应用心电图估测先天性心脏病合并肺动脉高压的分析

目的：探讨利用心电图（ECG）Ⅰ导联R波的高度＋S波的深度估测肺动脉收缩压（PASP）的价值。方法：选择60例先天性左向右分流型心脏病合并肺动脉高压（PAH）患者,行右心导管测量前3天内按常规方法完成十二导联心电图,由同一名医生进行心电图分析并测量,按照Ⅰ导联R波的高度＋S波的深=x mV,心电图x mV相当于x×10 mm Hg肺动脉收缩压的计算方法,将结果与用右心导管所测得的PASP比较。结果：用ECG检测法测定的PASP与术中右心导管测得的PASP的相关性r=0.817。结论：ECG是一种简便有效地诊断先天性心脏病合并PAH的检查方法,可为内科介入封堵术及外科手术时机提供判断,并对治疗的可行性做出初步预测。     

Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy

In this paper, a wearable and wireless ECG system is firstly designed with Bluetooth Low Energy (BLE). It can detect 3-lead ECG signals and is completely wireless. Secondly the digital Compressed Sensing (CS) is implemented to increase the energy efficiency of wireless ECG sensor. Different sparsifying basis, various compression ratio (CR) and several reconstruction algorithms are simulated and discussed. Finally the reconstruction is done by the android application (App) on smartphone to display the signal in real time. The power efficiency is measured and compared with the system without CS. The optimum satisfying basis built by 3-level decomposed db4 wavelet coefficients, 1-bit Bernoulli random matrix and the most suitable reconstruction algorithm are selected by the simulations and applied on the sensor node and App. The signal is successfully reconstructed and displayed on the App of smartphone. Battery life of sensor node is extended from 55 h to 67 h. The presented wireless ECG system with CS can significantly extend the battery life by 22 %. With the compact characteristic and long term working time, the system provides a feasible solution for the long term homecare utilization.     

小波变换和独立分量分析去除脑电信号中的噪声和干扰

目的：去除脑电信号中的噪声和心电干扰。方法：首先采用小波软门限法去除脑电中的噪声，然后使用扩展独立分量分析算法去除脑电信手中的心电干扰。该算法的优点在于不需要计算信号的高阶统计量，收敛速度快，同时适用于超高斯和亚高斯混合信号的分离。在提取独立分量之前，对观测信号进行白化处理，去除各信号之间的相关性。结果：消除了脑电信号中的噪声和心电干扰。结论：小波门限去噪结合独立分量分析可有效地去除脑电信手中的噪声和心电干扰。