Real-time Electrocardiogram Signal Research Articles

Novel Method to Efficiently Create an mHealth App: Implementation of a Real-Time Electrocardiogram R Peak Detector.

BackgroundIn parallel to the introduction of mobile communication devices with high computational power and internet connectivity, high-quality and low-cost health sensors have also become available. However, although the technology does exist, no clinical mobile system has been developed to monitor the R peaks from electrocardiogram recordings in real time with low false positive and low false negative detection. Implementation of a robust electrocardiogram R peak detector for various arrhythmogenic events has been hampered by the lack of an efficient design that will conserve battery power without reducing algorithm complexity or ease of implementation.ObjectiveOur goals in this paper are (1) to evaluate the suitability of the MATLAB Mobile platform for mHealth apps and whether it can run on any phone system, and (2) to embed in the MATLAB Mobile platform a real-time electrocardiogram R peak detector with low false positive and low false negative detection in the presence of the most frequent arrhythmia, atrial fibrillation.MethodsWe implemented an innovative R peak detection algorithm that deals with motion artifacts, electrical drift, breathing oscillations, electrical spikes, and environmental noise by low-pass filtering. It also fixes the signal polarity and deals with premature beats by heuristic filtering. The algorithm was trained on the annotated non–atrial fibrillation MIT-BIH Arrhythmia Database and tested on the atrial fibrillation MIT-BIH Arrhythmia Database. Finally, the algorithm was implemented on mobile phones connected to a mobile electrocardiogram device using the MATLAB Mobile platform.ResultsOur algorithm precisely detected the R peaks with a sensitivity of 99.7% and positive prediction of 99.4%. These results are superior to some state-of-the-art algorithms. The algorithm performs similarly on atrial fibrillation and non–atrial fibrillation patient data. Using MATLAB Mobile, we ran our algorithm in less than an hour on both the iOS and Android system. Our app can accurately analyze 1 minute of real-time electrocardiogram signals in less than 1 second on a mobile phone.ConclusionsAccurate real-time identification of heart rate on a beat-to-beat basis in the presence of noise and atrial fibrillation events using a mobile phone is feasible.

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications.

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).

Performance analysis of two stage adaptive FIR Filter Algorithms for PLI and BW artifact cancellation in ECG

Electrocardiogram (ECG) is a measure of the electrical movement of the heart, and is obtained by surface electrodes at standardized locations on the patient’s chest. During acquisition, various artifacts/noises such as power-line interference (PLI), baseline wander (BW), muscle artifacts (MA) and motion artifacts (EM) obscure the ECG. It is important that these artifacts are minimized for the clinicians to make better diagnosis on heart problems. This paper researches the creative idea of adaptive noise cancellation (ANC) using two stage form of adaptive filters. The concept of cascading and its algorithm for real-time application is simulated on MATLAB. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately which results in good adaptation and performance. The objective of the present investigation is to provide solution in order to improve the performance of noise canceller in terms of filter parameters which are obtained with the help of adaptive algorithms. Different kinds of two stage ANC algorithms are used to eliminate artifacts in ECG by considering the noises such as power line interference and baseline wander. The simulation results show that the performance of the two stage ANC is superior to the conventional single stage ANC system in terms of higher signal-to-noise ratio. Two stage adaptive algorithms are applied on real time ECG signals and compared their performance with the conventional single stage adaptive algorithms in terms of parameters Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Distortion.

ECG Abnormality Detection Algorithm

The monitoring and early detection of abnormalities in the cardiac cycle morphology have significant impact on the prevention of heart diseases and their associated complications. Electrocardiogram (ECG) is very effective in detecting irregularities of the heart muscle functionality. In this work, we investigate the detection of possible abnormalities in ECG signal and the identification of the corresponding heart disease in real-time using an efficient algorithm. The algorithm relies on cross-correlation theory to detect abnormalities in ECG signal. The algorithm incorporates two cross-correlations steps. The first step detects abnormality in a real-time ECG signal trace while the second step identifies the corresponding disease. The optimization of search-time is the main advantage of this algorithm.

Design and implementation of hand-held electrocardiogram monitor

A hand-held electrocardiogram (ECG) monitor with capacitive coupling is designed in this study that can rapidly detect ECG signals through clothing. This new device improves many deficiencies of the traditional ECG monitor, such as infection due to direct skin contacting, inconvenience, and time-consuming. In specificity, the hand-held ECG monitor consists of two parts, a sensor and an embedded terminal. ECG signals are initially detected by a sensing electrode placed on the chest through clothing, then treated by single ended differential amplification, filtering and master amplification, and later processed through A/D conversion and ECG signal transmission by CC2540 module. The waveform and heart rate are finally displayed on the screen based on digital filtering and data processing for the received ECG signal on the embedded terminal. Results confirm that the newly developed hand-held ECG monitor is capable of detecting real-time ECG signals through clothing with advantages of simple operation, portability and rapid detection.

Generation of biometric sequence using real time electrocardiogram signal

Generation of biometric sequence using real time electrocardiogram signal

Efficient Skin Temperature Sensor and Stable Gel-Less Sticky ECG Sensor for a Wearable Flexible Healthcare Patch.

Wearable, flexible healthcare devices, which can monitor health data to predict and diagnose disease in advance, benefit society. Toward this future, various flexible and stretchable sensors as well as other components are demonstrated by arranging materials, structures, and processes. Although there are many sensor demonstrations, the fundamental characteristics such as the dependence of a temperature sensor on film thickness and the impact of adhesive for an electrocardiogram (ECG) sensor are yet to be explored in detail. In this study, the effect of film thickness for skin temperature measurements, adhesive force, and reliability of gel-less ECG sensors as well as an integrated real-time demonstration is reported. Depending on the ambient conditions, film thickness strongly affects the precision of skin temperature measurements, resulting in a thin flexible film suitable for a temperature sensor in wearable device applications. Furthermore, by arranging the material composition, stable gel-less sticky ECG electrodes are realized. Finally, real-time simultaneous skin temperature and ECG signal recordings are demonstrated by attaching an optimized device onto a volunteer's chest.

Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring.

Automatic detection and classification of noises can play a vital role in the development of robust unsupervised electrocardiogram (ECG) analysis systems. This paper proposes a novel unified framework for automatic detection, localization, and classification of single and combined ECG noises. The proposed framework consists of the modified ensemble empirical mode decomposition (CEEMD), the short-term temporal feature extraction, and the decision-rule-based noise detection and classification. In the proposed framework, ECG signals are first decomposed using the modified CEEMD algorithm for discriminating the ECG components from the noises and artifacts. Then, the short-term temporal features such as maximum absolute amplitude, number of zerocrossings, and local maximum peak amplitude of the autocorelation function are computed from the extracted high-frequency and low-frequency signals. Finally, a decision rule-based algorithm is presented for detecting the presence of noises and classifying the processed ECG signals into six signal groups: noise-free ECG, ECG+BW, ECG+MA, ECG+PLI, ECG+BW+PLI, and ECG+BW+MA. The proposed framework is rigorously evaluated on five benchmark ECG databases and the real-time ECG signals. The proposed framework achieves an average sensitivity of 99.12%, specificity of 98.56%, and overall accuracy of 98.90% in detecting the presence of noises. Classification results show that the framework achieves an average sensitivity, positive predictivity, and classification accuracy of 98.93%, 98.39%, and 97.38%, respectively. The proposed framework not only achieves better noise detection and classification rates than the current state-of-the-art methods but also accurately localizes short bursts of noises with low endpoint delineation errors. Extensive studies on benchmark databases demonstrate that the proposed framework is more suitable for reducing false alarm rates and selecting appropriate noise-specific denoising techniques in automated ECG analysis applications.

Design of a Real-Time ECG Filter for Portable Mobile Medical Systems

Electrocardiogram (ECG) signal is a direct and effective way to find cardiovascular disease timely, which can intuitively reflect changes of the heart beat and activities of different parts. Due to the noise interference from surroundings, acquisition of real-time and high-quality ECG signal is a big challenge for portable mobile medical systems. Integer coefficients infinite impulse response (IIR) digital filter is suitable for portable mobile platform, but it will be a little bit of distortion. So, an improved integer IIR filter for portable ECG monitors is presented in this paper. The proposed IIR filter can instantaneously and effectively eliminate baseline drift of main interference frequency band and 50-Hz power frequency interference in ECG signal. The method is verified by a specific example and MIT/BIH Arrhythmia Database. The improved integer coefficients IIR filter was applied in a portable mobile medical system. It can meet the requirements of ECG filtering in real-time and filtering performance.

A revised scheme for real time ECG Signal denoising based on recursive filtering

In many healthcare applications, artifacts mask or corrupt important features of Electrocardiogram (ECG) signals. In this paper we describe a revised scheme for ECG signal denoising based on a recursive filtering methodology. We suggest a suitable class of kernel functions in order to remove artifacts in the ECG signal, starting from noise frequencies in the Fourier domain. Our approach does not require high computational requirements and this feature offers the possibility of an implementation of the scheme directly on mobile computing devices. The proposed scheme allows local denoising and hence a real time visualization of the signal by means of a strategy based on boundary conditions. Experiments on real datasets have been carried out in order to test, in terms of computation and accuracy, the proposed algorithm. Finally, comparative results with other well-known denoising methods are shown.

A Real-Time FPGA-Based Accelerator for ECG Analysis and Diagnosis Using Association-Rule Mining

Telemedicine provides health care services at a distance using information and communication technologies, which intends to be a solution to the challenges faced by current health care systems with growing numbers of population, increased demands from patients, and shortages in human resources. Recent advances in telemedicine, especially in wearable electrocardiogram (ECG) monitors, call for more intelligent and efficient automatic ECG analysis and diagnostic systems. We present a streaming architecture implemented on Field-Programmable Gate Arrays (FPGAs) to accelerate real-time ECG signal analysis and diagnosis in a pipelining and parallel way. Association-rule mining is employed to generate early diagnostic results by matching features of ECG with generated association rules. To improve performance of the processing, we propose a hardware-oriented data-mining algorithm named Bit_Q_Apriori . The corresponding hardware implementation indicates a good scalability and outperforms other hardware designs in terms of performance, throughput, and hardware cost.

Implementation of Wireless Electrocardiogram Monitoring System

We describe the design and development of wireless electrocardiogram monitoring system to meet the biomedical needs. Our system is able to acquire, display and record the patient’s real time electrocardiogram (ECG) data. Patient data received from this system can be further used for analysis of abnormal heart rate and new clinical applications. System hardware comprises of an electrode as part of signal input, signal conditioning components for manipulating signal, the 8051 microcontroller unit to perform signal processing and wireless communication module. Software was developed in Visual Basic 6 to record and visualise ECG signal in real time. Therefore, we implemented ECG monitor system, and this can be used to advanced ECG study.

Android based self-diagnostic electrocardiogram system for mobile healthcare.

Cardiovascular diseases are the most common cause of death worldwide and are characterized by arrhythmia (i.e. irregular rhythm of heartbeat). Arrhythmia occasionally happens under certain conditions, such as stress. Therefore, it is difficult to be diagnosed using electrocardiogram (ECG) devices available in hospitals for just a few minutes. Constant diagnosis and monitoring of heartbeat is required to reduce death caused by cardiovascular diseases. Mobile healthcare system has emerged as a potential solution to assist patients in monitoring their own heart condition, especially those who are isolated from the reference hospital. This paper proposes a self-diagnostic electrocardiogram system for mobile healthcare that has the capability to perform a real-time ECG diagnostic. The self-diagnostic capability of a real-time ECG signal is achieved by implementing a detrended fluctuation analysis (DFA) method. The result obtained from DFA is used to display the patient's health condition on a smartphone anytime and anywhere. If the health condition is critical, the system will alert the patient and his medical practitioner for further diagnosis. Experimental results verified the validity of the developed ECG diagnostic application on a smartphone. The proposed system can potentially reduce death caused by cardiovascular diseases by alerting the patient possibly undergoing a heart attack.

A Power-Line Interference Canceler Based on Sliding DFT Phase Locking Scheme for ECG Signals

A power-line interference (PLI) removal method is proposed based on a previously published sliding discrete Fourier transform (SDFT) phase locking scheme (PLL), which extracts the nonstationary sinusoids from the electrocardiogram (ECG) signal. The proposed PLI canceler consists of SDFT PLL as a vital element, which can track the variation in center frequency of the PLI either 50 or 60 Hz. The PLI canceler involves the adaptive sampling frequency control in which the sampling frequency of SDFT filter is adaptively adjusted according to the center frequency variation of the interference. Since the in-phase and quadrature components of SDFT filter can provide instantaneous sinusoidal and cosinusoidal interference signals, the SDFT PLL is capable of tracking amplitude, phase, and frequency of the interference. Additional resonators of different bin indices are augmented with SDFT PLL to handle the dominant odd harmonics of PLI. Furthermore, the SDFT PLL is cascaded with another SDFT bin to eliminate the baseline wander present in the real-time ECG signal. The adaptive PLI canceler based on SDFT PLL offers the tracking capability of variant PLI, attenuation of 40 dB, less acquisition time of 0.2 s for a variant PLI of ±5 Hz, removal of dominant odd harmonics, and baseline wander with expense of sampling frequency. Experimental results prove the efficacy of the proposed method in nonstationary sinusoidal interference extraction. Experimental investigation on SDFT PLL using field programmable gate array demonstrates the feasibility of digital implementation of the proposed algorithm.

Implementation of a portable device for real-time ECG signal analysis

BackgroundCardiac disease is one of the main causes of catastrophic mortality. Therefore, detecting the symptoms of cardiac disease as early as possible is important for increasing the patient’s survival. In this study, a compact and effective architecture for detecting atrial fibrillation (AFib) and myocardial ischemia is proposed. We developed a portable device using this architecture, which allows real-time electrocardiogram (ECG) signal acquisition and analysis for cardiac diseases.MethodsA noisy ECG signal was preprocessed by an analog front-end consisting of analog filters and amplifiers before it was converted into digital data. The analog front-end was minimized to reduce the size of the device and power consumption by implementing some of its functions with digital filters realized in software. With the ECG data, we detected QRS complexes based on wavelet analysis and feature extraction for morphological shape and regularity using an ARM processor. A classifier for cardiac disease was constructed based on features extracted from a training dataset using support vector machines. The classifier then categorized the ECG data into normal beats, AFib, and myocardial ischemia.ResultsA portable ECG device was implemented, and successfully acquired and processed ECG signals. The performance of this device was also verified by comparing the processed ECG data with high-quality ECG data from a public cardiac database. Because of reduced computational complexity, the ARM processor was able to process up to a thousand samples per second, and this allowed real-time acquisition and diagnosis of heart disease. Experimental results for detection of heart disease showed that the device classified AFib and ischemia with a sensitivity of 95.1% and a specificity of 95.9%.ConclusionsCurrent home care and telemedicine systems have a separate device and diagnostic service system, which results in additional time and cost. Our proposed portable ECG device provides captured ECG data and suspected waveform to identify sporadic and chronic events of heart diseases. This device has been built and evaluated for high quality of signals, low computational complexity, and accurate detection.

Implementation of Extended Kalman Filter for Real-Time Noncontact ECG Signal Acquisition in Android-Based Mobile Monitoring System

Noncontact electrocardiogram (ECG) measurement using capacitive-coupled technique is a very reliable long-term noninvasive health-care remote monitoring system. It can be used continuously without interrupting the daily activities of the user and is one of the most promising developments in health-care technology. However, ECG signal is a very small electric signal. A robust system is needed to separate the clean ECG signal from noise in the measurement environment. Noise may come from many sources around the system, for example, bad contact between the sensor and body, common-mode electrical noise, movement artifacts, and triboelectric effect. Thus, in this paper, the extended Kalman filter (EKF) is applied to denoise a real-time ECG signal in capacitive-coupled sensors. The ECG signal becomes highly stable and noise-free by combining the common analog signal processing and the digital EKF in the processing step. Furthermore, to achieve ubiquitous monitoring, android-based application is developed to process the heart rate in a realtime ECG measurement.

Adaptive Wavelet Thresholding for Noise reduction in Electrocardiogram (ECG) Signals

In diagnosis of diseases Ultrasonic devices are frequently used by healthcare professionals. The medical imaging devices namely X-ray, CT/MRI and ultrasound are producing abundant images which are used by medical practitioners in the process of diagnosis . The main problem faced by them is the noise introduced due to the consequence of the coherent nature of the wave transmitted. These noises corrupt the image and often lead to incorrect diagnosis. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference and high frequency noises during data acquisition. In the recent paper we have considered the Discrete Wavelet Transform (DWT) based wavelet Denoising have incorporated using different Thresholding techniques to remove major sources of noises from the acquired ECG signals. The experimental results shows the significant reduction of White Gaussian noise and it retains the ECG signal morphology effectively. Different performance measures were considered to select the appropriate wavelet function and Thresholding rule for efficient noise removal methods such as Mean Square Error (MSE),Peak Signal to Noise Ratio (PSNR) and Percentage Root Mean Square Difference (PRD) . The experimental result shows the db wavelet and BayesShrink Thresholding rule is optimal for reducing noise in the real time ECG signals.

WBAN Based Long Term ECG Monitoring

Healthcare solutions using anytime, and anywhere remote healthcare surveillance devices, have become a major challenge. The patients with chronic diseases who need only therapeutic supervision are not advised to occupy a hospital bed. Using Wearable Wireless Body/Personal Area Network (WWBAN), intelligent monitoring of heart can supply information about medical conditions. Electrocardiogram (ECG) is the core reference in the diagnosis and medication process. An approach on healthcare solution WBAN based, for real-time ECG signal monitoring and long-term recording will be presented. Low-power wireless sensor nodes with local processing and encoding capabilities in order to achieve maximum mobility and flexibility are our main goal. ZigBee wireless technology will be used for transmission. Sensor device will be programmed to process locally the ECG signal and to raise an alert. Low-power and miniaturization are essential physical requirements.

A Real Time Approach: ECG Noise Reduction in Chebyshev Type II Digital Filter

is well known that biomedical signals carry important information about the behavior of the living systems under study. With the analysis of the Electrocardiogram (ECG) signal it may be possible to predict heart problems or monitor patient recovery after a heart intervention. A proper processing of these signals enhances their physiological and clinical information. The quality of biomedical signal is degraded mainly by many sources of noise such as power line interference (PLI), baseline drift, muscle contraction noise etc. The objective of this paper is to implement the Chebyshev Type II digital filter to overcome degradation by improving ECG signal quality for quality clinical diagnosis. Different artifacts are the reason in corruption of the ECG signal. Removing noise from the biomedical signal is still challenging and a rapidly expanding field with a wide range of applications in ECG noise reduction. Present paper deals with the design of Chebyshev Type II filters including low pass, high pass, notch filters. Also the performance is tested using cascading of filters. For real time application 1711 add on card is used. Different ECG signals from MIT/BIH arrhythmia database are used for validation and compared with real time ECG signals. The objective of this paper is to implement the Chebyshev Type II digital filter to overcome degradation by improving ECG signal quality for quality clinical diagnosis. Finally results indicate noise reduction in the ECG. KeywordsSignal, ECG, PLI, Chebyshev Type II, Digital Filter.

ECG Signal Denoising Using Wavelet Thresholding Techniques in Human Stress Assessment

In recent years, Electrocardiogram (ECG) plays an imperative role in heart disease diagnostics, Human Computer Interface (HCI), stress and emotional states assessment, etc. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference, and high frequency noises during data acquisition. In order to retain the ECG signal morphology, several researches have adopted using different preprocessing methods. In this work, the stroop color word test based mental stress inducement have done and ECG signals are acquired from 10 female subjects in the age range of 20 years to 25 years. We have considered the Discrete Wavelet Transform (DWT) based wavelet denoising have incorporated using different thresholding techniques to remove three major sources of noises from the acquired ECG signals namely, power line interference, baseline wandering, and high frequency noises. Three wavelet functions (db4, coif5 and sym7) and four different thresholding methods are used to denoise the noise in ECG signals. The experimental result shows the significant reduction of above considered noises and it retains the ECG signal morphology effectively. Four different performance measures were considered to select the appropriate wavelet function and thresholding rule for efficient noise removal methods such as, Signal to Interference Ratio (SIR), noise power, Percentage Root Mean Square Difference (PRD) and finally periodogramof Power Spectral Density (PSD). The experimental result shows the coif5 wavelet andrigrsurethresholding rule is optimal for unknown Signal to Noise Ratio (SNR) in the real time ECG signals.