A nonlinear Bayesian filtering framework for ECG denoising.

The American College of Cardiology (ACC)/American Heart Association (AHA) Task Force on Practice Guidelines was formed to make recommendations regarding the diagnosis and treatment of patients with known or suspected cardiovascular disease. Coronary artery disease (CAD) is the leading cause of death in the United States. Unstable angina (UA) and the closely related condition non–ST-segment elevation myocardial infarction (NSTEMI) are very common manifestations of this disease. These life-threatening disorders are a major cause of emergency medical care and hospitalizations in the United States. In 1996, the National Center for Health Statistics reported 1 433 000 hospitalizations for UA or NSTEMI. In recognition of the importance of the management of this common entity and of the rapid advances in the management of this condition, the need to revise guidelines published by the Agency for Health Care Policy and Research (AHCPR) and the National Heart, Lung and Blood Institute in 1994 was evident. This Task Force therefore formed the current committee to develop guidelines for the management of UA and NSTEMI. The present guidelines supersede the 1994 guidelines. The customary ACC/AHA classifications I, II, and III summarize both the evidence and expert opinion and provide final recommendations for both patient evaluation and therapy: Class I: Conditions for which there is evidence and/or general agreement that a given procedure or treatment is useful and effective . Class II: Conditions for which there is conflicting evidence and/or a divergence of opinion about the usefulness/efficacy of a procedure or treatment. Class IIa: Weight of evidence/opinion is in favor of usefulness/efficacy. Class IIb: Usefulness/efficacy is less well established by evidence/opinion. Class III: Conditions for which there is evidence and/or general agreement that the procedure/treatment is not useful/effective and in some cases may be harmful. The weight of the evidence was ranked highest (A) if the data …

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

Accurate heart rate measurement during work is required for many industrial hygiene and ergonomics situations. The purpose of this investigation was to determine the validity of heart rate measurements obtained by a simple, lightweight, commercially available wrist-worn heart rate monitor (HRM) during work (cycle exercise) sessions conducted in the laboratory and also during the particularly challenging work environment of space flight. Three different comparisons were made. The first compared HRM data to simultaneous electrocardiogram (ECG) recordings of varying heart rates that were generated by an ECG simulator. The second compared HRM data to ECG recordings collected during work sessions of 14 subjects in the laboratory. Finally, ECG downlink and HRM data were compared in four astronauts who performed cycle exercise during space flight. The data were analyzed using regression techniques. The results were that the HRM recorded virtually identical heart rates compared with ECG recordings for the data set generated by an ECG simulator. The regression equation for the relationship between ECG versus HRM heart rate data during work in the laboratory was: ECG HR = 0.99 x (HRM) + 0.82 (r2 = 0.99). Finally, the agreement between ECG downlink data and HRM data during space flight was also very high, with the regression equation being: Downlink ECG HR = 1.05 x (HRM) -5.71 (r2 = 0.99). The results of this study indicate that the HRM provides accurate data and may be used to reliably obtain valid data regarding heart rate responses during work.

Studies suggest that electrocardiogram (ECG) recordings are chaotic in nature and for years they are being studied using various non-linear methods. These approaches can be applied to study the nonlinear properties of ECG signals from pregnant women, termed as Maternal ECG (MECG). For a better perspective of how the nonlinear properties of MECG signal is different from normal ECG we include normal sinus ECG signal collected from a group of women. In this study we apply four different non-linear methods, namely Rescaled Range analysis (R/S), Higuchi's Fractal Dimension analysis (HFD), Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA) method to characterize non-linearity of the maternal ECG signal. Comparing the results of these methods we find that all the methods successfully distinguishes different properties of the ECG signals, but MF-DFA method being a multi-scale method, separates MECG signal from normal group most accurately.

Arrhythmia is not uncommon in the etiology of syncope which mimics epilepsy. Data about the epilepsy induced vagal tonus abnormalities have being increasingly reported. So we aimed to evaluate what a neurologist may gain by a simultaneous electrocardiogram (ECG) and electroencephalogram (EEG) recording in the patients who underwent EEG testing due to prediagnosis of epilepsy. We retrospectively evaluated and detected ECG abnormalities in 68 (18%) of 376 patients who underwent EEG testing. A minimum of 20 of minutes artifact-free recording were required for each patient. Standard 1-channel ECG was simultaneously recorded in conjunction with the EEG. In all, 28% of females and 14% of males had ECG abnormalities. Females (mean age 49 years, range 18-88 years) were older compared with the male group (mean age 28 years, range 16-83 years). Atrial fibrillation was more frequent in female group whereas bradycardia and respiratory sinus arrhythmia was higher in male group. One case had been detected a critical asystole indicating sick sinus syndrome in the female group and treated with a pacemaker implantation in the following period. Simultaneous ECG recording in conjunction with EEG testing is a clinical prerequisite to detect and to clarify the coexisting ECG and EEG abnormalities and their clinical relevance. Potentially rare lethal causes of syncope that mimic seizure or those that could cause resistance to antiepileptic therapy could effectively be distinguished by detecting ECG abnormalities coinciding with the signs and abnormalities during EEG recording.

Studies on retrospective electrocardiogram (ECG) recordings of patients during cardiac arrest have shown significant changes in heart rate variability (HRV) indices prior to the onset of cardiac arrhythmia. The early detection of these changes in HRV indices increases the chances for a successful medical intervention by increasing the response time window. A portable, handheld remote ECG monitor designed in this research detects the QRS complex and calculates short-term HRV indices in real-time. The QRS detection of the ECG recordings of subjects from the MIT-Arrhythmia database yielded a mean sensitivity of 99.34% and a specificity of 99.31%. ECG recordings from normal subjects and subjects with congestive heart failure were used to identify the differences in HRV indices. An increase in heart rate, high-frequency spectral power (HFP), total spectral power, the ratio of HFP to low-frequency spectral power (LFP), and a decrease in root mean square sum of RR differences were observed. No difference was found on comparison of the standard deviation of normal to normal interval between adjacent R-waves, LFP, and very-low-frequency spectral power. Based on these, additional analytical calculations could be made to provide early warnings of impending cardiac conditions.

Prediction of countershock success using single features from multiple ventricular fibrillation frequency bands and feature combinations using neural networks

What the Journal of Electrocardiology can do for electrocardiogram technologists: An electrocardiogram Technologist's Perspective

—ECG (electrocardiogram) currently is the most effective way to detect the heart condition. Previously people have to do the ECG recording in hospital. But the ECG abnormal situation is unsustainable, because the symptoms usually disappear when the patient is sent to the hospital. This may lead to losing the early treatment opportunity. In this paper, a Holter system is designed based on STM32 and FPGA dual-core. ECG data acquisition, LCD display, and data storage in SD card are accomplished. In addition, an ECG de-noised method which combines Stationary Wavelet Transform with Wavelet Transform Scale factor Estimate is accomplished on FPGA to eliminate the noise of ECG. The system can be used anywhere at any time, so it's an ideal device for remote health care. Keywords-ECG; FPGA; STM32; SWT; WTSE; de-noise I. I NTRODUCTION Along with the rapid economic development as well as changes in lifestyle, cardiovascular disease has become one of the most important reasons that cause death recently. People may lose lives even when the heart doesn’t work only for a second. ECG signal objectively expresses the heart activity and is a typical biological electric signal. The correct diagnosis of cardiovascular disease depends on the accuracy of ECG acquisition. ECG recorders are often called Holter recorders, named after the first ECG recorder developed by N. J. Holter in 1961[1]. ECG signal is a kind of very weak electrical signal whose amplitude is only 0.05mV-5mV, and the spectrum scope mainly focus on 0.05 ~ 100Hz[2], so it's often drowned in Strong Noise Jamming. Therefore ECG signal has a disadvantage of low signal-to-noise ratio (SNR). However we can’t extract useful ECG characteristics from the distortion waveform, so restraining and removing the noise from ECG effectively is very important in ECG single processing. The Holter recorder described in this paper is based on STM32 and FPGA dual-core. Taking as main processor, STM32 is used to manage the whole system, such as input scan, wave display, data storage in SD card, and operate the FPGA. Taking as co-processor, FPGA consists a large number of programmable logic blocks, which can each implement an amount of digital logic, and have a high execution speed, so FPGA is used to run the filtering algorithm to meet the requirements of real-time processing, the Analog-to-digital converter module is also operated by FPGA. Two cores are connected by Parallel bus. II. HARDWARE DESIGN In the acquisition system, the hardware consists of electrodes, amplifier circuit, Analog-to-digital converter module, FPGA, STM32, SD Card and LCD module. The system-level description is shown in Figure1.

Currently used 24-hour electrocardiogram (ECG) monitors have been shown to skip detecting arrhythmias that may not occur frequently or during standardized ECG test. Hence, online ECG processing and wearable sensing applications have been becoming increasingly popular in the past few years to solve a continuous and long-term ECG monitoring problem. With the increase in the usage of online platforms and wearable devices, there arises a need for increased storage capacity to store and transmit lengthy ECG recordings, offline and over the cloud for continuous monitoring by clinicians. In this work, a discrete cosine transform (DCT) compressed segmented beat modulation method (SBMM) is proposed and its applicability in case of ambulatory ECG monitoring is tested using Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG Compression Test Database containing Holter tape normal sinus rhythm ECG recordings. The method is evaluated using signal-to-noise (SNR) and compression ratio (CR) considering varying levels of signal energy in the reconstructed ECG signal. For denoising, an average SNR of 4.56 dB was achieved representing an average overall decline of 1.68 dBs (37.9%) as compared to the uncompressed signal processing while 95 % of signal energy is intact and quantized at 6 bits for signal storage (CR=2) compared to the original 12 bits, hence resulting in 50% reduction in storage size.

Surface Electrocardiogram Recording: Baseline 12-lead and Ambulatory Electrocardiogram Monitoring.

The deceleration capacity – a new measure of heart rate variability evaluated in patients with schizophrenia and antipsychotic treatment

A precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary for a better understanding of the mechanisms behind atrial fibrillation (AF). Blind source separation (BSS) techniques have proven useful in extracting the AA source from ECG recordings. However, the automated selection of the AA source among the other sources after BSS is still an issue. In this scenario, the present work proposes two contributions: i) the use of the normalized mean square error of the TQ segment (NMSE-TQ) as a new feature to quantify the AA content of a source, and ii) an automated classification of AA and non-AA sources using three well-known machine learning algorithms. The tested classifiers outperform the techniques present in literature. A pattern in the mean and standard deviation of the used features, for AA and non-AA sources, is also observed.

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.

Electrode reversal errors in standard 12-lead electrocardiograms (ECG) can produce significant ECG changes and, in turn, misleading diagnoses. Their detection is important but mostly limited to the design of criteria using ECG databases with simulated reversals, without Wilson’s central terminal (WCT) potential change. This is, to the best of our knowledge, the first study that presents an algebraic transformation for simulation of all possible ECG cable reversals, including those with displaced WCT, where most of the leads appear with distorted morphology. The simulation model of ECG electrode swaps and the resultant WCT potential change is derived in the standard 12-lead ECG setup. The transformation formulas are theoretically compared to known limb lead reversals and experimentally proven for unknown limb–chest electrode swaps using a 12-lead ECG database from 25 healthy volunteers (recordings without electrode swaps and with 5 unicolor pairs swaps, including red (right arm—C1), yellow (left arm—C2), green (left leg (LL) —C3), black (right leg (RL)—C5), all unicolor pairs). Two applications of the transformation are shown to be feasible: ‘Forward’ (simulation of reordered leads from correct leads) and ‘Inverse’ (reconstruction of correct leads from an ECG recorded with known electrode reversals). Deficiencies are found only when the ground RL electrode is swapped as this case requires guessing the unknown RL electrode potential. We suggest assuming that potential to be equal to that of the LL electrode. The ‘Forward’ transformation is important for comprehensive training platforms of humans and machines to reliably recognize simulated electrode swaps using the available resources of correctly recorded ECG databases. The ‘Inverse’ transformation can save time and costs for repeated ECG recordings by reconstructing the correct lead set if a lead swap is detected after the end of the recording. In cases when the electrode reversal is unknown but a prior correct ECG recording of the same patient is available, the ‘Inverse’ transformation is tested to detect the exact swapping of the electrodes with an accuracy of (96% to 100%).