Development of a digital signal processor-based new 12-lead synchronization electrocardiogram automatic analysis system

The diagnostic values of the aVR lead or "Vereckei algorithm," and the lead II R-wave peak time (RWPT) criterion, recently devised for the differential diagnosis of wide QRS complex tachycardias (WCTs), were compared. A total of 212 WCTs (142 ventricular tachycardias [VTs], 62 supraventricular tachycardias [SVT], and eight preexcitation SVTs) from 145 patients with proven electrophysiologic diagnoses were retrospectively analyzed by seven examiners blinded to the electrophysiologic diagnoses. The overall test accuracy of the Vereckei algorithm was superior to that of the RWPT criterion (84.3% vs. 79.6%; p = 0.0003). The sensitivity of the Vereckei algorithm for VT diagnosis was greater than that of RWPT criterion (92.4% vs. 79.1%; p < 0.0001). The negative predictive value (NPV) for the Vereckei algorithm was also greater (77.8%; 95% confidence interval [CI] = 73.6% to 82.1%) than that of the RWPT criterion (61.6%; 95% CI = 57.6% to 65.6%). The specificity of the Vereckei algorithm was lower than that of the RWPT criterion (64.7% vs. 80.9%; p < 0.0001). The positive predictive value (PPV) was also lower for the Vereckei algorithm (86.4%; 95% CI = 84.4% to 88.4%) than for the RWPT criterion (90.9%; 95% CI = 89.1% to 92.8%). Incorrect diagnoses made by the Vereckei algorithm were mainly due to misdiagnosis of SVT as VT (65.7% of cases), and those made by the RWPT criterion were due to the more dangerous misdiagnosis of VT as SVT (72.5% of cases). The Vereckei algorithm was superior in overall test accuracy, sensitivity, and NPV for VT diagnosis and inferior in specificity and PPV to the RWPT criterion. All of these parameters were lower in "real life" than those reported by the original authors for each of the particular electrocardiographic methods.

Setting a New Gold Standard

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.

Objective. Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the development of deep learning, multi-class arrhythmia classification based on automatic feature extraction of ECG has become increasingly attractive. However, the majority of studies cannot accept varied-length ECG signals and have limited performance in detecting multi-class arrhythmias. Approach. In this study, we propose a multi-branch signal fusion network (MBSF-Net) for multi-label classification of arrhythmia in 12-lead varied-length ECG. Our model utilizes the complementary power between different structures, which include Inception with depthwise separable convolution (DWS-Inception), spatial pyramid pooling (SPP) Layer, and multi-scale fusion Resnet (MSF-Resnet). The proposed method can extract features from each lead of 12-lead ECG recordings separately and then effectively fuse the features of each lead by integrating multiple convolution kernels with different receptive fields, which can achieve the information of complementation between different angles of the ECG signal. In particular, our model can accept 12-lead ECG signals of arbitrary length. Main results. The experimental results show that our model achieved an overall classification F1 score of 83.8% in the 12-lead ECG data of CPSC-2018. In addition, the F1 score of the MBSF-Net performed best among the MBF-Nets which are removed the SPP layer from MBSF-Net. In comparison with the latest ECG classification algorithms, the proposed model can be applied in varied-length signals and has an excellent performance, which not only can fully retain the integrity of the original signals, but also eliminates the cropping/padding signal beforehand when dealing with varied-length signal database. Significance. MBSF-Net provides an end-to-end multi-label classification model with outperfom performance, which allows detection of disease in varied-length signals without any additional cropping/padding. Moreover, our research is beneficial to the development of computer-aided diagnosis.

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 …

Efficient lossless compression scheme for multi-channel ECG signal processing

Development of entropy based algorithm for cardiac beat detection in 12-lead electrocardiogram

Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): ITEA. Background Accurate detection of myocardial ischemia and arrhythmia’s during free-living exercise could play a pivotal role in optimizing screening and monitoring of individuals with underlying cardiovascular disease. Although remote electrocardiogram (ECG) solutions are emerging rapidly, these applications are generally not designed nor validated for continuous use during vigorous exercise. Purpose In this proof-of-concept study, we evaluated the usability, signal quality and arrhythmia detection of a new self-adhesive dry electrode ECG patch, the Nighthawk Vital Signs patch, during exercise and prolonged continuous ECG monitoring in individuals with chronic coronary syndrome (CCS). Methods Six consecutive CCS patients scheduled for an exercise stress test as part of routine cardiac follow-up were recruited. The traditional 12-lead exercise ECG was combined with monitoring with the 1-lead patch. Following the exercise test, the subjects continuously wore the sensor for five days. Intraclass correlation coefficients (ICC) and Wilcoxon signed-rank tests were used to assess the utility to detect arrhythmias with the patch. Signal quality for ECG recording was evaluated by using a signal quality indicator (SQI) based on the average QRS complex. Connection time and signal-to-noise ratio (SNR) were additionally calculated for prolonged ECG monitoring. Comfort and usability of the patch were evaluated by an online questionnaire. Results Six male patients with CCS (mean age 69.8 ± 6.2 years) completed the protocol. The mean time the patch was worn, was 118.3 hours (SD 5.6). The level of agreement between the patch and the 12-lead ECG was excellent for amount of premature atrial contractions (PAC’s) and premature ventricular contractions (PVC’s) during exercise stage, recovery stage and total test duration (ICC = 0.998, 0.998, 1.000, 1.000, 0.998, 1.000). There were no significant differences in the total amount of PAC’s and PVC’s detected with both methods during the exercise test (p = 0.785 and 0.180, respectively). One episode of atrial fibrillation was detected identically on both recorded ECG’s. Signal quality based on average QRS SQI during exercise did decrease slightly during (sub)maximal effort. Total connection time during recording was above 88% in all subjects and the SNR was above the target limit of 18dB. There were no reports on skin irritation, erythema or pain. Conclusion The Nighthawk Vital Signs ECG patch based on self-adhesive dry electrodes technology can be used for accurate arrhythmia detection during vigorous exercise. The sensor is also usable for prolonged ECG monitoring in free-living conditions and can therefore be of potential use in cardiac rehabilitation and telemonitoring for prevention of exercise-related cardiovascular events. However, more research is needed in order to analyze the signal quality over time together with a larger-scale validation study focusing on both arrhythmia and ischemia detection.

Funding Acknowledgements Type of funding sources: None. Background Integrating clinical data to distinguish hypertrophic cardiomyopathy (HCM) phenotypes is relevant in clinical practice. Machine learning (ML) can help - deep learning (DL) networks can automate detection and segmentation of 12-lead electrocardiograms (ECGs), whereas unsupervised learning can group patients to compare ECG, imaging and genetic characteristics. The aim is to automate ECG morphology analysis from all 12 ECG leads and multiple beats, and relate this to HCM genotypes and imaging phenotypes. Methods The single-center cohort included phenotype- and genotype-positive (G+) HCM patients (n = 104) and their phenotype-negative relatives (n = 50, 42% G+). All patients had a digital 12-lead ECG, echocardiography, and a magnetic resonance (CMR) study performed. The workflow is shown in Fig 1. A U-Net DL network was used for ECG delineation (P, QRS, T onsets/offsets) for all cardiac cycles. Three heartbeats were selected for each patient based on their morphology, with the aim of capturing beat-to-beat variability. An unsupervised representation learning algorithm was used to fuse ECG data and assess inter-patient similarities. Patients were clustered based on similarities of ECG biomarkers, and compared with regards to genotypes, family history of sudden cardiac death (SCD), history of ventricular arrhythmias/syncope/aborted SCD, implanted defibrillators (ICD), left ventricular (LV) obstruction, maximal wall thickness, late gadolinium enhancement (LGE), and HCM risk-SCD score. Results ML based on ECG biomarkers provided a good separation of HCM patients and relatives (Fig 1A), also showing G- and patients with variants of uncertain significance grouping together (Fig 1B). Clustering resulted in 6 ECG phenogroups (C1-6). C1 and 2 were related to less comorbidities, cardiac remodeling, and HCM risk score, capturing the majority of G- patients. C3 and 4 were related to LV obstruction – where C4 consisted of symptomatic patients with high ICD implantation and event rates, high LGE, and impaired systolic function. C5 captured patients with high comorbidities, extremely remodeled hearts, but no obstruction, whereas C6 patients with positive family history and high arrhythmic events (Fig 1C, Table 1). The average ECG morphology is shown side-by-side for C1 and C5 in Fig 1D – negative T waves, increased R/S wave amplitudes, left axis deviation (LAD) and ST elevation can be recognized as macro-biomarkers in C5 (yellow arrows). Conclusion ML can automate the analysis of complex clinical data, simultaneously taking into account the morphology of all ECG components in all 12-leads, throughout multiple beats, compare it with clinical and imaging data, and identify clinically sensible phenogroups as validated by structural and functional findings, as well as with genotypes and clinical information. Automated and comprehensive cardiac data analysis has diagnostic and research potential to help screen populations and phenotype disease etiologies. Abstract Figure 1: analysis pipeline Abstract Table 1: clinical variables

This chapter briefly overviews the digital signal processing with an outline of introduction and importance of biomedical signal processing. Biomedical signal processing plays an important role in analysis of human body. For this, bioelectric potential needs to be recorded digitally and processed through digital processing system. In general, all the real-world signals are analog in nature. Initially, these analog signals are converted into digital signals, and then digital filters and other signal processing techniques are applied over them. Same is also followed in case of biomedical signals. A brief overview of this conversion process is also given in this chapter. Mainly, this chapter explores about generation process, recording, signal processing and common problems of electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Real signals are also visualized that are taken from known datasets. This chapter provides a good base for any beginner in the field of biomedical signal processing.

Digital filters are extensively used in video processing, digital communications, frequency analysis, auto control and so on. Digital filter is one of the most important part of digital signal process, almost appeared in all digital signal process system. Based on theory, the digital filters can be divided into FIR digital filter and IIR digital filter. FIR digital filter is achieved for these requirements and then widely used in modern signal processing. IIR digital filter is a kind of digital filter that its unit pulse response is unlimited long, and is also an important research task in modern electronics, communication and signal processing, many scholars did much research on the optimization design of IIR digital filter. High-density FPGA have real-time performance and flexibility, and it can provide powerful hardware support for the implementation of digital filters. In this dissertation, author will make some analysis on digital filters, and make realization of digital filters based on FPGA.

Digital filtering and analysis for a semiconductor X-ray detector data acquisition

BackgroundWearables with integrated electrocardiogram (ECG) acquisition have made single-lead ECGs widely accessible to patients and consumers. However, the 12-lead ECG remains the gold standard for most clinical cardiac assessments. In this study, we developed a neural network to reconstruct 12-lead ECGs from single-lead and dual-lead ECGs, and evaluated the mathematical accuracy.MethodsWe used lead I or leads I and II from 9514 individuals from the Physikalisch-Technische Bundesanstalt (PTB-XL) cohort and a generative adversarial network, with the aim of recreating the missing leads from the 12-lead ECG. ECGs were divided into training, validation, and testing (10%). Original and recreated leads were measured with a commercially available algorithm. Differences in means and variances were assessed with Student’s t-tests and F-tests, respectively. Calibration and bias were assessed with Bland-Altman plots. Inter-lead correlations were compared in original and recreated ECGs.ResultsThe variability of precordial ECG amplitudes is significantly reduced in recreated ECGs compared to real ECGs (all p < 0.05), indicating regression-to-the-mean. Amplitude averages are recreated with bias (p < 0.05 for most leads). Reconstruction errors depend on the real amplitudes, suggesting regression-to-the-mean (R2 between target and error in R-peak amplitude in lead V3: 0.92). The relations between lead markers have a similar slope but are much stronger due to reduced variance (R-peak amplitude R2 between leads I and V3, real ECGs: 0.04, recreated ECGs: 0.49). Using two leads does not significantly improve 12-lead recreation.ConclusionsAI-based 12-lead ECG reconstruction results in a regression-to-the-mean effect rather than personalized output, rendering it unsuitable for clinical use.

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0175 and 0.0654, Pearson correlation coefficients of 0.7772 and 0.7287. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410.

Perhaps the most useful means of monitoring the critically ill patient is the electrocardiogram (ECG). A 12lead ECG is useful to rule out cardiac complications in the acutely injured patient, the postoperative patient, and the septic patient [1]. Limb lead II or chest lead V may be continuously monitored for evidence of progressive cardiac disease and complicating dysrhythmias [1]. This section summarizes the usefulness and indications of the ECG in monitoring of the high-risk, critically ill patient and possibilities for future clinical use. Interpretation of the ECG waveform provides the basis for diagnosis and therapy of numerous conditions associated with disorders of the myocardium in the intensive care unit (ICU) [1]. Continuous ECG monitoring is essential for the adult patient with acute myocardial infarction because dysrhythmias are the most common life-threatening complication [1]. Continuous ECG monitoring of the postoperative general surgical patient is not as useful because the incidence of lifethreatening dysrhythmias is lower [1,2]. However, in adults and children with decreased myocardial function, hypovolemia, or hypoxemia, the ECG may show PVCs, PACs, ST segments changes, altered T-waves, and bradycardia [1]. These abnormalities may be produced by inadequate oxygen delivery to the myocardium and therefore serve to alarm the physician of potentially signi~cant pathophysiologic disturbances. One of the main uses for the ECG is the determination of heart rate or pulse. Most ICUs measure heart rate as a calculated time-averaged signal from the bedside ECG monitor. Heart rate is a very nonspeci~c cardiorespiratory variable. However, tachycardia may signal signi~cant and abrupt cardiovascular compromise such as hypovolemia, hypoperfusion, or diminished myocardial contractility in the critically ill patient [3]. The degree of tachycardia is roughly proportional to the severity of the cardiovascular impairment. Tachycardia is also present in association with infection, anxiety, stress, fever, exercise, pain, and anxiety. Bradycardia may occur with inferior myocardial infarction, arteriosclerotic heart disease, or severe systemic hypoxemia [3]. Heart rate may also be used during invasive hemodynamic monitoring with thermodilution pulmonary arterial catheters to calculate stroke volume and other hemodynamic variables. The ECG may be used directly or as an adjunctive test in the diagnosis of numerous diseases and conditions affecting the critically ill patient. Myocardial ischemia may occur in the critically ill patient with shock, even in the absence of primary coronary artery disease [1]. Myocardial ischemia ultimately results in T-wave inversion. Myocardial injury due to ischemia may cause elevation of the ST segment if the ischemia is transmural or ST segment depression if the ischemia is nontransmural and predominantly subendocardial [1]. Progressive ischemia results in myocardial infarction which is often manifested in the ECG by Qwave deepening and distortion, loss of R-wave voltage, and/or an increased R-wave amplitude [1]. However, ECG abnormalities characterizing myocardial ischemia and infarction are nonspeci~c. Serial ECGs should be followed to speci~cally diagnose and localize myocardial ischemia and infarction [1]. Table 1 lists characteristic changes in the ECG associated with other disease states commonly seen in critically ill patients. ECG changes observed in drug overdose or poisoning, and electrolyte and metabolic disturbances are shown in Tables 2 and 3. Abnormal cardiac rhythms may occur during critical illness at any time and are the most common mechanism of death in adults [1]. Automated arrhythmia detection algorithms are commercially available and in use in many ICUs. These algorithms ~lter the incoming ECG signal, detect and classify the QRS complex, identify ectopic events and ventricular rhythms, and generate alarms. For ventricular ectopic beats, the system has a reported sensitivity of up to 90% with a false positive rate of #1%. However, the algorithms may result in false positive alarms if the Tor P-waves are $50% of the R-wave amplitude, and are not useful for detection of aberrantly conducted beats, atrial ~brillation and _utter, and intermittent bundle branch block. In summary, the clinical utility and cost effectiveness of these and other automated forms of ECG monitoring in the ICU, such as automated ST segment monitoring [5] and signal-averaged electrocardiography (detailed in subsequent sections), have yet to be determined. The ECG waveform itself may be an important source of clinical information that has only recently