A spectral analysis of the normal resting electrocardiogram.

World Health Organization (WHO) reported that heart disease, stroke, lower respiratory infections and chronic obstructive lung disease have remained the top major killers in the world during the past decade. In Indonesia, it covers 37% or about 1.8 millions mortality cases. Hence, human vital sign monitoring such as electrocardiograph (ECG) monitoring system is very essential for mitigating and preventing people hearth's fail to be handled a proper assistance from healthcare physicians. For that purpose, we propose a real time ECG monitoring system, which the ECG data is monitored wirelessly directly without any server for data pooling (centralized database). ECG QRS wave and arrhythmia detection are important features for ECG monitoring system. Up to now, there are several studies ECG wave detection, however, they are mainly focus on internet of things - based telemedicine system for data transfer. In this case, real time monitoring system could be disrupted by a pooling time and packet loss due to its IP-based connection. Hence, we propose a wireless ECG patient monitoring system based on point to point communication scheme at 2.4 GHz band by using 3-lead acquisition system on single channel wireless ECG device. The system is developed based on AD8232 chip for acquisition, NRF24101 wireless link platform for data transfer and Arduino™microcontroller for ECG data processing. In order to evaluate the performance of wireless ECG system, our developed wearable textile antennas (as Tx and Rx antenna) are attached on the human body and experimentally evaluated within indoor environment, simulating off-/on-body communication system. The wearable textile antenna is lightweight, small and insensitive to the resonant frequency due to the human body effect. The measurement is conducted by embedding the antennas at some body positions of the volunteers. The experimental results show that the proposed ECG monitoring system is able to transfer and display real time ECG signals well for off-/on-body communication schemes. In addition, to validate the results, the obtained ECG data are compared and analyzed with the healthcare clinic equipment. According to QRS wave analysis, the ECG signals have reasonable RR, PR, QRS and ST wave interval in comparison with the standard ECG signals. We convince that our developed ECG patient monitoring system is stable by applying our developed textile antennas and it is promising to be adopted in the hospital for future implementation.

Funding Acknowledgements None Background Acute myocarditis is a clinical and pathological condition defined as an inflammation of the myocardium. Its diagnosis is often challenging and requires multiple information derived from different diagnostic modalities. Purpose The aim of the study is to evaluate the correlation between electrocardiographic and imaging data in patients with acute myocarditis. Methods We made a retrospective analysis of 102 patients admitted to our Centre between January 2012 and April 2019 for suspected acute myocarditis. Diagnosis was confirmed with cardiac magnetic resonance (CMR) by identification of myocardial edema in T2-weighted images and/or typical subepicardial or midwall pattern of late gadolinium enhancement (LGE). Significant coronary artery disease was ruled out with coronary angiography. Electrocardiogram (ECG) was analysed on admission - in order to evaluate the presence of ST segment abnormalities, atrio-ventricular or bundle-branch block and heart rhythm disorders - and at the time of discharge. Every patient underwent echocardiography and CMR: from both these exams we reported the presence of regional wall motion abnormalities and left ventricular ejection fraction (LVEF). Results Mean age of our population was 39 ± 18 years; 92 people (90%) were males. At admission, 85 patients (83%) presented ECG abnormalities; the most frequent was ST-segment elevation (65 cases). Conduction or rhythm disorders were observed in 26 cases (25%). At the time of discharge, 41 out of 85 patients had complete regression of ECG changes. Mean value of LVEF measured with echocardiography was 56.4 ± 7.6%. In patients with normal ECG on admission it was 59.9 ± 3.1%, whereas in patients with abnormal ECG 55.7 ± 7.9% (p = 0.045). Considering CMR, mean LVEF in the population was 58.5 ± 8.6%, varying between 64.0 ± 8.9% in the group with normal ECG and 57.4 ± 10.1% in the group with ECG abnormalities (p = 0.02). Moreover, subjects with altered ECG on admission had a higher prevalence of wall motion abnormalities both in echocardiography (47/85 – 55% vs 3/17 – 18%, p < 0.01) and in CMR (45/85 – 53% vs 3/17 – 18%, p < 0.01). Patients with ECG normalization at discharge had a higher prevalence of ST-segment elevation (88 vs 66%, p = 0.02), while the group with persistent ECG alterations had a higher incidence of AV or bundle-branch block (23 vs 7%, p = 0.048). No statistical difference was noted between these two groups regarding echocardiographic or CMR values. Conclusion In our experience evaluation of ECG at admission in patients with suspected acute myocarditis identifies a subgroup of individuals with lower values of LVEF and a higher prevalence of wall motion abnormalities both in echocardiography and in CMR, while data derived by imaging techniques had no significant predictive value on ECG evolution at the time of discharge.

Blood pressure (BP) and the electrocardiographic (ECG) signal, or electrical signal of the heart, are physical measurements that provide insight into the behavior of the cardiac system. This paper presents a novel methodology, where for the first time the relationship between BP and ECG signal is shown. Initially, to perform this study, a signal sampling of ECG signals was performed on 20 patients: eighteen healthy, between 17 and 26 years old, and two with normal BP between 50 and 78 years old. Powerlab equipment was used to record the ECG signal, with electrodes used to capture the heart signal through the lead. Once the signal samples were obtained, the R and T waves in particular were studied with the aim of reading the systolic and diastolic blood pressure separately. In order to obtain the BP with the ECG signals, we used a wavelet transform to identify the R waves and T waves, and then to perform segmentation on the signal and extract the systole and diastole portions from the original signal. Following this procedure, neural networks were applied in order to have a system with systolic and diastolic pressure values based on the ECG signals. This application led to a total success rate of 97.305% for systole and 95.65% for diastole. In conclusion, this article can be said to demonstrate the existence of a relationship between BP and ECG signals. Blood pressure (BP) and the electrocardiographic (ECG) signal, or electrical signal of the heart, are physical measurements that provide insight into the behavior of the cardiac system. This paper presents a novel methodology, where for the first time the relationship between BP and ECG signal is shown. Initially, to perform this study, a signal sampling of ECG signals was performed on 20 patients: eighteen healthy, between 17 and 26 years old, and two with normal BP between 50 and 78 years old. Powerlab equipment was used to record the ECG signal, with electrodes used to capture the heart signal through the lead. Once the signal samples were obtained, the R and T waves in particular were studied with the aim of reading the systolic and diastolic blood pressure separately. In order to obtain the BP with the ECG signals, we used a wavelet transform to identify the R waves and T waves, and then to perform segmentation on the signal and extract the systole and diastole portions from the original signal. Following this procedure, neural networks were applied in order to have a system with systolic and diastolic pressure values based on the ECG signals. This application led to a total success rate of 97.305% for systole and 95.65% for diastole. In conclusion, this article can be said to demonstrate the existence of a relationship between BP and ECG signals.

Deep learning using electrocardiographs

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 …

Atrial fibrillation: prevalence in primary care patients in Brazil

The fast Fourier transform (FFT) algorithm is widely used as a standard tool to carry out spectral analysis because of its computational efficiency. However, the presence of multiple tones frequently requires a fine frequency resolution to achieve sufficient accuracy, which imposes the use of a large number of FFT points that results in large area and power overheads. In this paper, an FFT method is proposed for on-chip spectral analysis of multi-tone signals with particular harmonic and intermodulation components. This accurate FFT analysis approach is based on coherent sampling, but it requires a significantly smaller number of points to make the FFT realization more suitable for on-chip built-in testing and calibration applications that require area and power efficiency. The technique was assessed by comparing the simulation results from the proposed method of single and multiple tones with the simulation results obtained from the FFT of coherently sampled tones. The results indicate that the proper selection of test tone frequencies can avoid spectral leakage even with multiple narrowly spaced tones. When low-frequency signals are captured with an analog-to-digital converter (ADC) for on-chip analysis, the overall accuracy is limited by the ADC's resolution, linearity, noise, and bandwidth limitations. Post-layout simulations of a 16-point FFT showed that third-order intermodulation (IM3) testing with two tones can be performed with 1.5-dB accuracy for IM3 levels of up to 50 dB below the fundamental tones that are quantized with a 10-bit resolution. In a 45-nm CMOS technology, the layout area of the 16-point FFT for on-chip built-in testing is 0.073 mm2, and its estimated power consumption is 6.47 mW.

To apply both autoregressive (AR) and fast Fourier transform (FFT) spectral analysis at rest, during two different dynamic exercise intensities and in recovery from maximal exercise and to compare raw and normalized powers obtained with both methods. Sixteen participants (age 22.3 +/- 4.3 year) performed resting, submaximal and maximal protocols. The submaximal protocol consisted of two 5-min walks at 4 km h(-1) at treadmill grades of 0 and 7.5%. Beat-to-beat R-R series were recorded. FFT and AR analyses were preformed on the same R-R series. Compared to AR, FFT provided higher total power (TP) and raw high-frequency (HF) power at rest and exercise. Furthermore, FFT LF/HF ratio was lower than with the AR, except under resting conditions. Both methods showed reductions in TP, raw HF and LF powers during exercise and recovery. Only the AR revealed a significant reduction for normalized HF power and increase for normalized LF power in transition from rest to exercise conditions. AR and FFT methods are not interchangeable at rest or during dynamic exercise conditions. The AR method is more sensitive to the effects of exercise on the normalized power spectra of heart rate variability (HRV) than FFT. Finally, as both approaches are equally insensitive to the increase of exercise relative intensity, there is no practical advantage of performing HRV spectral analyses by the AR or FFT at higher workloads.

Wireless body area network is a key enabler for connected healthcare but recent cyberattacks have compromised its security and trustworthiness. This paper investigates heartbeat-based key generation to secure body area networks. The interpulse intervals (IPIs) between any two adjacent peaks of heartbeat signals are random and state-of-the-art literature has demonstrated that IPI is a good random source to be extracted as cryptographic keys. Heartbeat signals can be measured by electrocardiography (ECG) and photoplethysmography (PPG) sensors. A general heartbeat-based key generation framework applicable to both ECG and PPG signals is proposed. A robust peak detection algorithm is designed to capture noisy peaks and a simple yet efficient IPI alignment algorithm to align the common IPIs. A key establishment protocol is used to convert analog IPIs to digital binaries and reconcile them between legitimate devices. We evaluate the performance for both ECG signals from an online public database, MIT PhysioBank, and PPG signals collected from our testbed. The results demonstrate that our algorithm is robust and heartbeat-based key generation can be completed for both ECG and PPG signals. We finally create a PPG-based prototype and a demonstration video to show the practicality of our framework.

The detection of heartbeats is a fundamental task in the diagnosis of cardiovascular diseases. In a clinical setting, heartbeats are commonly detected from the electrocardiographic (ECG) signal, but it can also be estimated from hemodynamic signals, such as arterial blood pressure (ABP), pulmonary arterial pressure (PAP) and central venous pressure (CVP). In this paper, we conceived three HMM-based detectors that jointly exploit the information from ECG and ABP (\( D_{ECG \& ABP}\)), ECG and PAP (\( D_{ECG \& PAP}\)), and ECG and CVP (\( D_{ECG \& CVP}\)). The HMM-based detectors are based on the comparison of the difference of log-likelihoods of observation of two competing models that learned the dynamics of the presence and absence of a heartbeat, respectively. The detection performances of the bivariate centralized detectors were similar, but the detector that simultaneously considers ECG and ABP provide the best result (sensitivity = 98.91% and positive predictivity = 99.11%). These results agree when a univariate HMM-based detector only considers the ECG signal in the decision process. The centralized bivariate detection approach proposed in this paper make uses of redundant information in the decision process and is particularly useful when one of the signals is too noisy or missing.

The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods’ performances are still not robust or satisfactory. This study aims to develop a solution for CA classification in two dimensions by introducing the recurrence plot (RP) combined with an Inception-ResNet-v2 network. The proposed method for nine types of CA classification was tested on the 1st China Physiological Signal Challenge 2018 dataset. During implementation, the optimal leads (lead II and lead aVR) were selected, and then 1D ECG segments were transformed into 2D texture images by the RP approach. These RP-based images as input signals were passed into the Inception-ResNet-v2 for CA classification. In the CPSC, Georgia, and the PTB_XL ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the RP-based method achieved an average F1-score of 0.8521, 0.8529, and 0.8862, respectively. The results suggested the excellent generalization ability of the proposed method. To further assess the performance of the proposed method, we compared the 2D RP-image-based solution with the published 1D ECG-based works on the same dataset. Also, it was compared with two traditional ECG transform into 2D image methods, including the time waveform of the ECG recordings and time-frequency images based on continuous wavelet transform (CWT). The proposed method achieved the highest average F1-score of 0.844, with only two leads of the 12-lead ECG original data, which outperformed other works. Therefore, the promising results indicate that the 2D RP-based method has a high clinical potential for CA classification using fewer lead ECG signals.

The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep testing. Fortunately, most cortical arousal events are associated with autonomic nervous system activity that could be observed on an electrocardiography (ECG) signal. ECG data have lower noise and are easier to record at home than EEG. In this study, we developed a deep learning-based cortical arousal detection algorithm that uses a single-lead ECG to detect arousal during sleep. This study included 1,547 polysomnography records that met study inclusion criteria and were selected from the Multi-Ethnic Study of Atherosclerosis database. We developed an end-to-end deep learning model consisting of convolutional neural networks and recurrent neural networks which: (1) accepted varying length physiological data; (2) directly extracted features from the raw ECG signal; (3) captured long-range dependencies in the physiological data; and (4) produced arousal probability in 1-s resolution. We evaluated the model on a test set (n = 311). The model achieved a gross area under precision-recall curve score of 0.62 and a gross area under receiver operating characteristic curve score of 0.93. This study demonstrated the end-to-end deep learning approach with a single-lead ECG has the potential to be used to accurately detect arousals in home sleep tests.

Electrocardiography (ECG) data acquisition, data preprocessing, feature extraction and emotion recognition based on ECG feature classification were effectively implemented. Joy and sad movies were selected and presented to 154 subjects whose ECG data were recorded at the movie presentation time. The automatic location of QRS complex, which is of critical importance for ECG feature extraction by the computer, was performed by non-linear transformation on the first derivative of the ECG. By means of tabu search, the best combination of the ECG features was selected for classification. The ECG data were classified into certain cluster by Fisher projection.

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.

BackgroundAutomatic classification of arrhythmias based on electrocardiography (ECG) data faces several significant challenges, particularly due to the substantial volume of clinical data involved in ECG signal analysis. The volume of clinical data has increased considerably, especially with the emergence of new clinical symptoms and signs in various arrhythmia conditions. These symptoms and signs, which serve as distinguishing features, can number in the tens of thousands. However, the inclusion of irrelevant features can lead to inaccurate classification results.MethodTo identify the most relevant and optimal features for ECG arrhythmia classification, common feature extraction techniques have been applied to ECG signals, specifically shallow and deep feature extraction. Additionally, a feature selection technique based on a metaheuristic optimization algorithm is utilized following the ECG feature extraction process.ResultsOur findings indicate that shallow feature extraction based on the time-domain analysis, combined with feature selection using a metaheuristic optimization algorithm, outperformed other ECG feature extraction and selection techniques. Among eight features of time-domain anaylsis, the selected feature is one to three features from RR-interval assesment, achieving 100% accuracy, sensitivity, specificity, and precision for ECG arrhythmia classification.ConclusionThe proposed end-to-end architecture for ECG arrhythmia classification demonstrates simplicity in parameters and low complexity, making it highly effective for practical applications.