A new real-time lossless data compression algorithm for ECG and PPG signals

Highlights. The article presents a novel and unique method for assessment of left ventricular systolic disfunction using electrocardiography and photoplethysmography data. This method will improve and simplify the detection of cardiovascular diseases.Aim. To evaluate left ventricular (LV) systolic function using electrocardiogram (ECG) and photoplethysmogram (PPG) signals recorded by a single-channel ECG and PPG-based monitor.Methods. The prospective study included 489 patients over 18 years old with various cardiovascular diseases. All participants underwent echocardiography to determine the main indicators of LV systolic function: LV ejection fraction (EF), LV outflow tract velocity time integral (LVOT VTI), and global longitudinal strain (GLS). Moreover, all patients underwent 1-lead ECG and PPG recording using a single-channel ECG and PPG-based monitor (CardioQvark). The obtained data were analyzed, and ROC curve analysis was performed.Results. We have identified ECG and PPG parameters associated with a decrease in LV contractile function. During the analysis, the ECG, T-wave amplitude (TA) and RonsF parameters showed the highest diagnostic accuracy. With EF below 55%, the area under the ROC curve (AUC) was 0.822, sensitivity (Se) 80%, specificity (Sp) 69% in EF below 55% in TA; in RonsF AUC was 0.743, Se 81%, Sp 77%. With EF below 40%, AUC was 0.915, Se 85%, Sp 83% in TA, and in RonsF AUC was 0.844, Se 82%, Sp 82%. Diagnostic accuracy of ECG signals in case of LVOT VTI lower than 16 cm was measured: TA (AUC 0.755, Se 82%, Sp 70%), RonsF (AUC 0.620, Se 77%, Sp 72%). PPG signals were not significantly associated with reduced EF; however, the pulse wave parameters were associated with lower LVOT VTI: in DP-B0 AUC was 0.687, Se 71%, Sp 74%. The combination of ECG and PPG signals was significantly associated with EF below 40% (RonsF * DP-SEP (AUC 0.877, Se 86%, Sp 85%). ECG and PPG signals were not associated with LV GLS.Conclusion. Assessment of LV systolic function can be performed by analyzing ECG and PPG signals recorded using a portable single-channel CardioQvark monitor.

Blood pressure measurement is a significant part of preventive healthcare and has been widely used in clinical risk and disease management. However, conventional measurement does not provide continuous monitoring and sometimes is inconvenient with a cuff. In addition to the traditional cuff-based blood pressure measurement methods, some researchers have developed various cuff-less and noninvasive blood pressure monitoring methods based on Pulse Transit Time (PTT). Some emerging methods have employed features of either photoplethysmogram (PPG) or electrocardiogram (ECG) signals, although no studies to our knowledge have employed the combined features from both PPG and ECG signals. Therefore this study aims to investigate the performance of a predictive, machine learning blood pressure monitoring system using both PPG and ECG signals. It validates that the employment of the combination of PPG and ECG signals has improved the accuracy of the blood pressure estimation, compared with previously reported results based on PPG signal only. © 2018 Institution of Engineering and Technology. All rights reserved.

PO-04-175 RECONSTRUCTING TWELVE-LEAD ELECTROCARDIOGRAM SIGNALS FROM A SINGLE-LEAD SIGNAL USING GENERATIVE ADVERSARIAL NETWORKS

Emotions are complex, and people vary greatly in their accuracy in recognizing their own emotions and those of others. With advances in computer science and neuroscience, there is a desire to use automated techniques to help people identify emotions. Bio-electrical signals have been proven effective for emotion detection, but the acquisition of conventional electrocardiogram (ECG) and EEG requires medical-specific equipment, which is very expensive, uncomfortable, and inconvenient due to the large number of electrodes and the hair-covered scalp. In this article, a novel emotion recognition method based on the feature fusion of single-lead EEG and ECG signals is proposed, using the long short term memory (LSTM)-MLP-based model and the CNN-based model for feature fusion and classification, respectively, with fivefold cross-validation for validation. The ECG and EEG signals of 15 participants were collected in five states: 1) happy; 2) relaxed; 3) calm; 4) sad; and 5) afraid, each of which was stimulated using the participants’ own proposed music. Various time-domain features, frequency-domain features, and nonlinear features were extracted from the ECG and EEG signals. Experimental results demonstrate that the accuracy of emotion recognition and classification of signals captured by the proposed device can reach 92.08% using the CNN model. While using the LSTM-MLP feature fusion model, the accuracy figure can be improved to 95.07%. The results of the ablation experiment indicate that the feature fusion approach does improve the accuracy of recognition. It is demonstrated that the proposed device and emotional recognition approach are effective and feasible.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder and highly affects the quality of human life. Currently, gold standard for OSA detection is polysomnogram. Since this method is time consuming and cost inefficient, practical systems focus on the usage of electrocardiogram (ECG) signals for OSA detection. In this paper, a novel automatic OSA detection method using a single-lead ECG signal has been proposed. A nonlinear feature extraction using wavelet transform (WT) coefficients obtained by an ECG signal decomposition is employed. In addition, different classification methods are investigated. ECG signals are decomposed into eight levels using a Symlet function as a mother Wavelet function with third order. Then, the entropy-based features including fuzzy/approximate/sample/correct conditional entropy as well as other nonlinear features including interquartile range, mean absolute deviation, variance, Poincare plot, and recurrence plot are extracted from WT coefficients. The best features are chosen using the automatic sequential forward feature selection algorithm. In order to assess the introduced method, 95 single-lead ECG recordings are used. The support vector machine classifier having a radial basis function kernel leads to an accuracy of 94.63% (sensitivity: 94.43% and specificity: 94.77%) and 95.71% (sensitivity: 95.83% and specificity: 95.66%) for minute-by-minute and subject-by-subject classifications, respectively. The results show that applying entropy-based features for extracting hidden information of the ECG signals outperforms other available automatic OSA detection methods. The results indicate that a highly accurate OSA detection is attained by just exploiting the single-lead ECG signals. Furthermore, due to the low computational load in the proposed method, it can easily be applied to the home monitoring systems.

Recently numerous methods have been proposed for estimating average heart rate using photoplethysmography (PPG) during physical activity, overcoming the significant interference that motion causes in PPG traces. We propose a new algorithm framework for extracting instantaneous heart rate from wearable PPG and Electrocardiogram (ECG) signals to provide an estimate of heart rate variability during exercise. For ECG signals, we propose a new spectral masking approach which modifies a particle filter tracking algorithm, and for PPG signals constrains the instantaneous frequency obtained from the Hilbert transform to a region of interest around a candidate heart rate measure. Performance is verified using accelerometry and wearable ECG and PPG data from subjects while biking and running on a treadmill. Instantaneous heart rate provides more information than average heart rate alone. The instantaneous heart rate can be extracted during motion to an accuracy of 1.75 beats per min (bpm) from PPG signals and 0.27 bpm from ECG signals. Estimates of instantaneous heart rate can now be generated from PPG signals during motion. These estimates can provide more information on the human body during exercise. Instantaneous heart rate provides a direct measure of vagal nerve and sympathetic nervous system activity and is of substantial use in a number of analyzes and applications. Previously it has not been possible to estimate instantaneous heart rate from wrist wearable PPG signals.

Coronary artery disease (CAD) is one of the most common causes of heart ailments; many patients with CAD do not exhibit initial symptoms. An electrocardiogram (ECG) is a diagnostic tool widely used to capture the abnormal activity of the heart and help with diagnoses. Assessing ECG signals may be challenging and time-consuming. Identifying abnormal ECG morphologies, especially in low amplitude curves, may be prone to error. Hence, a system that can automatically detect and assess the ECG and treadmill test ECG (TMT-ECG) signals will be helpful to the medical industry in detecting CAD. In the present work, we developed an intelligent system that can predict CAD, based on ECG and TMT signals more accurately than any other system developed thus far. The distinct convolutional neural network (CNN) architecture deals with single-lead and multi-lead (12-lead) ECG and TMT-ECG data effectively. While most artificial intelligence-based systems rely on the universal dataset, the current work used clinical lab data collected from a renowned hospital in the neighborhood. ECG and TMT-ECG graphs of normal and CAD patients were collected in the form of scanned reports. One-dimensional ECG data with all possible features were extracted from the scanned report with the help of a modified image processing method. This feature extraction procedure was integrated with the optimized architecture of the CNN model leading to a novel prediction system for CAD. The automated computer-assisted system helps in the detection and medication of CAD with a high prediction accuracy of 99%.

SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals

The diagnosis of hypertrophic cardiomyopathy (HCM) is of great significance for the early risk classification of sudden cardiac death and the screening of family genetic diseases. This research proposed a HCM automatic detection method based on convolution neural network (CNN) model, using single-lead electrocardiogram (ECG) signal as the research object. Firstly, the R-wave peak locations of single-lead ECG signal were determined, followed by the ECG signal segmentation and resample in units of heart beats, then a CNN model was built to automatically extract the deep features in the ECG signal and perform automatic classification and HCM detection. The experimental data is derived from 108 ECG records extracted from three public databases provided by PhysioNet, the database established in this research consists of 14,459 heartbeats, and each heartbeat contains 128 sampling points. The results revealed that the optimized CNN model could effectively detect HCM, the accuracy, sensitivity and specificity were 95.98%, 98.03% and 95.79% respectively. In this research, the deep learning method was introduced for the analysis of single-lead ECG of HCM patients, which could not only overcome the technical limitations of conventional detection methods based on multi-lead ECG, but also has important application value for assisting doctor in fast and convenient large-scale HCM preliminary screening.

Electrocardiogram (ECG) signals are frequently utilized for detecting important cardiac events, such as variations in ECG intervals, as well as for monitoring essential physiological metrics, including heart rate (HR) and heart rate variability (HRV). However, the accurate measurement of ECG traditionally requires a clinical environment, thereby limiting its feasibility for continuous, everyday monitoring. In contrast, Photoplethysmography (PPG) offers a non-invasive, cost-effective optical method for capturing cardiac data in daily settings and is increasingly utilized in various clinical and commercial wearable devices. However, PPG measurements are significantly less detailed than those of ECG. In this study, we propose a novel approach to synthesize ECG signals from PPG signals, facilitating the generation of robust ECG waveforms using a simple, unobtrusive wearable setup. Our approach utilizes a Transformer-based Generative Adversarial Network model, designed to accurately capture ECG signal patterns and enhance generalization capabilities. Additionally, we incorporate self-supervised learning techniques to enable the model to learn diverse ECG patterns through specific tasks. Model performance is evaluated using various metrics, including heart rate calculation and root mean squared error (RMSE) on two different datasets. The comprehensive performance analysis demonstrates that our model exhibits superior efficacy in generating accurate ECG signals (with reducing 83.9% and 72.4% of the heart rate calculation error on MIMIC III and Who is Alyx? datasets, respectively), suggesting its potential application in the healthcare domain to enhance heart rate prediction and overall cardiac monitoring. As an empirical proof of concept, we also present an Atrial Fibrillation (AF) detection task, showcasing the practical utility of the generated ECG signals for cardiac diagnostic applications. To encourage replicability and reuse in future ECG generation studies, we have made both the dataset and the code publicly available.

Blood pressure (BP) serves as a vital indicator of cardiovascular health; however, achieving accurate and continuous BP monitoring continues to present significant challenges. To enhance the performance of continuous BP estimation using electrocardiogram (ECG) and photoplethysmography (PPG) signals, this paper introduces a method that integrates the ReliefF feature weighting algorithm with a Sparrow Search Algorithm-optimized Iterative Random Forest (SSA-IRF) regression model. First, a comprehensive set of time-domain, frequency-domain, and time-frequency features is extracted from PPG and ECG signals. The ReliefF algorithm is then applied to select highly sensitive features strongly correlated with BP, thereby reducing redundancy and improving monitoring efficiency. Subsequently, a hybrid prediction model is developed by combining the Sparrow Search Algorithm (SSA) with an Iterative Random Forest (IRF) regression model to learn the mapping between the selected features and BP values. A novel fitness function is designed to balance prediction accuracy and consistency. Finally, ablation and comparative experiments were conducted using ECG and PPG signals from 200 subjects in the MIMIC-III database, validating the effectiveness and advancement of the proposed approach. Experimental results show that the method achieves a mean absolute error (MAE) of 2.73 mmHg and a standard deviation (STD) of 3.88 mmHg for systolic BP prediction, and an MAE of 1.62 mmHg with an STD of 2.31 mmHg for diastolic BP prediction. Its performance not only complies with the Association for the Advancement of Medical Instrumentation (AAMI) standards but also meets Grade A criteria according to the British Hypertension Society (BHS) protocol.

NABNet: A Nested Attention-guided BiConvLSTM network for a robust prediction of Blood Pressure components from reconstructed Arterial Blood Pressure waveforms using PPG and ECG signals

A novel method for reducing arrhythmia classification from 12-lead ECG signals to single-lead ECG with minimal loss of accuracy through teacher-student knowledge distillation

An improved deep regression model with state space reconstruction for continuous blood pressure estimation

BackgroundHyperkalemia monitoring is very important in patients with chronic kidney disease (CKD) in emergency medicine. Currently, blood testing is regarded as the standard way to diagnose hyperkalemia (ie, using serum potassium levels). Therefore, an alternative and noninvasive method is required for real-time monitoring of hyperkalemia in the emergency medicine department.ObjectiveThis study aimed to propose a novel method for noninvasive screening of hyperkalemia using a single-lead electrocardiogram (ECG) based on a deep learning model.MethodsFor this study, 2958 patients with hyperkalemia events from July 2009 to June 2019 were enrolled at 1 regional emergency center, of which 1790 were diagnosed with chronic renal failure before hyperkalemic events. Patients who did not have biochemical electrolyte tests corresponding to the original 12-lead ECG signal were excluded. We used data from 855 patients (555 patients with CKD, and 300 patients without CKD). The 12-lead ECG signal was collected at the time of the hyperkalemic event, prior to the event, and after the event for each patient. All 12-lead ECG signals were matched with an electrolyte test within 2 hours of each ECG to form a data set. We then analyzed the ECG signals with a duration of 2 seconds and a segment composed of 1400 samples. The data set was randomly divided into the training set, validation set, and test set according to the ratio of 6:2:2 percent. The proposed noninvasive screening tool used a deep learning model that can express the complex and cyclic rhythm of cardiac activity. The deep learning model consists of convolutional and pooling layers for noninvasive screening of the serum potassium level from an ECG signal. To extract an optimal single-lead ECG, we evaluated the performances of the proposed deep learning model for each lead including lead I, II, and V1-V6.ResultsThe proposed noninvasive screening tool using a single-lead ECG shows high performances with F1 scores of 100%, 96%, and 95% for the training set, validation set, and test set, respectively. The lead II signal was shown to have the highest performance among the ECG leads.ConclusionsWe developed a novel method for noninvasive screening of hyperkalemia using a single-lead ECG signal, and it can be used as a helpful tool in emergency medicine.