ECG Signal Research Articles

Novel Metric for Non-Invasive Beat-to-Beat Blood Pressure Measurements Demonstrates Physiological Blood Pressure Fluctuations during Pregnancy.

Beat-to-beat (B2B) variability in biomedical signals has been shown to have high diagnostic power in the treatment of various cardiovascular and autonomic disorders. In recent years, new techniques and devices have been developed to enable non-invasive blood pressure (BP) measurements. In this work, we aim to establish the concept of two-dimensional signal warping, an approved method from ECG signal processing, for non-invasive continuous BP signals. To this end, we introduce a novel BP-specific beat annotation algorithm and a B2B-BP fluctuation (B2B-BPF) metric novel for BP measurements that considers the entire BP waveform. In addition to careful validation with synthetic data, we applied the generated analysis pipeline to non-invasive continuous BP signals of 44 healthy pregnant women (30.9 ± 5.7 years) between the 21st and 30th week of gestation (WOG). In line with established variability metrics, a significant increase (p < 0.05) in B2B-BPF can be observed with advancing WOGs. Our processing pipeline enables robust extraction of B2B-BPF, demonstrates the influence of various factors such as increasing WOG or exercise on blood pressure during pregnancy, and indicates the potential of novel non-invasive biosignal sensing techniques in diagnostics. The results represent B2B-BP changes in healthy pregnant women and allow for future comparison with those signals acquired from women with hypertensive disorders.

Advanced Deep Learning for ECG Anomaly Detection in Imbalanced Data

Segmentation of ECG to obtain significant and relevant features has been an inevitable step to reduce the dimensionality of dataset in automated heart disease diagno- sis systems.Accurate and speedy classification of heart beats is required to reduce high mortality rate which is prevalent due to cardiovascular diseases(CVD). Nonstationarity and high variability exhibited by ECG signal leads to increase in com- plexity of analysis in time and frequency domain.Challenges in processing are further enhanced due to imbalanced and vague datasets.Deep learning based methods have been used in litera- ture to combat the problem of imbalanced datasets.This paper employs an effective recurrent neural network with long short term memory layers(LSTM) to classify the heart beats into two classes.It has been observed that LSTM network can effectively extract the sequential timing information in the input ECG samples.To remove the imbalance in the datasets,oversampling and focal loss based weight balancing techniques have been used which eventually enhance the accuracy of classification. MIT- BIH database has been used for experimental evaluation.The proposed approach ,LSTM network with oversampling tech- nique,provides an accuracy of 99.54% which is far better as compared to the traditional approaches which yield accuracy around 95%.Moreover this method is insensitive to quality of ECG signals due to the involvement of fuzzification procedure in the initial steps.Deployment of the proposed method for biosignal telemetry or pharmaceutical research to assist the physicians in their work is a possible future advancement in this domain. Index Terms—ECG, RNN, LSTM,Classification, imbalance.

Emotion Detection Through Electrocardiogram Signal Classification in an IOT Environment with Deep Neural Networks

An ECG detects the health and rhythm of the heart by measuring the electric activity of the heart. It has also been demonstrated that a person's emotions may influence the electrical activity of the heart. As a result, studying the electrical behaviour of the heart may simply determine a person's cardiac state and emotional wellness. IoT is a new technology that is quickly gaining acceptance throughout the world. Anybody, at any time, from anywhere, may connect to any network or service because to the extraordinary power and capacity of IoT. IoT-enabled devices have revolutionized the medical business by providing new capabilities such as remote patient monitoring and self-monitoring. This research proposed an IoT-based ECG monitoring system that employs a heart rate sensor to generate data and an intelligent hybrid classification algorithm to categorize the data. ECG monitoring has become a widely used method for detecting cardiac problems. The following are the primary contributions of this paper: To begin, this paper describes WISE (Wearable IoT-cloud-based health monitoring system), a one-of-a-kind system for real-time personal health monitoring. WISE makes use of the BASN (body area sensor network) technology to provide real-time health monitoring. WISE rapidly transfers data from the BASN to the cloud, and a lightweight wearable LCD may be included to enable quick access to real-time data. This model can address the issue of class imbalance in the ECG dataset, assisting in the development of an IoT-based smart and accurate healthcare system. Pre-processing, feature extraction, and classification are the three steps in any classification technique, whether it is emotion classification or heart health classification. Sensors are used to collect an ECG signal from a person's outside body. The provided ECG signal is first pre-processed using the Butterworth Filtering Method, which effectively reduces noise from the signal. Following pre-processing, the Adaptive Discrete Wavelet Transform technique is used to anticipate the signal's attributes. Lastly, a decision making classification approach based on relational weights is used to determine if the ECG signal is normal or abnormal.

Arrhythmia Classification from ECG Signals Using Transformers and Data Balancing Techniques

While many arrhythmias pose minimal threat, certain heart rhythm irregularities elevate the potential for stroke or heart failure. The complexity arises particularly with the supraventricular premature heartbeat which has a resemblance to a normal beat and occurs infrequently. Consequently, this research proposes a data balancing and classification technique that enhances the accuracy of identifying mentioned hard-to-classify heartbeats while maintaining robust metrics for other classes. The study introduces a deep learning framework combined with a multi-head attention transformer, for balancing – under-sampling and synthetic minority oversampling are used. To evaluate the proposed model, various experiments based on real data were conducted. The results were compared with an existing model used in chest belt heartbeat monitoring, and the results show that the transformer model achieved better performance for supraventricular premature heartbeats, at the same time reaching high overall and per-class metrics.

Enhanced ECG Signal features transformation to RGB matrix imaging for advanced deep learning classification of myocardial infarction and cardiac arrhythmia

Enhanced ECG Signal features transformation to RGB matrix imaging for advanced deep learning classification of myocardial infarction and cardiac arrhythmia

ECG waveform generation from radar signals: A deep learning perspective

Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy. Additionally, ECG monitoring usually requires highly trained professionals and specialized equipment, which increases the treatment's complexity and cost. In critical care scenarios, such as continuous monitoring of hospitalized patients, wearable sensors for collecting ECG data may be difficult to use. Although there are issues with ECG, it remains a valuable tool for diagnosing and monitoring cardiac disorders due to its non-invasive nature and the detailed information it provides about the heart. The goal of this study is to present an innovative method for generating continuous ECG waveforms from non-contact radar data by using Deep Learning. The method can eliminate the need for invasive or wearable biosensors and expensive equipment to collect ECGs. In this paper, we propose the MultiResLinkNet, a one-dimensional convolutional neural network (1D CNN) model for generating ECG signals from radar waveforms. With the help of a publicly accessible radar benchmark dataset, an end-to-end DL architecture is trained and assessed. There are six ports of raw radar data in this dataset, along with ground truth physiological signals collected from 30 participants in five distinct scenarios: Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. By using strong temporal and spectral measurements, we assessed our proposed framework's ability to convert ECG data from Radar signals in three distinct scenarios, namely Resting, Valsalva, and Apnea (RVA). ECG segmentation performed better by MultiResLinkNet than by state-of-the-art networks in both combined and individual cases. As a result of the simulations, the resting, valsalva, and RVA scenarios showed the highest average temporal values, respectively: 66.09523 ± 19.33, 60.13625 ± 21.92, and 61.86265 ± 21.37. In addition, it exhibited the highest spectral correlation values (82.4388 ± 18.42 (Resting), 77.05186 ± 23.26 (Valsalva), 74.65785 ± 23.17 (Apnea), and 79.96201 ± 20.82 (RVA)), along with minimal temporal and spectral errors in almost every case. The qualitative evaluation revealed strong similarities between generated and actual ECG waveforms. As a result of our method of forecasting ECG patterns from remote radar data, we can monitor high-risk patients, especially those undergoing surgery.

Rhythm-adaptive statistical estimation methods of probabilistic characteristics of cyclic random processes

The paper is devoted to the rhythm-adaptive statistical estimation methods of cyclic random processes probabilistic characteristics. The main goal of the article is development and research of rhythm-adaptive methods of cyclic random processes statistical processing. Based on the fundamental fact of invariance of probabilistic characteristics of a cyclic random process to time shifts determined by its rhythm function, rhythm-adaptive statistical estimations of initial, central and mixed moment functions of cyclic random processes of discrete parameter were obtained. The article analytically proves the unbiasedness and consistency of rhythm-adaptive statistical estimates of the mathematical expectation and the initial moment functions of the higher order of cyclic random process. By conducting a series of computer simulation experiments, it has been demonstrated, that the absolute errors of rhythm-adaptive methods of statistical estimation are significantly smaller than the corresponding errors for classical non-rhythm adaptive methods of statistical estimation of cyclic stochastic signals probabilistic characteristics. The effectiveness of using rhythm-adaptive methods of statistical estimation of probabilistic characteristics of ECG signals has been demonstrated. The results obtained in the work clearly demonstrate the essential advantages of rhythm-adaptive methods of statistical processing of cyclic stochastic signals with a variable (irregular) rhythm in comparison with non-rhythm-adaptive analogues.

Design of Low Noise Low Power Instrumentation Amplifier for Biomedical Applications

Advancements in integrated circuit (IC) technology and circuit design are enabling devices to replace complex biological applications. Despite this progress, achieving necessary functionality at low power levels is crucial for fully integrated systems. Biomedical devices like pacemakers, which require ongoing battery charging, operate at nanowatt power levels over several years. To ensure long-term pacemaker survival, an Analog Front End (AFE) with low to moderate power consumption and high precision is essential. The front-end Instrumentation Amplifier (INA) is vital for signal acquisition, significantly improving the effectiveness of ECG signal collection systems.The primary aim of this research is to develop an integrated ECG instrumentation amplifier (INA) that can deliver top-notch performance while keeping noise and power consumption to a minimum. This amplifier will enable seamless monitoring of the ECG signal without interruptions. Using an AC connected Common Mode Feedback system [1]. in conjunction with an Operational Trans-conductance Amplifier (OTA) that uses a Chopper stabilization technique, the study's band pass filtering response within the ECG signal frequency range of 0-100Hz is obtained as anticipated. CMOS 45 nm technology is used throughout the entire process using Mentor Graphics EDA Tools

K-B2S+: A one-dimensional CNN model for AF detection with short single-lead ECG waves from wearable devices

Wearable signal analysis is an important technology for monitoring physiological signals without interfering with an individual's daily behavior. As detecting cardiovascular diseases can dramatically reduce mortality, arrhythmia recognition using ECG signals has attracted much attention. In this paper, we propose a single-channel convolutional neural network to detect Atrial Fibrillation (AF) based on ECG signals collected by wearable devices. It contains 3 primary modules. All recordings are firstly uniformly sized, normalized, and Butterworth low-pass filtered for noise removal. Then the preprocessed ECG signals are fed into convolutional layers for feature extraction. In the classification module, the preprocessed signals are fed into convolutional layers containing large kernels for feature extraction, and the fully connected layer provides probabilities. During the training process, the output of the previous pooling layer is concatenated with the vectors of the convolutional layer as a new feature map to reduce feature loss. Numerous comparison and ablation experiments are performed on the 2017 PhysioNet/CinC Challenge dataset, demonstrating the superiority of the proposed method.

LabVIEW-Enabled Synthetic Signal for Empowering Fetal-Maternal Healthcare

Biomedical signal processing has advanced to the point that tools and methods are now available to doctors to diagnose and track medical conditions connected to pregnancy. However, it is extremely difficult for researchers to look into novel procedures and approaches to uncover underlying pathological abnormalities associated with high-risk pregnancies due to the scarcity of high-quality medical databases of pregnant women. In this study, a LabVIEW software environment is used to precisely design a bio-physiological signal generator (BPSG) for use in feto-maternal health assessment applications. McSharry’s dynamical ECG model served as inspiration for the methods utilized to create the proposed time-domain mathematical model. The BPSG is capable of generating various realistic synthetic signals like respiration signal, pulse plethysmography (PPG) signal, phonocardiography (PCG) signal, maternal ECG (MECG) signal, fetal ECG (FECG) signal, abdominal ECG (AECG) signa,l and umbilical blood flow (UBF) velocimetry signals with corresponding Doppler indices. It is possible to create synthetic signals for both healthy and unhealthy conditions. Synthetic signal facilitates the testing and calibration of new diagnostic procedures, denoising algorithms, feature extraction processes, and instrumentation, all of which contribute to the prompt prediction of an overall health state of expectant mother.

Acquisition and Analysis of Cardio Graphic Signal Using Various Transforms

In the modern days, cardiac problems among various age group people have increased by a large ratio. Electro cardiograph is widely used to monitor the electrical activities of the heart. With the help of ECG, we can able to easily detect and classify the irregular activities of the heart. Hence in this study, we have acquired ECG signals from ten different subjects of various age groups using the BIOPAC system. All the acquired ECG signals are pre-processed using the infinite impulse response bandpass filter. Then the most vital R wave is detected by using the peak detection method present in-built in the BIOPAC system. This R wave detection plays a very vital role in diagnosing heart rhythm irregularities, calculating the heart rate variability, recognizing the heart rate variability, and measuring the heart rate. The pre-processed ECG signal is processed using highly advanced windowing techniques to extract the power spectrum and frequency-based values of the ECG signal. The results of the Fourier transform and spectral density of the processed ECG signals are displayed in this study. The features extracted from ECG signals acquired from ten different subjects have been tabulated in this study.

HEART RATE VARIABILITY BASED DRIVER DROWSINESS DETECTION

Driver drowsiness may cause traffic injuries and death. Various methods have been given to detect drowsiness, for instance, image-based biometric-signals-based. In this project, a new approach using heart rate and head tilt is discussed. The aim of the project is to provide a device with simple components that enables the driver to save his life by avoiding accidents caused by frequent sleepiness by giving a sound alert (alarm), which forces the driver to wake up and regain control of the car This device is not considered a treatment for the problem of sleep, but rather a preventive tool from accidents daily the project was designed with simple components, the most important of which are (ECG sensor and acceleration sensor Arduino, alarm device). ECG sensor: it used to give an ECG signal to Arduino to analysis it. ADXL335 sensor: it is placed behind the driver's head. This sensor senses the fall of the driver's head when he sleeps. It was added to sense better.

Multimodal image fusion on ECG signals for congestive heart failure classification

Multimodal image fusion on ECG signals for congestive heart failure classification

Conditional generative adversarial network driven variable-duration single-lead to 12-lead electrocardiogram reconstruction

The standard 12-lead ECG can represent the cardiac status from twelve different perspectives, while the single-lead ECG just involves a single view, so it is challenging for the single-lead ECG to provide information as rich as the 12-lead ECG. Therefore, this study adopts the improved generative adversarial network (GAN) framework to model the potential mapping relation between single-lead and 12-lead ECG. Taking a heartbeat (1024 points) in lead I as input, in which the R-peak aligning strategy will be used for variable-duration reconstruction. The average correlation coefficients(CC) between the generated and the real signals are 0.742 at the heartbeat level and 0.744 at the segment level, and the CC in lead aVR could be up to 0.91 at the segment level. This study demonstrates that the generated 12-lead ECG signals effectively retain pathological features, thereby providing valuable multi-scale diagnostic information. To assess the efficacy of the diagnostic information derived from the generated signals, this study employs the arrhythmia classifiers for both single-lead and 12-lead ECG, achieving accuracy of 0.71 and 0.81, and macro-F1 scores of 0.66 and 0.77. For the generated 12-lead ECG, the accuracy and macro-F1 for arrhythmia classification could be 0.74 and 0.70, successfully bridging the gap between the single-lead ECG and the 12-lead ECG. In addition, the supplement experiments for other cases have been shown and compared. The source code is available at https://github.com/Zehui-Zhan/12-lead-reconstruction.git.

Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals

ECG signals are one of the most common tools used to diagnose cardiovascular diseases. ECG signals are obtained by measuring electrical changes on the skin surface. Arrhythmias occurring in the heart are diagnosed because the expert evaluates ECG signals. This diagnosis depends on the experience of the specialist and is a subjective evaluation. With the widespread use of computer-aided diagnostic systems, evaluations dependent on the expert's experience are objectified, and support is provided to the physician for diagnosis. For computer-aided ECG classification, beats are detected from ECG signals, and arrhythmias are detected by analyzing the structure of these beats. In recent years, deep learning models have been successful in classifying ECG signals. The data to be used in the classification process is realized with the help of morphological features or images of the signal. The main objective of this study is to compare the classification performance of digital and visual heartbeat data for ECG signal classification. For this purpose, 1D-CNN and 2D-CNN architectures are used for the type of ECG signals. As inputs of the 1D-CNN model, numerical values of the heartbeat signal and hand-crafted features obtained from these numerical values were used. The inputs of the 2D-CNN model are the raw signal image, spectrogram, scalogram, Mel-spectrogram, GFCC, and CQT images, which are visual representations of the heartbeat signal. The results show that the numerical model of the ECG signal fails for classification, while the hand-crafted features provide 85.2% accuracy. The results obtained with the visual representation of the signal provided over 99% classification accuracy for all images. The highest success rate was 99.9% with the visualization of the raw signal. In line with these findings, the 2D-CNN architecture and the visual representation of the heartbeat signal were found to be the most suitable method for classifying ECG signals.

IoT-Based Virtual Primary Clinic: Remote Patient Monitoring and Consultation System

Using Internet of Things technology, the Virtual Primary Clinic project is a cutting-edge healthcare endeavor that enables remote patient monitoring and consultation. The project collects essential health metrics including body temperature, oxygen saturation, heartbeat, and ECG signals by integrating a variety of sensors, including NodeMCU ESP32, SpO2, DHT11, and AD8232 ECG. The information collected by these sensors is sent via API to a specially created website and an online platform called ThingSpeak. With features including patient registration, appointment scheduling, access to medical histories, and doctor-patient contact, the website acts as a comprehensive interface between patients and physicians. Patients can register on the website, provide their medical information, and get medications sent to them remotely based on their conditions. However, without having to see patients in person, doctors can examine patient data, diagnose ailments, and write prescriptions or offer medical advice.The Virtual Primary Clinic project intends to improve healthcare accessible, especially in remote or underserved locations, by enabling remote monitoring and consultation. In addition to providing healthcare providers with an effective platform for remote patient care, it gives patients the ability to take charge of their health. This project is a major step toward using cutting-edge IoT-based technologies to improve patient outcomes and democratize healthcare.

Recent advances of ECG monitoring and webserver health monitoring applications: A review

In response to recent improvements in mobile technology and cloud computing-based real-time monitoring systems, numerous cloud-based healthcare services and devices have been developed. In the cloud system, it is feasible to collect and distribute medical data immediately from anywhere to medical specialists, and patients can receive network-based feedback. This paper reviews the most recent developments and trends in wearable technology and biomedical equipment (mostly ECG), as well as the development of a cloud-based solution for mobile device and web browser users. The system contains ECG enhancement, ECG quality grading, and ECG parameter extraction methods. A use case was utilized to showcase the system, in which a web server processed ECG data from a mobile phone in real-time. The system has been proved to be functional, accurate, and efficient. We review the primary principles underlying the operation of the ECG and its fundamental operation, as well as the recording and transmission paradigm for ECG signals. Detailed processes were also provided for the system components, their configuration, and the employed technologies. In conclusion, we examine the complete study and the project's future scope.

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

Диагностика сигналов ЭКГ на основе цифровой обработки и оптимизированной модели трансформера

Heartbeat disorders are considered one of the main maladies that cause mortality. Therefore, their precocious diagnosis via ECG signal is critical for introducing prompt therapy. The advanced automatic classification of ECG signals has the potential to save cardiologists a tremendous amount of time while simultaneously decreasing the chance of misdiagnosis. The dilemma of massive parameters is troubling the current methods of ECG signal classification. Most recent methods exhibit inadequate performance for diagnosing ECG signals in the inter-patient mode. In an attempt to deal with the above limitations, this study offers an innovative, efficient, and end-to-end model. The suggested model uses the optimized transformer framework to classify the heartbeats according to the "Association for the Advancement of Medical Instrumentation, AAMI," and obeys the inter-patient setting. We constructed an efficient architecture called the optimized network to substitute the Self Attention Unit (SAU) in the encoder part of the transformer model. The suggested model, which includes an optimized network, outperforms the SAU-based transformer model and requires fewer computations. A robust embedding architecture based on a Convolutional Neural Network (CNN) with a Squeeze and Excitation (SE) network-based attention scheme that has been used for weighting the Local Heartbeat Shape Pattern (LHSP) features is presented. The introduced model exceeds the state-of-the-art. An extensive test has been done to compare the achievements of the suggested model with those of the cardiologists. The results proved the closeness of their performances.

Electrocardiogram-based Emotion Recognition using Convolutional Neural Networks

Introduction: Electrocardiogram-based facial emotion identification has been a widely spread field in the last few decades. Due to its non-linearity, non-stationary and noisy properties, it is a very difficult job to create a framework that is capable of recognizing emotions with a high recognition rate. background: Electrocardiogram based facial emotion identification has been widely spread field in last some decades. Due to its non-linearity, non-stationary and noisy properties, it is very difficult job to create a framework that is capable of recognizing emotions with high recognition rate. Method: In this work, we introduce a new framework for facial emotion detection based on feature creation using a topographic representation of ECG signal properties. The feature map is created using deep learning techniques, and further, extricated features are then used for classification techniques to detect facial emotion recognition. Result: The recognition results are achieved on two publicly available facial expression datasets, i.e., Ascertain and Dreamer. We illustrated the usefulness of our framework by comparing results with other existing methods. Conclusion: The recognition results prove that the introduced framework can enhance the identifying rate on various given datasets.