Fine-Grained ECG Monitoring Based on Stretchable Organic Electrochemical Transistors with Anisotropic Gel Electrolytes for Biometric Identification.

Two-path all-pass based half-band infinite impulse response decimation filters and the effects of their non-linear phase response on ECG signal acquisition

This paper introduces a new method of simultaneous acquisition of electrocardiogram (ECG) signals and electrical impedance tomography (EIT) measurements on active electrodes, i.e. electrodes that are applying current, in an EIT system. Howland noise reduction followed by an integrate-and-dump filter and adaptive filtering are used to extract the ECG signal without loss of amplitude. Results of simultaneous ECG signals and EIT reconstructions on human subjects are shown. It is also demonstrated that the recovered ECG waveforms from 32 electrodes can be used to reconstruct the heart current source vectors during a cardiac cycle, yielding an approximate solution to the inverse problem of electrocardiography. Acquiring ECG signals from all electrodes attached to the body simultaneously with EIT data acquisition eliminates the need for a separate system to collect ECG signals. Time-aligned ECG signals are helpful in interpreting pulsatile perfusion images by providing exact timing of each image in relation to the cardiac cycle. Additionally, collecting ECG signals from many or all of the EIT electrodes enables reconstruction of the heart's moving dipole moment at the same time as the EIT images.

Dual phase dependent RLS filtering approach for baseline wander removal in ECG signal acquisition

The goal of this article is to present an algorithm for QRS complex detection in an electrocardiogram (ECG) signal, realized in Matlab software. The algorithm was tested on real ECG signals acquired with a commercial monitoring system Alive Heart Monitor and also for reference on signals from MIT-BIH online ECG signal database. The goal of our research is to import signals from the monitoring system to a PDA or a Smart Phone via wireless network for signal processing and use in telehealthcare and telemonitoring. Therefore, we examine the concept of wireless acquisition and digital signal processing of ECG (electrocardiogram) signal, which, in addition to traditional medicine is increasingly used in the field of telemedicine as well as in completely non-medical areas, such as sport, entertainment, marketing, etc. Furthermore, we describe the fundamental architecture of an electrocardiograph and describe and overview the basic methods for ECG signal processing.

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.

Quantitative analysis of electrocardiogram (ECG) signals plays a pivotal role in objectively and quantitatively assessing cardiac electrical activity. This paper presents an innovative approach for quantitative ECG signal analysis utilizing extremum energy decomposition (EED). The methodology encompasses multiple steps: acquisition of unknown ECG signal under specific time and sampling conditions, denoising of acquired ECG signals, and subsequent decomposition of denoised ECG signals into a set of extremum modal function components alongside a residual. The n extremum modal function components obtained effectively represent different frequency bands. By evaluating these n extremum modal function components, the presence and severity of abnormalities within the ECG signal can be determined. The results showcased the effectiveness of the method in accurately identifying abnormal ECG signals, and the technique demonstrated robustness against noise interference, enhancing its practical utility in clinical and diagnostic settings. This research contributes to the field of ECG analysis by offering a quantitative toolset that enhances the objectivity and accuracy of abnormality assessment in cardiac electrical activity.

This paper investigates the application of the newly proposed Kronecker-based method for the reconstruction of compressively sensed electrocardiogram (ECG) signals. By applying the Kronecker-based method, ECG signal acquisition is done in smaller lengths. Collection of smaller length of ECG signals leads to fewer arithmetic operations during the compression phase. Instead of recovering individually in smaller lengths, recovery is done over several concatenated segments. This newly proposed recovery method improves the quality of the reconstructed signal when compared to the traditional recovery done without concatenation. Two random measurement matrices, namely the Gaussian and the Bernoulli matrices, are considered as sensing matrices in this study and the methodology is evaluated using 10 ECG signals acquired from the MIT arrhythmia database. The random Bernoulli matrix is found to provide better quality of recovered compressed ECG signal even with the traditional compressive recovery techniques. Recovery by the newly proposed Kroneckerbased method results in higher SNR in all the ECG signals when the compression ratio (CR) is 25% or 50% and when the CR is 75%, improvement is observed in majority of the ECG signals. Lower CRs provide better reconstruction than higher CRs. The Kronecker-based recovery method may be useful for wearable ECG devices.

Telemonitoring of vital signs is an established option in treatment of patients with chronic heart failure (CHF). In order to allow for early detection of atrial fibrillation (AF) which is highly prevalent in the CHF population telemonitoring programs should include electrocardiogram (ECG) signals. It was therefore the aim to extend our current home monitoring system based on mobile phones and Near Field Communication technology (NFC) to enable patients acquiring their ECG signals autonomously in an easy-to-use way. We prototypically developed a sensing device for the concurrent acquisition of blood pressure and ECG signals. The design of the device equipped with NFC technology and Bluetooth allowed for intuitive interaction with a mobile phone based patient terminal. This ECG monitoring system was evaluated in the course of a clinical pilot trial to assess the system's technical feasibility, usability and patient's adherence to twice daily usage. 21 patients (4f, 54 ± 14 years) suffering from CHF were included in the study and were asked to transmit two ECG recordings per day via the telemonitoring system autonomously over a monitoring period of seven days. One patient dropped out from the study. 211 data sets were transmitted over a cumulative monitoring period of 140 days (overall adherence rate 82.2%). 55% and 8% of the transmitted ECG signals were sufficient for ventricular and atrial rhythm assessment, respectively. Although ECG signal quality has to be improved for better AF detection the developed communication design of joining Bluetooth and NFC technology in our telemonitoring system allows for ambulatory ECG acquisition with high adherence rates and system usability in heart failure patients.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, necessitating innovative solutions for early detection and continuous monitoring. This research aims to develop an Android-based remote cardiac monitoring device for real-time electrocardiogram (ECG) signal acquisition, transmission, and analysis. The system comprises hardware for acquiring ECG signals, algorithms for processing and machine learning models for anomaly classification. The hardware unit captures ECG data using electrodes and sensors. The signals are filtered, processed, and transmitted to the cloud infrastructure enabling real-time monitoring and analysis. Machine learning models including support vector machines, ensemble methods and Artificial neural networks are trained on ECG datasets to classify signals and detect cardiac abnormalities. Comprehensive testing validates the system's capabilities in real-time signal acquisition, processing, anomaly detection and data transmission. The integration of hardware, algorithms and machine learning enables round-the-clock monitoring of cardiac activity, facilitating prompt interventions and improved patient outcomes. This affordable and user-friendly system demonstrates potential for enhanced accessibility and effectiveness of preventive cardiac care.

Security biometrics is a secure alternative to traditional methods of identity verification of individuals, such as authentication systems based on user name and password. Recently, it has been found that the electrocardiogram (ECG) signal formed by five successive waves (P, Q, R, S and T) is unique to each individual. In fact, better than any other biometrics’ measures, it delivers proof of subject’s being alive as extra information which other biometrics cannot deliver. The main purpose of this work is to present a low-cost method for online acquisition and processing of ECG signals for person authentication and to study the possibility of providing additional information and retrieve personal data from an electrocardiogram signal to yield a reliable decision. This study explores the effectiveness of a novel biometric system resulting from the fusion of information and knowledge provided by ECG and EMG (Electromyogram) physiological recordings. It is shown that biometrics based on these ECG/EMG signals offers a novel way to robustly authenticate subjects. Five ECG databases (MIT-BIH, ST-T, NSR, PTB and ECG-ID) and several ECG signals collected in-house from volunteers were exploited. A palm-based ECG biometric system was developed where the signals are collected from the palm of the subject through a minimally intrusive one-lead ECG set-up. A total of 3750 ECG beats were used in this work. Feature extraction was performed on ECG signals using Fourier descriptors (spectral coefficients). Optimum-Path Forest classifier was used to calculate the degree of similarity between individuals. The obtained results from the proposed approach look promising for individuals’ authentication.

Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

Electrocardiogram (ECG) has become a primary tool for proper diagnosis of cardiovascular disease. During the acquisition of ECG signal, it is embedded with noise. Presence of noise in the signal may lead to variation in the amplitude and time intervals, which in turn falsely represents the abnormalities associated with the heart. This may lead to false diagnosis of arrhythmia. Hence to facilitate proper analysis, this paper presents a novel ECG signal denoising method based on S-transform based time-frequency filtering approach. The performance of the proposed method is evaluated by taking different normal and abnormal ECG signals from the MIT/BIH arrhythmia database. Two different types of noises are added with the data, white Gaussian noise and muscle noise. The results show that the proposed method is promising in terms of better signal to noise ratio (SNR), minimum mean square error (MSE) and percent root mean square difference (PRD).

ECG-based biometric under different psychological stress states

The electrocardiogram (ECG) signal provides a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end. The overlapping interference affects the quality of the ECG waveform, leading to a false detection and recognition of wave groups, and thus causing misdiagnosis or faulty treatment. Therefore, the elimination of the interference of the ECG signal and the subsequent wave group identification technology has been a hot research topic, and their study has important significance. Since when the signal is non-stationary, like the ECG signal, neither the regular power spectrum nor the bispectrum can handle this problem because they do not reflect the time variation of the process characteristics. With the recent introduction of the evolutionary bispectrum (EB) in digital signal processing, a new approach to the analysis problem has been devised. The work in this paper is focusing on the reduction of the noise interferences by introducing a new algorithm based on the EB. This approach exploits the fact that the EB contains information regarding both the phase and the magnitude of the system. Also, we will show that if the ECG signal is corrupted by stationary/non-stationary noise with symmetric distribution, the noise can be removed using the EB. To show the effectiveness of the proposed method, some simulation is declared.

PurposeTo investigate the feasibility of cardiac synchronized gating in stereotactic body radiation therapy (SBRT) of ventricular tachycardia (VT) using a real‐time electrocardiogram (ECG) signal acquisition.Methods and materialsStability of beam characteristics during simulated ECG gating was examined by developing a microcontroller interface to a Varian Clinac iX linear accelerator allowing gating at frequencies and duty cycles relevant to cardiac rhythm. Delivery accuracy was evaluated by measuring dose linearity with an ionization chamber, and flatness and symmetry with a two‐dimensional detector array, for different gating windows within typical human cardiac cycle periods. To establish a practical method of gating based on actual ECG signals, an AD8232 Heart Monitor board was used to acquire the ECG signal and synchronize the beam delivery. Real‐time cardiac gated delivery measurements were performed for a single 10 × 10 cm2 field and for a VT‐SBRT plan using intensity‐modulated radiation therapy (IMRT).Results and discussionDose per monitor unit (MU) values were found to be linear within most gating windows investigated with maximum differences relative to non‐gated delivery of <2% for gating windows ≥200 ms and for >10 MUs. Beam profiles for both gated and non‐gated modes were also found to agree with maximum differences of 0.5% relative to central axis dose for all sets of beam‐on/beam‐off combinations. Comparison of dose distributions for intensity‐modulated SBRT plans between non‐gating and cardiac gating modes provided a gamma passing rate of 97.2% for a 2% 2 mm tolerance.ConclusionsBeam output is stable with respect to linearity, flatness, and symmetry for gating windows within cardiac cycle periods. Agreement between dose distributions for VT‐SBRT using IMRT in non‐gated and cardiac cycle gated delivery modes shows that the proposed methodology is feasible. Technically, gating for delivery of SBRT for VT is possible with regard to beam stability.