Electrocardiogram Signal Processing Research Articles

Heart Diseases Recognition Model Based on HRV Feature Extraction over 12-Lead ECG Signals.

Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field.

Study on the optimization process and application of FIR digital filter

As one of the components of digital signal processing technology, the FIR digital filter has been widely used in real life and played an increasingly important role due to its advantages of system stability and ease of linear phase. This paper will summarize and analyze the research results of some past scientific research teams on FIR digital filters, and show the optimization process of this technology and the application examples in real life. This paper focuses on the optimization process of noise reduction performance, delay and size, and energy consumption optimization of FIR digital filters. This study summarizes three examples of its applications in medical, audio processing, and radar. In the medical field, the processing of electrocardiogram (ECG) signals, the audio processing fields are the frequency shaping, noise removal, and signal enhancement of audio signals, and the precise filtering and processing of received signals in the radar field. At the end of this paper, the FIR digital filter has an irreplaceable role in all fields in the world and gradually develops in the direction of miniaturization and low power consumption in the future.

A review of signal processing techniques for electrocardiogram (ECG) signals

The accurate diagnosis of cardiovascular conditions relies heavily on Electrocardiogram (ECG) signals, yet persistent interference challenges, including baseline wander, powerline interference, and muscle artifacts, compromise clinical accuracy. This study comprehensively explores cutting-edge preprocessing techniques aimed at addressing multifaceted challenges in ECG signal processing. Investigating noise removal, baseline correction, feature extraction, arrhythmia detection, and heart rate variability (HRV) analysis, we synthesize insights from recent research to provide a thorough understanding of current state-of-the-art methodologies. Each facet plays a crucial role in enhancing the reliability of ECG signals for accurate cardiovascular diagnoses. In a landscape where clinical accuracy is paramount, this review critically assesses advancements in signal processing techniques, shedding light on innovative strategies and potential breakthroughs.

Organic Synaptic Transistors with Environmentally Friendly Core/Shell Quantum Dots for Wavelength-Selective Memory and Neuromorphic Functions.

Organic transistors based on organic semiconductors together with quantum dots (QDs) are attracting more and more interest because both materials have excellent optoelectronic properties and solution processability. Electronics based on nontoxic QDs are highly desired considering the potential health risks but are limited by elevated surface defects, inadequate stability, and diminished luminescent efficiency. Herein, organic synaptic transistors based on environmentally friendly ZnSe/ZnS core/shell QDs with passivating surface defects are developed, exhibiting optically programmable and electrically erasable characteristics. The synaptic transistors feature linear multibit storage capability and wavelength-selective memory function with a retention time above 6000 s. Various neuromorphic applications, including memory enhancement, optical communication, and memory consolidation behaviors, are simulated. Utilizing an established neuromorphic model, accuracies of 92% and 91% are achieved in pattern recognition and complicated electrocardiogram signal processing, respectively. This research highlights the potential of environmentally friendly QDs in neuromorphic applications and health monitoring.

REAL-TIME PROCESSING STAGES OF ELECTROCARDIOGRAM SIGNAL: A REVIEW

Signal analysis is a multidisciplinary field that combines various processes to create robust pipelines for automating data analysis. Within the medical field, its application revolves around physiological signals. Electrocardiogram (ECG) signal provides vital information concerning various cardiac conditions affecting the human heart. ECG analysis is a central pillar of medical research with the goal of detecting and preventing potentially fatal cardiac events. This review article aims to provide a comprehensive analysis of real-time processing techniques for electrocardiogram signals. It discusses the different methods and algorithms used for detecting and analyzing ECG signals in real-time, with a focus on their effectiveness and efficiency. Where, it evaluates the use of various techniques such as ECG signal preprocessing (denoising), ECG fiducial points detecting and ECG signal classification. It also highlights the challenges faced in real-time ECG signal processing, such as High-fidelity signal acquisition and noise reduction, and computational efficiency and resource constraints. Furthermore, the article presents an overview of the existing QRS-peak detection strategies, including methods such as Haar wavelet transform, and modified MaMeMi Filter.

EMD AND WAVELET-BASED ECG SIGNAL DENOISING AND QRS COMPLEX DETECTION

This project presents a novel method for noise removal and QRS complex detection in electrocardiogram (ECG) signals, employing Empirical Mode Decomposition (EMD), windowing, and wavelet thresholding techniques. Noise is added to the ECG signal, and EMD is applied to obtain denoised components. Local minima detection and peak matching identify QRS complex boundaries. Windowing isolates the QRS regions, followed by wavelet thresholding for further denoising. Evaluation metrics like improved Signal-to-Noise Ratio, Mean Square Error, and Percent Root Mean Square Difference are calculated at different input SNR values, demonstrating the method's effectiveness in preserving the QRS complex while removing noise.. Keywords: ECG signal processing, QRS complex detection, Empirical Mode Decomposition (EMD), Windowing, Wavelet thresholding, QRS complex extraction, Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Percent Root Mean Square Difference (PRD).

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.

IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring

Information systems are currently developing very rapidly, and this is inseparable from the role of internet of things (IoT) technology, especially in the world of telemedicine. MitApp is an open-source application that can be used to monitor electrocardiogram (ECG) signals in real-time. The aim of this study is to develop an IoT-based ECG signal monitoring system that utilizes the MitApp application to detect abnormal ECG signals that are characterized by symptoms of cardiac arrhythmias. To process ECG signal data obtained from lead electrode results, the research method utilizes Arduino Uno as a microcontroller. The result is then displayed on the thin film transistor (TFT) layer using the Nextion module. The ESP32 module is used as a Wi-Fi module to send data to the MitApp app on a smartphone. The results showed that the results of the comparison test of ECG signal module data with ECG simulator tools with beats per minute values of 60, 80, 100, 120, and 140 obtained an error rate of 0.05. Based on these results, there is potential to develop this feature and integrate the system with the patient management system to improve the effectiveness of remote monitoring.

Hardware Implementation of FIR Filter for ECG Signal Processing: Design, Optimization, and Performance Analysis on an FPGA

Electrocardiogram (ECG) signals are commonly used to diagnose heart-related diseases. However, noise induced during the measurement process can affect the accuracy of the diagnosis. Digital filters, such as the Finite Impulse Response (FIR) filter, are widely used to filter out noise from the ECG signal. Nevertheless, the processing speed of software-based FIR filters is slow for large ECG datasets due to serial processing. This paper presents a hardware implementation of the FIR filter for ECG signal processing to overcome the processing speed issue. The filter is designed using the Kaiser Window method and implemented on the Intel Cyclone IV Field Programmable Gate Array (FPGA). The filter is first designed in MATLAB to obtain the filter coefficients where the ECG data were obtained from Physionet database. From the difference equation, we designed the signal flow graph (SDFG) and then mapped into hardware logics to enable parallel processing. Simulation results of the software (MATLAB) and hardware (FPGA) implementations are obtained and compared. The results show that the FPGA-based FIR filter can process the ECG signal up to 1,250 times faster than software implementations. To further optimize the design and reduce hardware cost, we introduce optimized designs by applying the operation scheduling and constrained resource allocation techniques. The maximum operating frequency, logic utilization, and power consumption of each design were analysed and compared. This study demonstrates that custom-designed hardware logic for digital signal processing can significantly outperform software implementations due to its parallel processing capabilities. The proposed optimization techniques reduce the hardware cost while maintaining high processing speed and accuracy. The hardware implementation of the FIR filter for ECG signal processing has numerous applications in diagnosing heart-related diseases and real-time monitoring of ECG signals in critical care settings.

Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals

The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in a given patient’s quick-Sequential Organ Failure Assessment score, using a retrospective dataset. We applied this to both a small, critically ill cohort and a broader cohort of patients in the intensive care unit. Within the smaller cohort, privileged information proved helpful in a signal-informed model, and across both cohorts, electrocardiogram data proved to be informative to creating the prediction. Although learning using privileged information did not significantly improve results in this study, it is a paradigm worth studying further in the context of using signal processing for sepsis prognosis.

Denoising ECG Signals using Weighted Iterative UFIR Filtering

The electrocardiogram (ECG) holds paramount importance in diagnosing heart disease, and as it persists leading cause of global mortality. Over the past decades, diverse techniques have emerged for processing ECG signals, with denoising taking a prominent role in enhancing feature extraction. Nonetheless, achieving heightened accuracy remains an enduring challenge. In this study, we introduce an innovative approach involving the application of a weighted unbiased finite impulse response (UFIR) filter. Under the same noise conditions and in terms of the root mean square error (RMSE) and signal-to-noise ratio (SNR), our proposed method showcases worthy performance in comparison to the weighted Savitzky-Golay (SG) filter. This research contributes to the progressive evolution of ECG signal processing, offering the potential for more precise and dependable detection of cardiac diseases.

Поиск оптимального вейвлет-фильтра и уровня разложения сигнала ЭКГ для его шумоподавления

The denoising process represents one of the most important preprocessing steps for Electrocardiogram (ECG) signal processing and assists the specialist in making the right diagnosis for the patient. Five wavelet filters (WFs) closest in morphology to the pattern of the ECG signal were nominated, and their performances were analyzed at different noise, and number of decomposition (No. Dec) levels to determine the optimum, among them for noise reduction task. These Filters are Daubechies 4 (DB4), Daubechies 6 (DB6), Coiflet 4 (Coif4), Symlet 6 (Sym6) and Symlet 8 (Sym8). The results of the standard ECG signals (downloaded from MIT-BIH) revealed that the DB6 filter with four decomposition levels is optimal for removing noise of the ECG signal in terms of three metrics "Mean Square Error" (MSE), "Output Signal to Noise Ratio" (SNRo), and "Correlation Coefficient Index" (CCI). In addition, a simple and efficient threshold rule was adapted to be used in the proposed method. The suggested approach was successfully applied to reduce the noise of the ECG signals recorded using a simple proposed electronic circuit. Finally, the performance of the introduced scheme was compared with that of the standard ECG equipment, the "Biocare iE300", and the outcomes were very close.

Comparison of Digital Filter Orders in Single Lead Electrocardiographic Design for Reducing Interference Motion Artifact

Developed a new method to overcome motion artifacts in Electrocardiogram (ECG) signals, which often interfere with accurate clinical analysis. Motion artifacts, such as body movements, can cause significant distortions in the ECG signal, resulting in incorrect interpretation and affecting medical diagnosis. The main objective of this research is to design and implement an infinite impulse response (IIR) filter with a predetermined sequence, namely orders 2, 4, 6, and 8 to reduce motion artifacts in the ECG signal. We aim to improve ECG signal quality by preserving important ECG signal information and reducing noise caused by motion artifacts. This research contributes to developing more precise and reliable ECG signal processing techniques. The proposed method provides an effective approach to handling motion artifacts, enabling more accurate and reliable ECG interpretation by medical professionals. We used an ECG simulator that provides body movement simulation as a basis for experiments. The detected ECG signal is processed with a predetermined order IIR filter. We compare the filtered signal to the original signal to measure the effectiveness of reducing motion artifacts. Experimental results show that the applied IIR filter efficiently reduces motion artifacts in the ECG signal. The SNR assessment showed a significant improvement, proving the success of this method in maintaining ECG signal quality. The result is that in the 2nd order, the SNR value is 22.25 dB, in the 4th order the SNR value is 22.75 dB, in the 6th order the SNR value is 22.99 dB, in the 6th order the SNR value is 22.99 dB. 8 obtained an SNR value of 23dB. This study successfully demonstrated that using IIR filters in a specified order effectively reduces motion artifacts in the ECG signal, increases SNR, and maintains the integrity of clinical information in the ECG signal. The implications of this research extend to medical technology development and clinical applications, providing a strong foundation for continued research in more efficient and reliable ECG signal processing.

Development of an Adaptive Acquisition and Transmission System for Digital Processing of ECG Signals under Variable n-QAM Schemes

In the post-pandemic era, it is critical to monitor and transmit biomedical signals, specifically ECG. This study aims to develop a platform that enables signal acquisition, adaptation, and transmission using different n-QAM modulation schemes. The system comprises an acquisition stage implemented in the 2.5 GHz band employing the Olimex module and electrodes equipped with an Ag/AgCl type sensor. To effectively manage appropriate bandwidths during implementation of the various n-QAM modulation schemes, an adaptive algorithm was developed and applied to the system. The power amplifier was operated in the linear region to enhance the crest factor and achieve an ACPR close to 30 dBc, demonstrating an appropriate demodulation of the electrocardiogram (ECG) signal, it is feasible to shift to modulation schemes above 64-QAM in order to detect high frequencies and perform a subsequent Fourier analysis. As a telemedicine proposal, the developed system offers flexibility in signal acquisition, data storage, and digitalization, in addition to a multivariable n-QAM scheme; the hardware implementation ensures n-QAM scheme compatibility. For the purpose of contributing to telemedicine via RF transmission, the system was executed on an AD9361 transceiver, which removes the requirement for a traditional signal vector generator and enables optimal control of the tones to be transmitted.

A novel transformer-based ECG dimensionality reduction stacked auto-encoders for arrhythmia beat detection.

Electrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long-distancereliance. To reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto-encoders model using Transformer for ECG-based arrhythmia detection. And overcome the challenges of long-term dependencies and limited parallelizability in traditional RNNs when applied to ECG signalprocessing. In this paper, a Transformer-Based ECG Dimensionality Reduction Stacked Auto-encoders model is proposed for ECG-based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low-dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimizeoverfitting. The proposed method is benchmarked on the MIT-BIH Arrhythmia database. In the 10-fold cross validation of beat-based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record-based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively. Compared to other existing ECG-based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record-based data division method makes our approach more suitable for clinicalpractice.

Heartbeat classification based on single lead-II ECG using deep learning

The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.

Cross-modal multiscale multi-instance learning for long-term ECG classification

At present, deep learning models have been widely used in electrocardiogram (ECG) classification. However, when processing ECG signals over a long period of time, it is difficult for existing deep learning methods to effectively diagnose and classify ECG signals based on a few abnormal heartbeats in these long-term time series data. In this paper, we present a novel cross-modal multiscale multi-instance learning approach for long-term electrocardiogram (ECG) classification. Our method leverages the foundational paradigm of multi-instance learning (MIL) to tackle the challenge of processing long-term ECG signals, while incorporating cross-modal information to guide the global integration of instances. Furthermore, to enhance model performance and bolster its robustness, we introduce multiscale data into the modeling process. The experimental results show that our model achieves excellent performance in different patterns of different datasets, outperforming a series of other traditional deep learning methods and MIL-based methods. Our model achieves impressive F1 scores of 0.8512 and 0.8293 on the St. Petersburg INCART Arrhythmia dataset and MIT-BIH Arrhythmia dataset, respectively. In the context of intrapatient patterns, our model outperforms the suboptimal models, exhibiting improvements of 1.41% and 0.48% on F1, and 2.07% and 0.93% on Recall. Similarly, for interpatient patterns, our model demonstrates superior performance, surpassing the suboptimal models with substantial gains of 4.21% and 3.07% on F1, and 4.71% and 0.55% on Recall.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments.

Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

This paper describes a unique study that uses multiple FIR adaptive filter algorithms to denoise adult electrocardiogram (ECG) data. The study looks at how power line interference, external electromagnetic fields, random body motions, and breathing impact ECG measurement accuracy. The article takes a fresh look at Savitzky-Golay filtering techniques by implementing and evaluating them inside the FIR adaptive filter architecture. Matlab is used to evaluate the performance of the Affine projection FIR adaptive filter (AP), Direct-form Normalized least-mean-square FIR adaptive filter (NLMS), and Sliding-window Recursive least-squares FIR adaptive filter (SWRLS). The results show how different strategies compare in terms of performance and their influence on recorded waveform quality. The study extends to our understanding of the efficiency of FIR adaptive filter algorithms in decreasing ECG signal noise and helps us better understand their potential uses in ECG signal processing. Based on reliable ECG data, the research findings assist the development of new approaches for diagnosing aberrant cardiac rhythms and examining the origins of chest discomfort. The originality of this work comes in its thorough assessment, comparison, and unique use of Savitzky-Golay filtering techniques inside FIR adaptive filter algorithms, which contributes to the area of ECG signal denoising. According to a comparative investigation, the SWRLS FIR adaptive filter method improves ECG signal denoising by 91.53% noise reduction.

ECG Signals Segmentation Using Deep Spatiotemporal Feature Fusion U-Net for QRS Complexes and R-Peak Detection

To detect and identify QRS complexes and R-peak is one of the crucial steps in the field of electrocardiogram (ECG) signals research, and their detection accuracy directly affects the performance of the subsequent ECG signal processing and analysis. However, the task involving the detection and identification of the ECG features becomes so complex for the traditional threshold-based detection methods. At present, researchers turn to deep learning approaches but little work has been done on the detection of spatiotemporal features of the QRS complexes. In this paper, for better QRS complexes and R-peak detection, we propose a novel method based on the improved U-net model, called ST-Res U-net. This uniquely designed component of the improved U-net model is made up of four levels of ST Block (the block extracting spatial-temporal features) and Res Path (the residual path). The entire framework contains three steps, data preprocessing dealing with denoising of the raw ECG signals, the key component ST-Res U-net dealing with the spatiotemporal feature extraction, and a threshold screening algorithm for locating the R-peaks. Our experiments are purposedly designed to test various combination structures of the ST blocks. The training and testing are from the MIT-BIH arrhythmia database (MITDB) and the CPSC2019 database. We adopt a commonly used set of evaluation criteria with the following experimental results: 99.76% and 90.01% for sensitivity, 99.87% and 93.5% for positive predictivity rate, 99.81% and 91.75% for F1 value, and 0.37% and 15.24% for detection error rate. The former numbers are for MITDB and the latter CPSC2019. Further, we test the proposed method on the PTB-XL database (without labeled R-peak positions) and the method can accurately locate QRS complexes and R-peaks The experimental results demonstrate that the proposed model and method is quite effective for the automatic classification and annotation of ECG signals and thus greatly improves the accuracy of diagnosis of arrhythmia diseases.