De-Noising of ECG Signals by Design of an Optimized Wavelet

In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB.

Attention emotion recognition via ECG signals

Background:Physiological signal-based research has been a hot topic in affective computing.Previous works mainly focus on some strong,short-lived emotions(e.g.,joy,anger),while the attention,which is a weak and long-lasting emotion,receives less attraction.In this paper,we present a study of attention recognition based on electrocardiogram(ECG)signals,which contain a wealth of information related to emotions.Methods:The ECG dataset is derived from 10 subjects and specialized for attention detection.To relieve the impact of noise of baseline wondering and power-line interference,we apply wavelet threshold denoising as preprocessing and extract rich features by pan-tompkins and wavelet decomposition algorithms.To improve the generalized ability,we tested the performance of a variety of combinations of different feature selection algorithms and classifiers.Results:Experiments show that the combination of generic algorithm and random forest achieve the highest correct classification rate(CCR)of 86.3%.Conclusion:This study indicates the feasibility and bright future of ECG-based attention research.

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

Preliminary Biometrics of ECG Signal Based on Temporal Organization through the Implementation of a Multilayer Perceptron Neural Network

The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.

Multimodal Emotion Recognition Based on Facial Expression and ECG Signal

As a key link in human-computer interaction,emotion recognition can enable robots to correctly perceive user emotions and provide dynamic and adjustable services according to the emotional needs of different users,which is the key to improve the cognitive level of robot service.Emotion recognition based on facial expression and electrocardiogram has numerous industrial applications.First,three-dimensional convolutional neural network deep learning architecture is utilized to extract the spatial and temporal features from facial expression video data and electrocardiogram(ECG)data,and emotion classification is carried out.Then two modalities are fused in the data level and the decision level,respectively,and the emotion recognition results are then given.Finally,the emotion recognition results of single-modality and multi-modality are compared and analyzed.Through the comparative analysis of the experimental results of single-modality and multi-modality under the two fusion methods,it is concluded that the accuracy rate of multi-modal emotion recognition is greatly improved compared with that of single-modal emotion recognition,and decision-level fusion is easier to operate and more effective than data-level fusion.

Visualization of the Machine Learning Process Using J48 Decision Tree for Biometrics through ECG Signal

The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.

A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.

Efficient ECG classification based on Chi-square distance for arrhythmia

This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.

QRS waves detection algorithm based on positive-negative adaptive threshold method

In order to accurately detect the occasional negative R waves in electrocardiography （ECG） signals, the positive-negative adaptive threshold method is adopted to determine the positive R waves and the negative R waves, according to difference characteristics of ECG signals. The Q and S waves can then be accurately positioned based on the basic characteristics of QRS waves. Finally, the algorithm simulation is made based on the signals from MIT-BIH database with MATLAB. The ex- perimental results show that the algorithm can improve the detection accuracy rate to 99. 91% and o- vercome the problem of larger computation load for wavelet transform and other methods, so the al- gorithm is suitable for real-time detection.

A Healthcare System for Internet of Things (IoT) Application: Machine Learning Based Approach

Internet of things (IoT) has become an interesting topic in the field of technological research. It is basically interconnecting of devices with each other over the internet. Beside its general use in terms of autonomous cars and smart homes, but some of the best applications of IoT technology in fields of health care monitoring is worth mentioning. The main purpose of this research work is to provide comport services for patients. It can be used to promote basic nursing care by improving the quality of care and patient safety from patient home environment. Rural area of a country lacks behind the proper patient monitoring system. So, remote monitoring and prescribing by sharing medical information in an authenticated manner is very effective for betterment of medical facilities in rural area. We have proposed a healthcare system which can analyze ECG report using supervise machine learning techniques. Analyzing report can be stored in cloud platform which can be further used to prescribe by the experienced medical practitioner. For performance evaluation, ECG data is analyzed using six supervised machine learning algorithms. Data sets are divided into two groups: 75 percent data for training the model and rest 25 percent data for testing. To avoid any kind of anomalies or repetitions, cross validation and random train-test split was used to obtain the result as accurate as possible.

Implementation of a Multi-sampling-frequency System for RR-interval Timing and Blood Pressure Detection

In order to analyze the experimental cardiovascular signal with high accuracy, a system, integrating real-time monitoring and off-line further analysis, was developed and verified. The design, data processing and analysis methods as well as testing results are described. With 5 sampling frequency choices and 8 channel data acquisition, the system achieved high performances in beat-to-beat monitoring, signal processing and analysis. Tests were carried out to validate its performance in real-time monitoring, effectiveness of digital filters, QRS and blood pressure detection reliability, and RR-interval timing accuracy. The QRS detection rate was at least 99.46% for the records with few noises from MIT-BIH arrhythmia database using the algorithm for real-time monitoring, and no less than 96.43% for the records with some noises. In the condition that noise amplitude levels were less than 80%,the standard deviations for RR-interval timing were less than 1 ms with a generated ECG corrupted with various noises from MIT-BIH Noise Stress Test Database. Besides, the system is open for function expansion to meet further study-specific needs.

Removal of baseline wander from ECG signal based on a statistical weighted moving average filter

Baseline wander is a common noise in electrocardiogram (ECG) results.To effectively correct the baseline and to preserve more underlying components of an ECG signal,we propose a simple and novel filtering method based on a statistical weighted moving average filter.Supposed a and b are theminimum and maximum of all sample values within a moving window,respectively.First,the whole region [a,b] is divided into M equal sub-regions without overlap.Second,three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region,denoted as [a 0,b 0 ] for simplicity.Third,for every sample point in the moving window,its weight is set to 1 if its value falls in [a 0,b 0 ];otherwise,its weight is 0.Last,all sample points with weight 1 are averaged to estimate the baseline.The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org.The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and wavelet package translation did.

An OTA-C filter for ECG acquisition systems with highly linear range and less passband attenuation

A fifth order operational transconductance amplifier-C （OTA-C） Butterworth type low-pass filter with highly linear range and less passband attenuation is presented for wearable bio-telemetry monitoring applications in a UWB wireless body area network. The source degeneration structure applied in typical small transconduc- tance circuit is improved to provide a highly linear range for the OTA-C filter. Moreover, to reduce the passband attenuation of the filter, a cascode structure is employed as the output stage of the OTA. The OTA-based circuit is operated in weak inversion due to strict power limitation in the biomedical chip. The filter is fabricated in a SMIC 0.18-μm CMOS process. The measured results for the filter have shown a passband gain of -6.2 dB, while the -3-dB frequency is around 276 Hz. For the 0.8 Vpp sinusoidal input at 100 Hz, a total harmonic distortion （THD） of-56.8 dB is obtained. An electrocardiogram signal with noise interference is fed into this chip to validate the function of the designed filter.

Micro EEG/ECG signal's chopper-stabilization amplifying chip for novel drycontact electrode

Facing the body＇s EEG（electroencephalograph, 0.5–100 Hz, 5–100 μV） and ECG＇s（electrocardiogram,〈 100 Hz, 0.01–5 mV） micro signal detection requirement, this paper develops a pervasive application micro signal detection ASIC chip with the chopping modulation/demodulation method. The chopper-stabilization circuit with the RRL（ripple reduction loop） circuit is to suppress the ripple voltage, which locates at the single-stage amplifier＇s outputting terminal. The single-stage chopping core＇s noise has been suppressed too, and it is beneficial for suppressing noises of post-circuit. The chopping core circuit uses the PFB（positive feedback loop） to increase the inputting resistance, and the NFB（negative feedback loop） to stabilize the 40 dB intermediate frequency gain. The cascaded switch-capacitor sample/hold circuit has been used for deleting spike noises caused by non-ideal MOS switches, and the VGA/BPF（voltage gain amplifier/band pass filter） circuit is used to tune the chopper system＇s gain/bandwidth digitally. Assisted with the designed novel dry-electrode, the real test result of the chopping amplifying circuit gives some critical parameters： 8.1 μW/channel, 0.8 μVrms（@band-widthD100 Hz）, 4216–11220 times digitally tuning gain range, etc. The data capture system uses the NI CO＇s data capturing DAQmx interface,and the captured micro EEG/ECG＇s waves are real-time displayed with the PC-Labview. The proposed chopper system is a unified EEG/ECG signal＇s detection instrument and has a critical real application value.

Analysis of female drivers’ ECG characteristics within the context of connected vehicles

Purpose–This study aims to analyze the differences of electrocardiograph(ECG)characteristics for female drivers in calm and anxious states during driving.Design/methodology/approach–The authors used various materials(e.g.visual materials,auditory materials and olfactory materials)to induce drivers’mood states(calm and anxious),and then conducted the real driving experiments and driving simulations to collect driver’s ECG signal dynamic data.Physiological changes in ECG during the stimulus process were recorded using PSYLAB software.The paired T-test analysis was conducted to determine if there is a significant difference in driver’s ECG characteristics between calm and anxious states during driving.Findings–The results show significant differences in the characteristic parameters of female driver’s ECG signals,including(average heart rate),(atrioventricular interval),(percentage of NN intervals>50ms),(R wave average peak),(Root mean square of successive),(Q wave average peak)and(S wave average peak),in time domain,frequency domain and waveform in emotional states of calmness and anxiety.Practical implications–Findings of this work show that ECG can be used to identify driver’s anxious and calm states during driving.It can be used for the development of personalized driver assistance system and driver warning system.Originality/value–Only a few attempts have been made on the influence of human emotions on physiological signals in the transportationfield.Hence,there is a need for transport scholars to begin to identify driver’s ECG characteristics under different emotional states.This study will analyze the differences of ECG characteristics for female drivers in calm and anxious states during driving to provide a theoretical basis for developing the intelligent and connected vehicles.

Intra-Patient and Inter-Patient Multi-Classification of Severe Cardiovascular Diseases Based on CResFormer

Severe cardiovascular diseases can rapidly lead to death.At present,most studies in the deep learning field using electrocardiogram(ECG)are performed on intra-patient experiments for the classification of coronary artery disease(CAD),myocardial infarction,and congestive heart failure(CHF).By contrast,actual conditions are inter-patient experiments.In this study,we proposed a deep learning network,namely,CResFormer,with dual feature extraction to improve accuracy in classifying such diseases.First,fixed segmentation of dual-lead ECG signals without preprocessing was used as input data.Second,one-dimensional convolutional layers performed moderate dimensionality reduction to accommodate subsequent feature extraction.Then,ResNet residual network block layers and transformer encoder layers sequentially performed feature extraction to obtain key associated abstract features.Finally,the Softmax function was used for classifications.Notably,the focal loss function is used when dealing with unbalanced datasets.The average accuracy,sensitivity,positive predictive value,and specificity of four classifications of severe cardiovascular diseases are 99.84%,99.68%,99.71%,and 99.90%in intra-patient experiments,respectively,and 97.48%,93.54%,96.30%,and 97.89%in inter-patient experiments,respectively.In addition,the model performs well in unbalanced datasets and shows good noise robustness.Therefore,the model has great application potential in diagnosing CAD,MI,and CHF in the actual clinical environment.