ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset

3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record the heart's electrical activity. In recent years, there has been a notable surge in ECG recordings, driven by the widespread use of wearable devices. However, the limited availability of medical experts to analyze these recordings underscores the necessity for automated ECG analysis using computer-aided methods. In this study, we introduced 3DECG-Net, a deep learning model designed to detect and classify seven distinct heart states through the analysis of data fusion from 12-lead ECG in a multi-label framework. Our model leverages a residual architecture with a multi-head attention mechanism, undergoing training within a five-fold cross-validation scheme. By transforming 12-lead ECG signals into 3D data with the help of Recurrent Plot technique, 3DECG-Net achieves a noteworthy micro F1-score of 80.3 %, surpassing the performance of other state-of-the-art deep learning models developed for this specific task. Also, we present an ECG preprocessing framework to generate compact, high-quality ECG signals for potential application in future studies within this domain. We conduct an explainable AI experiment using Local Interpretable Model-agnostic Explanations (LIME) to elucidate the significance of each lead in accurately diagnosing specific arrhythmias, ensuring the logical processing of ECG data by 3DECG-Net. The findings of this study suggest that the proposed model is trustworthy and has the potential to be used as an effective diagnostic toolset for identifying heart arrhythmias. Its effectiveness can improve the diagnostic process, facilitate early treatment, and enhance overall efficiency in medical settings.

A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essential in the fields of biomedical engineering and clinical cardiac disease diagnosis. Intelligent ECG diagnosis techniques encompass Recurrent Neural Networks (RNN), Transformers, and Convolutional Neural Networks (CNN). While CNN is capable of extracting local spatial information from images, it lacks the ability to learn global spatial features and temporal memory features. Conversely, RNN relies on time and can retain significant sequential features. However, they are not proficient in extracting lengthy dependencies of sequence data in practical scenarios. The self-attention mechanism in the Transformer model has the capability of global feature extraction, but it does not adequately prioritize local features and cannot extract spatial and channel features. This paper proposes STFAC-ECGNet, a model that incorporates CAMV-RNN block, CBMV-CNN block, and TSEF block to enhance the performance of the model by integrating the strengths of CNN, RNN, and Transformer. The CAMV-RNN block incorporates a coordinated adaptive simplified self-attention module that adaptively carries out global sequence feature retention and enhances spatial-temporal information. The CBMV-CNN block integrates spatial and channel attentional mechanism modules in a skip connection, enabling the fusion of spatial and channel information. The TSEF block implements enhanced multi-scale fusion of image spatial and sequence temporal features. In this study, comprehensive experiments were conducted using the PTB-XL large publicly available ECG dataset and the China Physiological Signal Challenge 2018 (CPSC2018) database. The results indicate that STFAC-ECGNet surpasses other cutting-edge techniques in multiple tasks, showcasing robustness and generalization.

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.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

Impact of ECG data format on the performance of machine learning models for the prediction of myocardial infarction

Background We aim to determine which electrocardiogram (ECG) data format is optimal for ML modelling, in the context of myocardial infarction prediction. We will also address the auxiliary objective of evaluating the viability of using digitised ECG signals for ML modelling. Methods Two ECG arrangements displaying 10s and 2.5 s of data for each lead were used. For each arrangement, conservative and speculative data cohorts were generated from the PTB-XL dataset. All ECGs were represented in three different data formats: Signal ECGs, Image ECGs, and Extracted Signal ECGs, with 8358 and 11,621 ECGs in the conservative and speculative cohorts, respectively. ML models were trained using the three data formats in both data cohorts. Results For ECGs that contained 10s of data, Signal and Extracted Signal ECGs were optimal and statistically similar, with AUCs [95% CI] of 0.971 [0.961, 0.981] and 0.974 [0.965, 0.984], respectively, for the conservative cohort; and 0.931 [0.918, 0.945] and 0.919 [0.903, 0.934], respectively, for the speculative cohort. For ECGs that contained 2.5 s of data, the Image ECG format was optimal, with AUCs of 0.960 [0.948, 0.973] and 0.903 [0.886, 0.920], for the conservative and speculative cohorts, respectively. Conclusion When available, the Signal ECG data should be preferred for ML modelling. If not, the optimal format depends on the data arrangement within the ECG: If the Image ECG contains 10s of data for each lead, the Extracted Signal ECG is optimal, however, if it only uses 2.5 s, then using the Image ECG data is optimal for ML performance.

MVKT-ECG: Efficient single-lead ECG classification for multi-label arrhythmia by multi-view knowledge transferring

Electrocardiogram (ECG) is a widely used technique for diagnosing cardiovascular disease. The widespread emergence of smart ECG devices has sparked the demand for intelligent single-lead ECG-based diagnostic systems. However, it is challenging to develop a single-lead-based ECG interpretation model for multiple disease diagnosis due to the lack of some key disease information. We aim to improve the diagnostic capabilities of single-lead ECG for multi-label disease classification in a new teacher-student manner, where the teacher trained by multi-lead ECG educates a student who observes only single-lead ECG We present a new disease-aware Contrastive Lead-information Transferring (CLT) to improve the mutual disease information between the single-lead-based ECG interpretation model and multi-lead-based ECG interpretation model. Moreover, We modify the traditional Knowledge Distillation into Multi-label disease Knowledge Distillation (MKD) to make it applicable for multi-label disease diagnosis. The whole knowledge transferring process is inter-lead Multi-View Knowledge Transferring of ECG (MVKT-ECG). By employing the training strategy, we can effectively transfer comprehensive disease knowledge from various views of ECG, such as the 12-lead ECG, to a single-lead-based ECG interpretation model. This enables the model to extract intricate details from single-lead ECG signals and enhances the model's capability of diagnosing and identifying single-lead signals. Extensive experiments on two commonly used public multi-label datasets, ICBEB2018 and PTB-XL demonstrate that our MVKT-ECG yields exceptional diagnostic performance improvements for single-lead ECG. The student outperforms its baseline observably on the PTB-XL dataset (1.3 % on PTB.super, and 1.4 % on PTB.sub), and on ICBEB2018 dataset (3.2 %).

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System

Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors can be combined as numerous clinical devices to store the biosignals generated by the physiological actions of the human body. Meanwhile, a familiar method with a noninvasive and rapid biomedical electrocardiogram (ECG) signal can be used to diagnose and examine cardiovascular disease (CVD). As the growing number of patients is destroying the classification outcome because of major changes in the ECG signal patterns among numerous patients, computer-assisted automatic diagnostic tools are needed for ECG signal classification. Therefore, this study presents a mud ring optimization technique with a deep learning-based ECG signal classification (MROA-DLECGSC) technique. The presented MROA-DLECGSC approach recognizes the presence of heart disease using ECG signals. To accomplish this, the MROA-DLECGSC technique initially preprocessed the ECG signals to transform them into a uniform format. In addition, the Stacked Autoencoder Topographic Map (SAETM) approach was utilized for the classification of ECG signals to identify the presence of CVDs. Finally, the MROA was applied as a hyperparameter optimizer, which assisted in accomplishing enhanced performance. The experimental outcomes of the MROA-DLECGSC algorithm were tested on the benchmark database, and the results show the better performance of the MROA-DLECGSC methodology compared to other recent algorithms.

Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies

Electrocardiogram (EKG/ECG) is a key diagnostic tool to assess patient's cardiac condition and is widely used in clinical applications such as patient monitoring, surgery support, and heart medicine research. With recent advances in machine learning (ML) technology there has been a growing interest in the development of models supporting automatic EKG interpretation and diagnosis based on past EKG data. The problem can be modeled as multi-label classification (MLC), where the objective is to learn a function that maps each EKG reading to a vector of diagnostic class labels reflecting the underlying patient condition at different levels of abstraction. In this paper, we propose and investigate an ML model that considers class-label dependency embedded in the hierarchical organization of EKG diagnoses to improve the EKG classification performance. Our model first transforms the EKG signals into a low-dimensional vector, and after that uses the vector to predict different class labels with the help of the conditional tree structured Bayesian network (CTBN) that is able to capture hierarchical dependencies among class variables. We evaluate our model on the publicly available PTB-XL dataset. Our experiments demonstrate that modeling of hierarchical dependencies among class variables improves the diagnostic model performance under multiple classification performance metrics as compared to classification models that predict each class label independently.

Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network

BACKGROUND

The demand for early and precise identification of autism spectrum disorder (ASD) presented a challenge to the prediction of ASD with a non-invasive neuroimaging method.

NEW METHOD

A deep learning model was proposed to identify children with ASD using the resting-state functional near-infrared spectroscopy (fNIRS) signals. In this model, the input was the pattern of brain complexity represented by multiscale entropy of fNIRS time-series signals, with the purpose to solve the problem of deep learning analysis when the raw signals were limited by length and the number of subjects. The model consisted of a two-branch deep learning network, where one branch was a convolution neural network and the other was a long short-term memory neural network based on an attention mechanism.

RESULTS

Our model could achieve an identification accuracy of 94%. Further analysis used the SHapley Additive exPlanations (SHAP) method to balance the accuracy and the number of optical channels, thus reducing the complexity of fNIRS experiment. COMPARISON WITH PREVIOUSLY USED METHOD(S): in identification accuracy, our model was about 14% higher than previously used deep learning models with the same input and 4% higher than the same model but directly using fNIRS signals as input. We could obtain a discriminative accuracy of 90% with nearly half of the measurement channels by the SHAP method.

CONCLUSIONS

Using the pattern of brain complexity as input was effective in the deep learning model when the fNIRS signals were insufficient. With the SHAP method, it was possible to reduce the number of optical channels, while maintaining high accuracy in ASD identification.

Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review

Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.

Cloud-based healthcare framework for real-time anomaly detection and classification of 1-D ECG signals

Real-time data collection and pre-processing have enabled the recognition, realization, and prediction of diseases by extracting and analysing the important features of physiological data. In this research, an intelligent end-to-end system for anomaly detection and classification of raw, one-dimensional (1D) electrocardiogram (ECG) signals is given to assess cardiovascular activity automatically. The acquired raw ECG data is pre-processed carefully before storing it in the cloud, and then deeply analyzed for anomaly detection. A deep learning-based auto-encoder(AE) algorithm is applied for the anomaly detection of 1D ECG time-series signals. As a next step, the implemented system identifies it by a multi-label classification algorithm. To improve the classification accuracy and model robustness the improved feature-engineered parameters of the large and diverse datasets have been incorporated. The training has been done using the amazon web service (AWS) machine learning services and cloud-based storage for a unified solution. Multi-class classification of raw ECG signals is challenging due to a large number of possible label combinations and noise susceptibility. To overcome this problem, a performance comparison of a large set of machine algorithms in terms of classification accuracy is presented on an improved feature-engineered dataset. The proposed system reduces the raw signal size up to 95% using wavelet time scattering features to make it less compute-intensive. The results show that among several state-of-the-art techniques, the long short-term memory (LSTM) method has shown 100% classification accuracy, and an F1 score on the three-class test dataset. The ECG signal anomaly detection algorithm shows 98% accuracy using deep LSTM auto-encoders with a reconstructed error threshold of 0.02 in terms of absolute error loss. Our approach provides performance and predictive improvement with an average mean absolute error loss of 0.0072 for normal signals and 0.078 for anomalous signals.

A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory

Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-invasive approach for cardiologists to diagnose and classify the nature and severity of variant cardiac diseases including cardiac arrhythmia. However, the interpretation and analysis of ECG are highly working-load demanding, and the subjective may lead to false diagnoses and heartbeats classification. In recent years, many deep learning works showed an excellent role in accurate heartbeats classification. However, the imbalance of heartbeat classes is universal in most of the available ECG databases since abnormal heartbeats are always relatively rare in real life scenarios. In addition, many existing approaches achieved prominent results by removing noise and extracting features in data preprocessing, which relies heavily on powerful computers. It is a pressing need to develop efficient and automatic light weighted algorithms for accurate heartbeats classification that can be used in portable ECG sensors. Objective: This study aims at developing a robust and efficient deep learning method, which can be embedded into wearable or portable ECG monitors for classifying heartbeats. Methods: We proposed a novel and light weighted deep learning architecture with weight-based loss based on a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) that can automatically identify five types of ECG heartbeats according to the AAMI EC57 standard. It was also true that the raw ECG signals were simply segmented without noise removal and other feature extraction processing. Moreover, to tackle the challenge of classification bias due to imbalanced ECG datasets for different types of arrhythmias, we introduced a weight-based loss function to reduce the influence of over-weighted categories in the ECG dataset. For avoiding the influence of the division of validation dataset, k-fold method was adopted to improve the reliability of the model. Results: The proposed algorithm is trained and tested on MIT-BIH Arrhythmia Database, and achieves an average of 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F₁ score.

Classification of ECG signal using FFT based improved Alexnet classifier

Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.

Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification

The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine's signals and determine the heart's health condition and related causes of ECG signal abnormalities. However, cardiologist shortage is a challenge in both developing and developed countries. Moreover, the experience of a cardiologist matters in the accurate interpretation of the ECG signal, as the interpretation of ECG is quite tricky even for experienced doctors. Therefore, developing computer-aided ECG interpretation is required for its wide-reaching effect. 12-lead ECG generates a 1D signal with 12 channels among the well-known time-series data. Classical machine learning can develop automatic detection, but deep learning is more effective in the classification task. 1D-CNN is being widely used for CVDS detection from ECG datasets. However, adopting a deep learning model designed for computer vision can be problematic because of its massive parameters and the need for many samples to train. In many detection tasks ranging from semantic segmentation of medical images to time-series data classification, multireceptive field CNN has improved performance. Notably, the nature of the ECG dataset made performance improvement possible by using a multireceptive field CNN (MRF-CNN). Using MRF-CNN, it is possible to design a model that considers semantic context information within ECG signals with different sizes. As a result, this study has designed a multireceptive field CNN architecture for ECG classification. The proposed multireceptive field CNN architecture can improve the performance of ECG signal classification. We have achieved a 0.72 F 1 score and 0.93 AUC for 5 superclasses, a 0.46 F 1 score and 0.92 AUC for 20 subclasses, and a 0.31 F 1 score and 0.92 AUC for all the diagnostic classes of the PTB-XL dataset.

ECG Classification Using Orthogonal Matching Pursuit and Machine Learning

Health monitoring and related technologies are a rapidly growing area of research. To date, the electrocardiogram (ECG) remains a popular measurement tool in the evaluation and diagnosis of heart disease. The number of solutions involving ECG signal monitoring systems is growing exponentially in the literature. In this article, underestimated Orthogonal Matching Pursuit (OMP) algorithms are used, demonstrating the significant effect of concise representation parameters on improving the performance of the classification process. Cardiovascular disease classification models based on classical Machine Learning classifiers were defined and investigated. The study was undertaken on the recently published PTB-XL database, whose ECG signals were previously subjected to detailed analysis. The classification was realized for class 2, class 5, and class 15 cardiac diseases. A new method of detecting R-waves and, based on them, determining the location of QRS complexes was presented. Novel aggregation methods of ECG signal fragments containing QRS segments, necessary for tests for classical classifiers, were developed. As a result, it was proved that ECG signal subjected to algorithms of R wave detection, QRS complexes extraction, and resampling performs very well in classification using Decision Trees. The reason can be found in structuring the signal due to the actions mentioned above. The implementation of classification issues achieved the highest Accuracy of 90.4% in recognition of 2 classes, as compared to less than 78% for 5 classes and 71% for 15 classes.

CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive

(1) Background and objective: Cardiovascular disease is one of the most common causes of death in today’s world. ECG is crucial in the early detection and prevention of cardiovascular disease. In this study, an improved deep learning method is proposed to diagnose abnormal and normal ECG accurately. (2) Methods: This paper proposes a CNN-FWS that combines three convolutional neural networks (CNN) and recursive feature elimination based on feature weights (FW-RFE), which diagnoses abnormal and normal ECG. F1 score and Recall are used to evaluate the performance. (3) Results: A total of 17,259 records were used in this study, which validated the diagnostic performance of CNN-FWS for normal and abnormal ECG signals in 12 leads. The experimental results show that the F1 score of CNN-FWS is 0.902, and the Recall of CNN-FWS is 0.889. (4) Conclusion: CNN-FWS absorbs the advantages of convolutional neural networks (CNN) to obtain three parts of different spatial information and enrich the learned features. CNN-FWS can select the most relevant features while eliminating unrelated and redundant features by FW-RFE, making the residual features more representative and effective. The method is an end-to-end modeling approach that enables an adaptive feature selection process without human intervention.

Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset

The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists of P, QRS, and T waves. Information provided from the signal based on the intervals and amplitudes of these waves is associated with various heart diseases. The first step in isolating the features of an ECG begins with the accurate detection of the R-peaks in the QRS complex. The database was based on the PTB-XL database, and the signals from Lead I–XII were analyzed. This research focuses on determining the Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification. The study was conducted by training Deep Convolutional Neural Networks to recognize 2, 5, and 20 different heart disease classes. The results of the FSL network were compared with the evaluation score of the neural network performing softmax-based classification. The neural network proposed for this task interprets a set of QRS complexes extracted from ECG signals. The FSL network proved to have higher accuracy in classifying healthy/sick patients ranging from 93.2% to 89.2% than the softmax-based classification network, which achieved 90.5–89.2% accuracy. The proposed network also achieved better results in classifying five different disease classes than softmax-based counterparts with an accuracy of 80.2–77.9% as opposed to 77.1% to 75.1%. In addition, the method of R-peaks labeling and QRS complexes extraction has been implemented. This procedure converts a 12-lead signal into a set of R waves by using the detection algorithms and the k-mean algorithm.

Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset

Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw ECG signal, entropy-based features computed from raw ECG signals, extracted QRS complexes, and entropy-based features computed from extracted QRS complexes. The tests were based on the classification of 2, 5, and 20 classes of heart diseases. The research was carried out on the data contained in a PTB-XL database. An innovative method of extracting QRS complexes based on the aggregation of results from established algorithms for multi-lead signals using the k-mean method, at the same time, was presented. The obtained results prove that adding entropy-based features and extracted QRS complexes to the raw signal is beneficial. Raw signals with entropy-based features but without extracted QRS complexes performed much worse.

IoT Application of Transfer Learning in Hybrid Artificial Intelligence Systems for Acute Lymphoblastic Leukemia Classification

Acute lymphoblastic leukemia is the most common cancer in children, and its diagnosis mainly includes microscopic blood tests of the bone marrow. Therefore, there is a need for a correct classification of white blood cells. The approach developed in this article is based on an optimized and small IoT-friendly neural network architecture. The application of learning transfer in hybrid artificial intelligence systems is offered. The hybrid system consisted of a MobileNet v2 encoder pre-trained on the ImageNet dataset and machine learning algorithms performing the role of the head. These were the XGBoost, Random Forest, and Decision Tree algorithms. In this work, the average accuracy was over 90%, reaching 97.4%. This work proves that using hybrid artificial intelligence systems for tasks with a low computational complexity of the processing units demonstrates a high classification accuracy. The methods used in this study, confirmed by the promising results, can be an effective tool in diagnosing other blood diseases, facilitating the work of a network of medical institutions to carry out the correct treatment schedule.