PTB Database Research Articles

Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis.

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground-background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT-BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis.

MI-CSBO: a hybrid system for myocardial infarction classification using deep learning and Bayesian optimization

Myocardial Infarction (MI) refers to damage to the heart tissue caused by an inadequate blood supply to the heart muscle due to a sudden blockage in the coronary arteries. This blockage is often a result of the accumulation of fat (cholesterol) forming plaques (atherosclerosis) in the arteries. Over time, these plaques can crack, leading to the formation of a clot (thrombus), which can block the artery and cause a heart attack. Risk factors for a heart attack include smoking, hypertension, diabetes, high cholesterol, metabolic syndrome, and genetic predisposition. Early diagnosis of MI is crucial. Thus, detecting and classifying MI is essential. This paper introduces a new hybrid approach for MI Classification using Spectrogram and Bayesian Optimization (MI-CSBO) for Electrocardiogram (ECG). First, ECG signals from the PTB Database (PTBDB) were converted from the time domain to the frequency domain using the spectrogram method. Then, a deep residual CNN was applied to the test and train datasets of ECG imaging data. The ECG dataset trained using the Deep Residual model was then acquired. Finally, the Bayesian approach, NCA feature selection, and various machine learning algorithms (k-NN, SVM, Tree, Bagged, Naïve Bayes, Ensemble) were used to derive performance measures. The MI-CSBO method achieved a 100% correct diagnosis rate, as detailed in the Experimental Results section.

Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections.

The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder-decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder-decoders based on a fully convolutional architecture (CED-Net) and its modifications with a recurrent layer (CED-LSTM-Net), residual connections between symmetrical encoder and decoder feature maps (CED-U-Net), and sequential residual blocks (CED-Res-Net). All DNNs transform 12-lead representative beats to three diagnostic ECG intervals (P-wave, QRS-complex, QT-interval) used for the global delineation of the representative beat (P-onset, P-offset, QRS-onset, QRS-offset, T-offset). All DNNs were trained and optimized using the large PhysioNet ECG database (PTB-XL) under identical conditions, applying an advanced approach for machine-based supervised learning with a reference algorithm for ECG delineation (ETM, Schiller AG, Baar, Switzerland). The test results indicate that all DNN architectures are equally capable of reproducing the reference delineation algorithm's measurements in the diagnostic PTB database with an average P-wave detection accuracy (96.6%) and time and duration errors: mean values (-2.6 to 2.4 ms) and standard deviations (2.9 to 11.4 ms). The validation according to the standard-based evaluation practices of diagnostic electrocardiographs with the CSE database outlines a CED-Net model, which measures P-duration (2.6 ± 11.0 ms), PQ-interval (0.9 ± 5.8 ms), QRS-duration (-2.4 ± 5.4 ms), and QT-interval (-0.7 ± 10.3 ms), which meet all standard tolerances. Noise tests with high-frequency, low-frequency, and power-line frequency noise (50/60 Hz) confirm that CED-Net, CED-Res-Net, and CED-LSTM-Net are robust to all types of noise, mostly presenting a mean duration error < 2.5 ms when compared to measurements without noise. Reduced noise immunity is observed for the U-net architecture. Comparative analysis with other published studies scores this research within the lower range of time errors, highlighting its competitive performance.

A lightweight deep learning approach for detecting electrocardiographic lead misplacement

Objective. Electrocardiographic (ECG) lead misplacement can result in distorted waveforms and amplitudes, significantly impacting accurate interpretation. Although lead misplacement is a relatively low-probability event, with an incidence ranging from 0.4% to 4%, the large number of ECG records in clinical practice necessitates the development of an effective detection method. This paper aimed to address this gap by presenting a novel lead misplacement detection method based on deep learning models. Approach. We developed two novel lightweight deep learning model for limb and chest lead misplacement detection, respectively. For limb lead misplacement detection, two limb leads and V6 were used as inputs, while for chest lead misplacement detection, six chest leads were used as inputs. Our models were trained and validated using the Chapman database, with an 8:2 train-validation split, and evaluated on the PTB-XL, PTB, and LUDB databases. Additionally, we examined the model interpretability on the LUDB databases. Limb lead misplacement simulations were performed using mathematical transformations, while chest lead misplacement scenarios were simulated by interchanging pairs of leads. The detection performance was assessed using metrics such as accuracy, precision, sensitivity, specificity, and Macro F1-score. Main results. Our experiments simulated three scenarios of limb lead misplacement and nine scenarios of chest lead misplacement. The proposed two models achieved Macro F1-scores ranging from 93.42% to 99.61% on two heterogeneous test sets, demonstrating their effectiveness in accurately detecting lead misplacement across various arrhythmias. Significance. The significance of this study lies in providing a reliable open-source algorithm for lead misplacement detection in ECG recordings. The source code is available at https://github.com/wjcai/ECG_lead_check.

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge

Objective. Myocardial infarction (MI) is a serious cardiovascular disease that can cause irreversible damage to the heart, making early identification and treatment crucial. However, automatic MI detection and localization from an electrocardiogram (ECG) remain challenging. In this study, we propose two models, MFB-SENET and MFB-DMIL, for MI detection and localization, respectively. Approach. The MFB-SENET model is designed to detect MI, while the MFB-DMIL model is designed to localize MI. The MI localization model employs a specialized attention mechanism to integrate multi-instance learning with domain knowledge. This approach incorporates handcrafted features and introduces a new loss function called lead-loss, to improve MI localization. Grad-CAM is employed to visualize the decision-making process. Main Results. The proposed method was evaluated on the PTB and PTB-XL databases. Under the inter-patient scheme, the accuracy of MI detection and localization on the PTB database reached 93.88% and 67.17%, respectively. The accuracy of MI detection and localization on the PTB-XL database were 94.89% and 85.83%, respectively. Significance. Our method achieved comparable or better performance than other state-of-the-art algorithms. The proposed method combined deep learning and medical domain knowledge, demonstrates effectiveness and reliability, holding promise as an efficient MI diagnostic tool to assist physicians in formulating accurate diagnoses.

Abstract 18317: Implications of Noise on Deep Learning Models for 12-Lead ECG Construction

Introduction: ECG construction with deep learning can help standardize ECGs and remove noise and artifacts, transforming potentially unusable ECGs into clinically useful ones. However, noise levels, often unpredictable in clinical settings, may impact model performance. Hypothesis: This study explores the relationship between ECG noise levels and deep learning model performance in constructing noise-free ECGs. We hypothesize that beyond a certain noise threshold, the model's output ceases to be clinically usable. Aims: Our study aims to determine noise effects on the performance of deep learning models in constructing 12-Lead ECGs, providing insights into the model's robustness and the optimal conditions for its application. Methods: Using 250 digital 12-Lead ECGs from the PTB database, we injected Gaussian noise (mean 0.0, standard deviations between 0.0 and 1.0) into these ECGs, all processed and scaled to unitless values (-1 to +1) and resampled at 100Hz. A model was asked to construct a noise-free ECG from a full 12-lead ECG with noise injection, which was compared to the original noise-free ECG. Results: The findings reveal two distinct linear phases in the relationship between noise and model performance (measured by mean absolute error, MAE). Between standard deviations of 0.0 and 0.03, the MAE increased marginally, while a sharp increase occurred between 0.03 and 1.0. Importantly, critical features of the original ECG were retained for noise levels up to 0.08, implying a noise threshold for clinical usability. Conclusions: Deep learning models, though not entirely resistant to noise, demonstrate efficacy in constructing ECGs up to certain noise levels (standard deviation 0.03). Beyond this, performance declines markedly. These findings outline the limits and potential of deep learning models like ECGio in clinical practice. Future research should focus on resilience to higher noise levels or noise-reduction preprocessing strategies.

Accelerating deep convolutional neural network on FPGA for ECG signal classification

Abnormal activity of the heart is known as cardiac arrhythmia which must be recognized in earlier stage to prevent sudden death and premature death. The occurrence of arrhythmia increases with age, and it is detected using an electrocardiogram (ECG) signal. Conversely, it is very complex to manually achieve the quick and exact classification due to the complexity, non-linearity and low amplitude of the ECG signal. As a result, the healthcare field requires an automatic system to recognize abnormal heartbeats from a huge amount of ECG records. So, the deep learning algorithm named as Deep Convolutional Neural Network (DCNN) is proposed in this research to analyze the ECG signal on a field-programmable gate array (FPGA). Before performing a classification process, two different processes called signal pre-processing as well as feature extraction are required. For the process of signal pre-processing, the Error Normalised Least Mean Square (ENLMS) algorithm is utilized in our work, and Discrete Wavelet Transform (DWT) technique is performed to take out the relevant features from the ECG signal. Finally, FPGA based one-dimensional DCNN with 3 convolutional layers, 3 pooling layers, and 3 fully connected layers is proposed to classify the signals with proper complex features. The publicly available MIT-BIH arrhythmia and PTB databases are exploited in this research to process ECG signals on the multi-input structure. In addition, different performance parameters like classification accuracy, specificity, sensitivity, and precision are engaged to evaluate the proposed methodology; also, it is compared with different FPGA based existing classifiers. The analysis shows that the proposed design accomplishes 98.6 % classification accuracy on the MIT-BIH arrhythmia database and 99.67 % accuracy on the PTB database, which is 0.304 % higher than a multilayer perception (MLP) and 0.47 % higher than decision-based classifier. Moreover, the proposed FPGA based DCNN accelerator consumes 0.45 mW, 185.426 MHz operation frequency and takes 15 s to complete the classification process.

Importance of Electrode Selection and Number in Reconstructing Standard Twelve Lead Electrocardiograms.

Many clinical and consumer electrocardiogram (ECG) devices collect fewer electrodes than the standard twelve-lead ECG and either report less information or employ algorithms to reconstruct a full twelve-lead signal. We assessed the optimal electrode selection and number that minimizes redundant information collection while maximizing reconstruction accuracy. We employed a validated deep learning model to reconstruct ECG signals from 250 different patients in the PTB database. Different numbers and combinations of electrodes were removed from the ECG before reconstruction to measure the effect of electrode inclusion on reconstruction accuracy. The Left Leg (LL) electrode registered the largest drop in average reconstruction accuracy, from an R2 of 0.836 when the LL was included to 0.737 when excluded. Additionally, we conducted a correlation analysis to identify leads that behave similarly. We demonstrate that there exists a high correlation between leads I, II, aVL, aVF, V4, V5, and V6, which all occupy the bottom right quadrant in an ECG axis interpretation, and likely contain redundant information. Based on our analysis, we recommend the prioritization of electrodes RA, LA, LL, and V3 in any future lead collection devices, as they appear most important for full ECG reconstruction.

Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics.

Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People's Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20-99.76%) and 97.62% (95% confidence interval: 96.80-98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices.

Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.

ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

ECGs have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits. However, the lack of public data and standard experimental protocols makes the evaluation and comparison of novel ECG methods difficult. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. We consider verification and identification tasks, single- and multi-session settings, and single- and multi-lead ECGs recorded with traditional and user-friendly devices. We also present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database, and evaluate it with detailed experimental protocol and public databases. With the popular PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in single- and multi-session verification. The results achieved prove the soundness of ECGXtractor across multiple scenarios and databases. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field.

Classification of cardiac electrical signals between patients with myocardial infarction and normal subjects by using nonlinear dynamics features and different classification models

Myocardial infarction (MI) is one of the leading causes of human mortality and morbidity around the world. Despite that much progress has been made for MI detection based on medical image analysis in recent years, most of them suffer from their expensiveness and invasive nature. In this paper, we demonstrate abnormalities of electrocardiogram (ECG) in a new quantifiable manner using nonlinear dynamics features and different classification models. Time-varying ECG data are represented as three-dimensional vectorcardiogram (VCG) and the underlying cardiac dynamics. Nonlinear dynamics measures, including entropy variability measures, complexity measures and chaotic measures, are calculated and fed into different machine learning methods for the classification task. The extracted nonlinear dynamics measures reflect in-depth cardiac dynamics characteristic, which is shown to be more sensitive to subtle ECG modifications. Therefore, it is expected to provide an early detection tool for latent ECG modifications before obvious diagnostic changes are observed. Experiments on the PTB database are conducted to demonstrate the efficiency of the proposed method.

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.

A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection.

An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances.

Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals.

Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning

Individual differences among patients and the high cost of manual labeling are major challenges for electrocardiogram (ECG) diagnosis algorithms. To tackle this problem, we develop a novel deep active semi-supervised learning framework for myocardial infarction localization based on 12-lead ECG signals. First, a new deep learning model named multi-branch densely connected convolutional network (MB-DenseNet) is designed to automatically extract and fuse heartbeat features from 12-lead ECG signals. Moreover, to improve the classification results for new patients, we propose a novel active semi-supervised learning (ASSL) mechanism to update the model. Active learning (AL) is employed to improve the classification ability of the initial model firstly, and then a new semi-supervised learning method named self-training with spatial matching (STSM) is designed to update the model further. Based on the artificial knowledge from AL, STSM combines spatial matching algorithm and the trained model to label valuable unlabeled samples automatically. We conduct experiments based on intra-patient and patient-specific schemes using the PTB database. The MB-DenseNet yields an accuracy of 99.87% under the intra-patient scheme. For the patient-specific scheme, the updated model achieves an accuracy of 96.09%. Compared with state-of-the-art methods, our method can effectively reduce manual labeling while achieving comparable performance.

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review.

Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.

A new vision of a simple 1D Convolutional Neural Networks (1D-CNN) with Leaky-ReLU function for ECG abnormalities classification

Artificial Intelligence (AI) is increasingly impacting the healthcare field, due to its computational power that reduces time, cost and efforts for both healthcare professionals and patients. Diagnosing cardiac abnormalities using AI represents a very attractive subject for both medical and technical professionals. Cardiac abnormalities are characterized by the ECG signal, which is known by its variable morphology and intense affection by noises and artifacts. In this context, the presented study aims to propose a simple yet efficient version of Convolutional Neural Networks (CNN) to classify those abnormalities. This version increases the ability to detect several heart rate arrhythmias and severe cardiac abnormalities based only on the original 1D format of the ECG signal, which reserve the main feature of this signal and can be very suitable for ready-to-use and real-time applications. The main used training datasets are the MIT-BIH arrhythmias and the PTB databases. The proposed architectures are mainly inspired by the most recent CNN models and introduce several modifications on functions and layers, such as the use of the Leaky-ReLU instead of the ReLU activation function. The results of the proposed model are varying from an accuracy of 97%–99% in classifying Normal (n), Supraventricular (s), Ventricular (v), Fusion of ventricular and normal (f), and noisy (q) beats, in addition to the Myocardial Infarction (MI) case. A continuous performance was achieved while testing the model on real data, and after its migration to real mobile devices.

Deep Cardiac Telemonitoring for Clinical Cloud Healthcare Applications

Cardiac telemonitoring purpose is to allow cardiac professionals to interpret the medical measurements remotely, necessary for monitoring a patient and, if necessary, to make a decision about health care. The proposed board is a professional issue, in terms of management, support, and monitoring in the deployment of our virtual platform MCPCS (Medical Cyber-Physical Cloud Systems). In addition to the measured parameters, such as heart and respiratory rate, blood Spo2 level, blood pressure, body temperature, and movement exercises, our solution also includes a learning-based ECG classification system in depth (CNN) to detect myocardial infarction (MI). With the ADS1298 chip, our board measures the ECG signal, analyzes it and determines whether a person is having a cardiac arrhythmia or not. When an arrhythmia appears, our prototype activates an alarm in order to report the patient's condition to a caregiver and to the MCPCS healthcare center. The measured healthcare parameters are originated in an e-health sensor platform connected to a Raspberry Pi. For ECG denoising method and R-peak detection in the heartbeat segmentation step, we choose to use the wavelet transform (WT). Using 11 CNN layers, our method showed its superiority, in terms of detection and accuracy, over similar methods. We tested our model with 50,728 instances from PTB database. Our objective in this study is to outperform accuracy for the detection of MI. We achieved an average accuracy of 96.67%.

Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals

Many recent studies have focused on the automatic classification of electrocardiogram (ECG) signals using deep learning (DL) methods. Most rely on existing complex DL methods, such as transfer learning or providing the models with carefully designed extracted features based on domain knowledge. A common assumption is that the deeper and more complex the DL model is, the better it learns. In this study, we propose two different DL models for automatic feature extraction from ECG signals for classification tasks: A CNN-LSTM hybrid model and an attention/transformer-based model with wavelet transform for the dimensional embedding. Both of the models extract the features from time series at the initial layers of the neural networks and can obtain performance at least equal to, if not greater than, many contemporary deep neural networks. To validate our hypothesis, we used three publicly available data-sets to evaluate the proposed models. Our model achieved a benchmark accuracy of 99.92% for fall detection and 99.93% for the PTB database for myocardial infarction versus normal heartbeat classification.