Abnormal Heartbeats Research Articles

Integrated Pacemaker System with Piezoelectric Energy Harvesting and Laser-Based Heartbeat Abnormality Detection

Cardiovascular diseases remain the leading cause of mortality globally, highlighting the critical need for innovative cardiac care technologies. This study presents an advanced pacemaker system integrating piezoelectric energy harvesting, chemical sensors, and laser-based heartbeat abnormality detection. By converting body thermal and mechanical energy into electrical power, the system ensures continuous operation. The integration of diode laser sensors provides non-invasive, real-time monitoring of blood flow and oxygen saturation, enabling early detection of arrhythmias. A mobile application supports remote monitoring and emergency alerts, enhancing patient safety. The system's novel architecture aims to reduce surgical interventions and improve cardiac outcomes, setting a benchmark for future advancements in biomedical engineering.

XECG-Beats: an explainable deep transfer learning approach for ECG-based heartbeat classification

Early detection of abnormal heartbeats is of great importance for cardiologists for early diagnosis of cardiac diseases. This will help patients to receive in time diagnosis and prevention. Conventionally, physicians provide cardiac diagnoses by visual examination of electrocardiograms (ECGs). However, this can be a very time consuming and demanding task and, in some cases, may lead to overlooking and wrong diagnosis of life-threatening heart diseases. Therefore, an intelligent model can help to automatically analyze these huge amount of ECGs captured by different devices in clinical practice. A deep transfer learning approach is used to utilize the capability of different trained deep neural networks and to test them on new unseen datasets without the need to fully re-train the model. Two deep neural networks, namely, Visual Geometry Group (VGG) and Residual Network (ResNet) are utilized for classification of ECGs heartbeats. The models are evaluated using two unseen ECG datasets (i.e., SVDB and INCARTDB) by only optimizing their last classification layers. The overall area under curve for receiver operating characteristic (AUCROC) of two VGG and ResNet models are 0.961 and 0.966 on the SVDB dataset, respectively, and both models achieve 0.981 on the INCARTDB. This paper proposes an accurate and explainable model to classify ECG heartbeats into five categories recommended by the ANSI/AAMI standard. The proposed method paves the way to use pre-trained deep neural networks in real-time monitoring of heart patients using ECG data and to help clinicians understand the decision made by the models on each case using an explainable approach.

Pre-trained Bi-LSTM model for automated classification of ventricular arrhythmias using 1-D and 2-D ECG

Number of cardiac conditions have been associated with abnormal heartbeat (arrhythmia) such as ventricular fibrillation (Vfib), ventricular flutter (Vfl), and ventricular tachycardia (Vta). This is a difficult and essential job for timely clinical assessment and identification of these potentially life-threatening heart arrhythmias. With the aid of a one-dimensional electrocardiogram (ECG) signal and its associated two-dimensional image, the suggested method provides a strategy for the detection of time-frequency interpretation (Vfib, Vfl, and Vta). A four-stage cascaded Savitzky-Golay (SG) filter is used after a 2-stage median filter to preprocess the ECG signal. This technique employs z-score normalisation after brief (2 sec) ECG readings. The classification of these ECG segments (1-D) and associated time-frequency representation pictures (2-D) was explored separately using a bi-directional long short-term memory-based network. Eight distinct categorization scenarios were examined, and then an average accuracy of 99.67% for 1-D ECG and 99.87% for 2-D ECG signal was attained.

High Blood Pressure-Associated Symptoms: Insights From a Population-Based Study in Pakistan.

Background All over the world, millions die of hypertension (HTN) every year. Given the influence of healthcare expenses, HTN represents a serious public health issue in developed and developing countries. HTN is common in Pakistan; however, there are several myths about the symptoms of raised blood pressure that need to be identified. Objective The objective of this study is to compare the frequency of high blood pressure-associated symptoms in normotensive and hypertensive adult populations in Karachi, Pakistan. Methodology A community-based cross-sectional observational study was conducted among 277 patients aged 18 years and above who were attending the OPD of different community health centers in Karachi, with and without HTN. Ethical approval was obtained, and data were collected using a convenient sampling technique on a predesigned questionnaire and analyzed using IBM SPSS Statistics for Windows, Version 20.0 (Released 2011; IBM Corp., Armonk, NY, USA). Results Out of the total study population, 88 (31.65%) were normotensive, and 189 (67.98%) were hypertensive. In the hypertensive group, approximately 100 (52.9%) were men and 89 (47.1%) were women. The mean ages of normotensives and hypertensives were 44.13 + 11.0 and 49.2 + 13.1 years, respectively. Mean age, heart rate, and smoking status were significantly different between the two groups. Among several perceived blood pressure symptoms like vision problems, sleep apnea, and abnormal heartbeat was significantly higher in the hypertensive group. Conversely, although headache, vertigo, edema, and epistaxis were more frequent in the hypertensive group, the difference was not statistically significant. Conclusions In our study, >65% of patients visiting OPD had high blood pressure. Several symptoms were found to be more prevalent in hypertensive individuals compared with non-hypertensive ones. More large-scale studies are recommended to further explore the common symptoms associated with HTN in our population.

Identification and molecular mechanisms of novel antioxidant peptides in black soldier fly larvae protein: simulated gastrointestinal digestion, molecular docking and in vitro/in vivo studies

SummaryThis study aims to identify novel antioxidant peptides within black soldier fly larvae protein (BSFLP) using simulated gastrointestinal processes, LC–MS/MS analysis, and virtual screening. Two peptides, PFCPK and ADFW, exhibited significant antioxidant properties in both in vitro radical scavenging experiments and in vivo evaluations using zebrafish embryos induced with 2,2′‐azobis(2‐methylpropionamidine) dihydrochloride. Treatment with 25 μg mL−1 of PFCPK or ADFW significantly (P < 0.05) enhanced zebrafish embryo survival, reduced abnormal heartbeats and lowered ROS generation rate by 62.98% and 83.44%, lipid peroxidation by 52.54% and 68.35%, and cell death rate by 86.92% and 97.54%, respectively. Moreover, they restored antioxidant enzyme activities and MDA levels to near‐normal values, demonstrating significant therapeutic effects. The observed antioxidant effects may potentially be linked to the modulation of the Keap1/Nrf2 pathway. This study provides valuable insights for developing novel natural antioxidants and maximising the high‐value applications of BSFLP.

Elderly People Health Care Monitoring System Using Internet of Things (IOT) For Exploratory Data Analysis

The majority of senior persons are suffering a loss of physical condition their health is vital, the prevalence of illnesses has increased in the current age compared to previous generations, making health management in hospital sector. One of the concepts that can harness the advantages of the Internet of Things to better the older lifestyle is health monitoring for active and aided functioning. This approach aims to create a wireless communication system designed to remotely monitor patients. Each patient is wirelessly monitored based on Body temperature is measured via a temperature sensor, which gives an accuracy of around some certain value. The sensor carries a heart rate monitor with a pulse oximetry sensor. There are two light-emitting in it; one produces red light and the other infrared light. For tracking the amount of oxygen in the blood, two infrared LEDs are needed, but only one is needed to calculate the heart rate. The ESP 8266 (Node MCU) controller gathers the data and sends it to the specific application and Communication will be sent again to the consumers based on the data examination. The outcome results manage each patient's data separately and are used in hospitals, old age homes, and physically challenged people. Every sensor of data, such as temperature, heart rate, and abnormal heartbeat, will be managed in the database processed with GPS coordinates and data analyses using Think Speak Web Application is for informing about personal health data.

IoMT-Based Smart Healthcare Detection System Driven by Quantum Blockchain and Quantum Neural Network.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.

InfusedHeart: A Novel Knowledge-Infused Learning Framework for Diagnosis of Cardiovascular Events

In the undertaken study, we have used a customized dataset termed ``Cardiac-200'' and the benchmark dataset ``PhysioNet.'' which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur, extrasystole, artifact, and other unlabeled heartbeat acoustic events. The primary reason for designing a customized dataset, ``cardiac-200,'' is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events is 10-12 s. In the undertaken study, we have analyzed and evaluated various heartbeat acoustic events using audio processing libraries such as Chromagram, Chroma-cq, Chroma-short-time Fourier transform (STFT), Chroma-cqt, and Chroma-cens to extract more information from the recorded heartbeat sound signals. The noise removal process has been carried out using local binary pattern (LBP) methodology. The noise-robust heartbeat acoustic images are classified using long short-term memory (LSTM)-convolutional neural network (CNN), recurrent neural network (RNN), LSTM, Bi-LSTM, CNN, K-means Clustering, and support vector machine (SVM) methods. The obtained results have shown that the proposed InfusedHeart Framework had outclassed all the other customized machine learning and deep learning approaches such as RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM-based classification methodologies. The proposed Knowledge-infused Learning Framework has achieved an accuracy of 89.36% (without augmentation), 93.38% (with augmentation), and a standard deviation of 10.64 (without augmentation), and 6.62 (with augmentation). Furthermore, the proposed framework has been tested for various signal-to-noise ratio conditions such as SignaltoNoiseRatio0, SignaltoNoiseRatio3, SignaltoNoiseRatio6, SignaltoNoiseRatio9, SignaltoNoiseRatio12, SignaltoNoiseRatio15, and SignaltoNoiseRatio18. In the end, we have shown a detailed comparison of texture and without texture approaches and have discussed future enhancements and prospective ways for future directions.

Association between acquiring SARS-CoV-2 during pregnancy and post-acute sequelae of SARS-CoV-2 infection: RECOVER electronic health record cohort analysis

Little is known about post-acute sequelae of SARS-CoV-2 infection (PASC) after acquiring SARS-CoV-2 infection during pregnancy. We aimed to evaluate the association between acquiring SARS-CoV-2 during pregnancy compared with acquiring SARS-CoV-2 outside of pregnancy and the development of PASC. This retrospective cohort study from the Researching COVID to Enhance Recovery (RECOVER) Initiative Patient-Centred Clinical Research Network (PCORnet) used electronic health record (EHR) data from 19 U.S. health systems. Females aged 18-49 years with lab-confirmed SARS-CoV-2 infection from March 2020 through June 2022 were included. Validated algorithms were used to identify pregnancies with a delivery at >20 weeks' gestation. The primary outcome was PASC, as previously defined by computable phenotype in the adult non-pregnant PCORnet EHR dataset, identified 30-180 days post-SARS-CoV-2 infection. Secondary outcomes were the 24 component diagnoses contributing to the PASC phenotype definition. Univariable comparisons were made for baseline characteristics between individuals with SARS-CoV-2 infection acquired during pregnancy compared with outside of pregnancy. Using inverse probability of treatment weighting to adjust for baseline differences, the association between SARS-CoV-2 infection acquired during pregnancy and the selected outcomes was modelled. The incident risk is reported as the adjusted hazard ratio (aHR) with 95% confidence intervals. In total, 83,915 females with SARS-CoV-2 infection acquired outside of pregnancy and 5397 females withSARS-CoV-2 infection acquired during pregnancy were included in analysis. Non-pregnant females with SARS-CoV-2 infection were more likely to be older and have comorbid health conditions. SARS-CoV-2 infection acquired in pregnancy as compared with acquired outside of pregnancy was associated with a lower incidence of PASC (25.5% vs 33.9%; aHR 0.85, 95% CI 0.80-0.91). SARS-CoV-2 infection acquired in pregnant females was associated with increased risk for some PASC component diagnoses including abnormal heartbeat (aHR 1.67, 95% CI 1.43-1.94), abdominal pain (aHR 1.34, 95% CI 1.16-1.55), and thromboembolism (aHR 1.88, 95% CI 1.17-3.04), but decreased risk for other diagnoses including malaise (aHR 0.35, 95% CI 0.27-0.47), pharyngitis (aHR 0.36, 95% CI 0.26-0.48) and cognitive problems (aHR 0.39, 95% CI 0.27-0.56). SARS-CoV-2 infection acquired during pregnancy was associated with lower risk of development of PASC at 30-180 days after incident SARS-CoV-2 infection in this nationally representative sample. These findings may be used to counsel pregnant and pregnant capable individuals, and direct future prospective study. National Institutes of Health (NIH) Other Transaction Agreement (OTA) OT2HL16184.

Detection of Cardiovascular Diseases from ECG Images Using Machine Learning and Deep Learning Methods

Abstract: Cardiovascular diseases (CVDs) are the leading cause of death globally, emphasizing the importance of early prediction and classification for saving lives. Electrocardiogram (ECG) is a widely used, cost-effective, and noninvasive tool for measuring heart electrical activity, crucial for detecting CVDs. This study leverages deep learning techniques to predict four major cardiac abnormalities—abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal individuals—using a public dataset of ECG images from cardiac patients. The research explores transfer learning with pretrained deep neural networks such as SqueezeNet and AlexNet, as well as introduces a novel convolutional neural network (CNN) architecture for cardiac abnormality prediction. Additionally, pretrained models and the new CNN architecture are utilized as feature extraction tools for traditional machine learning algorithms including support vector machine, K-nearest neighbors, decision tree, random forest, and Naïve Bayes. Experimental results showcase the superiority of the proposed CNN model over existing works, achieving 98.23% accuracy, 98.22% recall, 98.31% precision, and 98.21% F1 score. Furthermore, when the proposed CNN model is utilized for feature extraction, it achieves the highest score of 99.79% using the Naïve Bayes algorithm.

CARDIOVASCULAR DISEASES DETECTION IN ECG IMAGES USING DEEP LEARNING METHODS

Heart diseases are the leading cause of death world- wide. So,detecting and identifying them earlier can save many lives.Electrocardiogram (ECG) is a common and inexpensive tool for measuring the electrical activity of the heart and is used to detect cardiovascular disease.In this paper, the power of deep learning techniques was used to predict the four major cardiac abnormalities: abnormal heartbeat, myocardial infarction, his- tory of myocardial infarction, and normal person classes using the public ECG images dataset of cardiac patients. First, the preatrained models, namely SqueezeNet, AlexNet, proposed CNN and Xception were proposed for abnormality prediction.Second, these models were used as feature extraction tools for traditional machine learning algorithms, namely support vector machine, K- nearest neighbors, decision tree, random forest, and Na¨ıve Bayes. According to the experimental results, the performance metrics of the Xception model outperform the exciting works; it achieves 99.5% accuracy, 99.5% recall, 99.5% precision, and 99.5% F1 score. Moreover, when the Xception model is used for feature extraction, it achieves the best score of 99.8% using the RF and DT algorithms. Index Terms—Cardiovascular, deep learning, electrocardio- gram (ECG) images, feature extraction, machine learning, trans- fer learning.

Detection of Cardiovascular Disease Using ECG Images in Machine Learning and Deep Learning

One of the most prominent tools for detecting cardiovascular problems is the electrocardiogram (ECG). The electrocardiogram (ECG or EKG) is a diagnostic tool that is used to routinely assess the electrical and muscular functions of the heart. Even though it is a comparatively simple test to perform, the interpretation of the ECG charts requires considerable amounts of training. Till recently, the majority of ECG records were kept on paper. Thus, manually examining and re-examining the ECG paper records often can be a time-consuming and daunting process. If we digitize such paper ECG records, we can perform automated diagnosis and analysis. The main goal of this project is to use machine learning to convert ECG paper records into a 1-D signal. This can be achieved by extracting the P, QRS, and T waves that exist in ECG signals to demonstrate the electrical activity of the heart using various techniques. The techniques include splitting the original ECG report into 13 Leads, extracting and converting into the signal, smoothing, converting them to binary images using threshold and scaling. Post-feature-extraction, dimension reduction techniques like Principal Component Analysis are applied to understand the data. Multiple classifiers like k-nearest neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), and Voting Based Ensemble Classifier are implemented, and based on the acceptable criteria on the accuracy, precision, recall, f1-score, and support, the model will be finalized. This final model will aid in the diagnosing of cardiac diseases, to detect whether a patient has/had Myocardial Infarction, Abnormal Heartbeat, or the patient is hale and healthy by inferring the ECG reports

Cardiovascular Disease Detection using Deep Learning

Cardiovascular diseases have surged as the leading global cause of death, necessitating a focus on early detection and continuous monitoring. This study employs deep learning, specifically the MobileNet Architecture, on a public ECG dataset to predict four cardiac abnormalities: abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal cases. The developed model demonstrates a notable training accuracy of 97.34% and validation accuracy of 91.00%, showcasing its efficacy in disease classification. With the potential to save lives and reduce healthcare costs, this algorithmic approach offers a reliable, time-efficient alternative to manual diagnosis in detecting heart disorders, providing valuable support to medical professionals..

Effect of Crataegus Extract on the Relief of Heart Palpitation and Anxiety in Teenagers: An Interventional Quasi-Experimental Study

Background: Heart palpitation is a common complaint defined as an unpleasant or abnormal heartbeat feeling. This study aimed to investigate the effectiveness of Crataegus extract on the relief of heart palpitations and anxiety in adolescents. Methods: This is an interventional quasi-experimental study performed from February 19, 2016 to February 19, 2017 on 120 adolescents aged 13 to 19 years with a complaint of heart palpitations, referring to heart clinic in Afshar Hospital of Yazd. The participants' demographic information, heartbeat feeling, Hamilton anxiety scaling, blood pressure, heartbeat rate, and electrocardiogram (ECG) were recorded. Patients were first treated with 20 drops every 8 hours for three days and then 25 drops every 8 hours for one month with Crataegus edible drops. They were then re-evaluated for heart palpations and anxiety. Results: Of 120 adolescents referring to Afshar Hospital with heart palpitations, 98 of participants (81.7%) experienced improvement. Mean differences in systolic blood pressure, heart rate, and anxiety scores before and after treatment with Crataegus extract decreased significantly by 1.71, 8.52, 6.55, respectively, but the mean difference in diastolic blood pressure increased by 1.46 (P<0.05). Conclusion: According to the results, Crataegus extract might be effective in reducing palpitation and anxiety in adolescents. Further studies are suggested to approve the results of the study.

Comparative study of time-frequency transformation methods for ECG signal classification

In this study, we highlighted the growing need for automated electrocardiogram (ECG) signal classification using deep learning to overcome the limitations of traditional ECG interpretation algorithms that can lead to misdiagnosis and inefficiency. Convolutional neural networks (CNN) application to ECG signals is gaining significant attention owing to their exceptional image-classification capabilities. However, we addressed the lack of standardized methods for converting 1D ECG signals into 2D-CNN-compatible input images by using time-frequency methods and selecting hyperparameters associated with these methods, particularly the choice of function. Furthermore, we investigated the effects of fine-tuned training, a technique where pre-trained weights are adapted to a specific dataset, on 2D-CNNs for ECG classification. We conducted the experiments using the MIT-BIH Arrhythmia Database, focusing on classifying premature ventricular contractions (PVCs) and abnormal heartbeats originating from ventricles. We employed several CNN architectures pre-trained on ImageNet and fine-tuned using the proposed ECG datasets. We found that using the Ricker Wavelet function outperformed other feature extraction methods with an accuracy of 96.17%. We provided crucial insights into CNNs for ECG classification, underscoring the significance of fine-tuning and hyperparameter selection in image transformation methods. The findings provide valuable guidance for researchers and practitioners, improving the accuracy and efficiency of ECG analysis using 2D-CNNs. Future research avenues may include advanced visualization techniques and extending CNNs to multiclass classification, expanding their utility in medical diagnosis.

An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection

Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.

Transcriptome-based approach to identify mechanisms underlying locomotor abnormality induced by decabromodiphenyl ethane in zebrafish larvae

The brominated flame retardant decabromodiphenyl ethane (DBDPE) has been extensively used following restrictions on BDE-209 and thus, been frequently detected in aquatic environment. However, information on impact of DBDPE on fish development and the potential mechanisms remains scarce. In present study, developing zebrafish were employed as a study model. Embryos were exposed until 5 d to DBDPE at concentrations of 0, 3, 30, and 300 μg/L, following which the impact on larval development was investigated. DBDPE bioaccumulation and locomotor hyperactivity were observed in developing zebrafish exposed to DBDPE. Transcriptome and bioinformatics analyses indicated that pathways associated with cardiac muscle contraction and retinol metabolism were notably affected. The mechanisms of DBDPE to induce locomotor abnormality were further investigated by analyzing levels of retinol and retinol metabolites, eye and heart histology, heart rates, and ATPase activity. Our results indicate that locomotor hyperactivity observed in larvae exposed to DBDPE results from abnormal heartbeat, which in turn is attributable to inhibition of Na+/K+-ATPase activity. Furthermore, DBDPE did not change larval eye histology and contents of retinoid (retinol, retinal, and retinoic acid). This study provides insight into the mechanisms underlying DBDPE-induced developmental toxicity and highlights the need for addressing the environmental risks for aquatic organisms.

COVID-ECG-RSNet: COVID-19 classification from ECG images using swish-based improved ResNet model

Millions of humansworldwide have been impacted by consecutive COVID-19waves during the past threeyears. The correct treatment for COVID-19 has not yet been identified because vaccine receivers have also contracted the illness. People’s lives can be saved, and patients can be shielded from laborious treatments with the quick and accurate diagnosis of coronavirus. Investigators have utilized various clinical imaging modalities i.e.,CT-Scan and X-ray for the identification of coronavirus, while, less emphasis is spent on the utilization ofECG images in recognizing the COVID-19-affected patients. In contrast to CT-Scan and X-ray images, ECG samples arereadily accessible, hence we utilize them to diagnose coronavirus. Since scientists generally transform the ECG data into integer representation before using any approach, which indicates higher computing overhead. Accurateand quickcoronavirus identification from the ECG modalityis a difficult and cumbersome operation. In this study, we attempted to address these issues by leveraging the ECG filesdirectly in an improved deep learning (DL)methodology called the COVID-ECG-RSNet. Descriptively, a novel DL model is presented by altering the ResNet-50 model by introducing an optimized activation approach called the Swish method during the feature engineering process. Moreover, we introduced multiple dense layers at the end of the proposed CNN structure to ensure more robust sample features for classification purposes. The introduced COVID-ECG-RSNet model is proficient in categorizing the ECG samples as being normal, coronavirus, myocardial infarction (MI), abnormal heartbeats (AHB), and victims with Previous history of Myocardial Infarction (PMI) groups. A vast evaluation with the help of a complicated and publically accessible data sample of ECG images is accomplished to show the effectiveness of our approach in recognizing the coronavirus images in contrast to other categories of heart diseases and healthy samples via using the ECG modality. We have attained an accuracy score of 98.8%, along with precision, and recall scores of 98.9%, and 98.7% respectively indicating the efficacy of suggested approach.

Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.

Development and validation of a predictive model for early diagnosis of neonatal acute respiratory distress syndrome based on the Montreux definition.

Based on the Montreux definition, we aim to develop and validate a predictive model for the early diagnosis of neonatal acute respiratory distress syndrome (ARDS). A retrospective analysis of clinical data on 198 neonates with respiratory distress from January 2018 to January 2022 was conducted. Neonates meeting Montreux definition were classified as ARDS group (n = 79), while the rest were non-ARDS group (n = 119). Univariate analysis identified indicators for neonatal ARDS, followed by logistic regression to construct a predictive model for early diagnosis. The ability of predictors and models to predict neonatal ARDS was evaluated using area under the curve (AUC), and model performance was estimated through bootstrap resampling. Maternal prenatal fever, abnormal fetal heart beat, meconium-stained amniotic fluid (MSAF), white blood cell (WBC), absolute neutrophil count (ANC), neutrophil percentage (NE%), platelet count (PLT), C-reactive protein (CRP), procalcitonin (PCT), creatine kinase (CK), activated partial thromboplastin time (APTT), serum calcium (Ca) and sodium (Na)exhibited significant differences between the ARDS group and the non-ARDS group (P < 0.05). MSAF (OR=5.037; 95% CI: 1.523-16.657; P < 0.05), ANC (OR = 1.324; 95% CI: 1.172-1.495; P < 0.05), PLT (OR = 0.979; 95% CI: 0.971-0.986; P < 0.05), Ca (OR = 0.020; 95% CI: 0.004-0.088; P < 0.05) emerged as independent risk factors for the development of ARDS. The respective AUC values for MSAF, ANC, PLT, Ca, and the combined prediction models were 0.606, 0.691, 0.808, 0.761 and 0.931. Internal validation showed that the C-index for the model was 0.931. Early application of the model combining MSAF, ANC, PLT and Ca may have a good predictive effect on the early diagnosis of neonatal ARDS.