LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

[Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network].

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.

[Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks].

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor's diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.

Type I Kounis syndrome in a young woman without chest pain: a case report.

BACKGROUND: Kounis syndrome is defined as the concurrence of acute coronary syndromes in the setting of allergic or anaphylactic reactions. It primarily affects men aged 40-70 years and is often associated with chest pain. This syndrome is often unrecognized and undiagnosed in clinical practice due to a low level of awareness. Herein, we present a case of type I Kounis syndrome in a young woman without chest pain. CASE PRESENTATION: A 28-year-old Japanese woman with a history of atopic dermatitis received a glycyrrhizin, glutathione, and neurotropin preparation (a preparation of inflamed skin extract from rabbits inoculated with vaccinia virus) at a dermatology clinic to treat pruritus caused by atopic dermatitis. Immediately after the administration, the patient developed abdominal pain and generalized body wheals. The patient was diagnosed with anaphylaxis and was transported to our hospital. She had no chest pain on arrival at our hospital; however, a 12-lead electrocardiogram showed ST elevation in leads I, aVL, V2, and V3, and an echocardiogram showed decreased wall motion in the anterior and lateral walls of the left ventricle. Sublingual nitroglycerin administration improved ST-segment elevation and left ventricular wall motion abnormalities. The patient underwent emergency coronary angiography, which revealed no significant stenosis, and was diagnosed with type I Kounis syndrome. CONCLUSION: Kounis syndrome without chest pain is rare in young women. Since it can be fatal in cases with severe allergic symptoms such as anaphylaxis, the possibility of concurrent acute coronary syndrome should be considered when treating systemic allergic reactions, regardless of age, sex, or the presence or absence of chest symptoms.

Cough-induced severe bradycardia and syncope in a dog.

A 10-year-old spayed female shih tzu dog was brought to the hospital because of recurring syncope that occurred simultaneously with a cough. Physical examination did not reveal an abnormal heart rhythm or abnormal heart sounds. Electrocardiography revealed sinus arrest of 4.7 s with intermittent escape beats during coughing. Additional examinations, including thoracic radiography, clinical pathology, and echocardiography, revealed no abnormalities of concern. Forty-eight-hour Holter monitoring captured 1 syncopal episode following severe coughing, during which the longest sinus arrest lasted 16 s with intermittent escape beats. This observation confirmed our strong suspicion that coughing was the cause of varying degrees of sinus arrest in this dog. Theophylline, codeine, and short-term prednisolone were prescribed to treat the dog's cough. The daily episodes of syncope ceased and coughing decreased. Subsequent 48-hour Holter monitoring revealed no abnormal pauses, and the owner did not report syncope. Theophylline and codeine were continued for 5 mo, during which time no syncope occurred. To our knowledge, this case provides the first clear evidence of a correlation between cough-induced sinus arrest and syncope in a veterinary patient, as confirmed by Holter monitoring and electrocardiography. Key clinical message: Cough-induced severe bradycardia and syncope were identified in a shih tzu dog. After the antitussive medication was adjusted, the signs resolved.

Bradycardie sévère et syncope provoquées par la toux chez un chienUne chienne shih tzu stérilisée âgée de 10 ans a été amenée à l'hôpital en raison d'une syncope récurrente survenue simultanément avec une toux. L'examen physique n'a révélé aucun rythme cardiaque anormal ni bruits cardiaques anormaux. L'électrocardiographie a révélé un arrêt sinusal de 4,7 s avec des battements d'échappements intermittents lors de la toux. Des examens complémentaires, notamment une radiographie thoracique, des analyses en pathologie clinique et une échocardiographie, n'ont révélé aucune anomalie préoccupante. Une surveillance Holter de 48 heures a capturé 1 épisode syncopal à la suite d'une toux sévère, au cours duquel l'arrêt sinusal le plus long a duré 16 s avec des battements d'échappements intermittents. Cette observation a confirmé nos fortes suspicions selon lesquelles la toux était la cause de divers degrés d'arrêt sinusal chez ce chien. De la théophylline, de la codéine et de la prednisolone de courte durée ont été prescrites pour traiter la toux du chien. Les épisodes quotidiens de syncope ont cessé et la toux a diminué. Une surveillance Holter ultérieure de 48 heures n'a révélé aucune pause anormale et le propriétaire n'a pas signalé de syncope. La théophylline et la codéine ont été poursuivies pendant 5 mois, période pendant laquelle aucune syncope ne s'est produite. À notre connaissance, ce cas constitue la première preuve claire d'une corrélation entre l'arrêt sinusal induit par la toux et la syncope chez un patient vétérinaire, comme le confirme la surveillance Holter et l'électrocardiographie.Message clinique clé :Une bradycardie et une syncope sévères induites par la toux ont été identifiées chez un chien shih tzu. Après ajustement du traitement antitussif, les signes ont disparu.(Traduit par Dr Serge Messier).

Zebra II as A Novel System to Record Electrophysiological Signals in Zebrafish.

Zebrafish and their mutant lines have been extensively used in biomedical investigations, cardiovascular studies, and drug screening. In the current study, the commercial version of the novel system, Zebra II, is presented. The protocol demonstrates electrocardiogram (ECG) acquisition and analysis from multiple zebrafish within controllable working environments. The device is composed of an external and independent perfusion system, a 4-point electrode, temperature sensors, and an embedded electronic system. In previous studies, the device prototype underwent validation against the established iWORX system through several tests, demonstrating similar data quality and ECG response to drug interventions. Following this, the study delved into examining the impact of anesthetic drugs and temperature fluctuations on zebrafish ECG, necessitating instant data evaluation. Thanks to the apparatus's capacity for consistent delivery of anesthetics and drugs, it was possible to extend ECG data collection up to 1 h, markedly longer than the 5 min duration supported by current systems. This paper introduces a pioneering, cloud-based, automated analysis utilizing data from four zebrafish, offering an efficient method for conducting combination experiments and significantly reducing time and effort. The system proved effective in capturing and analyzing ECG, especially in detecting drug-induced arrhythmias in wild-type zebrafish. Additionally, the capability to gather data across multiple channels facilitated the execution of randomized controlled trials with zebrafish models. The developed ECG system overcomes existing limitations, showing the potential to greatly expedite drug discovery and cardiovascular research involving zebrafish.

In Vivo Thoracic Dorsal Root Ganglia (DRG) Calcium Imaging and ECG Recording for Studying Peripheral Nerve Stimulation.

The dorsal root ganglia (DRG), housing primary sensory neurons, transmit somatosensory and visceral afferent inputs to the dorsal horn of the spinal cord. They play a pivotal role in both physiological and pathological states, including neuropathic and visceral pain. In vivo calcium imaging of DRG enables real-time observation of calcium transients in single units or neuron ensembles. Accumulating evidence indicates that DRG neuronal activities induced by somatic stimulation significantly affect autonomic and visceral functions. While lumbar DRG calcium imaging has been extensively studied, thoracic segment DRG calcium imaging has been less explored due to surgical exposure and stereotaxic fixation challenges. Here, we utilized in vivo calcium imaging at the thoracic1 dorsal root ganglion (T1-DRG) to investigate changes in neuronal activity resulting from somatic stimulations of the forelimb. This approach is crucial for understanding the somato-cardiac reflex triggered by peripheral nerve stimulations (PENS), such as acupuncture. Notably, synchronization of cardiac function was observed and measured by electrocardiogram (ECG), with T-DRG neuronal activities, potentially establishing a novel paradigm for somato-visceral reflex in the thoracic segments.

Cardiovascular risk and its influencing factors during exercise in apparently healthy Chinese adult population.

BACKGROUND: There are few studies on the safety of sub-maximal exercise testing of aerobic exercise in apparently healthy Chinese populations. The purpose of this study was to explore the frequency of exercise electrocardiography (ECG) abnormalities and the corresponding exercise intensities, as well as the associated influencing factors, during a symptom-limited stepwise incremental cardiopulmonary exercise test (CPET) in an apparently healthy Chinese population. METHODS: A cross-sectional study was done in four communities, one urban and one rural in the North (Beijing) and in the South (Hezhou, Guangxi) of China from 1 January 2017 to 31 December 2018, respectively. Total of 1642 participants was recruited, 918 were eligible and completed demographic indicators, routine blood indicators, physical activity status, symptom-limited CPET and exercise ECG were included in the analysis. RESULTS: Of the exercise ECG outcomes, 10 (1.1%) were positive and occurred at exercise intensities ≥ 62.50% heart rate reserve (HRR); 44 (4.8%) were equivocal and 864 (94.1%) were normal. Individuals with Cardiovascular Disease Risk Factor (CVDRF) = 3-4 were 2.6 times more likely to have a equivocal and abnormal exercise ECG than those with CVDRF = 0-2. Exercise ECGs of individuals with CVDRF = 5-7 were 5.4 times more likely to be positive and abnormal than exercise ECGs of individuals with CVDRF = 0-2. CONCLUSIONS: The exercise intensity of 62.5% HRR can be used as a safe upper limit for safe participation in exercise in apparently healthy Chinese population; the greater the number of CVDRFs, the greater the likelihood of cardiovascular risk during exercise.

Takotsubo Cardiomyopathy Mimicking Obstructive Hypertrophic Cardiomyopathy.

Takotsubo cardiomyopathy (TCM) is characterized by transient left ventricular dysfunction, diagnosed via echocardiography or left ventriculography. In most cases, TCM involves an emotional, physical, or combined trigger. Acute coronary syndrome is one of the most frequent misdiagnoses in TCM patients due to electrocardiogram (ECG) abnormalities and elevated cardiac biomarkers. Typically, coronary angiography reveals no stenosis or occlusion of the coronary arteries. Hypertrophic cardiomyopathy (HCM) is a distinct pathology characterized by a hypertrophied left ventricle with various phenotypes. However, some reports have described TCM cases mimicking obstructive-type HCM in some patients. We present a case of a female patient diagnosed with TCM based on clinical, laboratory, and imaging tests. Differentiating TCM from HCM was challenging due to ventriculography and echocardiography findings, as hyperdynamic contraction of the basal segments of the left ventricle caused an increased left ventricular outflow tract (LVOT) gradient and severe mitral valve regurgitation. Detailed evaluation and close echocardiographic follow-up are essential in such rare cases.

The Diagnostic Value of ECG Characteristics for Vasospastic and Microvascular Angina: A Systematic Review.

BACKGROUND: Coronary vascular dysfunction comprises VSA and/or MVA and is more common in women than in men with angina without obstructive coronary artery disease (ANOCA). Invasive coronary function testing is considered the reference test for diagnosis, but its burden on patients is large. We aimed to investigate the potential of electrocardiography (ECG) as noninvasive marker for vasospastic angina (VSA) and microvascular angina (MVA) diagnosis. METHODS: We systematically screened Pubmed and EMBASE databases for studies reporting on ECG characteristics in ANOCA patients with (a suspicion of) coronary vascular dysfunction. We assessed study quality using QUADAS-2. We extracted data on diagnostic values of different ECG characteristics and analyzed whether the studies were sex-stratified. RESULTS: Thirty publications met our criteria, 13 reported on VSA and 17 on MVA. The majority addressed repolarization-related ECG parameters. Only 1 of the 13 VSA papers and 4 of the 17 MVA papers showed diagnostic accuracy measures of the ECG characteristics. The presence of early repolarization, T-wave alternans, and inverted U waves showed of predictive value for VSA diagnosis. The QTc interval was predictive for MVA diagnosis in all six studies reporting on QTc interval. Sex-stratified results were reported in only 5 of the 30 studies and 3 of those observed sex-based differences. CONCLUSIONS: ECG features are not widely evaluated in diagnostic studies for VSA and MVA. Those features predictive for VSA and MVA diagnosis mostly point to repolarization abnormalities and may contribute to noninvasive risk stratification.

Genome-Wide Interaction Analyses of Serum Calcium on Ventricular Repolarization Time in 125 393 Participants.

BACKGROUND: Ventricular repolarization time (ECG QT and JT intervals) is associated with malignant arrhythmia. Genome-wide association studies have identified 230 independent loci for QT and JT; however, 50% of their heritability remains unexplained. Previous work supports a causal effect of lower serum calcium concentrations on longer ventricular repolarization time. We hypothesized calcium interactions with QT and JT variant associations could explain a proportion of the missing heritability. METHODS AND RESULTS: We performed genome-wide calcium interaction analyses for QT and JT intervals. Participants were stratified by their calcium level relative to the study distribution (top or bottom 20%). We performed a 2-stage analysis (genome-wide discovery [N=62 532] and replication [N=59 861] of lead variants) and a single-stage genome-wide meta-analysis (N=122 393, [European ancestry N=117 581, African ancestry N=4812]). We also calculated 2-degrees of freedom joint main and interaction and 1-degree of freedom interaction P values. In 2-stage and single-stage analyses, 50 and 98 independent loci, respectively, were associated with either QT or JT intervals (2-degrees of freedom joint main and interaction P value <5×10-8). No lead variant had a significant interaction result after correcting for multiple testing and sensitivity analyses provided similar findings. Two loci in the single-stage meta-analysis were not reported previously (SPPL2B and RFX6). CONCLUSIONS: We have found limited support for an interaction effect of serum calcium on QT and JT variant associations despite sample sizes with suitable power to detect relevant effects. Therefore, such effects are unlikely to explain a meaningful proportion of the heritability of QT and JT, and factors including rare variation and other environmental interactions need to be considered.

Detection of distant relatedness in biobanks to identify undiagnosed cases of Mendelian disease as applied to Long QT syndrome.

Rare genetic diseases are typically studied in referral populations, resulting in underdiagnosis and biased assessment of penetrance and phenotype. To address this, we develop a generalizable method of genotype inference based on distant relatedness and deploy this to identify undiagnosed Type 5 Long QT Syndrome (LQT5) rare variant carriers in a non-referral population. We identify 9 LQT5 families referred to a single specialty clinic, each carrying p.Asp76Asn, the most common LQT5 variant. We uncover recent common ancestry and a single shared haplotype among probands. Application to a non-referral population of 69,819 BioVU biobank subjects identifies 22 additional subjects sharing this haplotype, which we confirm to carry p.Asp76Asn. Referral and non-referral carriers have prolonged QT interval corrected for heart rate (QTc) compared to controls, and, among carriers, the QTc polygenic score is independently associated with QTc prolongation. Thus, our innovative analysis of shared chromosomal segments identifies undiagnosed cases of genetic disease and refines the understanding of LQT5 penetrance and phenotype.

Use of downstream stress imaging tests for risk stratification of patients presenting to the emergency department with chest pain and low HEART score.

BACKGROUND: Patients with low HEART (History, Electrocardiogram, Age, Risk factors, and Troponin level) risk scores who are discharged from the emergency department (ED) may present clinical challenges and diagnostic dilemmas. The use of downstream non-invasive stress imaging (NISI) tests in this population remains uncertain. Therefore, this study aims to investigate the value of NISI in risk stratification and predicting cardiac events in patients with low-risk HEART scores (LRHSs). METHODS: We prospectively included 1384 patients with LRHSs between March 2019 and March 2021. All the patients underwent NISI (involving myocardial perfusion imaging/stress echocardiography). The primary endpoints included cardiac death, non-fatal myocardial infarction and unplanned coronary revascularisation. Secondary endpoints encompassed cardiovascular-related admissions or ED visits. RESULTS: The mean patient age was 64±14 years, with 670 (48.4%) being women. During the 634±104 days of follow-up, 58 (4.2%) patients experienced 62 types of primary endpoints, while 60 (4.3%) developed secondary endpoints. Multivariable Cox models, adjusted for clinical and imaging variables, showed that diabetes (HR: 2.38; p=0.008), HEART score of 3 (HR: 1.32; p=0.01), history of coronary artery disease (HR: 2.75; p=0.003), ECG changes (HR: 5.11; p<0.0001) and abnormal NISI (HR: 16.4; p<0.0001) were primary endpoint predictors, while abnormal NISI was a predictor of secondary endpoints (HR: 3.05; p<0.0001). CONCLUSIONS: NISI significantly predicted primary cardiac events and cardiovascular-related readmissions/ED visits in patients with LRHSs.

Out-of-season, multiserotype acute coxsackie B myocarditis with concomitant thyrotoxicity.

Acute viral myocarditis and hyperthyroidism can present with acute coronary syndrome. However, the link between hyperthyroidism and myocarditis has hardly been explored apart from a small collection of published case reports. We report a case where a patient presents with severe chest pain and was found to have concomitant severe coronary vasospasm and acute myocarditis and was diagnosed with Graves' disease.

Cardiac Findings Following Cerebrovascular Disease.

BACKGROUND: Accumulating evidence suggests that cardiac findings after stroke are an important, yet understudied, manifestation of brain-heart interactions. Our aim was to investigate and compare cardiac findings after different cerebrovascular events (acute ischemic stroke, transient ischemic attack, and hemorrhagic stroke). METHODS AND RESULTS: There were 7113 patients screened who were treated between December 2013 and December 2020 at the University Hospital Zurich for ischemic stroke, transient ischemic attack, and hemorrhagic stroke. Seven hundred twenty-one patients without evidence of previous cardiac disease or presumed cardioembolic origin of their cerebrovascular disease and with at least 1 cardiac checkup were included. Clinical reports from the year following disease onset were screened for new cardiac findings, which were categorized as arrhythmia/electrocardiographic changes, myocardial alterations, valvular abnormalities, and coronary perfusion insufficiency. Differences in proportions of findings among groups were analyzed using the Pearson χ2 test or Fisher exact test. ECG changes were observed in 81.7% (n=474) of patients with ischemic stroke, 71.4% (n=70) of patients with transient ischemic attack, and 55.8% (n=24) of patients with hemorrhagic stroke (P<0.001). Myocardial alterations occurred often in all 3 groups (60.9% ischemic stroke [n=353], 59.2% transient ischemic attack [n=58], 44.2% hemorrhagic stroke [n=19]; P=0.396). CONCLUSIONS: Cardiac findings are frequent in patients with cerebrovascular disease, even without prior cardiac problems or suspected cardiac cause. Similarities, especially between patients with ischemic stroke and transient ischemic attack, were observed. Our data suggest that all patients with acute cerebrovascular events should receive thorough workup searching for cardiac manifestations.

[Clinical analysis of maternal autoantibody-mediated complete left bundle branch block in 9 children].

Objective: To analyze the clinical characteristics, treatment, and outcomes of children with complete left bundle branch block (CLBBB) mediated by maternal autoantibodies. Methods: A retrospective analysis was conducted on nine children diagnosed with maternal autoantibody-mediated CLBBB, treated at Beijing Anzhen Hospital and Fujian Provincial Hospital from March 2015 to August 2023. Their clinical characteristics, electrocardiographic and echocardiographic findings before and after treatment were reviewed. Paired sample t-test was used for inter-group comparison. Results: Among the mothers, 6 had positive antinuclear antibodies (ANA), 5 had anti-Sjogren syndrome antigen A antibodies, and 3 had anti-Ro-52 antibodies. The cohort included one female and eight male children, diagnosed with CLBBB at the age of 1 (2, 13) months. The positive autoantibodies in the infants, consisted with maternal antibodies, were detected within the first 3 months of life among 3 cases. Treatments included anti-heart failure therapy, myocardial nutritional support, intravenous immunoglobulin (IVIG) and glucocorticoids. Before treatment, the levels of troponin I (0.175 (0.060, 10.270) µg/L) and N-terminal pro-B-type natriuretic peptide (420 (327, 12 865) ng/L) were elevated, which normalized in most cases after treatment. Post-treatment, the QRS duration significantly shortened compared to pre-treatment ((137±15) vs.(169±25) ms, t=3.76, P<0.001), and the QTc interval significantly decreased ((433±41) vs. (514±27) ms, t=4.95, P=0.001). Before treatment, varying degrees of mitral and tricuspid regurgitation and marked interventricular septal dyskinesia were observed in echocardiography. After treatment, valve regurgitation and ventricular septum motion significantly improved, with a marked increase in left ventricular ejection fraction ((51±13)% vs. (27±6)%, t=-6.66, P<0.001). Conclusions: Maternal autoantibody-mediated CLBBB in children presents with chronic heart failure in infancy. Early treatment with anti-heart failure medications, IVIG and glucocorticoids can improve clinical symptoms.

A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning.

In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics.

Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals.

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response.