QRS Complex Research Articles

Preparing the Electrical Signal Data of the Heart by Performing Segmentation Based on the Neural Network U-Net

Research on the automated extraction of essential data from an electrocardiography (ECG) recording has been a significant topic for a long time. The main focus of digital processing processes is to measure fiducial points that determine the beginning and end of the P, QRS, and T waves based on their waveform properties. The presence of unavoidable noise during ECG data collection and inherent physiological differences among individuals make it challenging to accurately identify these reference points, resulting in suboptimal performance. This is done through several primary stages that rely on the idea of preliminary processing of the ECG electrical signal through a set of steps (preparing raw data and converting them into files that are read and then processed by removing empty data and unifying the width of the signal at a length of 250 in order to remove noise accurately, and then performing the process of identifying the QRS in the first place and P-T implicitly, and then the task stage is determining the required peak and making a cut based on it. The U-Net pre-trained model is used for deep learning. It takes an ECG signal with a customisable sampling rate as input and generates a list of the beginning and ending points of P and T waves, as well as QRS complexes, as output. The distinguishing features of our segmentation method are its high speed, minimal parameter requirements, and strong generalization capabilities, which are used to create data that can be used in diagnosing diseases or biometric systems.

ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks

We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal analysis, addressing both waveform delineation and beat type classification tasks. For beat type classification, we integrated two novel schemes into the deep learning model, significantly enhancing its performance. The first scheme is an adaptive beat segmentation method that determines the optimal duration for each heartbeat based on RR-intervals, mitigating segmenting errors from conventional fixed-period segmentation. The second scheme incorporates relative heart rate information of the target beat compared to neighboring beats, improving the model’s ability to accurately detect premature atrial contractions (PACs) that are easily confused with normal beats due to similar morphology. Extensive evaluations on the PhysioNet QT Database, MIT-BIH Arrhythmia Database, and real-world wearable device data demonstrated the proposed approach’s superior capabilities over existing methods in both tasks. The proposed approach achieved sensitivities of 99.81% for normal beats, 99.08% for premature ventricular contractions, and 97.83% for PACs in beat type classification. For waveform delineation, we achieved F1-scores of 0.9842 for non-waveform, 0.9798 for P-waves, 0.9749 for QRS complexes, and 0.9848 for T-waves. It significantly outperforms existing methods in PAC detection while maintaining high performance across both tasks. The integration of aforementioned two schemes into the deep learning model improved the accuracy of normal sinus rhythms and arrhythmia detection.

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.

Characteristics of electrocardiogram in fulminant myocarditis

Objective: To investugate the unique electrocardiogram (ECG) characteristics of fulminant myocarditis (FM) patients and provide important clues for the diagnosis of FM. Methods: This was a retrospective study. Patients diagnosed with acute myocarditis at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from February 2017 to April 2022 were enrolled and divided into fulminant myocarditis group (FM) and non-fulminant myocarditis group (NFM) according to clinical diagnosis. A total of 246 healthy people who underwent physical examination in the Health examination Center of Tongji Hospital at the same period were selected as the control group. The clinical data and ECG characteristics of the above 3 groups were analyzed and compared. Logistic regression model was used to analyze the influence of ECG parameters on left ventricular ejection fraction in FM patients. Receiver operating curves were constructed to evaluate the predictive value of different ECG parameters for FM. Results: A total of 180 patients were included in this study (FM group: n=123; NFM group: n=57), with an age of (35.0±16.2) years and 106 males (58.89%). Compared with NFM group, ECG was significantly abnormal in FM group, with a higher incidence of sinus tachycardia, ventricular tachycardia or ventricular fibrillation, escape rhythm, right bundle branch block, third degree atrioventricular block, ST-segment elevation, low voltage, prolonged QTc interval, and widened QRS wave in the FM group (all P<0.05). The ECG parameters showed that the amplitude of the full lead QRS wave in FM group was lower than that in NFM group (P<0.01). The average heart rate and QTc interval of FM group were significantly higher than those of NFM and control groups (all P<0.05). Although ST-segment elevation had a higher incidence in the FM group, ECG parameters showed that except for leads Ⅲ and aVF, the ST segment levels in all leads in the FM group were lower than those in the control group (all P<0.05). There was a statistically significant difference in some ST segment changes between FM and NFM groups, while there was no statistical difference between the NFM and control groups. Multivariate regression analysis showed that widened QRS wave and increased heart rate were the influencing factors for left ventricular systolic dysfunction in FM patients (OR=16.914, 95%CI: 1.367-209.224, P=0.028; OR=1.026, 95%CI: 1.010-1.042, P=0.001). Receiver operating curve analysis showed that heart rate>86.90 beat/min, QTc>431.50 ms, and RV5+SV1<1.72 mV had certain predictive value for FM diagnosis. Conclusions: FM patients displayed marked and severe ECG abnormalities, and characteristic changes in ECG can provide important first clues for the diagnosis of FM.

Evolving trends of pharmaceutical poisonings associated with QRS complex prolongation

Introduction Tricyclic antidepressants often cause drug-induced QRS complex prolongation in overdose but are now less commonly prescribed. We sought to determine, among a contemporary cohort of patients, the pharmaceuticals independently associated with QRS complex prolongation in acute overdose. Methods We performed secondary analysis of data from the Toxicology Investigators Consortium (ToxIC) Core Registry. We included adult patients presenting from January 2016 through March 2023 with acute or acute-on-chronic pharmaceutical exposures. The primary outcome was QRS complex prolongation >0.12 s. Secondary outcomes included cardiac arrest, death, ventricular dysrhythmia, intensive care unit admission, initiation of vasopressors, and treatment with sodium bicarbonate. We used a multivariable logistic regression model with QRS complex prolongation as the outcome and individual pharmaceuticals of interest as independent variables. We assessed yearly trends of the contribution of relevant pharmaceuticals to QRS complex prolongation since 2016. Results Of 11,945 patients in the total cohort (median age 37 years, 6,652 [55.7%] female), 366 (3.1%) developed QRS complex prolongation. Of 9,417 patients included in the model, 290 (3.1%) developed QRS complex prolongation. Amitriptyline, nortriptyline, doxepin, imipramine, noxiptiline, bupropion, flecainide, carvedilol, propranolol, diphenhydramine, and lamotrigine poisonings were independent predictors of QRS complex prolongation. Flecainide poisoning conferred the greatest odds of QRS complex prolongation (OR 574.1; 95% CI: 88.3–12,747). The contribution of tricyclic antidepressants to QRS complex prolongation decreased from 38.8% to 17.6% of all patients with QRS complex prolongation from 2016 to 2022. In 2022, the proportion of QRS complex prolongation from diphenhydramine (20.6%) surpassed that of tricyclic antidepressants. Discussion This study provides insights into contemporary pharmaceutical poisoning associated with QRS complex prolongation. Tricyclic antidepressants remain clinically relevant exposures but are no longer the most common cause of drug-induced QRS complex prolongation. Conclusions Bupropion, diphenhydramine, and antidysrhythmics are increasingly common causes of QRS complex prolongation, each associated with numerous severe outcomes in poisoning. Greater safety measures to protect patients from cardiovascular toxicity from these pharmaceuticals are warranted.

ECG changes following balloon pulmonary angioplasty in patients with chronic thromboembolic pulmonary hypertension: a retrospective study

PurposeThis research evaluates the effect of balloon pulmonary angioplasty (BPA) on cardiac electrophysiological changes in patients with chronic thromboembolic pulmonary hypertension (CTEPH).MethodsInvolving a retrospective analysis of 39 CTEPH patients (average age 61 ± 11), who had at least two BPAs and paired ECGs pre- and post-surgery, we examined changes in ECG indicators of right ventricular hypertrophy and their correlation with hemodynamic results.Results BPA yielded marked improvements in cardiac function and hemodynamics. ECG parameters, specifically the Lewis criteria and Butler-Leggett score, correlated strongly with hemodynamics and were predictive of a mean pulmonary arterial pressure (mPAP) ≥ 35mmHg. Notably, QRS complex axis normalization was observed in 25 patients, with 14 fully normalizing (range − 30° to + 90°). The qR pattern in V1 vanished in 9 cases, and 75% of the patients in qR pattern in V1 group had QRS complex electrical axis completely returned to normal range. The qR V1 group had higher mPAP and pulmonary vascular resistance (PVR), and lower cardiac output and index compared to the non-qR V1 group, alongside a higher Butler-Leggett score.ConclusionsBPA enhances cardiac function and hemodynamics in CTEPH patients, with certain ECG measures such as Lewis criteria and Butler-Leggett score reflecting the severity of hemodynamic impairment. The reversal of QRS axis deviation and the disappearance of the qR pattern in lead V1 may serve as valuable indicators for assessing post-BPA satisfaction in CTEPH patients.

Anatomical Ablation of the Atrioventricular Node.

Atrioventricular (AV) conduction ablation has been achieved by targeting the area of penetration of the conduction axis as defined by recording a His bundle potential. Ablation of the His bundle may reduce the possibility of a robust junctional escape rhythm. It was hypothesised that specific AV nodal ablation is feasible and safe. The anatomical position of the AV node in relation to the site of penetration of the conduction axis was identified as described in dissections and histological sections of human hearts. Radiofrequency (RF) ablation was accomplished based on the anatomical criteria. Specific anatomical ablation of the AV node was attempted in 72 patients. Successful AV nodal ablation was accomplished in 63 patients (87.5%), following 60 minutes (IQR 50-70 minutes) of procedure time, 3.4 minutes (IQR 2.4-5.5 minutes) of fluoroscopy time, and delivery of 4 (IQR 3-6) RF lesions. An escape rhythm was present in 45 patients (71%), and the QRS complex was similar to that before ablation in all 45 patients. Atropine was administered in six patients after the 10-min waiting period and did not result in restoration of conduction. In nine patients, AV conduction could not be interrupted, and AV block was achieved with ablation of the His after delivery of 12 (IQR 8-15) RF lesions. No cases of sudden death were encountered, and all patients had persistent AV block during a median 10.5 months (IQR 5-14 months) of follow-up. Anatomical ablation of the AV node is feasible and safe, and results in an escape rhythm similar to that before ablation.

Morphographic Changes in the Electrocardiogram of Colossoma macropomum Caused by Exposure to Manganese.

Manganese (Mn2+) is an abundant chemical element in the earth's crust and is present in soil, water, and industrial environments, including mining, welding, and battery manufacturing. Manganese (Mn) is an essential metal needed as a cofactor for many enzymes to maintain proper biological functions. Excessive exposure to Mn in high doses can result in a condition known as manganism, which results in disorders of the neurological, cardiac, and pulmonary systems. The aim of this study was to assess cardiac susceptibility to manganese intoxication in Colossoma macropomum subjected to a fixed concentration of 4 mg/mL for a period of up to 96 h. This study used 45 Tambaquis (30.38 ± 3.5 g) divided into five groups of 9 animals/treatment. The treated groups were exposed to the manganese concentration for a period of 24, 48, 72, and 96 h, after which the animals' ECGs were recorded, showing heart rate, R-R interval, P-Q interval, QRS complex duration and S-T interval. The results showed that cardiac activity decreased as the contact time increased, with an increase in the P-Q and S-T intervals. This indicates that the breakdown of circulatory homeostasis in these animals was caused by contact time with manganese.

Clinical and electrocardiographic characteristics of immune checkpoint inhibitor-related myocarditis

BackgroundImmune checkpoint inhibitor (ICI) has become a major breakthrough in the field of tumor therapy, leading to improved survival. This study evaluated the clinical and electrocardiographic characteristics of patients with ICI-related myocarditis. MethodsPatients with ICI-related myocarditis were enrolled from 4 centers in China until September 2023. Demographic data (age, sex, comorbidity), types of ICI, clinical manifestations, electrocardiogram (ECG) and treatment were analyzed retrospectively. Arrhythmia and characteristics of ECG were compared according to prognosis and grading. ResultsA total of 29 participants (13 females with a median age of 63.25 years) with ICI-related myocarditis were enrolled. Lung cancer was the most, with a proportion of 31.03 % (9/29). The median time from the first administration of ICI to the diagnosis of myocarditis was 50 days. Camrelizumab was the main type of ICI (9/29). Most patients had non-specific symptoms, dyspnea (n = 16) and palpitation (n = 9) were common. The overall mortality rate was 37.93 % (11/29) with a median follow-up of 9(4,11) days. Compared with the survivors, P-wave abnormality was more common in participants who were dead (24.14 %vs6.90 %, p = 0.010). A total of 19 patients with severe ICI-related myocarditis were included in this study. The proportions of sinus tachycardia (34.48 %vs0.00 %, p = 0.005), premature ventricular complex (27.59 %vs0.00 %, p = 0.027) and atrioventricular block (34.48 %vs3.45 %, p = 0.044) were higher in severe ICI-related myocarditis. ConclusionsClinical manifestations of ICI-related myocarditis usually lacked specificity. ECGs can be manifested as new-onset arrhythmias, ST-T segment changes, fragmented QRS complex, abnormal P wave, prolonged QTc interval and multi‑lead low voltage.

Prevalence of cardiomyopathy following pacemaker insertion in an iranian population; a cross-sectional study

Objectives: This study aimed to determine the prevalence of pacemaker-induced cardiomyopathy in patients with pacemaker implantation and to identify the associated risk factors. Methods: This cross-sectional study examined all patients who were implanted with a pacemaker at Afshar Hospital Yazd in 2019 and wore it for two to three years. A new EF test was performed for all patients and compared with the old EF. If a reduction in EF of more than 10% was observed, or the EF was less than 50% due to another reason, it was considered HF. All data were analyzed using SPSS, version 22. Results: One hundred eleven patients participated in this study, 52 (46.8%) men and 59 (53.2%) women. The results showed that ejection fraction (EF) was more than 50% in 93 patients (83.8%). The mean EF decreased from 62.1 to 36.2% over three years. Statistical analysis showed that there is a significant association between diabetes and the incidence of cardiomyopathies in these patients (p = 0.016), but there was no association with hypertension. The mean length of the QRS wave in the electrocardiogram was significantly longer in the group with cardiac pacemakers than in the other patients (P = 0.49). Conclusions: We concluded that 22.5% of referring patients with implantation of a right ventricular pacemaker will develop cardiomyopathy within 2-3 years.

Cardiac alterations induced by Heloderma horridum horridum venom in rats: An experimental study with ECG analysis using a linear regression algorithm

Envenomation by reptile venom, particularly from lizards, poses significant health risks and can lead to physiological and cardiovascular changes. The venom of Heloderma horridum horridum, endemic to Colima, Mexico, was tested on Wistar rats. Electrocardiographic (ECG) data were collected pre-treatment and at 5-min intervals for 1 h post-envenomation. A specially designed computational linear regression algorithm (LRA) was used for the segmentation analysis of the ECG data to improve the detection of fiducial points (P, Q, R, S, and T) in ECG waves. Additionally, heart tissue was analyzed for macroscopic and microscopic changes. The results revealed significant electrocardiographic alterations, including pacemaker migration, junctional extrasystoles, and intraventricular conduction aberrations. By applying a linear regression algorithm, the study compensated for noise and anomalies in the isoelectric line in an ECG signal, improving the detection of P and T waves and the QRS complex with an efficiency of 97.5%. Cardiac enzyme evaluation indicated no statistically significant differences between the control and experimental groups. Macroscopic and microscopic examination revealed no apparent signs of damage or inflammatory responses in heart tissues. This study enhances our understanding of the cardiovascular impact of Heloderma venom, suggesting a greater influence on changes in conduction and arrhythmias than on direct cardiac damage to the myocardium.

Can the efficacy of a medical treatment be predicted based on the type of idiopathic premature ventricular contraction?

BackgroundPremature ventricular contractions (PVCs) are common arrhythmias with diverse clinical implications. This retrospective study aimed to evaluate the efficacy of medical treatments using various clinical, imaging, and electrocardiographic parameters in patients with idiopathic PVCs. MethodsA total of 1051 patients with idiopathic PVCs were retrospectively analyzed. Patients were categorized into three groups based on treatment response: beta-blocker (BB) responders (479 patients), calcium-channel blocker (CCB) responders (335 patients), and class 1c antiarrhythmic (AA) responders (237 patients). Clinical, imaging, and electrocardiographic data were collected and analyzed to assess the factors influencing treatment response. ResultsAge, left ventricular ejection fraction (LVEF), PVC QRS duration, CI variability, and multiple PVC morphologies were identified as significant factors affecting treatment response. Older age and lower LVEF were associated with better response to BB treatment, whereas CCB responders showed narrower QRS complexes. BB responders also exhibited higher CI variability, possibly linked to automaticity mechanisms. Moreover, the BB responder group had a higher frequency of multiple PVC morphologies. ConclusionThese findings emphasize the importance of tailored treatment approaches based on individual patient characteristics.

Diagnosis of Narrow QRS Tachycardia

Narrow QRS tachycardia is a common clinical condition characterized by a heart rate exceeding 100 beats per minute and a QRS complex duration of less than 120 ms. This article provides an overview of the diagnostic approach to narrow QRS tachycardia, focusing on the differentiation between various supraventricular tachycardias, such as atrioventricular nodal reentrant tachycardia (AVNRT), atrioventricular reentrant tachycardia (AVRT), atrial tachycardia (AT), and sinus tachycardia. The discussion includes an analysis of the presenting symptoms, electrocardiographic (ECG) findings, and the use of vagal maneuvers and pharmacological agents in diagnosis.

QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.

A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs. The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervision. The telehealth ECGs, consisting of single-lead ECGs recorded between the hands, included a novel dataset of 479 ECGs collected in the SAFER study of screening for atrial fibrillation (AF). Performance was assessed against manual annotations. A total of 12 QRS detectors performed well on ECGs collected under clinical supervision (F1 score ≥0.96). However, fewer performed well on telehealth ECGs: five performed well on the TELE ECG Database; six performed well on high-quality SAFER data; and performance was poorer on low-quality SAFER data (three QRS detectors achieved F1 of 0.78-0.84). The presence of AF had little impact on performance. The Neurokit and University of New South Wales QRS detectors performed best in this study. These performed sufficiently well on high-quality telehealth ECGs, but not on low-quality ECGs. This demonstrates the need to handle low-quality ECGs appropriately to ensure only ECGs which can be accurately analysed are used for clinical decision making.

Improved bidirectional long short term memory-based QRS complex detection using autoencoder

In this paper, we propose a new technique to improve QRS complex detection. This technique consists of incorporating an autoencoder and bidirectional long short term memory (BiLSTM). The autoencoder used is a stacked autoencoder and functions as signal filtering. Meanwhile, BiLSTM is used as a detector. Exploration of the effect of hyperparameter in the autoencoder was also carried out to determine the effect on QRS complex detection. Furthermore, the dataset used in this study is the MIT-BIH arrhythmia database. Based on the experimental results, the hyperparameter in the autoencoder that gives a better effect on QRS complex detection is 16-8. Finally, the proposed method out-of-perform state of the art algorithm with accuracy 99.94%.

Cardiac amyloidosis prevalence and 1-year outcome in patients with aortic stenosis undergoing transaortic valve implantation: Findings from the CAMPOS-TAVI study

BackgroundTransthyretin amyloid cardiomyopathy (ATTR-CM) can manifest as rhythm disorders, heart failure, but also valvular degeneration. Despite aortic stenosis (AS) being prevalent among the elderly, data on ATTR-CM prevalence and outcome in patients with AS undergoing transaortic valve implantation (TAVI) remain scarce. AimTo determine ATTR-CM prevalence and evaluate 1-year survival in patients undergoing TAVI. MethodsBetween December 2020 and September 2021, 100 consecutive patients underwent TAVI and were screened prospectively for ATTR-CM using bone scintigraphy (BS). Monoclonal gammopathy was ruled out in case of cardiac uptake on BS. All patients were followed prospectively for 1year after TAVI. ResultsThe proportion of patients aged≥75years or with a EuroSCORE II>8% and possible femoral access was 99%. The abnormal cardiac uptake rate on BS was 7% (95% confidence interval: 2–12%); 86% of these patients were male. The RAISE (remodelling, age, injury, system and electrical) score, indicative of ATTR-CM risk, was higher in case of positive BS (P=0.04). Patients with positive BS were older and exhibited wider QRS complexes on electrocardiography (P=0.003), a higher frequency of reduced LVEF (57% vs. 17%), impaired basal LV strain (P=0.02) and a lower voltage/mass ratio (P=0.01). History of pacemaker implantation before TAVI was higher in the positive BS group (P=0.0004) and remained the only statistically significant factor after adjustment using the Holm–Bonferroni method. One-year survival of patients with positive BS did not differ from that of patients with isolated AS. ConclusionsPrevalence of ATTR-CM in patients treated with TAVI, underscoring the need for continued surveillance for potential development of ATTR-CM after TAVI. Caution is warranted regarding the 1-year survival because of the lack of study power. Further investigations are needed to define long-term prognosis of AS with ATTR-CM.

Factors that Determined a Positive Response to Resynchronization Therapy in Patients With Chronic Heart Failure and Cardiac Dyssynchrony. One Center Experience.

To evaluate the efficacy of cardiac resynchronization therapy (CRT) in patients with chronic heart failure (CHF) associated with cardiac dyssynchrony and to identify the factors that influence the CRT efficacy. This retrospective study included 155 patients after implantation of CRT devices. The CRT devices with a built-in cardioverter-defibrillator (CRT-D) and without it (CRT-P) were implanted in 139 (89.7%) and 16 (10.3%) patients, respectively. The follow-up period was 52.37±35.94 months. Based on the study results, two groups of patients were formed depending on the presence of a clinical response to CRT, responders and non-responders. The factors that influenced the clinical response to CRT were studied. The effect of the baseline state of patients on the effect of therapy was assessed. The need for CRT optimization and a possibility of using electrocardiographic criteria for that purpose were studied. Modern devices and leads for CRT, their functional capabilities and their influence on the CRT efficacy were characterized. Statistical analysis was performed with an IBM SPSS Statistics 21.0 (Chicago, USA) package. CRT implantation with the left ventricular lead placement according to the traditional technique, through the coronary sinus, was successful in 130 (87.9%) patients. Difficulties with the left ventricular lead placement were noted in 13 (8.3%) patients when other techniques were used. After 6 months, a hemodynamic and clinical response was observed in 112 (72.2%) patients, and no positive response in 43 (27.8%). The increase in left ventricular ejection fraction in the responder group was more than 21.8±3.7%, which was associated with an improvement of the 6-minute walk test results. Th clinical response was significantly influenced by the possibility of stimulation from the basal parts of the heart; the use of more modern devices for CRT and quadripolar left ventricular leads; timely CRT optimization; and persistent dyssynchrony in non-responders. During the follow-up period, 34 (21.9%) patients died. The death rate in the non-responder group was significantly higher than in the responder group, 18 (41.3%) vs. 16 (14.3%), p=0.001. The main cause of death in the group of non-responders was CHF. Heart transplantation was performed in 3 (1.9%) patients. CRT increases the life span and improves the quality of life in patients with CHF and cardiac dyssynchrony. There was a group of patients with no benefit from CRT in this study. Modern devices allow increasing the number of patients who benefit from CRT. Periodic optimization of CRT is necessary. When optimizing CRT, it is possible to use electrocardiographic criteria of effectiveness: duration of the QRS complex and changes in the position of the electrical axis of the heart.

Analysis of abnormalities in cardiac arrhythmia based on 12 - LEAD electrocardiography

Myocardial Infarction otherwise called heart attack occurs in human beings when blood flow decreases or stops to a part of the heart which in turn damages the heart muscle. Prediction of abnormalities in cardio arrhythmia disease is done by using standard 12-lead Electrocardiography (ECG) signals, which also detects Posterior Myocardial Infarction (PMI). The QRS complex is the merged output of different parts of graphical deflection seen on a typical Electro Cardio Gram (Electrocardiography). The main purpose of the paper is to monitor and analyze particularly the Rpeak upward deflections from the QRS complex. Denoising the ECG signal is done by butter worth filter. The denoised signals are used to detect R peaks and image plotting is done by segmentation. R peak images are used to classify the abnormalities in Myocardial Infarction (MI) with the help of the CNN image processing technique. The publicly available PTB diagnostic dataset is used to classify the abnormalities in PMI. The detection of the R peaks is used to guide Cardiologists must advance the Percutaneous Coronary Intervention treatment. Prediction has been done using probability weighted average method. Troponin level has been calculated to evaluate a person's health condition which also supports in close prediction of diseases and abnormalities. From experimental analysis of proposed Probability weighted average method in troponin level (PWAMT), the accuracy scores in the ensemble model were found to be 86 % respectively. The running of algorithm took 250 s–300 s to execute the program and display the prediction results.

A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time monitoring

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG.

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events. In this study, a DLM was trained on a dataset of 158,471 ECGs from 42,903 academic medical center patients, with additional validation involving 25,640 medical center patients and 26,538 community hospital patients. Primary analysis focused on predicting PMI within 90 days, while all-cause mortality, cardiovascular disease (CVD) mortality, and the development of various cardiovascular conditions were addressed with secondary analysis. The study's raw ECG DLM achieved area under the curve (AUC) values of 0.870, 0.878, and 0.883 for PMI prediction within 30, 60, and 90 days, respectively, along with sensitivities exceeding 82.0% and specificities over 81.9% in the internal validation. Significant ECG features included the PR interval, corrected QT interval, heart rate, QRS duration, P-wave axis, T-wave axis, and QRS complex axis. The AI-predicted PMI group had higher risks of PMI after 90 days (hazard ratio [HR]: 7.49, 95% CI: 5.40-10.39), all-cause mortality (HR: 1.91, 95% CI: 1.74-2.10), CVD mortality (HR: 3.53, 95% CI: 2.73-4.57), and new-onset adverse cardiovascular events. External validation confirmed the model's accuracy. Through ECG analyses, our AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.