Electrocardiogram Information Research Articles

Enhancement of single-lead dry-electrode ECG through wavelet denoising

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dry-electrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG’s QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10−6 and 1.65 × 10−7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG’s QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals.

CAT-Net: Convolution, attention, and transformer based network for single-lead ECG arrhythmia classification

Machine learning technologies have been applied extensively in the last decade to automatically detect and analyze various forms of arrhythmia from electrocardiogram (ECG) signals. Existing deep learning-based models focus on enhancing classification performance by exploring spatial–temporal ECG features or by implementing multi-modal and ensemble classifiers. Such approaches perform well but do not provide comprehensive accessibility for real-life applications due to the multi-lead ECG requirement. To address the issue, a single-lead ECG based network is considered. The proposed convolution, attention, and transformer-based network (CAT-Net) exhibits promising performance on arrhythmia classification by adeptly capturing local and global heartbeats’ morphological characteristics. Along with the local information captured by the convolution layer, the contextual ECG information is extracted by the multi-head attention layer present in the transformer encoder. In addition, most of the existing models suffer from lower predictive performance in minority-class arrhythmias due to the highly imbalanced ECG data. To enhance the predictive performance in minority classes, three balancing techniques – SMOTE-ENN, SMOTE-Tomek, and ADASYN – are systematically evaluated, and SMOTE-Tomek is ultimately integrated. To mitigate potential dataset bias, CAT-Net was assessed across two distinct datasets: MIT-BIH and INCART, respectively, and was shown to achieve state-of-the-art performance. CAT-Net establishes new benchmarks, achieving 99.14% overall accuracy and 94.69% macro F1 score on 5-class arrhythmia classification in the MIT-BIH dataset and 99.58% accuracy with 96.15% macro F1 score for 3-class classification in the INCART dataset.

Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three‑lead signals

BackgroundIn the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes due to considerations, such as portability and battery life. This situation leads to a contradiction between the demand for standard 12‑lead ECG information and the limited number of leads collected by portable devices. MethodsThis study introduces a composite ECG vector reconstruction network architecture based on convolutional neural network (CNN) combined with recurrent neural network by using leads I, II, and V2. This network is designed to reconstruct three‑lead ECG signals into 12‑lead ECG signals. A 1D CNN abstracts and extracts features from the spatial domain of the ECG signals, and a bidirectional long short-term memory network analyzes the temporal trends in the signals. Then, the ECG signals are inputted into the model in a multilead, single-channel manner. ResultsUnder inter-patient conditions, the mean reconstructed Root mean squared error (RMSE) for precordial leads V1, V3, V4, V5, and V6 were 28.7, 17.3, 24.2, 36.5, and 25.5 μV, respectively. The mean overall RMSE and reconstructed Correlation coefficient (CC) were 26.44 μV and 0.9562, respectively. ConclusionThis paper presents a solution and innovative approach for recovering 12‑lead ECG information when only three‑lead information is available. After supplementing with comprehensive leads, we can analyze the cardiac health status more comprehensively across 12 dimensions.

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning.

The value of Electrocardiogram (ECG) monitoring in early cardiovascular disease (CVD) detection is undeniable, especially with the aid of intelligent wearable devices. Despite this, the requirement for expert interpretation significantly limits public accessibility, underscoring the need for advanced diagnosis algorithms. Deep learning-based methods represent a leap beyond traditional rule-based algorithms, but they are not without challenges such as small databases, inefficient use of local and global ECG information, high memory requirements for deploying multiple models, and the absence of task-to-task knowledge transfer. In response to these challenges, we propose a multi-resolution model adept at integrating local morphological characteristics and global rhythm patterns seamlessly. We also introduce an innovative ECG continual learning (ECG-CL) approach based on parameter isolation, designed to enhance data usage effectiveness and facilitate inter-task knowledge transfer. Our experiments, conducted on four publicly available databases, provide evidence of our proposed continual learning method's ability to perform incremental learning across domains, classes, and tasks. The outcome showcases our method's capability in extracting pertinent morphological and rhythmic features from ECG segmentation, resulting in a substantial enhancement of classification accuracy. This research not only confirms the potential for developing comprehensive ECG interpretation algorithms based on single-lead ECGs but also fosters progress in intelligent wearable applications. By leveraging advanced diagnosis algorithms, we aspire to increase the accessibility of ECG monitoring, thereby contributing to early CVD detection and ultimately improving healthcare outcomes.

On the Impact of Synchronous Electrocardiogram Signals for Heart Sounds Segmentation.

In this paper we study the heart sound segmentation problem using Deep Neural Networks. The impact of available electrocardiogram (ECG) signals in addition to phonocardiogram (PCG) signals is evaluated. To incorporate ECG, two different models considered, which are built upon a 1D U-net - an early fusion one that fuses ECG in an early processing stage, and a late fusion one that averages the probabilities obtained by two networks applied independently on PCG and ECG data. Results show that, in contrast with traditional uses of ECG for PCG gating, early fusion of PCG and ECG information can provide more robust heart sound segmentation. As a proof of concept, we use the publicly available PhysioNet dataset. Validation results provide, on average, a sensitivity of 97.2%, 94.5%, and 95.6% and a Positive Predictive Value of 97.5%, 96.2%, and 96.1% for Early-fusion, Late-fusion, and unimodal (PCG only) models, respectively, showing the advantages of combining both signals at early stages to segment heart sounds.Clinical relevance- Cardiac auscultation is the first line of screening for cardiovascular diseases. Its low cost and simplicity are especially suitable for screening large populations in underprivileged countries. The proposed analysis and algorithm show the potential of effectively including electrocardiogram information to improve heart sound segmentation performance, thus enhancing the capacity of extracting useful information from heart sound recordings.

Residual neural networks based on empirical mode decomposition for mitral regurgitation prediction

A multi-modal residual neural network based on empirical mode decomposition (EMD) was proposed in this work and used for screening patients with mitral regurgitation (MR). EMD was used to decompose the synchronous electrocardiogram (ECG) and phonocardiogram (PCG) signals, and then the component with the highest degree of correlation with the original signals was selected for reconstruction, and then the reconstructed signals were converted into images by gramian angular difference field (GADF). Finally, the residual neural network was used to get the prediction results. In the present work, we established a database called Synchronized ECG and PCG Database for Patients with Mitral Regurgitation (SEP-MRDb) consisting of 1046 synchronous ECG and PCG recordings from patients with MR (n = 56) and without MR (n = 990). The experimental results showed that the accuracy rate of the proposed model was 96.90%, the precision was 97.10%, and recall was 97.10%, which can effectively capture the combined information of ECG and PCG signals and realize the recognition of MR.

Deep Convolutional Generative Adversarial Network with LSTM for ECG Denoising.

The electrocardiogram (ECG), as an essential basis for the diagnosis of cardiovascular diseases, is usually disturbed by various noise. To obtain accurate human physiological information from ECG, the denoising and reconstruction of ECG are critical. In this paper, we proposed an ECG denoising method referred to as LSTM-DCGAN which is based on an improved generative adversarial network (GAN). The overall network structure is composed of multiple layers of convolutional networks. Furthermore, the convolutional features can be connected to their time series order dependence by adding LSTM layers after each convolutional layer. To verify the effectiveness and the denoising performance of the improved network structure, we test the proposed algorithm on the famous MIT-BIH Arrhythmia Database with different levels of noise from the MIT-BIH Noise Stress Test Database. Experimental results show that our method can remove the single noise and the mixed noise while retaining the complete ECG information. For the mixed noise removal, the average SNRimp, RMSE, and PRD are 19.254 dB, 0.028, and 10.350, respectively. Compared with the state-of-the-art methods, DCGAN, and the LSTM-GAN methods, our method obtains the higher SNRimp and the lower RMSE and PRD scores. These results suggest that the proposed LSTM-DCGAN approach has a significant advantage for ECG processing and application in complex scenes.

Diagnosis of Dextrocardia with a Pictorial Rendition of Terminology and Diagnosis.

A significant number of patients with dextrocardia and other cardiac malpositions have other congenital heart defects (CHDs). The incidence of CHDs in subjects with cardiac malpositions is significantly greater than that in normal children, and the prevalence varies with the associated visceroatrial situs. The most useful approach to diagnosis is segmental analysis. Firstly, dextroposition should be excluded. In segmental analysis, the visceroatrial situs, ventricular location, status of atrioventricular connections, the great artery relationship, and conotruncal relationship are determined with the use of electrocardiogram (ECG), chest X-ray, and echocardiographic studies, and, when necessary, other imaging studies, including angiography. Following identification of the afore-mentioned segments, the associated defects in the atrial and ventricular septae, valvar and vascular stenosis or atresia may be determined by a review of the historical information, physical examination, and analysis of chest roentgenogram, ECG, and echocardiographic studies. Along the way, a pictorial rendition of the terminology and diagnosis of cardiac malpositions is undertaken.

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network.

The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret the ECG signals due to the large amount of data and complexity. Neural networks have been shown to be efficient recently in interpreting the biomedical signals including ECG and EEG. The novelty of the proposed work is using spectrograms instead of raw signals. Spectrograms could be easily reduced by eliminating frequencies with no ECG information. Moreover, spectrogram calculation is time-efficient through short-time Fourier transformation (STFT) which allowed to present reduced data with well-distinguishable form to convolutional neural network (CNN). The data reduction was performed through frequency filtration by taking a specific cutoff value. These steps makes architecture of the CNN model simple which showed high accuracy. The proposed approach reduced memory usage and computational power through not using complex CNN models. A large publicly available PTB-XL dataset was utilized, and two datasets were prepared, i.e., spectrograms and raw signals for binary classification. The highest accuracy of 99.06% was achieved by the proposed approach, which reflects spectrograms are better than the raw signals for ECG classification. Further, up- and down-sampling of the signals were also performed at various sampling rates and accuracies were attained.

Low-Power Multi-Lead Wearable ECG System With Sensor Data Compression

This study proposes a low-power wearable electrocardiogram (ECG) acquisition system for monitoring human health in daily life. A flexible sensing electrode was designed to replace traditional silver–silver chloride (Ag/AgCl) electrodes for measuring ECG signals from leads I, II, and V1. The traditional multi-lead-wired ECG detector limits the movements and activities of the patient and requires medical staff to assist in collecting ECG signals. The portable ECG acquisition device proposed in this article is not only comfortable to wear but also convenient for patients to use anytime, anywhere. To increase the sampling rate of the device to obtain more ECG signal information, this study proposes an innovative Huffman lossless ECG data compression algorithm based on variable-word length coding (VLC). This algorithm can efficiently realize data compression and reduce power consumption. It makes the whole system operate continuously for a long period of time and acquire abundant ECG information, which is helpful for clinical diagnosis. Experimental results show that when the sampling rate is 400 Hz, the working current is only 3.64 mA, and the compression ratio (CR) of the device can reach 2.38.

Medical Signal Processing via Digital Filter and Transmission Reception Using Cognitive Radio Technology

This research paper provides a viable solution for processing noise affected Electrocardiogram (ECG) signal via digital filter and transmission of ECG signal and reception via cognitive radio (CR) technology. Health assessment signals such as ECG signal, Electroencephalogram (EEG) signal, Electromyogram (EMG) signal are vital for diagnosis and rehabilitation of human welfare among which ECG attains prime importance due to its information on heart functioning. However, electrocardiogram signals are prone to addition of noise such as power line noise 50 Hz mainly due to improper shielding which can lead to wrong interpretation, incorrect diagnosis and at times will eventually lead to loss of human life. On combining signal processing into medical applications misconceptions can be eliminated and diagnosis can be done effectively through a designed digital filter. Effect of noise can be cancelled in an ECG signal and by using cognitive radio technology ECG information can be transmitted to a medical physician mobile terminal for remedial measures relating to medical treatment. Simulation results are shown in matrix laboratory (MATLAB) for cancelling noise in an ECG signal having noise using a digital filter which is designed represented by its transfer function. Also, ECG signal is transmitted and received in a CR system where the metric of probability of error is obtained which can be useful for signal processing fraternity.

ECG Biosignal Deidentification Using Conditional Generative Adversarial Networks.

Electrocardiogram (ECG) signals provide rich information on individuals' potential cardiovascular conditions and disease, ranging from coronary artery disease to the risk of a heart attack. While health providers store and share these information for medical and research purposes, such data is highly vulnerable to privacy concerns, similar to many other types of healthcare data. Recent works have shown the feasibility of identifying and authenticating individuals by using ECG as a biometric due to the highly individualized nature of ECG signals. However, to the best of our knowledge, there does not exist a method in the literature attempting to de-identify ECG signals. In this paper, to address this privacy protection gap, we propose a Generative Adversarial Network (GAN)-based framework for de-identification of ECG signals. We leverage a combination of a standard GAN loss, an Ordinary Differential Equations (ODE)-based, and identity-based loss values to train a generator that de-identifies a ECG signal while preserving structure the ECG signal and information regarding the target cardio vascular condition. We evaluate our framework in terms of both qualitative and quantitative metrics considering different weightings over the above-mentioned losses. Our experiments demonstrate the efficiency of our framework in terms of privacy protection and ECG signal structural preservation.

Development of a New Integrated System for Vital Sign Monitoring in Small Animals.

Monitoring the vital signs of mice is an essential practice during imaging procedures to avoid populational losses and improve image quality. For this purpose, a system based on a set of devices (piezoelectric sensor, optical module and thermistor) able to detect the heart rate, respiratory rate, body temperature and arterial blood oxygen saturation () in mice anesthetized with sevoflurane was implemented. Results were validated by comparison with the reported literature on similar anesthetics. A new non-invasive electrocardiogram (ECG) module was developed, and its first results reflect the viability of its integration in the system. The sensors were strategically positioned on mice, and the signals were acquired through a custom-made printed circuit board during imaging procedures with a micro-PET (Positron Emission Tomography). For sevoflurane concentration of 1.5%, the average values obtained were: 388 bpm (beats/minute), 124 rpm (respirations/minute) and 88.9% for the heart rate, respiratory rate and , respectively. From the ECG information, the value obtained for the heart rate was around 352 bpm for injectable anesthesia. The results compare favorably to the ones established in the literature, proving the reliability of the proposed system. The ECG measurements show its potential for mice heart monitoring during imaging acquisitions and thus for integration into the developed system.

A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Different from the conventional ECG classification techniques, this computation model is a end-to-end dynamic system that mimics the real-time signal flow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory efficiency by simplifying the computing procedure and minimizing the required memory for future wearable devices.

Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network.

Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples, K-means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.

Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification

Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification models are constructed based on one-dimensional convolutional neural network (1D-CNN) combined with a priority model integrated voting method to optimize the integrated classification effect. The experiments followed the recommended inter-patient scheme of the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and the practicability and effectiveness of THML ECG data are proved with ablation experiments. Results show that the average accuracy of the N, V, S, F, and Q classes is 94.82%, 98.10%, 97.28%, 98.70%, and 99.97%, respectively, with the positive predictive value of the N, V, S, and F classes being 97.0%, 90.5%, 71.9%, and 80.4%, respectively. Compared with current studies, the THML ECG data can effectively improve the morphological integrity and time continuity of ECG information and the 1D-CNN model of ECG sequence has a higher accuracy for arrhythmia classification. The proposed method alleviates the problem of insufficient samples, meets the needs of medical ECG interpretation and contributes to the intelligent dynamic research of cardiac disease.

Detection of T-wave Alternans in ECG Signals Using FRFT and Tensor Decomposition

T-wave alternans (TWA) refers to the periodic beat-to-beat variation in the amplitude of T-wave in the electrocardiogram (ECG) signal in an ABAB-pattern. TWA has been proven to be a very important indicator of malignant arrhythmia risk stratification. A new method to detect TWA by combining fractional Fourier transform (FRFT) and tensor decomposition is proposed. First, the T-wave vector is extracted from the ECG of each heartbeat, and its FRFT amplitudes at multiple orders are arranged to form a T-wave matrix. Then, a third-order tensor is composed of T-wave matrices of several consecutive heart beats. After tensor decomposition, projection matrices are obtained in three dimensions. The complexity of the projection matrix is measured by Shannon entropy to obtain feature vector to detect the presence of TWA. Results show that the sensitivity, specificity, and accuracy of the algorithm on the MIT-BIH database are 91.16%, 94.25%, and 92.68%, respectively. This method effectively utilizes the fractional domain information of ECG, and shows the promising potential of the FRFT in ECG signal processing.

Hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) diagnosis using echocardiography and electrocardiography

ABSTRACT Echocardiography and electrocardiogram (ECG) are the primary tools used by cardiologists to diagnose cardiovascular heart diseases. Contrary to the critical role of combining the echocardiography and ECG information in clinical examinations, to our knowledge, no study has considered this to classify heart diseases. Left ventricular hypertrophy (LVH) is caused by a variety of origins such as hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD). Differentiating HCM and HHD is challenging . We proposed an HCM and HHD patient classifier to use ECG and echocardiography information. Longitudinal strain and strain rate from echocardiography frames and amplitude and temporal features from ECG signals, are extracted. To eliminate incompetent features, Fisher’s discrimination ratio (FDR), information gain, and Relief-F weights are used. Finally, support vector machine (SVM) and K-nearest neighbours classifiers are used to classify the normal, HCM, and HHD subjects. The results on 30 subjects show that the best classification refers to SVM classifier using five selected features from ECG and echocardiography information using FDR. The precision, sensitivity, and F-measure are 97.62, 93.33 and 95.43%, respectively. According to the results, the combination of echocardiography and ECG information leads to diagnosis improvement compared to the classification based on separated information of ECG and echocardiography.

Automated ECG classification using a non-local convolutional block attention module

Background and objective: Recent advances in deep learning have been applied to ECG detection and obtained great success. The spatial and temporal information from ECG signals is fused by combining convolutional neural networks (CNN) with recurrent neural network (RNN). However, these networks ignore the different contribution of local and global segments of a feature map extracted from the ECG and the correlation relationship between the above two segments. To address this issue, a novel convolutional neural network with non-local convolutional block attention module(NCBAM) is proposed to automatically classify ECG heartbeats. Methods: Our proposed method consists of a 33-layer CNN architecture followed by a NCBAM module. Initially, preprocessed electrocardiogram (ECG) signals are fed into the CNN architecture to extract the spatial and channel features. Further, long-range dependencies of representative features along spatial and channel axis are captured by non-local attention. Finally, the spatial, channel and temporal information of ECG are fused by a learned matrix. The learned matrix is to mine rich relationship information across the above three types of information to make up for the different contribution. Results and conclusion: The proposed method achieves an average F1 score of 0.9664 on MIT-BIH arrhythmia database, as well as AUC of 0.9314 and Fmax of 0.8507 on PTB-XL ECG database. Compared with the state-of-the-art attention mechanism based on the same public database, NCBAM achieves an obvious improvement in classifying ECG heartbeats. The results demonstrate the proposed method is reliable and efficient for ECG beat classification.

Non-invasive mid-term ECG monitoring for the detection of atrial fibrillation in an outpatient population

Abstract Background Atrial fibrillation (AF) is a common arrhythmia with significant morbidity due to an increased risk of ischemic stroke. Outpatient electrocardiogram (ECG) monitoring is an integral part of the diagnosis of AF. Conventional 24 hour Holter monitoring can be cumbersome and often fails to diagnose patients with paroxysmal AF. Spyder ECG is a non-invasive ECG monitoring device that allows wireless transmission of ECG information for analysis. It is small and comfortable, allowing for easy application for the screening and detection of AF over a mid-term duration. Purpose This study aims to evaluate the incidence of AF in patients with no prior AF and CHADsVASC score of at least 1 with the use of the Spyder ECG mid-term ECG monitoring device. Methods Patients aged 21 to 85 years old with no prior history of AF and CHADsVASC score of at least 1 were recruited from outpatient clinics of 3 large tertiary hospitals in Singapore from December 2016 to April 2019. Patients wore the Spyder ECG device for up to 2 weeks, during which continuous ECG information was uploaded onto a central cloud database and analysed. Results There were 363 patients recruited. The mean age was 61±10.0 years and 65.1% were male. There were 80.3% Chinese, 11.6% Malay, 7.5% Indian and 20.6% of other races. 68.3% of the patients were non-smokers and 74.0% of them were non-alcohol drinkers. The mean BMI of 25.5±4.7 kg/m2. The patient population had significant co-morbidities. 76.3% of the patients had hypertension, 69.4% of them had hyperlipidemia and 40.5% of them had diabetes mellitus. 10.0% of them had congestive cardiac failure and 56.7% had ischaemic heart disease. 11.3% of patients had a previous stroke and 20.4% had a prior myocardial infarction. 7.8% of the patients had asthma, 5.8% of them had thyroid disease and 9.9% of them had chronic kidney disease. They were monitored for a mean of 5.4±2.9 days each. There were 15 (4.1%) patients in whom AF was detected. The patients with AF wore the device for a mean of 5.7±2.0 SD days. The mean burden of AF was 9.0% of monitored time. 46.7% of the patients with AF had detection of AF on the first day, 26.7% on the second day, 13.3% on the third day and 13.3% on the seventh day. The mean duration of the first episode of AF was 251±325 minutes. 7 out of 15 (46.7%) of patients had first episodes of AF lasting less than 10 minutes. Conclusion Continuous mid-term ECG monitoring was able to detect AF in 15 (4.1%) of a population of 363 patients with no prior AF and CHADsVASC score of at least one, monitored for a mean of 5.4 days. Most episodes (53.3%) of AF were detected after the first day of ECG monitoring. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Duke-NUS Medical School Singapore