A novel single-lead handheld atrial fibrillation detection system

2021 ◽  
Author(s):  
Ying Li ◽  
Jianqing Li ◽  
Chenxi Yang ◽  
Yantao Xing ◽  
Chengyu Liu
Keyword(s):  
Atrial Fibrillation ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Early Screening ◽  
Rr Intervals ◽  
Daily Monitoring ◽  
Standard Device ◽  
Memory Cost ◽  
Sensitivity Specificity

Abstract Objective: The single-lead handheld atrial fibrillation (AF) detection device is suitable for daily monitoring or early screening of AF in the hospital. However, the signal quality and the reliability of AF detection algorithm still need to be improved. This study proposed a novel AF detection system with a user-friendly interaction and a lightweight and accurate AF detection algorithm. Approach: The system consisted of a single-lead handheld electrocardiogram (ECG) device with a novel appearance like a gaming handle and a smartphone terminal embedded with AF detection. After feature optimization, the rule-based multi-feature AF detection algorithm had relatively good AF detection ability. Three types of experiments were designed to test the performance of the system. 1) Test the accuracy and time/memory cost of the AF detection algorithm. 2) Compare the proposed device with the standard device Shimmer. 3) Use the simulator to test the effectiveness of the system. Main results: The percentage of differences of successive RR intervals larger than 50 ms (PNN50), minimum value of RR intervals (minRR), and coefficient of sample entropy (COSEn) were features chosen for AF detection. 1) The sensitivity, specificity, and accuracy were 96.00%, 99.75%, 97.88% on the MIT-BIH AF database, and 98.50%, 94.50%, 96.50% on the clinical database we founded. The time/memory cost of the proposed algorithm was much smaller than that of Support Vector Machine (SVM). 2) The mean correlation coefficient of RR was 0.9950, indicating a high degree of consistency. 3) This system showed the effectiveness of AF detection. Significance: The proposed single-lead handheld AF detection system is demonstrated to be accurate, lightweight, consistent with the standard device, and efficient for AF detection.

scholarly journals Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoling Wei ◽  
Jimin Li ◽  
Chenghao Zhang ◽  
Ming Liu ◽  
Peng Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Complex Network ◽  
Individual Variation ◽  
Deep Neural Network ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Electrocardiogram Signal ◽  
Sensitivity Specificity ◽  
Atrial Fibrillation Detection

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

scholarly journals Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Syed Khairul Bashar ◽  
Dong Han ◽  
Shirin Hajeb-Mohammadalipour ◽  
Eric Ding ◽  
Cody Whitcomb ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
False Alarms ◽  
Time Frequency ◽  
Non Invasive ◽  
Accelerometer Signal ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Sensitivity Specificity

Abstract Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets.

A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

2022 ◽  
Vol 22 (3) ◽  
pp. 1-17
Author(s):  
Chaonan Shen ◽  
Kai Zhang ◽  
Jinshan Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Social Learning ◽  
Early Detection ◽  
Detection System ◽  
Particle Swarm ◽  
Detection Algorithm ◽  
Edge Computing ◽  
Internet Technology ◽  
Support Vector ◽  
Swarm Optimization

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

scholarly journals A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5222
Author(s):  
Liang-Hung Wang ◽  
Ze-Hong Yan ◽  
Yi-Ting Yang ◽  
Jun-Ying Chen ◽  
Tao Yang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Hybrid Model ◽  
Model Construction ◽  
Digital Data ◽  
Support Vector ◽  
Test Set ◽  
Svm Algorithm ◽  
Sensitivity Specificity

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.

scholarly journals Online Road Detection under a Shadowy Traffic Image Using a Learning-Based Illumination-Independent Image

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 707 ◽  
Author(s):  
Yongchao Song ◽  
Yongfeng Ju ◽  
Kai Du ◽  
Weiyu Liu ◽  
Jiacheng Song
Keyword(s):  
Feature Fusion ◽  
Detection System ◽  
Geometric Mean ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Light Illumination ◽  
Road Detection ◽  
The Road

Shadows and normal light illumination and road and non-road areas are two pairs of contradictory symmetrical individuals. To achieve accurate road detection, it is necessary to remove interference caused by uneven illumination, such as shadows. This paper proposes a road detection algorithm based on a learning and illumination-independent image to solve the following problems: First, most road detection methods are sensitive to variation of illumination. Second, with traditional road detection methods based on illumination invariability, it is difficult to determine the calibration angle of the camera axis, and the sampling of road samples can be distorted. The proposed method contains three stages: The establishment of a classifier, the online capturing of an illumination-independent image, and the road detection. During the establishment of a classifier, a support vector machine (SVM) classifier for the road block is generated through training with the multi-feature fusion method. During the online capturing of an illumination-independent image, the road interest region is obtained by using a cascaded Hough transform parameterized by a parallel coordinate system. Five road blocks are obtained through the SVM classifier, and the RGB (Red, Green, Blue) space of the combined road blocks is converted to a geometric mean log chromatic space. Next, the camera axis calibration angle for each frame is determined according to the Shannon entropy so that the illumination-independent image of the respective frame is obtained. During the road detection, road sample points are extracted with the random sampling method. A confidence interval classifier of the road is established, which could separate a road from its background. This paper is based on public datasets and video sequences, which records roads of Chinese cities, suburbs, and schools in different traffic scenes. The author compares the method proposed in this paper with other sound video-based road detection methods and the results show that the method proposed in this paper can achieve a desired detection result with high quality and robustness. Meanwhile, the whole detection system can meet the real-time processing requirement.

A NOVEL APPLICATION OF HIGHER ORDER STATISTICS OF R-R INTERVAL SIGNAL IN EMD DOMAIN FOR PREDICTING TERMINATION OF ATRIAL FIBRILLATION

2015 ◽  
Vol 23 (01) ◽  
pp. 115-130
Author(s):  
MARYAM MOHEBBI
Keyword(s):  
Atrial Fibrillation ◽  
Higher Order ◽  
Therapeutic Interventions ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Mean And Variance ◽  
Sensitivity Specificity ◽  
Method R

Predicting termination of atrial fibrillation (AF), based on noninvasive techniques, can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. Currently, no reliable method exists to predict the termination of AF. We propose an algorithm for predicting termination of AF using higher order statistical moments of R-R interval signal calculated in both time and empirical mode decomposition (EMD) domains. In the proposed method, R-R interval signal is decomposed into a set of intrinsic mode functions (IMF) and higher order moments including skewness, and kurtosis, as well as mean and variance, are calculated from the first four IMFs. The appropriateness of these features in predicting the termination of AF is studied using atrial fibrillation termination database (AFTDB) which consists of three types of AF episodes: N-type (non-terminated AF episode), S-type (terminated 1'min after the end of the record), and T-type (terminated immediately after the end of the record). By using a support vector machine (SVM) classifier for classification of AF episodes, we obtained sensitivity, specificity, and positive predictivity 92.47%, 95.29%, and 92.80%, respectively. The important advantage of the proposed method compared to the other existing approaches is that our algorithm can simultaneously discriminate the three types of AF episodes with high accuracy. The results demonstrate that the EMD domain is a particularly well-suited domain for analyzing nonstationary and nonlinear R-R interval signal in AF termination prediction application.

scholarly journals Support Vector Method with Radial Basis Function and Multi-Segment of Electrocardiogram for Paroxysmal Atrial Fibrillation Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 148
Author(s):  
Nuryani Nuryani
Keyword(s):  
Atrial Fibrillation ◽  
Radial Basis Function ◽  
Basis Function ◽  
Paroxysmal Atrial Fibrillation ◽  
Experimental Result ◽  
Support Vector ◽  
Rr Intervals ◽  
Vector Method ◽  
Radial Basis ◽  
New Strategy

Paroxysmal Atrial fibrillation (PAF) is a heart problem relating to irregular and rapid beating of the heart atria. It has risk of stroke and is independently associated with risk of mortality. Early information of PAF episode is important for a patient to have appropriate treatment to reduce atrial fibrillation complications. This article presents a new strategy to detect PAF with base of statistical electrocardiographic features and a support vector machine (SVM). R-peak series of electrocardiogram were segmented and were extracted to find the statistics of RR intervals. Different approaches in relation with the segmentation were investigated. Two-class SVM with radial basis function (RBF) and the statistics of RR intervals were examined for PAF detection. Using clinical data of patients with PAF, the proposed strategy showed excellent performance of 99.17% in terms of accuracy. The experimental result indicated that the appropriate statistics of RR intervals and SVM-RBF with its suitable parameters can perform well for PAF detection.

scholarly journals A comparison of over-the-counter available smartwatches and devices for electrocardiogram based detection of atrial fibrillation

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
J Scholten ◽  
A Mahes ◽  
J R De Groot ◽  
M M Winter ◽  
A H Zwinderman ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Atrial Flutter ◽  
Heart Rhythm ◽  
Detection Algorithm ◽  
Left Knee ◽  
Funding Sources ◽  
Cardiology Department ◽  
Normal Heart Rate ◽  
Golden Standard ◽  
Sensitivity Specificity

Abstract Background There is an increasing number of smartwatches and devices commercially available that can generate and automatically interpret an electrocardiogram (ECG). Such devices have an enormous potential to improve population screening and telemonitoring of atrial fibrillation (AF). Purpose There is limited data on the sensitivity, specificity and interpretability of these devices and comparative studies are lacking. Our purpose was to compare three frequently used devices for AF detection. Methods We performed a single-center, prospective study in consecutive patients with AF presenting for electrical cardioversion (ECV). We collected a standard 12-lead ECG recording immediately followed by four times 30 seconds of ECG recordings from different devices for every patient prior to the ECV. These paired measurements were considered simultaneous. If the ECV was performed, the same measurements were repeated afterwards. The standard 12L-ECGs were interpreted by a cardiologist and used as golden standard for heart rhythm. The different devices used for the 30 second ECGs were: Withings Move ECG (lead I), Apple Watch series 5 (lead I), Kardia Mobile 6L (six leads) and Withings/Apple (1:1 ratio) on left knee (lead II). Sensitivity and specificity were determined for each AF detection algorithm excluding patients with atrial flutter (AFL) or uninterpretable ECGs. In addition, proportions of uninterpretable ECGs were determined including all patients and including only patients with sinus rhythm (SR) and compared between devices using McNemar's test. Results A total of 220 patients were included (age 70±10 years, female 35%, first ECV 44%) and in total 415 12-lead ECGs were performed (45% SR, 45% AF, 10% AFL). The sensitivity/specificity were overall similar for all devices (Withings 98%/95%, Apple 94%/98%, Kardia 99%/91%. P>0.05 for all). In detail, Kardia was the most sensitive test with highest proportion of suspected AF (57%) whereas Apple was the most specific, as shown by the highest proportion of normal heart rate results by the device (55%, P=0.003 compared to Kardia (43%)). Overall, Withings, Apple and Kardia had a comparable proportion of uninterpretable ECGs (20%, 20%, 24%, respectively. P>0.05 for all). Lead II had higher proportion of uninterpretable ECGs (32%, p<0.01 compared to all). More specifically, Kardia had a higher rate of uninterpretable ECGs in those with SR (P<0.05 compared to Withings (lead I) and Apple (lead I)). Conclusion In all devices, we found sensitivity/specificity for AF detection between 91%-99%, better than previous studies reported, and 20–24% of uninterpretable ECGs. Kardia was the most sensitive device, but less useful to rule out atrial fibrillation whereas Apple had numerically highest specificity. We aim to further evaluate both cardiologist interpretation and accuracy of atrial flutter detection using different leads to inform clinical use. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Tergooi Cardiology department, J.P. Bokma was supported with a research grant by Amsterdam Cardiovascular Sciences Overview and comparison

Detection of Sliding Wheels and Hot Bearings Using Wayside Thermal Cameras

2016 ◽  
Author(s):  
Hanieh Deilamsalehy ◽  
Timothy C. Havens ◽  
Pasi Lautala
Keyword(s):  
Computer Vision ◽  
Detection System ◽  
Automatic Detection ◽  
Real Data ◽  
Detection Algorithm ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Excessive Heat ◽  
Research Areas

Train car wheels are subjected to different types of damages due to their interactions with the brake shoes and track. If not detected early, these defects can worsen, possibly causing damage to the bogie and rail. In the worst-case scenario, this rail damage can possibly lead to later derailments, a serious concern for the rail industry. Therefore, automatic inspection and detection of wheel defects are high priority research areas. An automatic detection system not only can prevent train and rail damage, but also can reduce operating costs as an alternative for tedious and expensive manned inspection. The main contribution of this paper is to develop a computer vision method for automatically detecting the defects of rail car wheels using a wayside thermal camera. We concentrate on identification of flat-spotted/sliding wheels, which is an important issue for both wheel and suspension hardware and also rail and track structure. Flat spots occur when a wheel locks up and slides while the vehicle is still moving. As a consequence, this process heats up local areas on the metal wheel, which can be observed and potentially detected in thermal imagery. Excessive heat buildup at the flat spot will eventually lead to additional wheel and possibly rail damage, reducing the life of other train wheels and suspension components, such as bearings. Furthermore, as a byproduct of our algorithm, we propose a method for detecting hot bearings. A major part of our proposed hot bearing detection algorithm is common with our sliding wheel detection algorithm. In this paper, we first propose an automatic detection and segmentation method that identifies the wheel and bearing portion of the image. We then develop a computer vision method, using Histogram of Oriented Gradients to extract features of these regions. These feature descriptors are input to a Support Vector Machine classifier, a fast classifier with a good detection rate, which can detect abnormalities in the wheel. We demonstrate our methods on several real data sets taken on a Union Pacific rail line, identifying sliding wheels and hot bearings in these images.

Sign in / Sign up

Export Citation Format

Share Document