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

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

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Evaluation of the device independent nature of a photoplethysmography-deriving smartphone app

EP Europace ◽

10.1093/europace/euab116.525 ◽

2021 ◽

Vol 23 (Supplement_3) ◽

Author(s):

H Gruwez ◽

S Evens ◽

T Proesmans ◽

C Smeets ◽

P Haemers ◽

...

Keyword(s):

Atrial Flutter ◽

Sensitivity And Specificity ◽

Heart Rhythm ◽

Smartphone Application ◽

Detection Algorithm ◽

Interquartile Range ◽

Detection Algorithms ◽

Funding Sources ◽

Successful Measurement ◽

Sensitivity Specificity

Abstract Funding Acknowledgements Type of funding sources: None. Background Smartphone apps using photoplethysmography (PPG) technology enable digital heart rhythm monitoring through their built-in camera, without the need for additional, specific, or costly hardware. This may positively impact the availability and scalability of remote monitoring. However, the diversity of smartphone specifications on the consumer market may raise concerns regarding the robustness of AF detection algorithms between various devices. Purpose To study the device independency of AF detection performance by a PPG-based smartphone application. Methods Patients from the cardiology department were consecutively enrolled. Patients were handed 7 iOS models and 1 Android model and were asked to consecutively perform one PPG measurement per device. A 12-lead electrocardiogram (ECG) was collected during the same consultation and interpreted by a cardiologist as reference diagnosis. To allow an objective comparison across the devices, patients who failed to perform one successful measurement on each device were excluded. Additional exclusions were atrial flutter rhythms and insufficient quality results. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was used for the head-to-head comparison of the sensitivity and specificity of the proprietary algorithm on the different smartphone devices. Results A total of 150 patients participated in the study with a median CHA2DS2-VASc score of 3 (interquartile range: 1-5). The median age of the study population was 70 (interquartile range: 56-79) years. In total, 54.7% of the population was male and the AF-prevalence was 35.3%. After the exclusion of patients with atrial flutter (n = 14) and patients who did not successfully perform a PPG measurement on each device (n = 5), diagnostic-grade results of 131 patients were used to calculate the performance of the proprietary algorithm. The sensitivity and specificity of the AF detection algorithm ranged from 90.9% (95% CI 75.7-98.1) to 100.0% (95% CI 91.0-100) and 94.5% (95% CI 86.6-98.5) to 100.0% (95% CI 94.6-100), respectively. The overall accuracy across the devices ranged from 94.4% (95% CI 88.3-97.9) to 99.0% (95% CI 94.6-100). Head-to-head comparisons of the results did not reveal significant differences in sensitivity (P = 0.125-1.000) or specificity (P = 0.375-1.000) of the proprietary AF detection algorithm among the different devices. Conclusion This study demonstrated the device-independent nature of the PPG-deriving smartphone application with respect to 12-lead ECG diagnosis.

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What affects the acceptance of illness in patients with atrial fibrillation?

European Journal of Cardiovascular Nursing ◽

10.1093/eurjcn/zvab060 ◽

2021 ◽

Vol 20 (Supplement_1) ◽

Author(s):

N Swiatoniowska-Lonc ◽

D Kasperczak ◽

B Jankowska-Polanska

Keyword(s):

Physical Activity ◽

Atrial Fibrillation ◽

Important Determinant ◽

Heart Rhythm ◽

Positive Influence ◽

Physically Active ◽

Funding Sources ◽

Cardiology Department ◽

Heart Rhythm Disorders ◽

Acceptance Of Illness

Abstract Funding Acknowledgements Type of funding sources: None. Background. Patients with atrial fibrillation (AF) have symptoms that require advanced treatment. The most common include palpitations, dyspnea, dizziness, tiredness, chest pain and anxiety. Both the symptoms and treatment and its complications adversely affect the perception of the disease among patients with AF. The research proves that acceptance of illness (AIS) is a factor positively influencing the quality of life, but also the adaptation to the therapeutic recommendations of patients with chronic diseases. There is little research on factors increasing the level of AIS among patients with AF. The aim of the study is to determine the level of acceptance of illness in patients with AF and factors influencing the level of acceptance of illness. Material and methods. 84 patients (including 51 men) aged 57.86 ± 17.72 years hospitalized in the cardiology department due to heart rhythm disorders. Standardized tools were used in the study: Acceptance of Illness Scale (AIS) to assess disease acceptance and International Physical Activity Questionnaire (IPAQ) to assess physical activity. Sociodemographic and clinical data were taken from the hospital register. Results. In the study group the average result of acceptance of illness (AIS) was 27.67 ± 7.70. 48.8% of patients had a high degree of disease acceptance, 38.09% average, and 13.11% had no acceptance. The examined patients showed a lack of physical activity (IPAQ = 0.92 ± 0.40). In comparative analyses women had lower level of AIS than men (27.36 ± 7.37 vs. 27.86 ± 7.97; p = 0.01), patients more often hospitalized due to AF lower than patients less frequently hospitalized (26.30 ± 6.11 vs. 30.55 ± 8.55; p = 0.02). Lower level of AIS was observed in patients with comorbidities (34 ± 6.25 vs. 22 ± 6.66; p < 0.001). Smokers had higher level of AIS than non-smokers (28.66 ± 6.65 vs. 27.00 ± 7.56; p = 0.02), similarly, physically active persons than inactive ones (26.48 ± 8.27 vs. 23.07 ± 10.58; p = 0.01). In correlation analysis, physical activity turned out to be an important determinant having a positive effect on the level of AIS (r = 0.220; p = 0.03). Conclusions. Patients with AF have moderate level of acceptance of illness and low level of physical activity. A higher level of AIS is observed in men, without comorbidities, less frequently hospitalized and smokers. The important determinant having positive influence on AIS is physical activity.

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

Journal of Probability and Statistics ◽

10.1155/2019/8057820 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 5

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.

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Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches

Scientific Reports ◽

10.1038/s41598-019-49092-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 11

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.

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Different implication in dementia between patients with atrial flutter and atrial fibrillation: a national cohort study

EP Europace ◽

10.1093/europace/euab116.160 ◽

2021 ◽

Vol 23 (Supplement_3) ◽

Author(s):

YL Chen

Keyword(s):

Atrial Fibrillation ◽

Atrial Flutter ◽

Research Database ◽

Funding Sources ◽

Valvular Surgery ◽

Previous Stroke ◽

Included Patient ◽

National Cohort ◽

History Of ◽

The Difference

Abstract Funding Acknowledgements Type of funding sources: None. Importance Atrial fibrillation (AF) has been reported with increasing the risk of stroke and dementia. Atrial flutter (AFL) is also a risk of stroke with different discrepancies in clinical outcome. Little is known about the difference in the risk of dementia between AF and AFL. Objective To investigate if the risk of dementia is difference between AF and AFL. Methods The patients with newly diagnosed AF and AFL during 2001–2013 was retrieved from Taiwan’s National Health Insurance Research Database. Patients with missing information, aged <20 years, history of valvular surgery, rheumatic heart disease, hyperthyroidism, and history of dementia were excluded. Propensity score matching (PSM) between AF and AFL was performed, which included patient comorbidities, past medical history, medications, and index date stratified by age. Primary outcome was defined as dementia at follow-up. Results A total of 232,425 AF and 7,569 AFL were eligible for analysis. After 4:1 PSM, we included 30,276 AF (aged 67.3 ± 15.7 years) and 100,065 AFL (aged 67.4 ± 16.0 years) for analysis. The risk of dementia was higher in AF patients compared with AFL patients (subdistribution HR (SHR)=1.52, 95% CI 1.39 - 1.66; p <0.0001) before PSM and still higher in AF patients (SHR = 1.14, 95% CI 1.04 to 1.25; p = 0.0064). The risk was higher in AF patients without previous stroke after PSM and there was no difference between AF and AFL patients with previous stroke history. Conclusions and relevance Our finding supports that risk of dementia is higher in AF patients than AFL patients. However, the risk of dementia between patients with AF and AFL varies depending on whether there is a previous stroke history.

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The influence of frailty syndrome on quality of life in patients with atrial fibrillation

EP Europace ◽

10.1093/europace/euab116.566 ◽

2021 ◽

Vol 23 (Supplement_3) ◽

Author(s):

K Lomper ◽

M Loboz-Rudnicka ◽

I Uchmanowicz ◽

J Jaroch

Keyword(s):

Quality Of Life ◽

Atrial Fibrillation ◽

Frailty Syndrome ◽

Adverse Health Outcomes ◽

Funding Sources ◽

Cardiology Department ◽

Tilburg Frailty Indicator ◽

Patient Prognosis ◽

Self Administered Questionnaire

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Atrial fibrillation (AF) is associated with decreased quality of life (QoL) compared to the general population and to other cardiovascular conditions. In addition, the co-occurrence of frailty syndrome (FS) affects the risk of developing adverse health outcomes including disability and rehospitalizations. This may further translate into a decreased subjective sense of QoL. Purpose To evaluate the prevalence of FS and its impact on subjective QoL in a group of patients with AF. Methodology 116 patients (mean age 75.21 ± 8.19) with diagnosis of AF hospitalized at the Cardiology Department were included in the study. Medical record analysis and self-administered questionnaire were used to obtain basic sociodemographic and clinical data. The prevalence of FS was assessed using the Tilburg Frailty Indicator (TFI) questionnaire. The standardized Arrhythmia-Specific questionnaire in Tachycardia and Arrhythmia (ASTA) assessing quality of life was used. Results FS was diagnosed in 67.24% of the patients. The group with FS was predominantly elderly (p < 0.001), female (p = 0.03), less educated (p = 0.04), single (p= <0.001), with coexisting heart failure (p = 0.015 ) and with higher EHRA classification (p = 0.014). Better QoL was demonstrated in the group of patients without FS in the total score (p = 0.004), psychological domain (p = 0.014) and physical domain (p = 0.004). There was a significant positive correlation of TFI total score with overall QoL (B = 0.383; p < 0.001) and psychological (B = 0.355; p < 0.001) and physical (B = 0.336; p < 0.001) domains. Conclusions FS is common in patients with AF. Assessment of FS occurrence is important for everyday clinical practice because FS lowers QoL. The consequence of FS and decreased QoL may be worse patient prognosis and increased number of hospitalizations.

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Emery-Dreifuss muscular dystrophy presenting as a heart rhythm disorders in children

European Heart Journal ◽

10.1093/eurheartj/ehab724.0286 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

T S Kovalchuk ◽

E V Yakovleva ◽

S G Fetisova ◽

T L Vershinina ◽

T M Pervunina ◽

...

Keyword(s):

Atrial Fibrillation ◽

Skeletal Muscle ◽

Muscular Dystrophy ◽

Atrial Flutter ◽

Sinus Node ◽

Heart Rhythm ◽

Sinus Node Dysfunction ◽

Supraventricular Arrhythmias ◽

Heart Rhythm Disorders ◽

The Mean

Abstract Introduction Emery-Dreifuss muscular dystrophy (EDMD) is an inherited muscle dystrophy often accompanied by cardiac abnormalities in the form of supraventricular arrhythmias, conduction defects, sinus node dysfunction. Cardiac phenotype typically arises years after skeletal muscle presentations, though, can be severe and life-threatening. The disease usually manifests during the third decade of life with elbow joint contractions and progressive muscle weakness and atrophy. Objective To present our clinical experience of diagnosis and treatment of arrhythmias in children with Emery-Dreifuss muscular dystrophy Materials and methods We enrolled 5 patients with different forms of EDMD (X-linked and autosomal dominant) linked to the mutations in EMD and LMNA genes, presented with early onset of cardiac abnormalities and no leading skeletal muscle phenotype. The predominant forms of cardiac pathology were atrial flutter, atrial fibrillation and conduction disturbances that progress over time. Clinical examination included physical examination, 12-lead electrocardiography, Holter ECG monitoring (HM), transthoracic echocardiography, neurological examination and biochemical and hormone tests. Also we performed CMR, electrophysiological study (EPS), treadmill test of some patients. One patient underwent an endomyocardial biopsy to exclude inflammatory heart disease. Target sequencing was performed using a panel of 108 or 172 genes Results We observed five patients with EDMD and cardiac debut during first-second decades of life: 3 with 1st subtype (variants in EMD gene) and 2 with 2nd subtype (variants in LMNA gene). All patients were males. The mean age of cardiac manifestation was 13,2±3,11 (from 9 to 16 y.o.). The mean follow-up period was 7,4±2,6 years. All patients presented with sinus node dysfunction and four out of five with AV conduction abnormalities. The leading arrhythmic phenotypes included various types of supraventricular arrhythmias: multifocal atrial tachycardia (AT) (n=4), premature atrial captures (PACs) (n=4), atrial flutter, (AF) (n=3), atrial fibrillation (AFib) (n=3) and AV nodal recurrent tachycardia (AVRNT). Heart rhythm disorders were the first manifestation in all three patients with 1st EDMD subtype. Radiofrequency ablation was performed in 2 patients, one of them received permanent pacemaker implantation. Conclusions In conclusion, while being the rare cases, heart rhythm disorders can represent the first and for a long time, the only clinical symptom of EDMD even in the pediatric group of patients. Therefore, thorough laboratory and neurological screening along with genetic studies, are of importance in each pediatric patient presenting with complex heart rhythm disorders of primary supraventricular origin to exclude EDMD or other neuromuscular disorders. FUNDunding Acknowledgement Type of funding sources: None.

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The efficacy and safety of a nurse-led electrical cardioversion service for atrial fibrillation over a two-year time period

European Journal of Cardiovascular Nursing ◽

10.1093/eurjcn/zvab060.005 ◽

2021 ◽

Vol 20 (Supplement_1) ◽

Author(s):

P Purkayastha ◽

A Ibrahim ◽

D Haslen ◽

R Gamma

Keyword(s):

Atrial Fibrillation ◽

Success Rate ◽

Atrial Flutter ◽

Dramatic Improvement ◽

Success Rates ◽

Funding Sources ◽

Hospital Readmission Rate ◽

Direct Current Cardioversion ◽

Significant Difference ◽

Chi Squared

Abstract Funding Acknowledgements Type of funding sources: None. Background & Purpose Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, with a significant impact on morbidity, mortality and utilisation of healthcare resources. Electrical direct-current cardioversion (DCCV) is offered to patients with ongoing symptoms despite medical management. In this study we aim to evaluate the safety and efficacy of a specialised nurse-led DCCV cardioversion service. Methods This was a retrospective cohort study analysing the outcome of patients presenting with atrial fibrillation or flutter, who were subsequently referred for a nurse-led DCCV procedure between August 2017 and December 2019. Results Analysis included a total of 341 patients (mean age = 68.37; STDV = 10.96) who presented with either atrial fibrillation (N = 267; 78.30%) or atrial flutter (N = 74; 21.70%). Approximately 30% of patients were female (N = 101); and 70% were male (N = 240). Of the 341 patients who underwent DCCV, 299 were successfully cardioverted (87.68%), whilst 42 patients remained in AF (12.32%). Of those patients successfully cardioverted, 167 remained in sinus rhythm after 6 weeks (55.85%); 93 patients reverted back to AF (31.10%). 38 patients were lost to follow up (12.71%). Of all 341 patients who underwent DCCV, only 24 patients were admitted to hospital during the subsequent 3 month period (7.04%). Of these admissions, 11 were due to persistent AF (45.83%), and 13 were due to other non-related reasons (54.17%). Importantly, no patients were admitted as a direct complication of the DCCV procedure. Using a Chi-squared analysis, we found a significant difference in cardioversion success rates between patients presenting with atrial flutter (97% success rate) versus those in atrial fibrillation (85% success rate) (χ2 = 8.089; p = 0.004; α<0.05). We did not find a significant difference in cardioversion success rates between males and females (χ2 = 1.651; p = 0.199; α<0.05); nor did we witness a significant impact from the presence of ischaemic heart disease (χ2 = 1.545; p = 0.214; α<0.05) or hypertension (χ2 = 2.075; p = 0.150; α<0.05). Similarly, we found negligible impact of LV ejection fraction (χ2 = 1.494; p = 0.684; α<0.05) or LA size (χ2 = 1.310; p = 0.727; α<0.05) upon cardioversion success rates. We witnessed a dramatic improvement in DC cardioversion success rates in patients taking antiarrhythmic medication in preference to a rate control strategy alone (χ2 = 11.825; p = 0.008; α<0.05). Conclusion Overall, data gathered from this study provides positive evidence to support the use of a nurse-led DCCV service. In addition to obtaining very successful cardioversion rates, we found low remission rates, with a very low hospital readmission rate for AF related issues after successful DCCV.

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A new electrophysiological triad for atrial flutter critical isthmus identification and localization

EP Europace ◽

10.1093/europace/euab116.101 ◽

2021 ◽

Vol 23 (Supplement_3) ◽

Author(s):

P Adragao ◽

D Nascimento Matos ◽

P Galvao Santos ◽

F Moscoso Costa ◽

G Rodrigues ◽

...

Keyword(s):

Atrial Fibrillation ◽

Atrial Flutter ◽

Cycle Length ◽

Low Voltage ◽

Prospective Analysis ◽

Electroanatomical Mapping ◽

Funding Sources ◽

Activation Times ◽

Density Zone ◽

Funding Acknowledgements

Abstract Funding Acknowledgements Type of funding sources: None. Introduction In a previous retrospective study it was demonstrated that an electrophysiological triad was able to identify critical isthmus in atrial flutter (AFL) patients. This triad is based in the Carto® electroanatomical mapping (EAM) version 7, which displays a histogram of the local activation times (LAT) of the tachycardia cycle length (TCL), in addition to the activation and voltage maps. This study aimed to prospectively assess the ability of an electrophysiological triad to identify and localize the AFL’s critical isthmus. Methods Prospective analysis of a unicentric registry of individuals who underwent left AFL ablation with Carto® EAM. All patients with non-left AFL, lack of high-density EAM, less than 2000 collected points or lack of mapping in any of the left atrium walls or structures were excluded. Ablation sites of arrhythmia termination were compared to an electrophysiological triad constituted by: areas of low-voltage (0.05 to 0.3mV), sites of deep histogram valleys (LAT-Valleys) with less than 20% density points relative to the highest density zone and a prolonged LAT-Valley duration that included 10% or more of the TCL. The longest LAT-Valley was designated as the primary valley, while additional valleys were named as secondary. Results A total of 12 patients (9 men, median age 72 IQR 67-75 years) were included. All patients presented with left AFL and 67% had a previous atrial fibrillation and/or flutter ablation. The median TCL and number collected points were 250 (230─290) milliseconds and 3150 (IQR 2340─3870) points, respectively. All AFL presented with at least 1 LAT-Valley in the analysed histograms, which corresponded to heterogeneous low-voltage areas (0.05 to 0.3mV) and encompassed more than 10% of TCL. Eleven of the 12 patients presented with at least 1 secondary LAT-Valley. All arrhythmias were effectively terminated after undergoing radiofrequency ablation in the primary or the secondary LAT-Valley location. Conclusion In a prospective analysis, an electrophysiological triad was able to identify the AFL critical isthmus in all patients. Further studies are needed to assess the usefulness of this algorithm to improve catheter ablation outcomes.

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A novel single-lead handheld atrial fibrillation detection system

Physiological Measurement ◽

10.1088/1361-6579/ac3d77 ◽

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.

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