ABSTRACT
Atrial fibrillation is often asymptomatic and intermittent making its detection challenging. A photoplethysmography (PPG) provides a promising option for atrial fibrillation detection. However, the shapes of pulse waves vary in atrial fibrillation decreasing pulse and atrial fibrillation detection accuracy. This study evaluated ten robust photoplethysmography features for detection of atrial fibrillation. The study was a national multi-center clinical study in Finland and the data were combined from two broader research projects (NCT03721601, URL: https://clinicaltrials.gov/ct2/show/NCT03721601 and NCT03753139, URL: https://clinicaltrials.gov/ct2/show/NCT03753139). A photoplethysmography signal was recorded with a wrist band. Five pulse interval variability, four amplitude features and a novel autocorrelation-based morphology feature were calculated and evaluated independently as predictors of atrial fibrillation. A multivariate predictor model including only the most significant features was established. The models were 10-fold cross-validated. 359 patients were included in the study (atrial fibrillation n = 169, sinus rhythm n = 190). The autocorrelation univariate predictor model detected atrial fibrillation with the highest area under receiver operating characteristic curve (AUC) value of 0.982 (sensitivity 95.1%, specificity 93.7%). Autocorrelation was also the most significant individual feature (p < 0.00001) in the multivariate predictor model, detecting atrial fibrillation with AUC of 0.993 (sensitivity 96.4%, specificity 96.3%). Our results demonstrated that the autocorrelation independently detects atrial fibrillation reliably without the need of pulse detection. Combining pulse wave morphology-based features such as autocorrelation with information from pulse-interval variability it is possible to detect atrial fibrillation with high accuracy with a commercial wrist band. Photoplethysmography wrist bands accompanied with atrial fibrillation detection algorithms utilizing autocorrelation could provide a computationally very effective and reliable wearable monitoring method in screening of atrial fibrillation.
ABSTRACT
BACKGROUND: Atrial fibrillation (AF) is the major cause of stroke since approximately 25% of all strokes are of cardioembolic-origin. The detection and diagnosis of AF are often challenging due to the asymptomatic and intermittent nature of AF. HYPOTHESIS: A wearable electrocardiogram (ECG)-device could increase the likelihood of AF detection. The aim of this study was to evaluate the feasibility and reliability of a novel, consumer-grade, single-lead ECG recording device (Necklace-ECG) for screening, identifying and diagnosing of AF both by a cardiologist and automated AF-detection algorithms. METHODS: A thirty-second ECG was recorded with the Necklace-ECG device from two positions; between the palms (palm) and between the palm and the chest (chest). Simultaneously registered 3-lead ECGs (Holter) served as a golden standard for the final rhythm diagnosis. Two cardiologists interpreted independently in a blinded fashion the Necklace-ECG recordings from 145 patients (66 AF and 79 sinus rhythm, SR). In addition, the Necklace-ECG recordings were analyzed with an automatic AF detection algorithm. RESULTS: Two cardiologists diagnosed the correct rhythm of the interpretable Necklace-ECG with a mean sensitivity of 97.2% and 99.1% (palm and chest, respectively) and specificity of 100% and 98.5%. The automatic arrhythmia algorithm detected the correct rhythm with a sensitivity of 94.7% and 98.3% (palm and chest) and specificity of 100% of the interpretable measurements. CONCLUSIONS: The novel Necklace-ECG device is able to detect AF with high sensitivity and specificity as evaluated both by cardiologists and an automated AF-detection algorithm. Thus, the wearable Necklace-ECG is a new, promising method for AF screening. CLINICAL TRIAL REGISTRATION: Study was registered in the ClinicalTrials.gov database (NCT03753139).
Subject(s)
Atrial Fibrillation , Electrocardiography , Wearable Electronic Devices , Aged , Algorithms , Atrial Fibrillation/diagnosis , Female , Humans , Male , Middle Aged , Reproducibility of ResultsABSTRACT
Atrial fibrillation (AF) is a significant cause of cardioembolic strokes. AF is often symptomless and intermittent, making its detection challenging. The aim of this study was to assess the possibility to use a chest strap (Suunto Movesense) to detect AF both by cardiologists and automated algorithms. A single channel electrocardiogram (ECG) from a chest strap of 220 patients (107 AF and 111 sinus rhythm SR with 2 inconclusive rhythms) were analyzed by 2 cardiologists (Doc1 and Doc2) and 2 different algorithms (COSEn and AFEvidence). A 3-lead Holter served as the gold standard ECG for rhythm analysis. Both cardiologists evaluated the quality of the chest strap ECG to be superior to the quality of the Holter ECG; p <0.05/p <0.001 (Doc1/Doc 2). Accurate automated algorithm-based AF detection was achieved with sensitivity of 95.3%/96.3% and specificity of 95.5/98.2% with 2 AF detection algorithms from chest strap and 93.5%/97.2% and 98.2%/95.5% from Holter, respectively. P waves were detectable in 93.7% (Doc1) and 94.6% (Doc2) of the cases from the chest strap ECG with sinus rhythm and 98.2% (Doc1) and 95.5% (Doc2) from the Holter (pâ¯=â¯n.s). In conclusion, the ECGs from both methods enabled AF detection by a cardiologist and by automated algorithms. Both methods studied enabled P-wave detection in sinus rhythm.
