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Observational Study
. 2020 Sep 26;20(19):5517.
doi: 10.3390/s20195517.

Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions

Malte Jacobsen  1   2 , Till A Dembek  3 , Athanasios-Panagiotis Ziakos  1   4 , Rahil Gholamipoor  5 , Guido Kobbe  6 , Markus Kollmann  5 , Christoph Blum  5 , Dirk Müller-Wieland  2 , Andreas Napp  2 , Lutz Heinemann  7 , Nikolas Deubner  1   4 , Nikolaus Marx  2 , Stefan Isenmann  1   8 , Melchior Seyfarth  1   4
Affiliations
Observational Study

Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions

Malte Jacobsen et al. Sensors (Basel). .

Abstract

Atrial fibrillation (AF) is the most common arrhythmia and has a major impact on morbidity and mortality; however, detection of asymptomatic AF is challenging. This study sims to evaluate the sensitivity and specificity of non-invasive AF detection by a medical wearable. In this observational trial, patients with AF admitted to a hospital carried the wearable and an ECG Holter (control) in parallel over a period of 24 h, while not in a physically restricted condition. The wearable with a tight-fit upper armband employs a photoplethysmography technology to determine pulse rates and inter-beat intervals. Different algorithms (including a deep neural network) were applied to five-minute periods photoplethysmography datasets for the detection of AF. A total of 2306 h of parallel recording time could be obtained in 102 patients; 1781 h (77.2%) were automatically interpretable by an algorithm. Sensitivity to detect AF was 95.2% and specificity 92.5% (area under the receiver operating characteristics curve (AUC) 0.97). Usage of deep neural network improved the sensitivity of AF detection by 0.8% (96.0%) and specificity by 6.5% (99.0%) (AUC 0.98). Detection of AF by means of a wearable is feasible in hospitalized but physically active patients. Employing a deep neural network enables reliable and continuous monitoring of AF.

Keywords: atrial fibrillation; clinical trial; deep neural network; photoplethysmography; wearable sensors.

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Conflict of interest statement

The author declares that there is no conflict of interest. This trial was an Investigator Initiated Trial. This study used the wearable “Everion”-Device provided by Biovotion AG, Switzerland. Biovotion did not provide any financial support for the research and had no impact on writing of the manuscript. Biovotion did not participate in the analysis of the data or influence the conclusions in any sense.

Figures

Figure A1
Figure A1
(a–c) The accuracy of heart rate estimation by the medical wearable compared to ECG was calculated by using the Spearmen correlation (left figure). Additionally, a Bland–Altman graph was plotted with 95% LoA (Limits of Agreement) (right figure). Automated ECG processing was performed using an open-source algorithm (Sedghamiz H. Complete Pan Tompkins Implementation ECG QRS detector. In: MATLAB Central File Exchange 2019.).
Figure A1
Figure A1
(a–c) The accuracy of heart rate estimation by the medical wearable compared to ECG was calculated by using the Spearmen correlation (left figure). Additionally, a Bland–Altman graph was plotted with 95% LoA (Limits of Agreement) (right figure). Automated ECG processing was performed using an open-source algorithm (Sedghamiz H. Complete Pan Tompkins Implementation ECG QRS detector. In: MATLAB Central File Exchange 2019.).
Figure 1
Figure 1
Flow-chart of patient disposition for algorithm development and group classification for the trial.
Figure 2
Figure 2
Recordings setup with the medical wearable attached to the left upper arm and ECG Holter (a); wearable (b); recorded signals (c) (first-row photoplethysmography (PPG), second-row ECG; showing an atrial fibrillation (AF) recording); Poincaré plot of PPG derived inter-beat intervals in AF (d) and sinus rhythm (e).
Figure 3
Figure 3
First two principle components of the latent space from the unsupervised Deep Learning approach for five-minute periods. Results of one-nearest neighbor classification for individual periods are shown that would be interpreted as AF (orange) or non-AF (blue).
Figure 4
Figure 4
Percentage of five-minute periods with a positive physical activity index of all patients for 24 h.
See this image and copyright information in PMC

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