PT  - JOURNAL ARTICLE
AU  - Ming-Zher Poh
AU  - Yukkee Cheung Poh
AU  - Pak-Hei Chan
AU  - Chun-Ka Wong
AU  - Louise Pun
AU  - Wangie Wan-Chiu Leung
AU  - Yu-Fai Wong
AU  - Michelle Man-Ying Wong
AU  - Daniel Wai-Sing Chu
AU  - Chung-Wah Siu
TI  - Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms
AID  - 10.1136/heartjnl-2018-313147
DP  - 2018 May 31
TA  - Heart
PG  - heartjnl-2018-313147
4099  - http://heart.bmj.com/content/early/2018/05/31/heartjnl-2018-313147.short
4100  - http://heart.bmj.com/content/early/2018/05/31/heartjnl-2018-313147.full
AB  - Objective To evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.Methods We trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison.Results In the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924–0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%).Conclusions In this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.