Dataset Information

Performance of an atrial fibrillation detection algorithm using continuous pulse wave monitoring.

ABSTRACT:

Background

Detecting asymptomatic and undiagnosed atrial fibrillation (AF) is increasingly important. Recently, we developed a wristwatch-based pulse wave monitor (PWM; Seiko Epson, Japan) capable of long-term recording, with an automatic diagnosis algorithm that uses frequency-based pulse wave analysis. The aim of this study was to evaluate the validity of continuous pulse wave monitoring for detection of AF.

Methods

During the electrophysiological study (EPS) in patients with AF, simultaneous pulse wave monitoring and Holter electrocardiograms (ECG) were recorded (n = 136, mean age 62.7 ± 10.9 years). The diagnostic accuracy of the PWM for AF was compared to the Holter ECG diagnosis. Standard performance metrics (sensitivity [Se], specificity [Sp], positive predictive value [PPV], and negative predictive value [NPV]) were calculated. The duration-based measurements were based on the diagnosis concordance ratios for the duration of time between diagnosis detected by the PWM and true diagnosis by the Holter ECG (AF or not AF). The episode-based performance metrics were based on the proportion of episodes appropriately detected with the PWM relative to episodes determined by the Holter ECG.

Results

The total recording time was 1,542,770 s (AF: 270,945 s). A high diagnostic Sp (patient average: 96.4%, cumulative: 97.7%) and NPV (patient average: 95.1%, cumulative: 96.8%) were obtained in the duration-based results. In the episode-based metrics, all indices significantly improved with longer AF episode durations.

Conclusions

Continuous pulse wave monitoring can provide accurate and dependable information to aid in AF diagnosis. A high validity in confirming freedom from AF was shown by a high NPV.

SUBMITTER: Kashiwa A

PROVIDER: S-EPMC6931792 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Performance of an atrial fibrillation detection algorithm using continuous pulse wave monitoring.

Kashiwa Asami A Koyama Fumio F Miyamoto Koji K Kamakura Tsukasa T Wada Mitsuru M Yamagata Kenichiro K Ishibashi Kohei K Inoue Yuko Y Okamura Hideo H Nagase Satoshi S Noda Takashi T Aiba Takeshi T Watanabe Shinichiro S Goto Akihiro A Yasuda Satoshi S Kusano Kengo K

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc 20181102 2

<h4>Background</h4>Detecting asymptomatic and undiagnosed atrial fibrillation (AF) is increasingly important. Recently, we developed a wristwatch-based pulse wave monitor (PWM; Seiko Epson, Japan) capable of long-term recording, with an automatic diagnosis algorithm that uses frequency-based pulse wave analysis. The aim of this study was to evaluate the validity of continuous pulse wave monitoring for detection of AF.<h4>Methods</h4>During the electrophysiological study (EPS) in patients with AF ...[more]

PMID: 30387545

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Project description: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.

Project description:AimsThe HeartLogic Index combines data from multiple implantable cardioverter defibrillators (ICDs) sensors and has been shown to accurately stratify patients at risk of heart failure (HF) events. We evaluated and compared the performance of this algorithm during sinus rhythm and during long-lasting atrial fibrillation (AF).Methods and resultsHeartLogic was activated in 568 ICD patients from 26 centres. We found periods of ≥30 consecutive days with an atrial high-rate episode (AHRE) burden <1 h/day and periods with an AHRE burden ≥20 h/day. We then identified patients who met both criteria during the follow-up (AHRE group, n = 53), to allow pairwise comparison of periods. For control purposes, we identified patients with an AHRE burden <1 h throughout their follow-up and implemented 2:1 propensity score matching vs. the AHRE group (matched non-AHRE group, n = 106). In the AHRE group, the rate of alerts was 1.2 [95% confidence interval (CI): 1.0-1.5]/patient-year during periods with an AHRE burden <1 h/day and 2.0 (95% CI: 1.5-2.6)/patient-year during periods with an AHRE-burden ≥20 h/day (P = 0.004). The rate of HF hospitalizations was 0.34 (95% CI: 0.15-0.69)/patient-year during IN-alert periods and 0.06 (95% CI: 0.02-0.14)/patient-year during OUT-of-alert periods (P < 0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95% CI: 1.67-55.31) during periods with an AHRE burden <1 h/day and 2.70 (95% CI: 1.01-28.33) during periods with an AHRE burden ≥20 h/day. In the matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95% CI: 0.12-0.60)/patient-year during IN-alert periods and 0.04 (95% CI: 0.02-0.08)/patient-year during OUT-of-alert periods (P < 0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44).ConclusionPatients received more alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio in comparison with non-AF periods and non-AF patients.Clinical trial registrationhttp://clinicaltrials.gov/Identifier: NCT02275637.

Dataset Information

Performance of an atrial fibrillation detection algorithm using continuous pulse wave monitoring.

Background

Methods

Results

Conclusions

Publications

Performance of an atrial fibrillation detection algorithm using continuous pulse wave monitoring.

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