Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation

Harju, Jarkko; Tarniceriu, Adrian; Parák, Jakub; Vehkaoja, Antti; Yli‐Hankala, Arvi; Korhonen, Ilkka

doi:10.1088/1361-6579/aac9a9

Cited by 50 publications

(39 citation statements)

References 41 publications

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“…It has been shown previously that IBI extracted from PPG corresponds closely with IBI extracted from ECG [67], [68]. This allowed us to exploit existing high-quality ECG datasets for this study.…”

Section: Discussionmentioning

confidence: 56%

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

Harper

Southern

2022

IEEE Trans. Affective Comput.

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Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple modalities − audio, visual, and physiological − to classify emotional state. However, practical considerations 'in the wild' limit collection of this physiological data to commoditised heartbeat sensors. Furthermore, real-world applications often require some measure of uncertainty over model output. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat data. We further propose a Bayesian framework for modelling uncertainty over valence predictions, and describe a procedure for tuning output according to varying demands on confidence. We benchmarked our framework against two established datasets within the field and achieved peak classification accuracy of 90%. These results lay the foundation for applications of affective computing in realworld domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty.

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Section: Discussionmentioning

confidence: 56%

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

Harper

Southern

2022

IEEE Trans. Affective Comput.

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“…Most of the studies include fewer than 100 volunteers. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] Six studies had a study population of more than 100 subjects [35][36][37][38][39][40] ; the largest monitored 1617 volunteers in an ambulatory setting. 36 Rhythm characteristics in the study populations varied among studies.…”

Section: Study Populationsmentioning

confidence: 99%

Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review

Eerikäinen

Bonomi

Dekker

et al. 2020

Cardiovascular Digital Health Journal

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Early detection and diagnosis of atrial fibrillation (AF) is essential in order to prevent stroke and other severe health consequences. The challenges in diagnosing AF arise from its intermittent and asymptomatic nature. Wrist-worn devices that use monitoring based on photoplethysmography have been proposed recently as a possible solution because of their ability to monitor heart rate and rhythm for long periods of time at low cost. Long-term continuous monitoring with implantable devices has been shown to increase the percentage of detected AF episodes, but the additional value of wrist-worn devices has yet to be determined. In this review, we present the state of the art in AF detection with wrist-worn devices, discuss the potential of the technology and current knowledge gaps, and propose directions for future research. The state-ofthe-art methods show excellent accuracy for AF detection. However, most of the studies were conducted in hospital settings, and more studies showing the accuracy of the technology for ambulatory long-term monitoring are needed. Objective comparison of results and methodologies among different studies currently is difficult due to the lack of adequate public datasets.

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“…Edgerton and colleagues 16 suggested that 7-days' monitoring after ablation of AF was ideal, but the optimal monitoring period after cardiac surgery without ablation has not been defined. Evolving developments in wearable technology 17,18 may represent an opportunity for more convenient ambulatory monitoring but need further validation of their accuracy. Patients with existing cardiac devices represent an opportunity to study the incidence of POAF by routinely interrogating such devices postoperatively.…”

Section: Future Directions: Opportunitiesmentioning

confidence: 99%

Treatment of postoperative atrial fibrillation: The long road ahead

Hui

Lee

2020

The Journal of Thoracic and Cardiovascular Surgery

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Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation

Abstract: Results suggest that wrist PPG measurement allows accurate HR and beat-to-beat HR monitoring also in AF patients, and could be used for differentiating between SR and AF with very good sensitivity.

Cited by 50 publications

References 41 publications

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review

Treatment of postoperative atrial fibrillation: The long road ahead

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