Automated real-time atrial fibrillation detection on a wearable wireless sensor platform

Rincon, Francisco; Grassi, Paolo Roberto; Khaled, N.; Atienza, David; Sciuto, D.

doi:10.1109/embc.2012.6346465

Cited by 35 publications

(18 citation statements)

References 10 publications

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“…This proves the suitability of CS as a promising low-power compression technique for wearable cardiac monitoring systems. In addition, similar results have been obtained for applications performing a diagnosis at a higher level of abstraction, such as Atrial Fibrillation (AF) detection [25]. This cardiac monitoring application uses the results of the ECG delineation to analyze the regularity of the heart beat rate as well as the shape of the P wave, which constitute two characteristic irregularities of AF episodes.…”

Section: Resultsmentioning

confidence: 50%

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

Braojos

Mamaghanian

Dias

et al. 2014

Proceedings of the 51st Annual Design Automation Conference

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Abstract-This paper presents the system-level architecture of novel ultra-low power wireless body sensor nodes (WBSNs) for real-time cardiac monitoring and analysis, and discusses the main design challenges of this new generation of medical devices. In particular, it highlights first the unsustainable energy cost incurred by the straightforward wireless streaming of raw data to external analysis servers. Then, it introduces the need for new cross-layered design methods (beyond hardware and software boundaries) to enhance the autonomy of WBSNs for ambulatory monitoring. In fact, by embedding more onboard intelligence and exploiting electrocardiogram (ECG) specific knowledge, it is possible to perform real-time compressive sensing, filtering, delineation and classification of heartbeats, while dramatically extending the battery lifetime of cardiac monitoring systems. The paper concludes by showing the results of this new approach to design ultra-low power wearable WBSNs in a real-life platform commercialized by SmartCardia. This wearable system allows a wide range of applications, including multi-lead ECG arrhythmia detection and autonomous sleep monitoring for critical scenarios, such as monitoring of the sleep state of airline pilots.

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Section: Resultsmentioning

confidence: 50%

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

Braojos

Mamaghanian

Dias

et al. 2014

Proceedings of the 51st Annual Design Automation Conference

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“…Comparing with the existing methods [22][23][24][25][26][27][28][29] to detect AF, the proposed method does not require complex numerical tools such as RdR maps [22] or Receiver Operating Characteristic (ROC) curves [25,28]. It also does not require computationally intensive algorithms [23,27] or probability-based data adaptive techniques [24], community-based screening [25], or mobile phone apps [29]. Instead, this research work focused on developing a noninvasive method to detect AF in a home setting based on low cost data acquisition hardware and low demand for computing power.…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Atrial Fibrillation Based on ECG Signals

Ahmed

Zhu

2020

Bioengineering

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Atrial fibrillation, often called AF is considered to be the most common type of cardiac arrhythmia, which is a major healthcare challenge. Early detection of AF and the appropriate treatment is crucial if the symptoms seem to be consistent and persistent. This research work focused on the development of a heart monitoring system which could be considered as a feasible solution in early detection of potential AF in real time. The objective was to bridge the gap in the market for a low-cost, at home use, noninvasive heart health monitoring system specifically designed to periodically monitor heart health in subjects with AF disorder concerns. The main characteristic of AF disorder is the considerably higher heartbeat and the varying period between observed R waves in electrocardiogram (ECG) signals. This proposed research was conducted to develop a low cost and easy to use device that measures and analyzes the heartbeat variations, varying time period between successive R peaks of the ECG signal and compares the result with the normal heart rate and RR intervals. Upon exceeding the threshold values, this device creates an alert to notify about the possible AF detection. The prototype for this research consisted of a Bitalino ECG sensor and electrodes, an Arduino microcontroller, and a simple circuit. The data was acquired and analyzed using the Arduino software in real time. The prototype was used to analyze healthy ECG data and using the MIT-BIH database the real AF patient data was analyzed, and reasonable threshold values were found, which yielded a reasonable success rate of AF detection.

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“…Analogously, we extract the same set of features for the T wave, and we define signal s t . The normalized power of this signal in the frequency bands [0, 5] Hz and [5,30] Hz, along with its Shannon entropy is calculated. These two frequency bands have been selected, as we have noticed clear differences in power for NSR and OthR signals.…”

Section: P-wave and T-wave Featuresmentioning

confidence: 99%

“…The combined analysis of atrial and ventricular responses can improve the accuracy in AF detection. In particular, Rincon et al [5] combine heart-rate analysis with a P-wave analysis to detect AF in real-time on a wearable device.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Hierarchical Cardiac Rhythm Classification Methodology Based on Electrocardiogram Fiducial Points

Sopic

Giovanni

Aminifar

et al. 2017

Computing in Cardiology Conference (CinC)

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Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals and abnormal P waves. However, discriminating AF from a normal sinus rhythm or from other types of rhythms remains a challenging problem today. Methods: We analyze the database of PhysioNet/Computing in Cardiology Challenge 2017 to validate our heart rhythm classification technique. The database contains short-term ECG recordings, labelled as normal sinus rhythm, AF, other types of rhythm, and noise. We extract different morphology-based features of ECG signals, and we design a multiclass classifier based on error-correcting output codes, along with a random forest classifier for binary decision making. Results: We test the performance of our classifiers based on the F 1 score of each class and the average F 1 score of all the classes. The final F 1 score obtained on the hidden test set of challenge is 80%. Conclusions: Our results show that our classifier is robust and that it is able to discriminate AF from normal sinus, other rhythms, and noise, based on the morphology of the ECG signal.

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Automated real-time atrial fibrillation detection on a wearable wireless sensor platform

Cited by 35 publications

References 10 publications

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

Early Detection of Atrial Fibrillation Based on ECG Signals

A Hierarchical Cardiac Rhythm Classification Methodology Based on Electrocardiogram Fiducial Points

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