Atrial Fibrillation Analysis for Real Time Patient Monitoring

Aliami, Ragheed; Stranieri, Andrew; Marzbanrad, Faezeh; Balasubramanian, Venki; Jelinek, Herbert F.

doi:10.22489/cinc.2017.118-272

Cited by 2 publications

(1 citation statement)

References 24 publications

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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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