An ECG monitoring system for prediction of cardiac anomalies using WBAN

Abstract

Abstract-Cardiovascular diseases (CVD) are known to be the most widespread causes to death. Therefore, detecting earlier signs of cardiac anomalies is of prominent importance to ease the treatment of any cardiac complication or take appropriate actions. Electrocardiogram (ECG) is used by doctors as an important diagnosis tool and in most cases, it's recorded and analyzed at hospital after the appearance of first symptoms or recorded by patients using a device named holter ECG and analyzed afterward by doctors. In fact, there is a lack of systems able to capture ECG and analyze it remotely before the onset of severe symptoms. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real time systems able to accurately analyze ECG and detect cardiac abnormalities. In this paper, we propose a new CVD detection system using Wireless Body Area Networks (WBAN) technology. This system processes the captured ECG using filtering and Undecimated Wavelet Transform (UWT) techniques to remove noises and extract nine main ECG diagnosis parameters, then the system uses a Bayesian Network Classifier model to classify ECG based on its parameters into four different classes: Normal, Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC) and Myocardial Infarction (MI). The experimental results on ECGs from real patients databases show that the average detection rate (TPR) is 96.1% for an average false alarm rate (FPR) of 1.3%.

Wearable Sensors for Remote Health Monitoring

“…The electrodes contain conducting gel in the middle of the pad that functions as a conduction medium between the skin and the electrode. This conducting gel has potential toxic and irritant effects on the skin and is thus not best suitable to use for long–term ambulatory monitoring system though currently, it is the only system available [ 10 , 27 ]. However, only a few numbers of electrodes are used in ambulatory ECG monitoring system at the cost of limited information ( Figure 2 c).…”

Online anomaly detection for long-term ECG monitoring using wearable devices

“…In practice, we reduce the overall cost of the OMP algorithm by a factor κ, from O(κpn) to O(pn). The following proves the equivalence of (6) and (30). Proposition 1.…”

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

“…Real-time Cloud-based ECG monitoring systems for arrhythmia detection were proposed in [175,176], whereas AF is commonly diagnosed based on ECG signals [57,67,[177][178][179]. ECG signals contribute to the detection of several cardiovascular abnormalities, which were addressed in [180][181][182]. Developing evolutionary, efficient, and accurate automatic diagnostic techniques have been always an open area of research to overcome many challenges, among which are medical device capabilities, ECG diagnostics overlapping patterns, and other ECG signal-related issues throughout the system's lifecycle.…”