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%.

International audienceElectrocardiogram (ECG) datasets are among the most challenging records that have been widely studied for early automatic prediction of cardiac anomalies. In order to achieve high performance automatic prediction, existing works make use of complex and time consuming techniques and/or show high rates of false positives. In this paper, we introduce a new method to analyze an ECG dataset and perform an efficient prediction of 7 ST-segment and T-wave anomalies related to Myocardial Infarction (MI) or Ischemia. Our method combines both Decision Trees Boosting and Random Under Sampling (RUS) techniques to respectively improve the prediction performance and solve the class imbalance problem. This method, named RUSBoost, has been validated using data of 7 leads, collected from a real ECG dataset [1], and the obtained results show a higher balance between true and false positives for all the 7 leads. Obtained average sensitivity and specificity are respectively 86% and 94.85%, which outperform the existing results of other related works

Abstract-Cardiovascular diseases are the leading cause of death in the world, and Myocardial Infarction (MI) is the most serious one among those diseases. Patient monitoring for an early detection of MI is important to alert medical assistance and increase the vital prognostic of patients. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real-time applications that are able to accurately detect MI non-invasively. In this paper, we propose a new approach for early detection of MI using wireless body area networks. The proposed approach analyzes the patient electrocardiogram (ECG) in real time and extracts from each ECG cycle the ST elevation which is a significant indicator of an upcoming MI. We use the sequential change point detection algorithm CUmulative SUM (CUSUM) to early detect any deviation in ST elevation time series, and to raise an alarm for healthcare professionals. The experimental results on the ECG of real patients show that our proposed approach can detect MI with low delay and high accuracy.

Various wearable devices are foreseen to be the key components in the future for vital signs monitoring as they offer a non-invasive, remote and real-time medical monitoring means. Among those, Wireless Body Sensors (WBS) for cardiac monitoring are of prominent help to early detect CardioVascular Diseases (CVD) by analyzing 24/24 and 7/7 collected cardiac data. Today, most of these WBS systems for CVD detection, include only limited automatic anomalies detection, particularly regarding ECG anomalies. Severe CVD, such as Myocardial Infarction or Ischemia, needs to achieve an advanced analysis of ECG waves known as P, Q, R, S and T. In particular, the T-wave and its specific changes. In this paper, we focus on T-wave anomalies detection in a context of WBS. Our study suggests an accurate and lightweight Twave changes detection model which suits well an ECG monitoring system based on WBS architecture. We performed a comparative study of 7 well-known supervised learning classification models, on real ECG data sets from 7 different leads. We compared the results from both perspectives of classification and processing times. Our results show that the C4.5 Decision Tree technique performs better results with 92.54% Accuracy, 96.06% Sensibility, 55.41% Specificity and 7.41% Error Rate.