Article

QRS complexes detection for ECG signal: the Difference Operation Method.

Department of Electrical Engineering, National Central University, Jhongli 320, Taiwan, ROC.
Computer Methods and Programs in Biomedicine (Impact Factor: 1.09). 10/2008; 91(3):245-54. DOI: 10.1016/j.cmpb.2008.04.006
Source: PubMed

ABSTRACT This paper proposes a simple and reliable method termed the Difference Operation Method (DOM) to detect the QRS complex of an electrocardiogram (ECG) signal. The proposed DOM includes two stages. The first stage is to find the point R by applying the difference equation operation to an ECG signal. The second stage looks for the points Q and S based on the point R to find the QRS complex. From the QRS complex, the T wave and P wave can be obtained by the existing methods. Some records (QRS complex and T and P waves) of ECG signals in MIT-BIH arrhythmia database is tested to show the DOM has a much more precise detection rate and faster speed than other methods.

11 Followers
 · 
698 Views
  • Source
    2012 IEEE Symposium on Industrial Electronics and Applications (ISIEA), Bandung, Indonesia; 09/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Cardiovascular system study using ECG signals have evolved tremendously in the domain of electronics and signal processing. However, there are certain floating challenges unresolved in the analysis and detection of abnormal performances of cardiovascular system. As the medical field is moving towards more automated and intelligent systems, wrong detection or wrong interpretations of ECG waveform of abnormal conditions can be quite fatal. Since the PQRST signals vary their positions randomly, the process of locating, identifying and classifying each feature can be cumbersome and it is prone to errors. Here we present an automated scheme using adaptive wavelet to detect prominent R-peak with extreme accuracy and algorithmically tag and mark the coexisting peaks P, Q, S, and T with almost same accuracy. The adaptive wavelet approach used in this scheme is capable of detecting R-peak in ECG with 99.99% accuracy along with the rest of the waveforms.
    Signal and Information Processing (ChinaSIP), 2013 IEEE China Summit & International Conference on; 01/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Continuous existence of negative emotions (disgust, anger, fear and sad) over a longer period of time induces emotional stress. This emotional stress can be analyzed through physiological signal characteristics such as Electrocardiogram (ECG), Electromyogram (EMG), etc. In this work, we have proposed a customized protocol experiment to induce emotional stress through audio-visual stimuli (video clips) and simultaneously acquired ECG signals. ECG signals are preprocessed using Elliptic filter and Discrete Wavelet Transform (DWT). Heart Rate Variability (HRV) signals is derived from ECG signals through QRS detection algorithm. Heart rate (HR) is used as a statistical feature to distinguish the emotional stress through a nonlinear classifier (K Nearest Neighbor (KNN)) into three different classes namely, negative emotions, positive emotions (surprise and happy) and neutral. We have analyzed the HRV signals based on segmenting the data into 5 and 10 segments. The maximum classification rate of 93.1% on positive emotion, 85.1% on negative emotion and 71.7% on neutral state is achieved using KNN. Indeed, the negative emotions are further categorized as emotional stress and emotional non-stress and achieved a maximum classification rate of 82.9% and 86.9%, respectively. This accuracy proved that the customized protocol experiment is successful in inducing emotional stress among subjects.
    Industrial Electronics and Applications (ISIEA), 2012 IEEE Symposium on; 01/2012

Preview

Download
46 Downloads
Available from