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In this paper a heuristic method, called Moving Window K-Nearest Neighbors (MW-KNN), for detecting QRS complexes was developed. To achieve this, a new simple 2-D geometrical feature space (feature space dimension was equal to 2) was extracted from the original electrocardiogram (ECG) signal. In this method, a sliding window was moved sample-by-sample on the preprocessed ECG signal. During each forward sliding, an artificial image was generated from the excerpted segment allocated in the window. Each image estimated by a 300×300 pixels matrix. Then, a pictorial-geometrical feature extraction technique based on curve-length was applied to each image for establishment of an appropriate feature space. Afterwards the K-Nearest Neighbors (KNN) Classification method was designed and implemented to the ECG signal. The proposed methods were applied to DAY general hospital high resolution holter data. For detection of QRS complex the average values of sensitivity Se = 99.93% and positive predictivity P+ = 99.88% were obtained.

Keywords: Feature Extraction, Electrocardiogram, QRS Complexes, MW- KNN, Fault detection

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