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Aims: Electrocardiography is a common non-invasive diagnostic tool used to evaluate the heart's electrical and muscular processes. Every nurse must understand the fundamental electrocardiogram rhythms. This study aimed to assess nurses’ knowledge of electrocardiogram interpretation at Al-Hussein Teaching Hospital in Al-Samawa City.
Instruments & Methods: This descriptive study was carried out on nurses at Al-Hussein Teaching Hospital in Al-Samawa City, Iraq, from 1st December 2022 to 30th February 2023. Fifty nurses were selected by a purposive sampling (non-probability) method. A researcher-made questionnaire was designed to collect data. This questionnaire consisted of two sections. The first section was related to demographic information, and the second section had 15 self-report questions to measure the knowledge level of nurses. Data were analyzed using SPSS 24.0 software.
Findings: Most of the nurses were women (64.0%) and in the age group of 25-29 years (36.0%), and more than half of them were married (74.0%). Most nurses graduated from the institute (42.0%), and the total years of service of 40% were 1-10 years. The nurses' knowledge regarding electrocardiogram Interpretation was at a poor level.
Conclusion: The knowledge of electrocardiogram interpretation in Al-Hossein Teaching Hospital in Al-Samawa City is at a poor level.
 

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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.