Exploratory data analysis based efficient QRS-complex detection technique with minimal computational load
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SCIENTIFIC PAPER
Exploratory data analysis based efficient QRS‑complex detection technique with minimal computational load Jagdeep Rahul1 · Marpe Sora2 · Lakhan Dev Sharma3 Received: 21 January 2020 / Accepted: 14 July 2020 © Australasian College of Physical Scientists and Engineers in Medicine 2020
Abstract Detection of QRS-complex in the electrocardiogram (ECG) plays a decisive role in cardiac disorder detection. We face many challenges in terms of powerline interference, baseline drift, and abnormal varying peaks. In this work, we propose an exploratory data analysis (EDA) based efficient QRS-complex detection technique with minimal computational load. This paper includes median and moving average filter for pre-processing of the ECG. The peak of filtered ECG is enhanced to third power of the signal. The root mean square (rms) of the signal is estimated for the decision making rule. This technique adapted the new concept for isoelectric line identification and EDA based QRS-complex detection. In this paper, total 10,70,981 beats were used for validation from MIT BIH-Arrhythmia Database (MIT-BIH), Fantasia Database (FDB), European ST-T database (ESTD), a self recorded dataset (SDB), and fetal ECG database (FTDB). Overall sensitivity of 99.65 % and positive predictivity rate of 99.84 % have been achieved. The proposed technique doesn’t require selection, setting, and training for QRS-complex detection. Thus, this paper presents a QRS-complex detection technique based on simple decision rules. Keywords ECG · QRS-Complex · Median filter · Moving average filter · Root mean square · RR-interval
Introduction Cardiovascular diseases (CVDs) are major cause of mortality in worldwide. In 2015, almost 17.7 million people lost their life due to CVDs. It is approximately 31% of all global mortality, where 6.7 million due to heart attack and 7.4 million due to coronary heart disease [1]. The death caused by impetuous cardiac arrest due to ventricular arrhythmia is almost 80% of total premature mortality [2]. As per the world health organization report, by 2030 the death due to CVDs may increase upto 23.3 million globally. Increased deaths due to CVDs, resulted in attention among the researchers to work in the field of cardiac health. The risk of heart disease increases with age and is greater at above the age of 55 years [3]. The developed countries spend * Jagdeep Rahul [email protected] 1
Department of Electronics and Communication Engineering, Rajiv Gandhi University, Itanagar, India
2
Department of Computer Science and Engineering, Rajiv Gandhi University, Itanagar, India
3
School of Electronics Engineering, VIT-AP University, Amaravati, India
around 1 to 2 % of their health care expenditure on the heart patient. Hence, heart failure is the most chronic disease in developed nations because of their elder population [4]. The number of heart patients has increased drastically over the past decades and in the future it can increase further due to rise in population of elders in the society [5]. The
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