m-QRS: An Efficient QRS Detection Algorithm for Mobile Health Applications Amir Mohammad Amiri1, Abhinav2, Kunal Mankodiya1 1
Dept. of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, RI, USA 2 Cardea Biomedical Technologies Pvt. Ltd, Cardea Labs, New Delhi, India
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Abstract –When using the available m-health systems, ECG data for a small duration is recorded and sent to a server for processing and arrhythmia detection. Since arrhythmia occurrence is not so frequent in early stages, a need is felt to develop a real time and continuous arrhythmia monitoring system on the phone itself. Since arrhythmia occurrence in early stages is less frequent, it calls for a need to develop algorithms to process ECG data in real-time to detect arrhythmia on the phone itself. This paper provides a novel approach to detect QRS complexes from a high fidelity ECG data obtained from ® B.E.A.T. hardware for arrhythmia monitoring in real time. Our approach referred to as m-QRS uses continuous wavelet transform at its kernel and its efficiency is compared to that of Pan-Tompkins’s which is a standard QRS detection algorithm widely used for arrhythmia detection. It was found that our algorithm uses lesser computation time when compared to PanTompkins and was found to be mobile friendly. This provides an opportunity to develop further algorithms to perform continuous and real-time arrhythmia monitoring on affordable smartphones without internet dependability. Keywords— ECG, B.E.A.T. , mobile health, m-QRS algorithm, Pan- Tompkins algorithm. ®
I. INTRODUCTION According to a report given by Cleveland clinic [1], Sudden Cardiac Death (SCD) is the largest cause of natural death in the United States, causing about 325,000 adult deaths in the United States each year. Most SCD are caused by abnormal heart rhythms called arrhythmias. The most common life-threatening arrhythmia is ventricular fibrillation. When it occurs, the heart is unable to pump blood and death will occur within minutes, if left untreated. According to another report [3], roughly 14.4 million people, or 1 in every 18 individuals, suffer from arrhythmia in United States alone. This problem is not just local but present in developing countries in a rampant fashion [2, 3]. For example, in India alone, around 45 million people are at high risk of a stroke. India accounts for 60% of the world’s cardiovascular diseases (CVDs) [4]. Moreover the incidences of CVDs have gone up considerably for people between the age groups of 25 and 69 to 24.8% [5].With the recent advances in the telecommunication and computer technology, the capability of smartphones with high-speed processors is phenomenal. Because of high competition among mobile phone manufacturers, advanced processors based phones are available at very affordable rates. This has allowed users to
look upon smartphones as a platform to develop consumer grade healthcare devices that will empower users to monitor their vital parameters on their smartphone itself [6]. If deployed in smartphones, early arrhythmia detection can help better management of heart diseases. A single lead high fidelity ECG is required to monitor arrhythmia. The existing devices [14] record the ECG data on the phone for a particular duration and upload the data to the server for processing and arrhythmia detection. This methodology is inefficient as the user might get a ‘Normal’ report whereas he/she might get an arrhythmia in the very next second. For developing countries, internet availability for such services is scarce. Since arrhythmia occurrence in early stages is less frequent, it calls for a need to develop algorithms to process ECG data in realtime to detect arrhythmia on the phone itself. As shown in Fig. 1, the ECG wave consists of major components such as P wave, PR interval, RR interval, QRS complex, pulse train, ST segment, T wave, QT interval and infrequent presence of U wave. Presence of arrhythmias can be seen in the changes within the QRS complex, RR interval and pulse train. For instance a narrow QRS complex (
