A Combined Syntactical and Statistical Approach for R Peak Detection in Real-Time Long-Term Heart Rate Variability Analysis  
[PERSON]  
1 Department of Human and Environmental Informatics, Graduate School of Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan; [EMAIL_ADDRESS]  
[PERSON]  
Because of its usefulness, our goal is to develop a system for a real-time long-term HRV analysis that can be embedded into a bedside monitor for use in intensive care units or high care units to provide a quantitative assessment of long-term outcomes for patients [5]. Such analysis requires that the analyzed HRV only reflects the ANS activity. Accordingly, ensuring that processed beats are normal beats is essential for an effective analysis. In addition, the presence of arrhythmic events, in which beat rhythms are not directly caused by the ANS, significantly affects HRV analysis. To eliminate such events from tracked data, some authors proposed excluding signals containing more than 10 ectopic beats per hour [6], while others recommended accepting ectopic beats comprising up to 5 [7], 8 [8], 10 [9; 10], or even 15% [11] of all RR intervals. The study reported herein represents a specific component of our work on HRV analysis systems, for which we intend to detect only the normal heartbeat and exclude ectopic or arrhythmic heartbeats [5].  
In long-term R peak detection, noises that can distort the ECG signal, such as muscle noise, some artefacts arising from electrode and body motion, and baseline wander caused by perspiration and respiration, have an increased probability of occurrence. Such noises make long-term R peak detection more difficult in terms of both accuracy and effect on the physiological variability of the QRS complex itself. A long-term HRV analysis requires a reliable and customized R peak detector to provide information on an uninterrupted series of normal RR intervals. In fact, a single misdetection of an R peak can undermine the results of further analyses (e.g., time-domain, frequency-domain, or nonlinear analysis). In this work, we propose a new method designed to accurately detect in real time the R peak of normal heartbeats from long-term ECG signals specifically intended for HRV analysis.  
A useful criterion in long-term ECG monitoring is the energy consumption efficiency. The proposed method meets this criterion and that of computational efficiency. Moreover, it is much faster than most existing methods. For instance, finite impulse response (FIR) filters are typically used in the ECG preprocessing stage and have computational costs of at least \((m\times n)\), where \(m\) and \(n\) are the kernel and data sizes, respectively. Cascades of more than one FIR filter are commonly used, as is done in bandpass or band-reject filters. Depending on the method, the detection stage can employ even more complex computations or, at least, computations that are not less complex than \(O(n)\). In contrast, the proposed method has a computational cost proportional to \(n\) at the preprocessing stage and a cost proportional to \(k\) at the detection stage, where \(k\) is a fraction of \(n\). This reduced computational complexity has been proven useful in our implementation of a client-based HRV detector [5]. It is also appropriate for other applications, in which energy consumption is a concern, or implementations that present limited processing resources (e.g., portable embedded devices).  
The remainder of this paper is organized as follows: Section 2 describes the preprocessing method and how R peaks are detected; Section 3 describes how performance is improved using the proposed algorithm and its overall low cost; and Section 4 concludes the paper.  
Figure 1: A typical electrocardiography (ECG) waveform and RR interval.