Research Article  Open Access
An Adaptive and TimeEfficient ECG RPeak Detection Algorithm
Abstract
Rpeak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and timeefficient Rpeak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative Rpeaks to positive ones. After that, local maximums were calculated by the firstorder forward differential approach and were truncated by the amplitude and time interval thresholds to locate the Rpeaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MITBIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MITBIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MITBIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional PanTompkins method.
1. Introduction
Electrocardiogram (ECG) can describe the electrical activity of the heart and is an essential tool for the diagnosis of cardiovascular diseases (CAD). With the rapid development of wearable and wireless ECG techniques, realtime and routine ECG monitoring is attracting more and more attention due to the increasing popularization of medical health, especially for the elderly people [1]. Recent years, lots of publications about ambulatory ECG monitoring devices have been reported [2–4], aiming to automatically monitor the heart activities and give the feedback of any CAD early warning in real time. However, this application still needs significant development due to the challenge of unexpected noise effects in ECG signal, such as baseline drift, electrode motion and stretching, motion artifacts, and muscle noise [5, 6], which impedes the automatic ECG processing technology to perform effectively. The primary sources of noises are electrical activities of muscles and baseline drift caused by respiration, poor contact of electrodes, and equipment or electronic devices [7, 8]. Electrode movement alters the signal baseline and brings the signal fluctuate perpendicularly to the baseline. If the electrode moves drastically enough to drop from the skin, baseline drift will overwhelm the signal and waveform distortion occurs [9]. Motion artifact is generally attributed to the variation of electrodeskin impedance during a subject’s motion. Changed impedance will be treated by the ECG amplifier as a different input, resulting in impedance mismatching and difficult identification of irregular fluctuation on small amplitude waveforms, such as P wave and T wave [10, 11]. Consequently, noise removal is the preliminary issue to consider for in ECG signal processing.
ECG features are essential characteristics for CAD diagnosis. Rpeak detection is the datum since all other features are extracted after the Rpeak location [12]. Accurate Rpeak detection is critical for arrhythmia diagnosis such as atrial premature contraction, tachycardia, and bradycardia [13]. Nevertheless, efficient Rpeak extraction is still difficult in the dynamic and noisy environment due to the timevarying waveform morphology. This would be more difficult when ECG signal is overwhelmed by noises with similar frequency in energy distribution [9].
Over the last decades, numerous techniques have been proposed for Rpeak detection. In [14], a thorough review on Rpeak detection methodologies for portable, wearable, batteryoperated, and wireless ECG devices was elaborated. The authors claimed that the thresholding methods were regarded as the most computationally efficient. However, the suitable parameter settings for thresholds were difficult. The most widely used Rpeak detection method, proposed by Pan and Tompkins [15], is the PanTomkins method. It is a thresholdbased method with low complexity. Other algorithms of Rpeak detection can be classified as pattern recognition [16, 17], wavelet transform [18], mathematical morphology [19], and digital filter [20]. In [10], a realtime Rpeak detector using adaptive thresholding was proposed. This algorithm consisted of preprocessing to initialize Rpeak threshold and thresholding to adaptively modify the threshold. It achieved sensitivity and positive predictivity higher than 99.3%. In [21], a different interferencebased method was developed. This method could effectively distinguish Rpeaks from high amplitude noises but failed to detect Rpeaks when abrupt jump of baseline appeared. Some researchers also conducted ECG feature extraction without predenoising [22, 23]. The detection accuracy could reach up to 94.8%, although much lower than that acquired from the denoised signals.
Time cost is important due to the fastresponding requirement in CAD early warning applications [4], especially in the realtime monitoring. Many ambulatory ECG devices are generally limited in power supply and computation [1]. The conventional feature extraction algorithms are, from a computational perspective, very intensive tasks, which are typically executed in mainframetype computational facilities. A significant power expenditure component in such systems is the energy required by the radio frontend for supporting continuous data transmission, which may not allow a longterm sustainable operation. To this end, some researchers have attempted to develop algorithms of low computational load. Apart from the aforementioned methods, in [24], the authors presented a lowcomplexity ECG feature extraction approach for mobile healthcare applications. This technique was based on the combination of wavelet analysis and timedomain morphology principles. Except for high accuracy and precision, low computation and fast response are also needed in ECG feature extraction.
In this study, an adaptive and timeefficient ECG Rpeak detection algorithm is proposed. The method takes advantage of waveletbased multiresolution analysis (WMRA) and adaptive thresholding. WMRA is applied to strengthen ECG signal representation by extracting ECG frequency interval of interest from widerange frequencies, which contain interference such as baseline drift and motion artifacts. All the noises produce considerable influence on the following thresholding operation. The adaptive thresholding is designed to exclude false Rpeaks in the reconstructed signal by WMRA. The proposed algorithm was tested by the MITBIH arrhythmia database (MITDB) and the QT database (QTDB) [25]. Both accuracy and time consumption of the algorithm were evaluated. By exploring the timefrequency property of ECG, this study aims to conduct preliminary and tentative research on adaptive and timeefficient Rpeak detection for lowquality ECG signals, promoting automatic ECG processing technology for clinical and daily use.
The remainder of the paper is organized as follows. Section 2 elaborated the detailed procedures of the proposed Rpeak detection algorithm. In Section 3, experiment setups were introduced, including the datasets and the evaluation indices. Section 4 demonstrated the experimental results over Rpeak detection accuracy, time consumption and time complexity, and the selection of optimal threshold coefficients. Section 5 discussed the advantages and the potential limitation of our algorithm. The summarization of this study was presented in Section 6.
2. Proposed RPeak Detection Algorithm
The Rpeak detection system is described in Figure 1. The purpose of this study is to develop an algorithm which can effectively identify Rpeaks mixed in different noises.
2.1. Step 1: WMRA Enhancement
WMRA enhances signals using wavelet transform to extract both time and frequency domain information. This method is very suitable for ECG processing since ECG is essentially nonstationary with small amplitude (0.01~5 mV) and low frequency (0.05~100 Hz) [26]. This method also provides low computational cost [27]. By WMRA, signal below 0.05 Hz and above 100 Hz can be filtered from the raw signal. These intervals are not the ECG frequency bands and contain most types of noises [28]. In addition, according to the Nyquist criterion, subfrequency band presented by each decomposition level is directly related to the sampling frequency [26]. Consequently, the ECG signals, sampled at 360 Hz in MITDB and 250 Hz in QTDB as illustrated in [25], are all decomposed up to 8 levels in this study.
Figure 2 shows the decomposition procedure of eightlevel WMRA by using bior6.8 wavelet. For MITDB, consist of frequency components in a range of 0.70–90 Hz, which is the ECG frequency band of interest. with frequency band 90~180 Hz and with frequency band 0~0.70 Hz are beyond the ECG frequency; they are not the considered coefficients containing baseline drift and other interference. Consequently, and are set to zeroes; are kept for reconstruction. Similarly, for QTDB, with frequency band 0~0.49 Hz is set to zero; with frequency components in a range of 0.49–125 Hz are kept. All the retained coefficients are then filtered by the wavelet shrinking threshold algorithm [29]. In this study, soft thresholding is adopted due to its good continuity and no Gibbs phenomenon on step points [30].
2.2. Step 2: Signal Mirroring
For some ECG patterns, such as premature ventricular contraction (PVC) beat, Rpeaks are presented with amplitude below the baseline but other features are above the baseline. To avoid the potential missing detection, signal mirroring is designed. The mirroring procedure for a PVC segment is described in Figure 3. Large negative amplitudes are mirrored by taking the baseline as their symmetrical axis. However, not all the negative amplitudes are mirrored, they should be significantly distinctive from adjacent negative values. This assumption is based on the fact that Rpeaks have steep slopes while other waves such as P wave and T wave have gentle ones [10]. Steep slope means drastic increment and decrement on both sides of local maximum, and the slope is finished within several sampling points. If the absolute amplitude of a negative point is 1.5 times larger than that of the adjacent points within 0.278 seconds (0.278 points) before and after it, then the negative point will be mirrored. where is the signal length, is the amplitude of large negative point with position number in signal, , and is the amplitude of point within 0.278 s before and after the large negative point.
In some literatures [15, 21, 31–33], authors recommend that signal with baseline drift removed could be squared to highlight the difference between true Rpeaks and false ones, such as highamplitude noise and highamplitude P waves. However, this operation may not be suitable in our method due to the differences among Rpeak amplitudes. If the signal is squared, amplitude values below 1 will be smaller than the original, and values above 1 will be enlarged. This increases the difference among true Rpeaks and is adverse for the amplitude threshold to detect potential Rpeaks, especially when a signal segment is mixed with large and small amplitude Rpeaks.
2.3. Step 3: Local Maximum Location and Adaptive Threshold Selection
Local maximums are located by implementing firstorder forward differential in the mirrored signal. The procedure is illustrated as follows. (1)Firstorder forward difference is implemented on ECG signal with .(2)For all the elements in , values less than, equal, and more than 0 are replaced by −1, 0, and 1, respectively, (3)Firstorder forward difference is implemented on the updated , and the value of is symbolized by −2, 0, and 2. Local maximums in original ECG signal are positions shifted by 1 sample to the right of −2.
The following threshold procedure depends significantly on the amplitude threshold and time interval threshold , which are adaptively determined by the location of local maximum instead of adopting fixed thresholds, since fixed thresholds do not copy with large or small amplitude Rpeak and slow or fast beat. The selection of the two thresholds is displayed in Figure 4. During an ECG segment, is selected to be a multiple of the amplitude maximum ; is selected to be a multiple of the average horizontal distance of each adjacent local maximum . If the positions and amplitudes of these local maximums change, the two thresholds will change correspondingly; hence, and will adjust adaptively according to the maximum variety.
However, the threshold selection is strongly dependent on the noise; and are coefficients designed to correct the noise influence. The detailed selection method for them is discussed in Section 4.3. In a segment, the positions of local maximums are fixed, correspondingly; and are deterministic. Hence, only and need to be decided. The thresholds are automatically updated by the shift of new coming segment. The superiority of automatic threshold substitution embodies in the corresponding adjustment on recognition for small amplitude and slow or fast cardiac beat, as fixed thresholds may fail to detect Rpeaks in these cases.
2.4. Step 4: Threshold Recognition
Actually, most of the local maximums are not true Rpeaks, such as burst points caused by highfrequency interference. The difficulty of Rpeak detection lies in the recognition of false Rpeaks with amplitudes approximate to or even larger than true Rpeaks. To this end, is designed to filter the local maximums with small amplitudes. In general, there should be no extra Rpeaks between two adjacent Rpeaks; otherwise, the extra Rpeaks are definitely false. Assisted by this knowledge, is designed to further remove false Rpeaks omitted by . The thresholding algorithm is plotted in Figure 5. The example ECG is from the Record 200 in MITDB with PVC beats. It comprises of large negative Rpeaks, and consequently, the signal needs to be mirrored. The marks M in Figure 5 signify the mirrored Rpeaks, where they should originally be large negative amplitudes. After the amplitude filtration, the time interval threshold algorithm is summarized as follows: (1)Step A. A local maximum and its following maximum are chosen as true reference (Tref) and comparative reference (Cref) respectively, turn to Step B. If there is no Cref, Tref is considered as a true Rpeak and the algorithm ends.(2)Step B. The time period (t_p) between Tref and Cref is calculated. If t_p<ti_t, it indicates that one of the two maximums is a false Rpeak, then turn to Step C. Otherwise, Tref is considered as a true Rpeak, Cref replaces Tref as true reference, then turn to Step A for next thresholding.(3)Step C. Widths along the baseline of Tref (Wr) and Cref (Wf) are compared, if Wr<Wf, Tref is considered to be a true Rpeak, if Wr>Wf, Cref is treated as a true Rpeak [34]. Then turn to Step E. If Wr=Wf, turn to Step D.(4)Step D. Amplitude of Tref (Ar) and Cref (Af) is compared, if Ar>Af, Tref is considered to be a true Rpeak, otherwise, Cref is treated as a true Rpeak [34]. Then turn to Step E.(5)Step E. The false maximum is replaced by the third local maximum (Rref) just behind the two maximums, which is treated as a new Cref, and then return to Step B. If there is no Rref in the time period, turn to Step A.
 
Pseudocode 1: The pseudocode of the threshold procedure. 
3. Experiment Designs
3.1. Datasets
The MITDB comprises 48 ECG records, and each contains 30minute ECG signal [35, 36], resulting in a total of 109966 beats that were all used. The ECG records have acceptable quality, sharp and tall P and T waves, negative R waves, small Rpeak amplitudes, wider R waves, muscle noise, baseline drift, sudden changes in heartbeat morphology, multiform PVC, long pauses, and irregular heart rhythms [25].
The QTDB contains a total of 105 15minute ECGs. ECGs in this database were chosen to represent a wide variety of QRS and STT morphologies with realworld variability to challenge the detection algorithms [35, 37]. A total of 86995 beats from 82 records were used, and the rest 23 records of sel30sel52 were excluded since the QRS annotations were not given.
It should be noted that both databases provide two channels of ECG signals. In this study, only the first channel was used for algorithm development and test.
3.2. Evaluation Indices
Experimental results are evaluated in terms of sensitivity , positive predictivity , and accuracy . The definitions of the indices are expressed in where (true positive) is the number of Rpeaks correctly recognized, (false negative) is the number of Rpeaks missed, and (false positive) is the number of false Rpeaks recognized as true Rpeaks. The , , and , verified by the annotations announced in [25], are calculated based on a tolerance window of 50 ms.
Time complexity is also tested, which quantifies the amount of time taken by an algorithm to run as a function of the length of string representing the input. It reflects the increment of time consumption when the input data increase. Time complexity of an algorithm is commonly expressed using O notation. If the number of input data n multiplies, the time consumption multiplies with an increment of , the algorithm is called to have an morder time complexity symbolized as . In this study, all the time cost experiments were carried out on a desktop (CPU i72600 3.40GHz, 8GB RAM, 64bit Windows 7 Enterprise) installed with Matlab 2016b.
4. Results
First, for both databases, and were initially set as 0.25 and 0.45, respectively. The length of shifting signal was set as 10 s for each thresholding operation. Then, we tested the influences of the parameters and .
4.1. RPeak Detection Results
The testing results on MITDB are summarized in Table 1. The results demonstrate a satisfactory performance on the records. The algorithm has a total detection failure of 1229 beats (668 beats and 561 beats) out of 109966 beats; the average , , and are 99.39%, 99.49%, and 98.89% respectively.

The testing results on QTDB are shown in Table 2. The algorithm has a total detection failure of 238 beats (147 beats and 91 beats); out of 86995 beats, the average , , and are 99.83%, 99.90%, and 99.73% respectively. Compared with MITDB, the ECG signals from QTDB have much better waveforms with higher quality; distractors such as motion artifacts, burst noise, large P, and T waves are much less. Consequently, the algorithm achieves a more satisfactory performance over QTDB.

Our algorithm is also compared with several existing methods, including the most widely used PanTompkins method, as shown in Table 3. The comparison indicates that our algorithm achieves comparable high performance.

4.2. Time Consumption and Time Complexity
The time consumption for each record from MITDB is described in Figure 6(a). In general, the PanTompkins method consumes more time than our method for most records. The mean time of this method is 1.256 s to process one record, while our algorithm consumes 0.872 s, achieving about 30.6% of time reduction. Figure 6(b) shows the time consumption ratio of the proposed method over the PanTompkins method. It is obvious that our method consumes less time for most records except for records 107, 109, 113, and 116, which contain large T waves that cause more frequent thresholding manipulations. In some cases, our algorithm economizes nearly 50% of time than the PanTompkins method.
(a) Time of processing one record (MITDB)
(b) Time ratio of processing one record (MITDB)
The time consumption for each record from QTDB is described in Figure 7(a) and Figure 8(a). It is obvious that our method consumes less time than the PanTompkins method for all the records. The mean time of the PanTompkins method is 1.137 s to process one record, while our algorithm consumes 0.763 s, achieving about 32.9% of time reduction. Figure 7(b) and Figure 8(b) show the time consumption ratio of the proposed method over the PanTompkins method. It can be seen that all the ratios are less than 1. The outstanding performance can be attributed to the highquality ECG signals in QTDB.
(a) Time of processing one record (first 41 records from QTDB)
(b) Time ratio of processing one record (first 41 records from QTDB)
(a) Time of processing one record (last 41 records from QTDB)
(b) Time ratio of processing one record (last 41 records from QTDB)
The time consumption reveals an important characteristic of the two methods. The number of sampling points of each QTDB ECG is 225000, and the number is 650000 of each MITDB ECG. Although the number has increased about two times from QTDB to MITDB, the time consumed increases only 12.5% using our method and 9.2% using the PanTomkins method. It indicates that when data multiplies, the time consumption increases slightly instead of multiplying correspondingly. Both our method and the PanTomkins method are not so sensitive to data increase.
However, for records 107, 109, 113, and 116 in MITDB, our method consumes the same and even more time than the PanTompkins method. The disadvantage of our method is plotted in Figure 9 versus the PanTompkins method in terms of time complexity. In each subfigure, the abscissa represents the quantitative increment of samples. The basic number of samples is 720; it is multiplied by the number shown in the abscissa. In the top row, the ordinate is the multiple increments of time consumed by processing the multiplied samples shown in the abscissa. In the bottom row, the ordinates are the increment of time multiples calculated from the ordinates in the top row. Intuitively, the four records have high increments of time consumption as the amount of data increases, especially when the data quantity is less than 500 times of the basic number 720. One important reason is that the four records contain numerous large T waves or P waves to be compared by time interval threshold or large negative amplitudes to be estimated for mirroring. Time interval comparison is more frequent for these waveforms, leading to higher time complexity than that of the PanTompkins method. But on the other hand, the curves prove that our algorithm is not so sensitive to data increase. The increments of time multiple fluctuate in small ranges, basically remaining unchanged. The multiples of time consumption have linear relationships with the increments of sample amount.
4.3. K_{amp} and K_{time}
Amplitude threshold takes a significant role in truncating burst points along the baseline; time interval threshold is a critical measure to further distinguish false Rpeaks. According to (3), the thresholds depend significantly on and . To determine optimal coefficients and validate the feasibility of adaptive thresholding, different and values are tested using the 48 ECG records in MITDB. RR intervals of the records range from 0.54 s to 1.19 s; an average of 0.82 s is adopted to cope with different heart rates. The validation results are summarized in Table 4.

It can be seen that the optimal is generally times the , with reaching the maximum , and as illustrated in Table 1. On one hand, large is beneficial for the algorithm to cut off points located near the baseline, but small true Rpeaks may be missed if is too large. As shown in the table, when is larger than 0.40, most of the false Rpeaks are filtered as well as true ones, resulting in high but low and correspondingly low but high . On the other, smaller generates fewer omitted Rpeaks, but this will increase the computational cost in time interval thresholding. The worst situation is that all the potential Rpeaks are compared, as revealed in the table when is smaller than 0.20. In this case, most of the true Rpeaks are included in local maximums as well as false ones, resulting in low but high and correspondingly high but low . To coordinate with different heart rates, is recommended to be selected from interval [0.2, 0.3] and from [0.42, 0.48]. After and are determined, and can be automatically updated by the shift of new coming signal.
5. Discussion
The proposed method has two advantages. One is from the time efficiency as indicated in Section 4.2. The main difference between our method and the PanTompkins method is that the latter calculates more measures for Rpeak recognition. It includes a search back operation after a complete detection circulation, thus resulting in a high computational complexity. Our method exclusively uses the thresholding method, and it does not require any search back operation. Besides, the amplitude threshold can also contribute to the calculation efficiency since it excludes most distractors and significantly reduces the amount of threshold comparisons.
Another advantage is from that there is no time length limitation for thresholding. As described in Section 2.4, the length of new coming segment is flexible, and the thresholding procedure can operate not only for a single heartbeat but also for a complete ECG record. With adaptive and , our method is suitable for different lengths of ECG signal and it requires no prelearning procedure.
From Table 1, we can see that about half of the failed beats (314 and 328 ) are from record 207. This record consists of numerous distorted heartbeats that are extremely difficult to be recognized even by a cardiologist. However, few literatures reported the results on this specific record. It is also unclear if record 207 is excluded in the evaluations to achieve a high score. This record is also the main interference that significantly reduces the detection accuracy of our method.
Apart from record 207, there are still some missing and false recognitions on some other records. The main methodological defect of the algorithm is that amplitude threshold may fail to detect small Rpeaks mixed in large ones (records 105, 106, 108, and 228). The is selected based on the local maximums; if a segment contains numerous large Rpeaks, will be larger than small Rpeaks. This weights against the identification for small Rpeaks because they are prone to be partitioned below the , as illustrated in Figure 10. The signal contains baseline drift, large T waves, and large negative amplitude Rpeaks. Although most of the Rpeaks are recognized, there are two FN detections for small Rpeaks. If an Rpeak is missed, would probably take the following maximum as the candidate Rpeak, which actually is a distractor.
(a) Record 223 with different interface
(b) Missing detection of small Rpeaks on record 223
6. Conclusions
In this study, an adaptive and timeefficient methodology has been developed for automatic ECG Rpeak detection. It is an adaptive method integrating WMRA, signal mirroring, local maximum detection, and amplitude and time interval thresholding. The accuracy performances were tested by using ECG records from MITDB and QTDB. Experimental results indicate that the proposed algorithm achieves average , , and of 99.39%, 99.49%, and 98.89% for MITDB, and 99.83%, 99.90%, and 99.73% for QTDB, respectively. In addition, time consumption and time complexity of the algorithm are computed to prove its time efficiency. By processing one ECG record, the average time cost is 0.872 s for MITDB and 0.763 s for QTDB, achieving 30.6% and 32.9%, respectively, of time reduction compared to the PanTompkins method. Experiments on time complexity demonstrate that the proposed method is provided with linear time complexity; both our method and the PanTompkins method are less sensitive to data increase.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This study is supported by the National Natural Science Foundation of China (61571113). This study is also supported by the Natural Science Foundation of Jiangsu Province of China (BK20160697), the International S&T Cooperation Program of China (2015DFA10490), and the China Scholarship Council (CSC).
References
 J. R. Windmiller and J. Wang, “Wearable electrochemical sensors and biosensors: a review,” Electroanalysis, vol. 25, no. 1, pp. 29–46, 2013. View at: Google Scholar
 L. Atallah, A. Serteyn, M. Meftah et al., “Unobtrusive ECG monitoring in the NICU using a capacitive sensing array,” Physiological Measurement, vol. 35, no. 5, pp. 895–913, 2014. View at: Publisher Site  Google Scholar
 B. Taji, V. G. Shirmohammadi, and I. Batkin, “Impact of skinelectrode interface on electrocardiogram measurements using conductive textile electrodes,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 6, pp. 1412–1422, 2014. View at: Publisher Site  Google Scholar
 P. Augustyniak, “Wearable wireless heart rate monitor for continuous longterm variability studies,” Journal of Electrocardiology, vol. 44, no. 2, pp. 195–200, 2011. View at: Publisher Site  Google Scholar
 Y. M. Chi, T. P. Jung, and G. Cauwenberghs, “Drycontact and noncontact biopotential electrodes: methodological review,” IEEE Reviews in Biomedical Engineering, vol. 3, pp. 106–119, 2010. View at: Publisher Site  Google Scholar
 H. F. PosadaQuintero, B. A. Reyes, K. Burnham, J. Pennace, and K. H. Chon, “Low impedance carbon adhesive electrodes with long shelf life,” Annals of Biomedical Engineering, vol. 43, no. 10, pp. 2374–2382, 2015. View at: Publisher Site  Google Scholar
 E. M. Fong and W. Y. Chung, “A hygroscopic sensor electrode for fast stabilized noncontact ECG signal acquisition,” Sensors, vol. 15, no. 8, pp. 19237–19250, 2015. View at: Publisher Site  Google Scholar
 H. Y. Lin, S.Y. Liang, Y.L. Ho, Y.H. Lin, and H.P. Ma, “Discretewavelettransformbased noise removal and feature extraction for ECG signals,” IRBM, vol. 35, no. 6, pp. 351–361, 2014. View at: Publisher Site  Google Scholar
 W. Tylman, T. Waszyrowski, A. Napieralski et al., “Realtime prediction of acute cardiovascular events using hardwareimplemented Bayesian networks,” Computers in Biology and Medicine, vol. 69, pp. 245–253, 2016. View at: Publisher Site  Google Scholar
 R. GutierrezRivas, J. J. García, W. P. Marnane, and Á. Hernández, “Novel realtime lowcomplexity QRS complex detector based on adaptive thresholding,” IEEE Sensors Journal, vol. 15, no. 10, pp. 6036–6043, 2015. View at: Publisher Site  Google Scholar
 H. Naseri and M. R. Homaeinezhad, “Electrocardiogram signal quality assessment using an artificially reconstructed target lead,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 18, no. 10, pp. 1126–1141, 2015. View at: Publisher Site  Google Scholar
 Z. Zidelmal, A. Amirou, M. Adnane, and A. Belouchrani, “QRS detection based on wavelet coefficients,” Computer Methods and Programs in Biomedicine, vol. 107, no. 3, pp. 490–496, 2012. View at: Publisher Site  Google Scholar
 R. Benali, F. B. Reguig, and Z. H. Slimane, “Automatic classification of heartbeats using wavelet neural network,” Journal of Medical Systems, vol. 36, no. 2, pp. 883–892, 2012. View at: Publisher Site  Google Scholar
 M. Elgendi, B. Eskofier, S. Dokos, and D. Abbott, “Revisiting QRS detection methodologies for portable, wearable, batteryoperated, and wireless ECG systems,” PloS One, vol. 9, no. 1, article e84018, 2014. View at: Publisher Site  Google Scholar
 J. Pan and W. J. Tompkins, “A real time QRS detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230–236, 1985. View at: Publisher Site  Google Scholar
 B. Abibullaev and H. D. Seo, “A new QRS detection method using wavelets and artificial neural networks,” Journal of Medical Systems, vol. 35, no. 4, pp. 683–691, 2011. View at: Publisher Site  Google Scholar
 F. Khelifi and J. M. Jiang, “KNN regression to improve statistical feature extraction for texture retrieval,” IEEE Transactions on Image Processing, vol. 20, no. 1, pp. 293–298, 2011. View at: Publisher Site  Google Scholar
 D. Clifton, P. S. Addison, M. K. Stiles et al., “Using wavelet transform reassignment techniques for ECG characterization,” 30th Annual Meeting on Computers in Cardiology, vol. 30, pp. 581–584, 2003. View at: Publisher Site  Google Scholar
 F. Zhang and Y. Lian, “QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks,” IEEE Transactions on Biomedical Circuits and Systems, vol. 3, no. 4, pp. 220–228, 2009. View at: Publisher Site  Google Scholar
 C. SoIn, C. Phaudphut, and K. Rujirakul, “Realtime ECG noise reduction with QRS complex detection for mobile health services,” Arabian Journal for Science and Engineering, vol. 40, no. 9, pp. 2503–2514, 2015. View at: Publisher Site  Google Scholar
 W. H. Jung and S. G. Lee, “An Rpeak detection method that uses an SVD filter and a search back system,” Computer Methods and Programs in Biomedicine, vol. 108, no. 3, pp. 1121–1132, 2012. View at: Publisher Site  Google Scholar
 S. Banerjee and M. Mitra, “A cross wavelet transform based approach for ECG feature extraction and classification without denoising,” in Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), pp. 162–165, Calcutta, India, 2014. View at: Publisher Site  Google Scholar
 J. Li, X. Li, B. Yang, and X. Sun, “Segmentationbased image copymove forgery detection scheme,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 3, pp. 507–518, 2015. View at: Publisher Site  Google Scholar
 E. B. Mazomenos, D. Biswas, A. Acharyya et al., “A lowcomplexity ECG feature extraction algorithm for mobile healthcare applications,” IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 2, pp. 459–469, 2013. View at: Publisher Site  Google Scholar
 http://physionet.org/cgibin/atm/ATM.
 S. Pal and M. Mitra, “Empirical mode decomposition based ECG enhancement and QRS detection,” Computers in Biology and Medicine, vol. 42, no. 1, pp. 89–92, 2012. View at: Publisher Site  Google Scholar
 J. H. Kim, “Discrete wavelet transformbased feature extraction of experimental voltage signal for liion cell consistency,” IEEE Transactions on Vehicular Technology, vol. 65, no. 3, pp. 1150–1161, 2016. View at: Publisher Site  Google Scholar
 M. A. Kabir and C. Shahnaz, “Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains,” Biomedical Signal Processing and Control, vol. 7, no. 5, pp. 481–489, 2012. View at: Publisher Site  Google Scholar
 D. L. Donoho, “Denoising by soft thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995. View at: Publisher Site  Google Scholar
 M. A. Awal, S. S. Mostafa, M. Ahmad, and M. A. Rashid, “An adaptive level dependent wavelet thresholding for ECG denoising,” Biocybernetics and Biomedical Engineering, vol. 34, no. 4, pp. 238–249, 2014. View at: Publisher Site  Google Scholar
 S. Satheeskumaran and M. Sabrigiriraj, “A new LMS based noise removal and DWT based Rpeak detection in ECG signal for biotelemetry applications,” National Academy Science Letters, vol. 37, no. 4, pp. 341–349, 2014. View at: Publisher Site  Google Scholar
 M. Merah, T. A. Abdelmalik, and B. H. Larbi, “Rpeaks detection based on stationary wavelet transform,” Computer Methods and Programs in Biomedicine, vol. 121, no. 3, pp. 149–160, 2015. View at: Publisher Site  Google Scholar
 S. D. Xie and Y. X. Wang, “Construction of tree network with limited delivery latency in homogeneous wireless sensor networks,” Wireless Personal Communications, vol. 78, no. 1, pp. 231–246, 2014. View at: Publisher Site  Google Scholar
 D. Komorowski, S. Pietraszek, E. Tkacz, and I. Provaznik, “The extraction of the new components from electrogastrogram (EGG) using both adaptive filtering and electrocardiographic (ECG) derived respiration signal,” Biomedical Engineering Online, vol. 14, 2015. View at: Publisher Site  Google Scholar
 A. L. Goldberger, L. A. Amaral, L. Glass et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000. View at: Google Scholar
 G. B. Moody and G. R. Mark, “The impact of the MITBIH arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001. View at: Google Scholar
 P. Laguna, R. G. Mark, A. Goldberg, and G. B. Moody, “A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG,” Computers in Cardiology, vol. 24, pp. 673–676, 1997. View at: Publisher Site  Google Scholar
 F. Chiarugi, V. Sakkalis, D. Emmanouilidou, T. Krontiris, M. Varanini, and I. Tollis, “Adaptive threshold QRS detector with best channel selection based on a noise rating system,” Computers in Cardiology, vol. 34, pp. 157–160, 2007. View at: Publisher Site  Google Scholar
 N. M. Arzeno, Z. D. Deng, and C. S. Poon, “Analysis of firstderivative based QRS detection algorithms,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 2, pp. 478–484, 2008. View at: Publisher Site  Google Scholar
 M. Elgendi, “Fast QRS detection with an optimized knowledgebased method: evaluation on 11 standard ECG databases,” PLoS One, vol. 8, no. 9, article e73557, 2013. View at: Publisher Site  Google Scholar
 I. I. Christov, “Real time electrocardiogram QRS detection using combined adaptive threshold,” Biomedical Engineering Online, vol. 3, no. 28, pp. 1–9, 2004. View at: Publisher Site  Google Scholar
 S. A. Chouakri, F. B. Reguig, and A. TalebAhmed, “QRS complex detection based on multi wavelet packet decomposition,” Applied Mathematics and Computation, vol. 217, no. 23, pp. 9508–9525, 2011. View at: Publisher Site  Google Scholar
 R. Rodríguez, A. Mexicano, J. Bila, S. Cervantes, and R. Ponce, “Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis,” Journal of Applied Research and Technology, vol. 13, no. 2, pp. 261–269, 2015. View at: Publisher Site  Google Scholar
 Y. C. Yeh and W. J. Wang, “QRS complexes detection for ECG signal: the difference operation method,” Computer Methods and Programs in Biomedicine, vol. 91, no. 3, pp. 245–254, 2008. View at: Publisher Site  Google Scholar
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Copyright © 2017 Qin Qin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.