R-peak Detection Method Research Articles

ECG Enhancement and R-Peak Detection Based on Window Variability.

In ECG applications, the correct recognition of R-peaks is extremely important for detecting abnormalities, such as arrhythmia and ventricular hypertrophy. In this work, a novel ECG enhancement and R-peak detection method based on window variability is presented, and abbreviated as SQRS. Firstly, the ECG signal corrupted by various high or low-frequency noises is denoised by moving-average filtering. Secondly, the window variance transform technique is used to enhance the QRS complex and suppress the other components in the ECG, such as P/T waves and noise. Finally, the signal, converted by window variance transform, is applied to generate the R-peaks candidates, and the decision rules, including amplitude and kurtosis adaptive thresholds, are applied to determine the R-peaks. A special squared window variance transform (SWVT) is proposed to measure the signal variability in a certain time window, and this technique reduces false detection rate caused by the various types of interference presented in ECG signals. For the MIT-BIH arrhythmia database, the sensitivity of R-peak detection can reach 99.6% using the proposed method.

Intelligent Exercise Guidance System Based on Smart Clothing

In this study, we designed and developed an intelligent exercise guidance system based on smart clothing. The system comprised smart clothing for electrocardiogram (ECG) signal acquisition and heart rate (HR) monitoring, an exercise control application program, and a cloud server. Music beats were used to guide the exercise routine. The use of an empirical mode decomposition (EMD)-based ECG signal denoising algorithm and a quadratic polynomial regression model (QPRM) of HR and running cadence (running steps per minute guided by music beats) were proposed for the system. Five types of experiments were conducted: Experiments I and II, R-peak detection; Experiment III, preset QPRMs; Experiment IV, degree of completion of exercises; and Experiment V, comparison of preset and trained QPRMs. The average accuracy and sensitivity of the EMD-based R-peak detection method were respectively 99.8% and 94.87% for ECG data from the MIT-BIH Arrhythmia Database and 96.46% and 98.75% for ECG data collected from university students during the walking exercise. The coefficient of determination and the mean absolute percentage error (MAPE) of the QPRMs were respectively 97.21% and 3.12% for increasing HR and 98.09% and 2.06% for decreasing HR. The average degrees of completion for warmup, training, and cooldown exercise stages were 97.05%, 91.91%, and 98.32%, respectively. The MAPEs of the preset and trained QPRMs were respectively 6.37% and 3.84% for increasing HR and 5.25% and 3.57% for decreasing HR. The experimental results demonstrated the effectiveness of the proposed system in exercise guidance.

Adaptive Fourier decomposition based R-peak detection for noisy ECG Signals.

An adaptive Fourier decomposition (AFD) based R-peak detection method is proposed for noisy ECG signals. Although lots of QRS detection methods have been proposed in literature, most detection methods require high signal quality. The proposed method extracts the R waves from the energy domain using the AFD and determines the R-peak locations based on the key decomposition parameters, achieving the denoising and the R-peak detection at the same time. Validated by clinical ECG signals in the MIT-BIH Arrhythmia Database, the proposed method shows better performance than the Pan-Tompkin (PT) algorithm in both situations of a native PT and the PT with a denoising process.

An improved method for R-peak detection by using Shannon energy envelope

R-peaks in electrocardiogram (ECG) play a vital role in diagnosis of heart rhythm irregularities and also estimating heart rate variability. However, almost all existing R-peak detectors suffer from the non-stationary of both QRS morphology and noise. To overcome these difficulties, we propose a four-stage improved method to detect R-peak using Shannon energy envelope. In the first stage, noise is suppressed and QRS complex is enhanced by using band pass filter, first order differentiation, and amplitude normalization. In the second stage, Shannon energy envelope is extracted. In the third stage, peak is estimated without considering any threshold amplitude. In the final stage, true R-peaks are detected. Our proposed R-peak detection method is validated using 48 first channel ECG records of the MIT-BIH arrhythmia database with the accuracy of 99.84%, sensitivity of 99.95% and positive predictability of 99.88%. Our proposed method outperforms other well-known methods in case of pathological ECG signals.

침대 패드 형태의 용량성 전극에서 측정된 심전도 신호를 처리하기 위한 자동 잡음 제거 및 피크 검출 알고리즘

Recent technological advances have increased interest in personal health monitoring. Electrocardiogram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascular system status. In this study, we developed a capacitive ECG measurement system that can be included within a cloth mattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However, it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high level of noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithm that can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated by determining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH Polysomnographic Database and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%, and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% for our capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of providing useful ECG information, even under noisy signal conditions.

A NOVEL R-PEAK DETECTION METHOD COMBINING ENERGY AND WAVELET TRANSFORM IN ELECTROCARDIOGRAM SIGNAL

QRS complex is the most important part in electrocardiogram (ECG) as it contains the most important information of heart activities. R-peak detection is the first, yet crucial, step in most ECG automatic diagnose methods. Due to the existence of noise in ECG signals and changes in QRS morphology, most existing methods are not robust in different conditions. In the field of intelligent remote health caring, in addition to the detection accuracy, timeliness is also an important research issue. In this paper, wavelet transform and energy window transform are introduced, which form the basis of a novel R-peak detection method. Wavelet transform is used to efficiently reduce noise and highlight useful ECG signal for it has good time-frequency resolution characters, and energy window transform converts time domain signal to energy domain, which makes it easier to isolate QRS complex from other signals. As a result, influence from QRS morphology changes can be effectively alleviated. To validate the effectiveness of this new method, ECG records of MIT-BIH arrhythmia database are used in the experiments. The experiment results show that the proposed method is efficient and robust to noise and QRS morphology changes. The computational cost of the proposed method has also been evaluated.

An R-peak detection method based on peaks of Shannon energy envelope

In a real-time electrocardiogram (ECG) monitoring system, to detect the R-peak in each beat with low delay, high accuracy and high speed is an important and fundamental task. Aiming at satisfying these requirements, a novel R-peak detection algorithm based on Shannon energy envelope (PSEE) is introduced. It extracts Shannon energy envelope (SEE) from ECG firstly, then finds out the R-peaks after SEE by three new introduced sub-processes: peak detection, false-R detection and false-noise detection. Owing to these sub-processes, the whole schema can deal with long pause or asystole case, and avoid segmenting ECG. Moreover, by splitting the calculation into every sampling time slot, the delay is guaranteed. The new method has been evaluated with all records in MIT–BIH with 99.83% detection rate of R-peaks and about 1.77μs computation time for each sample and with 251 records in Chinese Cardiovascular Disease Database with total accuracy 99.78%. Therefore, it not only satisfies real-time requirements but also improves the detection accuracy rate.

An R-peak detection method that uses an SVD filter and a search back system

In this paper, we present a method for detecting the R-peak of an ECG signal by using an singular value decomposition (SVD) filter and a search back system. The ECG signal was detected in two phases: the pre-processing phase and the decision phase. The pre-processing phase consisted of the stages for the SVD filter, Butterworth High Pass Filter (HPF), moving average (MA), and squaring, whereas the decision phase consisted of a single stage that detected the R-peak. In the pre-processing phase, the SVD filter removed noise while the Butterworth HPF eliminated baseline wander. The MA removed the remaining noise of the signal that had gone through the SVD filter to make the signal smooth, and squaring played a role in strengthening the signal. In the decision phase, the threshold was used to set the interval before detecting the R-peak. When the latest R–R interval (RRI), suggested by Hamilton et al., was greater than 150% of the previous RRI, the method of detecting the R-peak in such an interval was modified to be 150% or greater than the smallest interval of the two most latest RRIs. When the modified search back system was used, the error rate of the peak detection decreased to 0.29%, compared to 1.34% when the modified search back system was not used. Consequently, the sensitivity was 99.47%, the positive predictivity was 99.47%, and the detection error was 1.05%. Furthermore, the quality of the signal in data with a substantial amount of noise was improved, and thus, the R-peak was detected effectively.

A novel method for detecting R-peaks in electrocardiogram (ECG) signal

AbstractThe R-peak detection is crucial in all kinds of electrocardiogram (ECG) applications. However, almost all existing R-peak detectors suffer from the non-stationarity of both QRS morphology and noise. To combat this difficulty, we propose a new R-peak detector, which is based on the new preprocessing technique and an automated peak-finding logic. In this paper, we first demonstrate that the proposed preprocessor with a Shannon energy envelope (SEE) estimator is better able to detect R-peaks in case of wider and small QRS complexes, negative QRS polarities, and sudden changes in QRS amplitudes over that using the absolute value, energy value, and Shannon entropy features. Then we justify the simplicity and robustness of the proposed peak-finding logic using the Hilbert-transform (HT) and moving average (MA) filter. The proposed R-peak detector is validated using the first-channel of the 48 ECG records of the MIT-BITH arrhythmia database, and achieves average detection accuracy of 99.80%, sensitivity of 99.93% and positive predictivity of 99.86%. Various experimental results show that the proposed R-peak detection method significantly outperforms other well-known methods in case of noisy or pathological signals.