A novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform

Rahul, Jagdeep; Sora, Marpe; Sharma, Lakhan Dev

doi:10.1016/j.compbiomed.2021.104307

Cited by 33 publications

(21 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To meet a particular criterion normalization is done to maintain the peak amplitude within a limited range i.e., −1 to +1 and the variation that occurs in the thresholding value during the R-peak detection remains in control. The normalization of the ECG signal is done using the method [15]…”

Section: Wavelet Decomposition and Qrs Complex Detectionmentioning

confidence: 99%

“…DWT provides good time-frequency resolution features but a choice of mother wavelets and shift-invariance limits its applications. In [15], the author proposed a novel method for the detection of ECG waveform components based on adaptive thresholding and template waveform. The kurtosis coefficients were used to remove the false QRS complex but the limitation is that this method cannot work efficiently on higher heart rate abnormalities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

QRS complex detection using stationary wavelet transform and adaptive thresholding

Sharma¹,

Sunkaria²,

Sharma³

2022

Biomed. Phys. Eng. Express

Self Cite

View full text Add to dashboard Cite

Purpose- Electrocardiogram (ECG) signal is a record of the electrical activity of the heart and contains important clinical data about cardiovascular-related misfunctioning. The goal of the present work is to develop an improved QRS detection algorithm for the detection of heart abnormalities. Methods- In this present work stationary wavelet transforms (SWT) based method has been proposed for precise detection of QRS complex with ‘sym2’ mother wavelet. The stationary wavelet transform is a systematic mathematical tool to decompose the signal without downsampling using scale analysis and provides high detection of QRS complex and accurate localization of signal components. In the proposed method four level of decomposition is applied and the initial thresholding value is computed by the maximum amplitude of scale one at level four in SWT coefficients without the zero-crossing amplitude detection method. The multi-layered dynamic thresholding method has been applied to detect the true R-peak values and locate the QRS complex in the ECG signal. Results- For evaluation of results, the presented methodology is assessed on MIT-BIH, QTDB, and Noise stress test databases. In MIT-BIH, the sensitivity = 99.88%, positive predictivity = 99.93%, accuracy = 99.80% and detection error rate = 0.18% is achieved. In NSTD database, sensitivity = 97.46%, positive predictivity = 94.20%, accuracy = 91.95% and detection error rate = 8.47% and in QTDB, sensitivity = 99.95%, positive predictivity = 99.90%, accuracy = 99.71% and detection error rate = 0.16% is executed. Conclusion- In the presented proposed methodology, the computation complexity is low and exhibits a simple technique rather than an empirical approach. The proposed technique corroborates the performance for the detection of QRS complex with improved accuracy.

show abstract

Section: Wavelet Decomposition and Qrs Complex Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QRS complex detection using stationary wavelet transform and adaptive thresholding

Sharma¹,

Sunkaria²,

Sharma³

2022

Biomed. Phys. Eng. Express

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is typically accomplished by using fixed or adaptive thresholds. Hence, a heartbeat is associated with the time point at which these thresholds were exceeded [ 19 , 20 , 22 , 25 , 30 , 33 , 38 , 42 – 44 ]. The accuracy of these techniques in determining the temporal location of the heartbeats is between 96.69% and 99.99%.…”

Section: Introductionmentioning

confidence: 99%

“… [ 46 ] MBA; ESTT; INCART; MSA NN 98.09 [ 23 ] MBA BPF; MF 99.96 Integrated detection of P and T waves. [ 24 ] MBA BPF; SE; HT 99.69 [ 25 ] MBA; QT BPF; normalized cubic power; AT 99.84 Integrated detection of P and T waves. [ 47 ] QT; Lobachevsky NN 96.90 Integrated detection of P and T waves.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat detector from ECG and PPG signals based on wavelet transform and upper envelopes

et al. 2023

View full text Add to dashboard Cite

The analysis of cardiac activity is one of the most common elements for evaluating the state of a subject, either to control possible health risks, sports performance, stress levels, etc. This activity can be recorded using different techniques, with electrocardiogram and photoplethysmogram being the most common. Both techniques make significantly different waveforms, however the first derivative of the photoplethysmographic data produces a signal structurally similar to the electrocardiogram, so any technique focusing on detecting QRS complexes, and thus heartbeats in electrocardiogram, is potentially applicable to photoplethysmogram. In this paper, we develop a technique based on the wavelet transform and envelopes to detect heartbeats in both electrocardiogram and photoplethysmogram. The wavelet transform is used to enhance QRS complexes with respect to other signal elements, while the envelopes are used as an adaptive threshold to determine their temporal location. We compared our approach with three other techniques using electrocardiogram signals from the Physionet database and photoplethysmographic signals from the DEAP database. Our proposal showed better performances when compared to others. When the electrocardiographic signal was considered, the method had an accuracy greater than 99.94%, a true positive rate of 99.96%, and positive prediction value of 99.76%. When photoplethysmographic signals were investigated, an accuracy greater than 99.27%, a true positive rate of 99.98% and positive prediction value of 99.50% were obtained. These results indicate that our proposal can be adapted better to the recording technology.

show abstract

“…Flatten T-wave may show the presence of myocardial ischemia. The analysis of such T-wave can reduce premature casualty due to the sudden cardiac arrest [9,10]. The T on and T off points of the T-wave are used in estimating the other significant parameters for cardiac arrhythmia detection.…”

Section: Introductionmentioning

confidence: 99%

An enhanced T-wave delineation method using phasor transform in the electrocardiogram

Rahul

Sharma²

2021

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Accurate detection of key components plays a vital role in determining cardiovascular diseases in the ECG. In this method, we propose an enhanced T-wave delineation method using the phasor transform. Discrete Wavelet Transform (DWT) and median filters were used to suppress the high-frequency noise and baseline drift during pre-processing. The phasor transform was used to detect and locate the delineation points before and after the T-wave. The proposed method was tested on the QTDB for R-peak, T-peak, and Toff detection. It achieved both sensitivity (Se%) and positive predictivity (+P%) values of 100 for R-peak detection. In T-peak detection, method shows Se % = 99.46 and +P % = 99.54, respectively. This method has reported Se% = 99.34 and +P% = 99.48 for Toff detection in the ECG. The achieved results show that the method can be used for cardiac arrhythmia detection related to the morphology of T-wave.

show abstract

A novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform

Cited by 33 publications

References 36 publications

QRS complex detection using stationary wavelet transform and adaptive thresholding

QRS complex detection using stationary wavelet transform and adaptive thresholding

Heartbeat detector from ECG and PPG signals based on wavelet transform and upper envelopes

An enhanced T-wave delineation method using phasor transform in the electrocardiogram

Contact Info

Product

Resources

About