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

Accurate detection of key components in an electrocardiogram (ECG) plays a vital role in identifying cardiovascular diseases. In this work, we proposed a novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform. In the first stage, we proposed a QRS complex detector, which utilises a novel adaptive thresholding process followed by threshold initialisation. Moreover, false positive QRS complexes were removed using the kurtosis coefficient computation. In the second stage, the ECG segment from the S wave point to the Q wave point was extracted for clustering. The template waveform was generated from the cluster members using the ensemble average method, interpolation, and resampling. Next, a novel conditional thresholding process was used to calculate the threshold values based on the template waveform morphology for P and T peaks detection. Finally, the min-max functions were used to detect the P and T peaks. The proposed technique was applied to the MIT-BIH arrhythmia database (MIT-AD) and the QT database for QRS detection and validation. Sensitivity (Se%) values of 99.81 and 99.90 and positive predictivity (+P%) values of 99.85 and 99.94 were obtained for the MIT-AD and QT database for QRS complex detection, respectively. Further, we found that Se% = 96.50 and +P% = 96.08 for the P peak detection, Se% = 100 and +P% = 100 for the R peak detection, and Se% = 99.54 and +P% = 99.68 for the T peak detection when using the manually annotated QT database. The proposed technique exhibits low computational complexity and can be implemented on low-cost hardware, since it is based on simple decision rules rather than a heuristic approach.

Exploratory data analysis based efficient QRS-complex detection technique with minimal computational load.

Detection of QRS-complex in the electrocardiogram (ECG) plays a decisive role in cardiac disorder detection. We face many challenges in terms of powerline interference, baseline drift, and abnormal varying peaks. In this work, we propose an exploratory data analysis (EDA) based efficient QRS-complex detection technique with minimal computational load. This paper includes median and moving average filter for pre-processing of the ECG. The peak of filtered ECG is enhanced to third power of the signal. The root mean square (rms) of the signal is estimated for the decision making rule. This technique adapted the new concept for isoelectric line identification and EDA based QRS-complex detection. In this paper, total 10,70,981 beats were used for validation from MIT BIH-Arrhythmia Database (MIT-BIH), Fantasia Database (FDB), European ST-T database (ESTD), a self recorded dataset (SDB), and fetal ECG database (FTDB). Overall sensitivity of 99.65 % and positive predictivity rate of 99.84 % have been achieved. The proposed technique doesn't require selection, setting, and training for QRS-complex detection. Thus, this paper presents a QRS-complex detection technique based on simple decision rules.