Development of robust, fast and efficient QRS complex detector: a methodological review

Abstract

The basis and reliability for timely diagnosis of cardiovascular diseases depend on the robust and accurate detection of QRS complexes along with the fiducial points in the electrocardiogram (ECG) signal. Despite, the several QRS detection algorithms reported in the literature, the development of an efficient QRS detector remains a challenge in the clinical environment. Therefore, this article summarizes the performance analysis of various QRS detection techniques depending upon three assessment factors which include robustness to noise, computational load, and sensitivity validated on the benchmark MIT-BIH arrhythmia database. Moreover, the limitations of these algorithms are discussed and compared with the standard signal processing algorithms, followed by the future suggestions to develop a reliable and efficient QRS methodology. Further, the suggested method can be implemented on suitable hardware platforms to develop smart health monitoring systems for continuous and long-term ECG assessment for real-time applications.

Improved online event detection and differentiation by a simple gradient-based nonlinear transformation: Implications for the biomedical signal and image analysis

“…Since this algorithm relies on the values of the derivative, it successfully detects any abrupt changes in the signal, while remaining barely sensitive to the particular signal shape analyzed. Due to its simplicity and universality, different variants of the Pan-Tompkins algorithm remain a standard solution for wearable ECG analysis systems over several decades [20]. The disadvantage is that the derivative remains quite sensitive to the high-frequency noise components mainly represented by electrophysiological activity of the skeletal muscles, especially in those leads where the ECG signal amplitude (and thus also energy) are small.…”

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

“…To guarantee high performance, pre-processing and post-processing steps were usually required, at an increased computing cost. Other reviews and benchmarks focused on pre-processing and decision-making steps [ 62 , 63 , 64 ]. Van and Podmasteryev reviewed in detail four ML-based algorithms [ 65 ], including artificial neural network (ANN) [ 66 ], k-nearest neighbor (k-NN) algorithm [ 67 ], k-means [ 68 ], and SVM [ 69 ].…”

Algorithm for Mobile Platform-Based Real-Time QRS Detection

“…Many algorithms have been developed to recognize characteristics of the ECG, and the detection of the QRS complex is fundamental for analysis [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ] because it is the major landmark that allows the waveform to be segmented into heartbeats for determining the heart rate and its variability [ 18 ]. The accurate detection of the QRS signal is also fundamental to more detailed ECG processing [ 19 ].…”