A computationally efficient QRS detection algorithm for wearable ECG sensors

Heartbeat detection is the first step in automatic analysis of the electrocardiogram (ECG). For mobile and wearable devices, the detection process should be both accurate and computationally efficient. In this paper, we present a QRS detection algorithm based on moving average filters, which affords a simple yet robust signal processing technique. The decision logic considers the rhythmic and morphological features of the QRS complex. QRS enhancing is performed with channel-specific moving average cascades selected from a pool of derivative systems we designed. We measured the effectiveness of our algorithm on well-known benchmark databases, reporting F1 scores, sensitivity on abnormal beats and processing time. We also evaluated the performances of other available detectors for a direct comparison with the same criteria. The algorithm we propose achieved satisfying performances on par with or higher than the other QRS detectors. Despite the performances we report are not the highest that have been published so far, our approach to QRS detection enhances computational efficiency while maintaining high accuracy.

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EFFICIENT QRS COMPLEX DETECTION ALGORITHM IMPLEMENTATION ON SOC-BASED EMBEDDED SYSTEM

Jurnal Teknologi ◽

10.11113/jt.v78.9450 ◽

2016 ◽

Vol 78 (7-5) ◽

Cited By ~ 1

Author(s):

Muhammad Amin Hashim ◽

Yuan Wen Hau ◽

Rabia Baktheri

Keyword(s):

Embedded System ◽

Detection Algorithm ◽

Detection Accuracy ◽

Qrs Complex ◽

Qrs Detection ◽

Qrs Complex Detection ◽

Moving Windows ◽

Field Programmable ◽

Complex Detection ◽

On Chip

This paper studies two different Electrocardiography (ECG) preprocessing algorithms, namely Pan and Tompkins (PT) and Derivative Based (DB) algorithm, which is crucial of QRS complex detection in cardiovascular disease detection. Both algorithms are compared in terms of QRS detection accuracy and computation timing performance, with implementation on System-on-Chip (SoC) based embedded system that prototype on Altera DE2-115 Field Programmable Gate Array (FPGA) platform as embedded software. Both algorithms are tested with 30 minutes ECG data from each of 48 different patient records obtain from MIT-BIH arrhythmia database. Results show that PT algorithm achieve 98.15% accuracy with 56.33 seconds computation while DB algorithm achieve 96.74% with only 22.14 seconds processing time. Based on the study, an optimized PT algorithm with improvement on Moving Windows Integrator (MWI) has been proposed to accelerate its computation. Result shows that the proposed optimized Moving Windows Integrator algorithm achieves 9.5 times speed up than original MWI while retaining its QRS detection accuracy.

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A New QRS Detection Algorithm Based on Combined Fuzzy Logic and Wavelet Technique

IFMBE Proceedings - 5th European Conference of the International Federation for Medical and Biological Engineering ◽

10.1007/978-3-642-23508-5_109 ◽

2011 ◽

pp. 419-422

Author(s):

E. Timoshenko ◽

N. Dhungel

Keyword(s):

Fuzzy Logic ◽

Detection Algorithm ◽

Qrs Detection ◽

Wavelet Technique

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A Novel Wavelet-Based Algorithm for Detection of QRS Complex

Applied Sciences ◽

10.3390/app9102142 ◽

2019 ◽

Vol 9 (10) ◽

pp. 2142 ◽

Cited By ~ 8

Author(s):

Chun-Cheng Lin ◽

Hung-Yu Chang ◽

Yan-Hua Huang ◽

Cheng-Yu Yeh

Keyword(s):

Evaluation Method ◽

Decision Rules ◽

Detection Algorithm ◽

Qrs Complex ◽

Qrs Detection ◽

Qrs Complex Detection ◽

Frequency Components ◽

Noise Evaluation ◽

Complex Detection ◽

Almost All

Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.

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A Real Time QRS Detection Algorithm Based on ET and PD Controlled Threshold Strategy

Sensors ◽

10.3390/s20144003 ◽

2020 ◽

Vol 20 (14) ◽

pp. 4003 ◽

Cited By ~ 1

Author(s):

Aiyun Chen ◽

Yidan Zhang ◽

Mengxin Zhang ◽

Wenhan Liu ◽

Sheng Chang ◽

...

Keyword(s):

Real Time ◽

Detection Algorithm ◽

Qrs Detection ◽

Threshold Strategy ◽

Pd Control ◽

Ecg Signals ◽

Field Programmable ◽

Exponential Transform ◽

Current Threshold ◽

Basic Characteristics

As one of the important components of electrocardiogram (ECG) signals, QRS signal represents the basic characteristics of ECG signals. The detection of QRS waves is also an essential step for ECG signal analysis. In order to further meet the clinical needs for the accuracy and real-time detection of QRS waves, a simple, fast, reliable, and hardware-friendly algorithm for real-time QRS detection is proposed. The exponential transform (ET) and proportional-derivative (PD) control-based adaptive threshold are designed to detect QRS-complex. The proposed ET can effectively narrow the magnitude difference of QRS peaks, and the PD control-based method can adaptively adjust the current threshold for QRS detection according to thresholds of previous two windows and predefined minimal threshold. The ECG signals from MIT-BIH databases are used to evaluate the performance of the proposed algorithm. The overall sensitivity, positive predictivity, and accuracy for QRS detection are 99.90%, 99.92%, and 99.82%, respectively. It is also implemented on Altera Cyclone V 5CSEMA5F31C6 Field Programmable Gate Array (FPGA). The time consumed for a 30-min ECG record is approximately 1.3 s. It indicates that the proposed algorithm can be used for wearable heart rate monitoring and automatic ECG analysis.

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