scholarly journals ECG-Multichannel Frontend for Quick Stimulus Response Based on FPGA with Implemented Real-Time, Online QRS Detection Algorithm

2012 ◽  
Vol 57 (SI-1 Track-F) ◽  
Author(s):  
P. Fritzsche ◽  
S. Niemöller ◽  
D. Laqua ◽  
P. Husar
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Qrs Detection ◽  
Stimulus Response

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

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4003 ◽  
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.

A REAL-TIME QT INTERVAL DETECTION ALGORITHM

2008 ◽  
Vol 08 (02) ◽  
pp. 251-263 ◽  
Author(s):  
Z. E. HADJ SLIMANE ◽  
F. BEREKSI REGUIG
Keyword(s):  
Real Time ◽  
Qt Interval ◽  
Detection Algorithm ◽  
Qrs Detection ◽  
Signal To Noise ◽  
Ecg Signals ◽  
Wave Detection ◽  
T Wave ◽  
Electrocardiogram Ecg ◽  
Ventricular Depolarization

The QT interval is the electrocardiographic representation of the duration of ventricular depolarization and repolarization. In this paper, we have developed a new real-time QT interval detection algorithm for automatically locating the onset of QRS and the end of the T wave. The algorithm consists of several steps: signal-to-noise enhancement, QRS detection, QRS onset, and T-wave end definition. The detection algorithm is tested on electrocardiogram (ECG) signals from the universal MIT-BIH Arrhythmia Database. The resulting QRS detection algorithm has a sensitivity of 99.79% and a specificity of 99.72%. The QRS onset and T-wave detection algorithm is tested using several data records from the MIT/BIH Arrhythmia Database. The results obtained are shown to be highly satisfactory.

scholarly journals A Real-time QRS Detector Based on Low-pass Differentiator and Hilbert Transform

2018 ◽  
Vol 175 ◽  
pp. 02008 ◽  
Author(s):  
Daizong Yang ◽  
Yue Zhang
Keyword(s):  
Real Time ◽  
Hilbert Transform ◽  
High Sensitivity ◽  
Detection Algorithm ◽  
Center Frequency ◽  
Qrs Detection ◽  
Threshold Method ◽  
Physiological Signal ◽  
First Derivative ◽  
Adjustable Center

Electrocardiogram(ECG) is an important physiological signal of the human body. It is widely used in identification and arrhythmia detection. The first step of ECG application is signal segmentation, that is, the QRS detection. An effective and real-time QRS detection algorithm is proposed in this paper. A differentiator with adjustable center frequency is used to capture the first derivative information of the frequency band of the electrocardiogram. Then Hilbert transform is used to generate the envelope of the first derivative. After that, a dual threshold method is introduced to decrease FP and FN. Finally, a more precise R wave position is determined based on derivative method. The detector is validated on MIT-BIH arrhythmia database. The result show that the proposed algorithm has a high Sensitivity of 99.87%, Specificity of 99.84%, and the detection error rate is 0.28%. The average execution time of a 30 minutes record is 2.45s.

A Real-Time QRS Detection Algorithm

1985 ◽  
Vol BME-32 (3) ◽  
pp. 230-236 ◽  
Author(s):  
Jiapu Pan ◽  
Willis J. Tompkins
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Qrs Detection

scholarly journals A QRS-Detection Algorithm for Real-Time Applications

2021 ◽  
Vol 14 (1) ◽  
pp. 356-367
Author(s):  
Akram Khalaf ◽  
◽  
Samir Mohammed ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Real Time ◽  
Error Detection ◽  
High Efficiency ◽  
Detection Algorithm ◽  
Hybrid Technique ◽  
Qrs Detection ◽  
Artificial Neural

The QRS detection algorithm is substantial for healthcare monitoring and diagnostic applications. A low error detection without adding more computation is a big challenge for researchers. The proposed QRS detection algorithm is a simple, real-time, and high-performance hybrid technique based on decision tree and artificial neural networks (ANN). In this study, the five stages algorithm is designed, implemented, and evaluated for wearable healthcare applications. The first stage is filtering the original ECG signal to reduce the noise and baseline wandering. After that, a maximum or minimum moving-window for positive or negative peaks respectively is searching R-peaks for any expected value and finding the Q and S corresponding to this R-peak. Only these values from all ECG samples are passed to the next stage for feature extraction to reduce the algorithm computation. Stage four is excluded any unlikely points using the mean of the slope and level based on a simple decision tree. Finally, artificial neural networks are designed to classify the rest point for QRS detection using ANNs for each peak polarity to improve the network’s performance by separating the data as a positive or negative peak. The algorithm is evaluated based on MATLAB using the MIT-BIH Arrhythmia Database, and the results show a low error rate detection of 0.25%, high sensitivity of 99.86%, and high predictivity of 99.89%. We develop a new approach for real-time QRS detection with low resources and high efficiency compared with other approaches.

The Effect of Real-Time Feedback Using a Smartwatch on the Clinical Behavior of Novice Student Clinicians

2017 ◽  
Vol 2 (11) ◽  
pp. 79-90
Author(s):  
Courtney G. Scott ◽  
Trina M. Becker ◽  
Kenneth O. Simpson
Keyword(s):  
Real Time ◽  
Rating Scales ◽  
Qualitative Interviews ◽  
Communication Disorders ◽  
Preliminary Evidence ◽  
Single Subject ◽  
Clinical Behavior ◽  
Stimulus Response ◽  
Treatment Data ◽  
Computer Monitors

The use of computer monitors to provide technology-based written feedback during clinical sessions, referred to as “bug-in-the-eye” (BITi) feedback, recently emerged in the literature with preliminary evidence to support its effectiveness (Carmel, Villatte, Rosenthal, Chalker & Comtois, 2015; Weck et al., 2016). This investigation employed a single-subject, sequential A-B design with two participants to observe the effects of implementing BITi feedback using a smartwatch on the clinical behavior of student clinicians (SCs). Baseline and treatment data on the stimulus-response-consequence (S-R-C) contingency completion rates of SCs were collected using 10 minute segments of recorded therapy sessions. All participants were students enrolled in a clinical practicum experience in a communication disorders and sciences (CDS) program. A celeration line, descriptive statistics, and stability band were used to analyze the data by slope, trend, and variability. Results demonstrated a significant correlative relationship between BITi feedback with a smartwatch and an increase in positive clinical behaviors. Based on qualitative interviews and exit rating scales, SCs reported BITi feedback was noninvasive and minimally distracting. Preliminary evidence suggests BITi feedback with a smartwatch may be an effective tool for providing real-time clinical feedback.

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