QRS detection algorithm for long term Holter records

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 Data Analytics Framework for Analyzing the Effect of Frac Hits on Parent Well Production

10.2118/204160-ms ◽

2021 ◽

Author(s):

Yifei Guo ◽

Pradeepkumar Ashok ◽

Eric van Oort ◽

Ross Patterson ◽

Dandan Zheng ◽

...

Keyword(s):

Horizontal Wells ◽

Detection Algorithm ◽

Clear Understanding ◽

Pressure Data ◽

Daily Production ◽

Well Production ◽

Different Types ◽

Shale Formation ◽

Well Completions

Abstract Well interference, which is commonly referred to as frac hits, has become a significant factor affecting production in fractured horizontal shale wells with the increase in infill drilling in recent years. Today, there is still no clear understanding on how frac hits affect production. This paper aims to develop a process to automatically identify the different types of frac hits and to determine the effect of stage-to-well distance and frac hit intensity on long-term parent well production. First, child well completions data and parent well pressure data are processed by a frac hit detection algorithm to automatically identify different frac hit intensities and duration within each stage. This algorithm classifies frac hits based on the magnitude of the differential pressure spikes. The frac stage to parent well distance is also calculated. Then, we compare the daily production trend before and after the frac hits to determine the severity of its influence on production. Finally, any evident correlations between the stage-to-well distance, frac hit intensity and production change are identified and investigated. This work utilizes 3 datasets covering 22 horizontal wells in the Bakken Formation and 37 horizontal wells in the Eagle Ford Shale Formation. These sets included well trajectories, child well completions data, parent well pressure data and parent well production data. The frac hit detection algorithm developed can accurately detect frac hits in the available dataset with minimal false alerts. The data analysis results show that frac hit severity (production response) and intensity (pressure response) are not only affected by the distance between parent and child wells, but also affected by the directionality of the wells. Parent wells tend to experience more frac hits from the child frac stages with smaller direction angles and shorter stage-to-parent distances. Formation stress change with time is another factor that affects frac hit intensity. Depleted wells are more susceptible to frac hits even if they are further from the child wells. Also, we observe frac hits in parent wells due to a stimulation of a child well in a different shale formation. This paper presents a novel automated frac hit detection algorithm to quickly identify different types of frac hits. This paper also presents a novel way of carrying out production analysis to determine whether frac hits in a well have positive or negative influence long-term production. Additionally, the paper introduces the concept of the stage-to-well distance as a more accurate metric for analyzing the influence of frac hits on production.

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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 computationally efficient QRS detection algorithm for wearable ECG sensors

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2011.6091365 ◽

2011 ◽

Cited By ~ 22

Author(s):

Y. Wang ◽

C. J. Deepu ◽

Y. Lian

Keyword(s):

Detection Algorithm ◽

Qrs Detection ◽

Computationally Efficient

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Research on Singing Voice Detection Based on a Long-Term Recurrent Convolutional Network with Vocal Separation and Temporal Smoothing

Electronics ◽

10.3390/electronics9091458 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1458

Author(s):

Xulong Zhang ◽

Yi Yu ◽

Yongwei Gao ◽

Xi Chen ◽

Wei Li

Keyword(s):

Time Domain ◽

Short Term Memory ◽

Block Size ◽

Detection Algorithm ◽

Singing Voice ◽

Convolutional Network ◽

Frame Size ◽

Voice Detection ◽

Public Datasets

Singing voice detection or vocal detection is a classification task that determines whether a given audio segment contains singing voices. This task plays a very important role in vocal-related music information retrieval tasks, such as singer identification. Although humans can easily distinguish between singing and nonsinging parts, it is still very difficult for machines to do so. Most existing methods focus on audio feature engineering with classifiers, which rely on the experience of the algorithm designer. In recent years, deep learning has been widely used in computer hearing. To extract essential features that reflect the audio content and characterize the vocal context in the time domain, this study adopted a long-term recurrent convolutional network (LRCN) to realize vocal detection. The convolutional layer in LRCN functions in feature extraction, and the long short-term memory (LSTM) layer can learn the time sequence relationship. The preprocessing of singing voices and accompaniment separation and the postprocessing of time-domain smoothing were combined to form a complete system. Experiments on five public datasets investigated the impacts of the different features for the fusion, frame size, and block size on LRCN temporal relationship learning, and the effects of preprocessing and postprocessing on performance, and the results confirm that the proposed singing voice detection algorithm reached the state-of-the-art level on public datasets.

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Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Sensors ◽

10.3390/s19133021 ◽

2019 ◽

Vol 19 (13) ◽

pp. 3021 ◽

Cited By ~ 5

Author(s):

Wonju Seo ◽

Namho Kim ◽

Sehyeon Kim ◽

Chanhee Lee ◽

Sung-Min Park

Keyword(s):

Deep Learning ◽

Mental Stress ◽

Detection Algorithm ◽

Workplace Stress ◽

Physiological Signals ◽

Work Related ◽

Average Accuracy ◽

Conventional Machine ◽

End To End

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.

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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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