Sensitivity of QRS Detection Accuracy to Detector Temporal Resolution

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 Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data

Remote Sensing ◽

10.3390/rs10121992 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1992 ◽

Cited By ~ 6

Author(s):

Zixi Xie ◽

Weiguo Song ◽

Rui Ba ◽

Xiaolian Li ◽

Long Xia

Keyword(s):

Spatial Resolution ◽

Temporal Resolution ◽

Forest Fire ◽

Forest Fires ◽

Spatial Information ◽

Fire Detection ◽

High Temporal Resolution ◽

Detection Accuracy ◽

Contextual Model ◽

Forest Fire Detection

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data’s spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel’s diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires.

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Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases

Journal of Healthcare Engineering ◽

10.1155/2018/9050812 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 18

Author(s):

Feifei Liu ◽

Chengyu Liu ◽

Xinge Jiang ◽

Zhimin Zhang ◽

Yatao Zhang ◽

...

Keyword(s):

High Signal ◽

Signal Quality ◽

Detection Accuracy ◽

Qrs Detection ◽

Numerical Efficiency ◽

Ecg Signals ◽

Detection Algorithms ◽

High Efficient ◽

Ecg Database ◽

Evaluation Work

A systematical evaluation work was performed on ten widely used and high-efficient QRS detection algorithms in this study, aiming at verifying their performances and usefulness in different application situations. Four experiments were carried on six internationally recognized databases. Firstly, in the test of high-quality ECG database versus low-quality ECG database, for high signal quality database, all ten QRS detection algorithms had very high detection accuracy (F1 >99%), whereas the F1 results decrease significantly for the poor signal-quality ECG signals (all <80%). Secondly, in the test of normal ECG database versus arrhythmic ECG database, all ten QRS detection algorithms had good F1 results for these two databases (all >95% except RS slope algorithm with 94.24% on normal ECG database and 94.44% on arrhythmia database). Thirdly, for the paced rhythm ECG database, all ten algorithms were immune to the paced beats (>94%) except the RS slope method, which only output a low F1 result of 78.99%. At last, the detection accuracies had obvious decreases when dealing with the dynamic telehealth ECG signals (all <80%) except OKB algorithm with 80.43%. Furthermore, the time costs from analyzing a 10 s ECG segment were given as the quantitative index of the computational complexity. All ten algorithms had high numerical efficiency (all <4 ms) except RS slope (94.07 ms) and sixth power algorithms (8.25 ms). And OKB algorithm had the highest numerical efficiency (1.54 ms).

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ALGORITHM FOR AUTOMATIC DETECTION OF ECG WAVES

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519410003848 ◽

2011 ◽

Vol 11 (01) ◽

pp. 15-29 ◽

Cited By ~ 4

Author(s):

DIB. NABIL ◽

F. BEREKSI-REGUIG

Keyword(s):

Detection Accuracy ◽

Ecg Signal ◽

Qrs Complex ◽

Qrs Detection ◽

Accurate Identification ◽

Ecg Signals ◽

Good Ability ◽

Noisy Conditions ◽

Electrocardiogram Ecg ◽

T Waves

An accurate measurement of the different electrocardiogram (ECG) intervals is dependent on the accurate identification of the beginning and the end of the P, QRS, and T waves. Available commercial systems provide a good QRS detection accuracy. However, the detection of the P and T waves remains a serious challenge due to their widely differing morphologies in normal and abnormal beats. In this paper, a new algorithm for the detection of the QRS complex as well as for P and T waves identification is provided. The proposed algorithm is based on different approaches and methods such as derivations, thresholding, and surface indicator. The proposed algorithm is tested and evaluated on ECG signals from the universal MIT-BIH database. It shows a good ability to detect P, QRS, and T waves for different cases of ECG signal even in very noisy conditions. The obtained QRS, sensitivity and positive predictivity are respectively 95.39% and 98.19%. The developed algorithm is also able to separate the overlapping P and T waves.

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Deep Deconvolution for Traffic Analysis with Distributed Acoustic Sensing Data

10.31223/x5p345 ◽

2021 ◽

Author(s):

Martijn van den Ende ◽

André Ferrari ◽

Anthony Sladen ◽

Cédric Richard

Keyword(s):

Real Time ◽

Temporal Resolution ◽

Heavy Traffic ◽

Simultaneous Detection ◽

Detection Accuracy ◽

Acoustic Sensing ◽

Traffic Conditions ◽

Real Time Traffic ◽

Vibration Sensing ◽

Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) is a novel vibration sensing technology that can be employed to detect vehicles and to analyse traffic flows using existing telecommunication cables. DAS therefore has great potential in future "smart city" developments, such as real-time traffic incident detection. Though previous studies have considered vehicle detection under relatively light traffic conditions, in order for DAS to be a feasible technology in real-world scenarios, detection algorithms need to also perform robustly under heavy traffic conditions. In this study we investigate the potential of roadside DAS for the simultaneous detection and characterisation of the velocity of individual vehicles. To improve the temporal resolution and detection accuracy, we propose a self-supervised Deep Learning approach that deconvolves the characteristic car impulse response from the DAS data, which we refer to as a Deconvolution Auto-Encoder (DAE). We show that deconvolution of the DAS data with our DAE leads to better temporal resolution and detection performance than the original (non-deconvolved) data. We subsequently apply our DAE to a 24-hour traffic cycle, demonstrating the feasibility of our proposed method to process large volumes of DAS data, potentially in near-real time.

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Vivid Imagers Are Better at Detecting Salient Changes

Journal of Individual Differences ◽

10.1027/1614-0001.27.4.218 ◽

2006 ◽

Vol 27 (4) ◽

pp. 218-228 ◽

Cited By ~ 13

Author(s):

Paul Rodway ◽

Karen Gillies ◽

Astrid Schepman

Keyword(s):

Individual Differences ◽

Change Detection ◽

Visual Imagery ◽

Detection Task ◽

Level Of Detail ◽

Detection Accuracy ◽

Change Detection Task ◽

Term Change ◽

High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17 s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

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Right-Hemisphere Specialization for Contour Grouping

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000252 ◽

2014 ◽

Vol 61 (5) ◽

pp. 331-339 ◽

Cited By ~ 1

Author(s):

Gregor Volberg

Keyword(s):

Right Hemisphere ◽

Contour Detection ◽

Global Processing ◽

Detection Accuracy ◽

Frequency Spectra ◽

Visual Hemifield ◽

Spatial Frequencies ◽

A Chain ◽

The Right ◽

Hemisphere Specialization

Previous studies often revealed a right-hemisphere specialization for processing the global level of compound visual stimuli. Here we explore whether a similar specialization exists for the detection of intersected contours defined by a chain of local elements. Subjects were presented with arrays of randomly oriented Gabor patches that could contain a global path of collinearly arranged elements in the left or in the right visual hemifield. As expected, the detection accuracy was higher for contours presented to the left visual field/right hemisphere. This difference was absent in two control conditions where the smoothness of the contour was decreased. The results demonstrate that the contour detection, often considered to be driven by lateral coactivation in primary visual cortex, relies on higher-level visual representations that differ between the hemispheres. Furthermore, because contour and non-contour stimuli had the same spatial frequency spectra, the results challenge the view that the right-hemisphere advantage in global processing depends on a specialization for processing low spatial frequencies.

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