Algorithm for the automatic computation of the modified Anderson–Wilkins acuteness score of ischemia from the pre-hospital ECG in ST-segment elevation myocardial infarction

Fakhri, Yama; Sejersten, Maria; Schoos, Mikkel Malby; Melgaard, Jacob; Graff, Claus; Wagner, Galen S.; Clemmensen, Peter; Kastrup, Jens

doi:10.1016/j.jelectrocard.2016.11.005

Cited by 6 publications

(5 citation statements)

References 12 publications

(22 reference statements)

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“…The time from symptom onset to STEMI ECG acquisition has been categorized with a modified Anderson-Wilkins acuteness score that could provide a better measure for approaching treatment of the evolving STEMI. 26 Limitations There are several limitations to this study. These are data from a single-center in an urban-suburban geographic area with relatively short transport times.…”

Section: Discussionmentioning

confidence: 98%

“…This may involve increasing public awareness of STEMI symptoms, improving access to 9-1-1 including mobile phone localization, and enhancing efficient dispatch of resources. The time from symptom onset to STEMI ECG acquisition has been categorized with a modified Anderson-Wilkins acuteness score that could provide a better measure for approaching treatment of the evolving STEMI 26 …”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Weekly Checks Improve Real-Time Prehospital ECG Transmission in Suspected STEMI

D’Arcy

Bosson²,

Kaji³

et al. 2018

Prehosp. Disaster med.

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Implementation of weekly test ECG transmissions was associated with improvement in successful real-time transmissions from field to hospital, which provided a median advanced notification time of 16 minutes, but no decrease in FMC2B or D2B times. D'ArcyNT, BossonN, KajiAH, BuiQT, FrenchWJ, ThomasJL, ElizarrarazY, GonzalezN, GarciaJ, NiemannJT. Weekly checks improve real-time prehospital ECG transmission in suspected STEMI. Prehosp Disaster Med. 2018;33(3):245-249.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Weekly Checks Improve Real-Time Prehospital ECG Transmission in Suspected STEMI

D’Arcy

Bosson²,

Kaji³

et al. 2018

Prehosp. Disaster med.

View full text Add to dashboard Cite

show abstract

“…In fact, the denoising of the ) (i s signal is the restoration of the original signal ) (i f from the noise-contaminated signal while suppressing the noise ) (i e . In general, the use of wavelet transform threshold method for denoising is divided into the following three steps [11]: 1) Orthogonal wavelet transform with noise signal: select the appropriate wavelet and wavelet decomposition level Ⅳ, the use of wavelet signal decomposition algorithm to the fourth layer, get the corresponding wavelet decomposition coefficient.…”

Section: B Genetic Wavelet Filteringmentioning

confidence: 99%

Optimization of Human ECG Signal Acquisition Based on Wavelet Neural Network and Genetic Algorithm

Chen¹,

Liang²,

Liu³

et al. 2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

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Abstract-Human ECG signal is weak, the signal-to-noise ratio is small, the collected ECG signal amplification and filtering are often accompanied by interference, the interference from the body's own interference such as EMG interference, but also from outside interference such as 50Hz power frequency interference[2]. These disturbances degrade the signal-to-noise ratio of the system, and can even overwhelm the useful weak ECG signal, especially 50Hz power frequency interference. If not to eliminate to a very small extent, it will affect the subsequent signal processing, diagnosis, recognition accuracy. In addition the ECG signal also includes the following two kinds of interference: First, baseline drift [3]. Usually caused by human respiration and excitement of the heart and legs, the body's movement will lead to a certain amount of baseline drift, the frequency is lower than 1Hz, the performance of the slow changes in the curve; Second, EMG interference, which is caused by human body tremor, its frequency range is very wide. Usually between 5 ~ 2kHz[1], the performance of the rapid changes in irregular waveforms. In addition, due to the test sex, age and skin conductivity, so there are differences and the impact of equipment, system acquisition of different people's ECG signal is also very different. Therefore, in order to eliminate these interference and obtain an effective ECG signal, this paper presents a genetic neural network based on human heart ECG parameters acquisition system. By combining the nonlinear approximation ability of wavelet function with the self-learning characteristic of neural network, the output accuracy of sensor can be effectively improved, the effect of non-target parameter on detection result can be eliminated, and the detection accuracy of human physiological parameters can be improved. Through the experimental test, the ECG parameters of the device are accurate and reliable data has a good practical value.

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“…Heart disease is the leading cause of death with the third largest number of sufferers worldwide, early detection of heart disease is the right step in reducing the mortality rate (Fakhri et al, 2017). Electrocardiogram (ECG) is an important strategy in management to determine the next action in the handling of patients with cardiology diagnoses, electrocardiogram is thought to be the best marker of elevated serum levels (Zimmerman et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Many obstacles that have been identified prevent the initial recognition of STEMI including the lack of patients' ability to identify that they have ischemia or the ability of health workers to detect infarctions on electrocardiogram results (Muhlestein et al, 2015;Zhang & Hsu, 2013). Nurses have 99% ability to detect electrocardiogram waves when the patient is in lethal arrhythmia or a very life-threatening disorder, only the nurse's ability to detect ischemia or infarction on electrocardiogram results is still below 50% (Fakhri et al, 2017;Zimmerman et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Development Model of Basic ECG Algorithm Interpretation based on Android Application to Increase Ability of Nurses

Nursalam,

Sila,

Yusuf

et al. 2024

CMSNJ

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Introduction: ECG interpretation ability by nurses is still not appropriate, thus affecting the accuracy and speed of the next action that will be given to patients to reduce mortality. This study aimed to develop the use of an Android system based ECG interpretation algorithm application to improve the ability of nurse. Methods: Research development design to develop the Android system based ECG interpretation algorithm application. The sampling method used purposive sampling as many as 90 nurses in Universitas Airlangga Hospital, Surabaya, Indonesia. The independent variables were interpretation skills (rhythm, frequency, pacemaker, axis, extrasistole, block, enlargement of heart muscle, coronary heart disease, conclusion). The instruments that are used are ECG recording, ECG interpretation sheet, and focus group disscusion. The analysis used an independent t-test with a significance level of α ≤ 0.05. Results: The results of developing an ECG interpretation algorithm application based on the Android system have received recommendations from experts and feasibility tests have been carried out. There was a relationship between basic ECG interpretation capabilities with the use of an android system based ECG interpretation algorithm application. Increasing the ability of nurses had p value of 0,000. Conclusion: The application of an ECG interpretation algorithm based on an android system can improve nurses' abilities in interpreting basic ECG. For future researchers, it is necessary to develop and refine the Android system-based ECG interpretation algorithm application and this can be applied on a case-by-case basis.

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Algorithm for the automatic computation of the modified Anderson–Wilkins acuteness score of ischemia from the pre-hospital ECG in ST-segment elevation myocardial infarction

Cited by 6 publications

References 12 publications

Weekly Checks Improve Real-Time Prehospital ECG Transmission in Suspected STEMI

Weekly Checks Improve Real-Time Prehospital ECG Transmission in Suspected STEMI

Optimization of Human ECG Signal Acquisition Based on Wavelet Neural Network and Genetic Algorithm

Development Model of Basic ECG Algorithm Interpretation based on Android Application to Increase Ability of Nurses

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