Exercise Vital Signs Detection Employing FMCW Radar and Artificial Neural Networks

Background: Coronary angiography is gold standard method to diagnose coronary arteries diseases. The aim of this study was to estimate pain after coronary angiography based on vital signs for determining best position by using artiﬁcial neural networks ANN. Methodology: This study used a database containing 86 subjects that refer to angiography center. For each subject Vital signs were measured that included blood pressure, percent of blood oxygen saturation, heart rate, respiratory rate and temperature. The Numeric Rating scale (NRS) was used to determine pain intensity. The vital signs were the inputs and the pain value was the corresponding output. These data were applied to train the ANN in the learning process. The model was implemented in MATLAB software. The results of pain estimation were compared with the results of NRS method and the error rate was calculated. Results: The absolute error and error percentage between NRS method and the present method were 5.41 ± 2.63 mmHg, 4.09 ± 1.59%. The results indicated that the pain measurement by NRS method and pain value predicted with trained ANN differ by only less than 11%. It is obvious that the neural network prediction fit properly to the NRS results. Conclusion: The results of proposed method were closely in agreement with the results of the NRS. so this method can be suggested for reliving the pain and determining the best patient's position after the angiography procedure. Key words: Artiﬁcial neural network; Coronary angiography; Pain Citation: Heravi MAY, Yaghubi MS, Amini ZA, Salimi PS, Falahi ZZ, Gazerani AG. Pain estimation after coronary angiography based on vital signs by using artiﬁcial neural networks. Anaesth. pain intensive care 2021;25(1):27–32. DOI: 10.35975/apic.v25i1.1433 Received: 21 November 2020, Reviewed: 2 December 2020, Accepted: 12 December 2020

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Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

BioMed Research International ◽

10.1155/2015/536863 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 11

Author(s):

Muammar Sadrawi ◽

Shou-Zen Fan ◽

Maysam F. Abbod ◽

Kuo-Kuang Jen ◽

Jiann-Shing Shieh

Keyword(s):

Blood Pressure ◽

Neural Networks ◽

Artificial Neural Networks ◽

Cross Validation ◽

Vital Signs ◽

Eeg Signal ◽

Depth Of Anesthesia ◽

Ann Model ◽

Mean Values ◽

Artificial Neural

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.

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