scholarly journals Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jian Zhan ◽  
Zhuo-xi Wu ◽  
Zhen-xin Duan ◽  
Gui-ying Yang ◽  
Zhi-yong Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Deep Neural Network ◽  
Low Frequency ◽  
Support Vector ◽  
Low Frequency Power ◽  
Frequency Power

Abstract Background Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment. Methods A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method. Results The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods. Conclusions The incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method—with other evaluation methods, such as EEG—is expected to assist anaesthesiologists in the accurate evaluation of the DoA.

scholarly journals Heart Rate Variability-Derived Features Based on Deep Neural Network for Distinguishing Different Anaesthesia States

2020 ◽  
Author(s):  
Jian Zhan ◽  
Zuo-xi Wu ◽  
Zhen-xin Duan ◽  
Gui-ying Yang ◽  
Zhi-yong Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Deep Neural Network ◽  
Low Frequency ◽  
Support Vector ◽  
Low Frequency Power ◽  
Frequency Power

Abstract Background: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, the hypothesis that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states was investigated.Methods: A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived time and frequency domain features combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which used the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.Results: The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.Conclusions: The incorporation of four HRV-derived time and frequency domain features and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot of a feasibility study, providing a method to supplement DoA monitoring based on EEG features to improve the accuracy of DoA estimation.

scholarly journals Heart Rate Variability-Derived Features Based on Deep Neural Networks for Monitoring Depth of Anaesthesia

2020 ◽  
Author(s):  
Jian Zhan ◽  
Zuo-xi Wu ◽  
Zhen-xin Duan ◽  
Gui-ying Yang ◽  
Zhi-yong Du ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Deep Neural Networks ◽  
Low Frequency ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Depth Of Anaesthesia ◽  
Low Frequency Power ◽  
Frequency Power

Abstract Background: Estimating the depth of anaesthesia (DoA) is critical in clinical anaesthesiology. Electroencephalograms (EEGs) have been widely used for monitoring the DoA; however, they may be inaccurate under certain conditions. Methods: In this study, we propose a novel method to evaluate the DoA based on multiple heart rate variability (HRV)-derived features combined with a discrete wavelet transform and deep neural networks (DNNs). Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the DNN, which used the expert assessment of consciousness level as the reference output. Finally, the DNN was compared with the logistic regression (LR), support vector machine (SVM), and decision tree (DT) models. The data of 23 anaesthesia patients were used to assess the proposed method. Results: The results demonstrated that the accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (LR),87.5% (SVM),87.2% (DT), and 90.1%(DNN). Our method outperformed the LR, SVM, and DT methods.Conclusions: The proposed method could accurately distinguish between different anaesthesia states, thus, providing an alternative or supplementary method to EEG monitoring for the assessment of DoA.

Preeclamptic pregnancy is associated with increased sympathetic and decreased parasympathetic control of HR

2000 ◽  
Vol 278 (4) ◽  
pp. H1269-H1273 ◽  
Author(s):  
Cheryl C. H. Yang ◽  
Te-Chang Chao ◽  
Terry B. J. Kuo ◽  
Chang-Sheng Yin ◽  
Hsing I. Chen
Keyword(s):  
Heart Rate ◽  
Low Frequency ◽  
Normal Pregnancy ◽  
Frequency Domain Analysis ◽  
Natural Logarithm ◽  
Sympathetic Regulation ◽  
History Of ◽  
Low Frequency Power ◽  
Normalized Units ◽  
Frequency Power

Previous work from our laboratory using heart rate variability (HRV) has demonstrated that women before menopause have a more dominant parasympathetic and less effective sympathetic regulations of heart rate compared with men. Because it is still not clear whether normal or preeclamptic pregnancy coincides with alternations in the autonomic functions, we evaluated the changes of HRV in 17 nonpregnant, 17 normotensive pregnant, and 11 preeclamptic women who were clinically diagnosed without history of diabetic neuropathy, cardiac arrhythmia, and other cardiovascular diseases. Frequency-domain analysis of short-term, stationary R-R intervals was performed to evaluate the total variance, low-frequency power (LF; 0.04–0.15 Hz), high-frequency power (HF; 0.15–0.40 Hz), ratio of LF to HF (LF/HF), and LF in normalized units (LF%). Natural logarithm transformation was applied to variance, LF, HF, and LF/HF for the adjustment of the skewness of distribution. We found that the normal pregnant group had a lower R-R value and HF but had a higher LF/HF and LF% compared with the nonpregnant group. The preeclamptic group had lower HF but higher LF/HF compared with either the normal pregnant or nonpregnant group. Our results suggest that normal pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of heart rate, and such alterations are enhanced in preeclamptic pregnancy.

scholarly journals An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Abebe Belay Adege ◽  
Hsin-Piao Lin ◽  
Getaneh Berie Tarekegn ◽  
Yirga Yayeh Munaye ◽  
Lei Yen
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Neural Network ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Online Data ◽  
Location Services ◽  
Outdoor Environments ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for positioning through the integration of SVM and DNN. Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values. Fifthly, we project the online data through an LDA-based projected vector. Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN. The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework. The proposed method provides accurate and scalable positioning services in different scenarios. The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.

scholarly journals Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Divneet Mandair ◽  
Premanand Tiwari ◽  
Steven Simon ◽  
Kathryn L. Colborn ◽  
Michael A. Rosenberg
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Factors ◽  
Myocardial Infarction ◽  
Logistic Regression ◽  
Large Scale ◽  
Deep Neural Network ◽  
Low Frequency ◽  
Common Data Model ◽  
Electronic Health Record Data

Abstract Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data. Methods Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyper-parameters were identified using 10-fold cross-validation. Results Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest. Conclusions Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI.

scholarly journals Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

2020 ◽  
Vol 10 (15) ◽  
pp. 5047 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Danesh Zandi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Shallow Landslide ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Algorithm ◽  
Alternating Decision Tree ◽  
Bayesian Logistic Regression

This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

Heart rate response and parasympathetic modulation during recovery from exercise in boys and men

2014 ◽  
Vol 39 (8) ◽  
pp. 969-975 ◽  
Author(s):  
Justin P. Guilkey ◽  
Matthew Overstreet ◽  
Bo Fernhall ◽  
Anthony D. Mahon
Keyword(s):  
Heart Rate ◽  
Maximal Exercise ◽  
Ventilatory Threshold ◽  
Heart Rate Recovery ◽  
Low Frequency ◽  
Cycle Ergometer ◽  
Submaximal Exercise ◽  
Frequency Domains ◽  
Low Frequency Power ◽  
Frequency Power

The purpose of this study was to examine the influence of postexercise parasympathetic modulation, measured by heart rate variability (HRV), on heart rate recovery (HRR) in boys (n = 13, 10.1 ± 0.8 years) and men (n = 13, 23.9 ± 1.5 years) following maximal and submaximal exercise. Subjects completed 10 min of supine rest, followed by graded exercise on a cycle ergometer to maximal effort. On a separate day, subjects exercised at an intensity equivalent to ventilatory threshold. Immediately following both exercise bouts, 1-min HRR was assessed in the supine position. HRV was analyzed under controlled breathing during the final 5 min of rest and recovery in the time and frequency domains and transformed to natural log (ln) values. Boys had a greater 1-min HRR than men following maximal (58 ± 8 vs. 47 ± 11 beats·min−1) and submaximal (59 ± 8 vs. 47 ± 15 beats·min−1) exercise (p < 0.05). Following maximal exercise, boys had greater ln root mean square successive differences in R-R intervals (2.52 ± 0.95 ms), ln standard deviation of NN intervals (3.34 ± 0.57 ms), ln high-frequency power (4.32 ± 2.00 ms2), and ln low-frequency power (4.98 ± 1.17 ms2) than men (1.33 ± 0.37 ms, 2.52 ± 0.24 ms, 1.32 ± 1.06 ms2 and 2.80 ± 0.74 ms2, respectively) (p < 0.05). There were no differences in any HRV variables between groups following submaximal exercise (p > 0.05). In conclusion, it appears that greater parasympathetic modulation accounts for greater HRR following maximal exercise in boys versus men. Although submaximal HRR was greater in boys, parasympathetic responses were similar between groups.

scholarly journals Komparasi Algoritma Kasifikasi dengan Pendekatan Level Data Untuk Menangani Data Kelas Tidak Seimbang

2017 ◽  
Vol 3 (1) ◽  
pp. 1-6
Author(s):  
Ahmad Ilham
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Level Data ◽  
Under Sampling

Masalah data kelas tidak seimbang memiliki efek buruk pada ketepatan prediksi data. Untuk menangani masalah ini, telah banyak penelitian sebelumnya menggunakan algoritma klasifikasi menangani masalah data kelas tidak seimbang. Pada penelitian ini akan menyajikan teknik under-sampling dan over-sampling untuk menangani data kelas tidak seimbang. Teknik ini akan digunakan pada tingkat preprocessing untuk menyeimbangkan kondisi kelas pada data. Hasil eksperimen menunjukkan neural network (NN) lebih unggul dari decision tree (DT), linear regression (LR), naïve bayes (NB) dan support vector machine (SVM).

scholarly journals Evaluating structural safety of trusses using Machine Learning

2021 ◽  
Vol 15 (58) ◽  
pp. 308-318
Author(s):  
Tran-Hieu Nguyen ◽  
Anh-Tuan Vu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Roc Curve ◽  
Deep Neural Network ◽  
Structural Safety ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Model ◽  
Adaptive Boosting

In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed framework. Firstly, several truss cases with different cross-sectional areas are generated by employing the Latin Hypercube Sampling method. Stresses inside truss members as well as displacements of nodes are determined through finite element analyses and obtained values are compared with design constraints. According to the constraint verification, the safety state is assigned as safe or unsafe. Members’ sectional areas and the safety state are stored as the inputs and outputs of the training dataset, respectively. Three popular machine learning classifiers including Support Vector Machine, Deep Neural Network, and Adaptive Boosting are used for evaluating the safety of structures. The comparison is conducted based on two metrics: the accuracy and the area under the ROC curve. For the two first examples, three classifiers get more than 90% of accuracy. For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately 98%. In terms of the area under the ROC curve, the comparative results are similar. Overall, the Adaptive Boosting model outperforms the remaining models. In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model.

scholarly journals IDENTIFIKASI JENIS IKAN MENGGUNAKAN MODEL HYBRID DEEP LEARNING DAN ALGORITMA KLASIFIKASI

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
Author(s):  
Anifuddin Azis
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Deep Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Output

Indonesia merupakan negara dengan keanekaragaman hayati terbesar kedua di dunia setelah Brazil. Indonesia memiliki sekitar 25.000 spesies tumbuhan dan 400.000 jenis hewan dan ikan. Diperkirakan 8.500 spesies ikan hidup di perairan Indonesia atau merupakan 45% dari jumlah spesies yang ada di dunia, dengan sekitar 7.000an adalah spesies ikan laut. Untuk menentukan berapa jumlah spesies tersebut dibutuhkan suatu keahlian di bidang taksonomi. Dalam pelaksanaannya mengidentifikasi suatu jenis ikan bukanlah hal yang mudah karena memerlukan suatu metode dan peralatan tertentu, juga pustaka mengenai taksonomi. Pemrosesan video atau citra pada data ekosistem perairan yang dilakukan secara otomatis mulai dikembangkan. Dalam pengembangannya, proses deteksi dan identifikasi spesies ikan menjadi suatu tantangan dibandingkan dengan deteksi dan identifikasi pada objek yang lain. Metode deep learning yang berhasil dalam melakukan klasifikasi objek pada citra mampu untuk menganalisa data secara langsung tanpa adanya ekstraksi fitur pada data secara khusus. Sistem tersebut memiliki parameter atau bobot yang berfungsi sebagai ektraksi fitur maupun sebagai pengklasifikasi. Data yang diproses menghasilkan output yang diharapkan semirip mungkin dengan data output yang sesungguhnya.  CNN merupakan arsitektur deep learning yang mampu mereduksi dimensi pada data tanpa menghilangkan ciri atau fitur pada data tersebut. Pada penelitian ini akan dikembangkan model hybrid CNN (Convolutional Neural Networks) untuk mengekstraksi fitur dan beberapa algoritma klasifikasi untuk mengidentifikasi spesies ikan. Algoritma klasifikasi yang digunakan pada penelitian ini adalah : Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN),  Random Forest, Backpropagation.

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