Pengembangan Sistem Deteksi Kantuk Menggunakan Pengklasifikasi Random Forest pada Sinyal Elektrokardiogram

Kantuk merupakan salah satu penyebab utama kecelakaan dalam lalu lintas, industri manufaktur, maupun pada bidang lain. Untuk itu, sistem yang dapat mendeteksi kantuk secara dini merupakan hal yang sangat penting dalam rangka mengurangi angka kecelakaan akibat kantuk. Kantuk dapat dianalisis melalui Heart Rate Variability (HRV) dari sinyal EKG yang menunjukkan perubahan aktivitas saraf otonom. Pengklasifikasi Random Forest diketahui berkinerja sangat baik serta kuat terhadap overfitting. Oleh karena itu, pada makalah ini dikembangkan sistem deteksi kantuk menggunakan sinyal elektrokardiogram (EKG) dan Random Forest. Sistem deteksi kantuk dilatih menggunakan rekaman EKG dari database DROZY yang dilengkapi Karolinska Sleepiness Scale (KSS). Fitur masukan sistem diekstraksi berdasarkan metode ranah waktu dan ranah frekuensi. Tingkat kantuk diklasifikasikan berdasarkan KSS yang disederhanakan menjadi dua kelas, yaitu kantuk dan terjaga. Random Forest divalidasi dengan metode Out-of-Bag (OOB). Efek dari variasi jumlah estimator dan max feature terhadap kinerja sistem dievaluasi. Fitur diurutkan berdasarkan kepentingannya dan dikombinasikan sebagai masukan sistem dengan berbagai panjang segmentasi EKG. Kinerja terbaik sistem deteksi kantuk yaitu rata-rata akurasi 94,61%, sensitivitas 96,67%, dan specificity 91,67%, yang diperoleh dengan segmentasi 40 detik.

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Automatic identification of rapid eye movement sleep based on random forest using heart rate variability

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2019.121421 ◽

2019 ◽

Vol 527 ◽

pp. 121421

Author(s):

Yitian Wang ◽

DaiYan Wang ◽

Lulu Zhang ◽

Cong Liu ◽

Jin Li ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Random Forest ◽

Eye Movement ◽

Automatic Identification ◽

Rapid Eye Movement ◽

Rapid Eye Movement Sleep

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Assessment of fetal maturation age by heart rate variability measures using random forest methodology

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2016.01.020 ◽

2016 ◽

Vol 70 ◽

pp. 157-162 ◽

Cited By ~ 8

Author(s):

F. Tetschke ◽

U. Schneider ◽

E. Schleussner ◽

O.W. Witte ◽

D. Hoyer

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Random Forest ◽

Maturation Age

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The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/09544119211036806 ◽

2021 ◽

pp. 095441192110368

Author(s):

Mustafa B Selek ◽

Bartu Yesilkaya ◽

Saadet S Egeli ◽

Yalcin Isler

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Heart Rate ◽

Principal Component Analysis ◽

Congestive Heart Failure ◽

Heart Rate Variability ◽

Random Forest ◽

Principal Components ◽

Principal Component ◽

Machine Learning Algorithms

In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers’ inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.

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Random Forest-Based Classification of Heart Rate Variability Signals by Using Combinations of Linear and Nonlinear Features

XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010 - IFMBE Proceedings ◽

10.1007/978-3-642-13039-7_8 ◽

2010 ◽

pp. 29-32 ◽

Cited By ~ 5

Author(s):

Alan Jovic ◽

Nikola Bogunovic

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Random Forest ◽

Nonlinear Features ◽

Linear And Nonlinear

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Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter

Applied Computing and Informatics ◽

10.1016/j.aci.2019.06.002 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Cited By ~ 5

Author(s):

R. Shashikant ◽

P. Chetankumar

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Logistic Regression ◽

Cardiac Arrest ◽

Random Forest ◽

Decision Tree ◽

Machine Learning Technique ◽

Forest Model ◽

Learning Technique

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

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