scholarly journals ECG-Based Subject Identification Using Statistical Features and Random Forest

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Turky N. Alotaiby ◽  
Saud Rashid Alrshoud ◽  
Saleh A. Alshebeili ◽  
Latifah M. Aljafar
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Ecg Signal ◽  
Identification Algorithm ◽  
Statistical Features ◽  
Accuracy Rate ◽  
Subject Identification ◽  
Signal Segment ◽  
Electrocardiogram Ecg ◽  
Ecg Database

In this work, a nonfiducial electrocardiogram (ECG) identification algorithm based on statistical features and random forest classifier is presented. Two feature extraction approaches are investigated: direct and band-based approaches. In the former, eleven simple statistical features are directly extracted from a single-lead ECG signal segment. In the latter, the single-lead ECG signal is first decomposed into bands, and the statistical features are extracted from each segment of a given band and concatenated to form the feature vector. Nonoverlapping segments of different lengths (i.e., 1, 3, 5, 7, 10, or 15 sec) are examined. The extracted feature vectors are applied to a random forest classifier, for the purpose of identification. This study considers 290 reference subjects from the ECG database of the Physikalisch-Technische Bundesanstalt (PTB). The proposed identification algorithm achieved an accuracy rate of 99.61% utilizing the single limb lead (I) with the band-based approach. A single chest lead (V1), augmented limb lead (aVF), and Frank’s lead (Vx) achieved an accuracy rate of 99.37%, 99.76%, and 99.76%, respectively, using the same approach.

scholarly journals Species-specific audio detection: A comparison of three template-based classification algorithms using random forests

2017 ◽  
Author(s):  
Carlos J Corrada Bravo ◽  
Rafael Álvarez Berríos ◽  
T. Mitchell Aide
Keyword(s):  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Classification Algorithms ◽  
Statistical Features ◽  
Web Based ◽  
Average Accuracy ◽  
Species Specific ◽  
Web Based System

We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based classification. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.

scholarly journals A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

2019 ◽  
Vol 9 (1) ◽  
pp. 201 ◽  
Author(s):  
Di Wang ◽  
Yujuan Si ◽  
Weiyi Yang ◽  
Gong Zhang ◽  
Tong Liu
Keyword(s):  
Heart Rate ◽  
Principal Component ◽  
Identification Accuracy ◽  
Ecg Signal ◽  
Identification Task ◽  
Short Term ◽  
Ecg Signals ◽  
Subject Identification ◽  
Novel Method ◽  
Electrocardiogram Ecg

In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

scholarly journals Electrocardiogram Signal Denoising by Hilbert Transform and Synchronous Detection

2020 ◽  
Vol 24 (4) ◽  
pp. 323-336
Author(s):  
Mohammed Assam Ouali ◽  
◽  
Asma Tinouna ◽  
Mouna Ghanai ◽  
Kheireddine Chafaa
Keyword(s):  
Hilbert Transform ◽  
High Energy ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Synchronous Detection ◽  
Ecg Signals ◽  
Dominant Component ◽  
Electrocardiogram Signal ◽  
Electrocardiogram Ecg ◽  
Ecg Database

An efficient method for Electrocardiogram (ECG) signal denoising based on synchronous detection and Hilbert transform techniques is presented. The goal of the method is to decompose a noisy ECG signal into two components classified according to their energy: (1) component with high energy representing the dominant component which is the clean ECG signal, and (2) component with low energy representing the sub-dominant component which is the contaminant noise. The investigated approach is validated through out some experimentations on MIT-BIH ECG database. Experimental results show that random noises can be effectively suppressed from ECG signals.

scholarly journals Species-specific audio detection: A comparison of three template-based classification algorithms using random forests

2017 ◽  
Author(s):  
Carlos J Corrada Bravo ◽  
Rafael Álvarez Berríos ◽  
T. Mitchell Aide
Keyword(s):  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Classification Algorithms ◽  
Statistical Features ◽  
Web Based ◽  
Average Accuracy ◽  
Species Specific ◽  
Web Based System

We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based classification. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.

scholarly journals Species-specific audio detection: a comparison of three template-based detection algorithms using random forests

2017 ◽  
Vol 3 ◽  
pp. e113 ◽  
Author(s):  
Carlos J. Corrada Bravo ◽  
Rafael Álvarez Berríos ◽  
T. Mitchell Aide
Keyword(s):  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Statistical Features ◽  
Web Based ◽  
Detection Algorithms ◽  
Average Accuracy ◽  
Species Specific ◽  
Web Based System

We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based detection. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.

The Performance Improvement on 4-FB Face using Random Forest Classifier

2018 ◽  
Vol 10 (5) ◽  
pp. 1-12
Author(s):  
B. Nassih ◽  
A. Amine ◽  
M. Ngadi ◽  
D. Naji ◽  
N. Hmina
Keyword(s):  
Random Forest ◽  
Performance Improvement ◽  
Random Forest Classifier

Methodology for Malware Classification using a Random Forest Classifier

2018 ◽  
Author(s):  
Carlos Domenick Morales-Molina ◽  
Diego Santamaria-Guerrero ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana ◽  
Aldo Hernandez-Suarez
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Malware Classification

scholarly journals Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brain Tumors ◽  
Proton Transfer ◽  
Multilayer Perceptron ◽  
Random Forest Classifier ◽  
Amide Proton ◽  
Low Grade ◽  
Who Grade ◽  
Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

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