A novel approach for the identification of chronic alcohol users from ECG signals

2017 ◽  
Author(s):  
V Rakshith ◽  
V Apoorv ◽  
N K Akarsh ◽  
K Arjun ◽  
B N Krupa ◽  
...  
Keyword(s):  
Chronic Alcohol ◽  
Ecg Signals ◽  
Novel Approach ◽  
Alcohol Users

scholarly journals Design and evaluation of a novel approach to invisible electrocardiography (ECG) in sanitary facilities using polymeric electrodes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aline dos Santos Silva ◽  
Hugo Almeida ◽  
Hugo Plácido da Silva ◽  
António Oliveira
Keyword(s):  
Heart Rate ◽  
Pearson Correlation ◽  
Voluntary Action ◽  
Signal Loss ◽  
Standard Equipment ◽  
Self Monitoring ◽  
Battery Lifetime ◽  
Ecg Signals ◽  
Toilet Seat ◽  
Novel Approach

AbstractMultiple wearable devices for cardiovascular self-monitoring have been proposed over the years, with growing evidence showing their effectiveness in the detection of pathologies that would otherwise be unnoticed through standard routine exams. In particular, Electrocardiography (ECG) has been an important tool for such purpose. However, wearables have known limitations, chief among which are the need for a voluntary action so that the ECG trace can be taken, battery lifetime, and abandonment. To effectively address these, novel solutions are needed, which has recently paved the way for “invisible” (aka “off-the-person”) sensing approaches. In this article we describe the design and experimental evaluation of a system for invisible ECG monitoring at home. For this purpose, a new sensor design was proposed, novel materials have been explored, and a proof-of-concept data collection system was created in the form of a toilet seat, enabling ECG measurements as an extension of the regular use of sanitary facilities, without requiring body-worn devices. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard equipment, involving 10 healthy subjects. For the acquisition of the ECG signals on the toilet seat, polymeric electrodes with different textures were produced and tested. According to the results obtained, some of the textures did not allow the acquisition of signals in all users. However, a pyramidal texture showed the best results in relation to heart rate and ECG waveform morphology. For a texture that has shown 0% signal loss, the mean heart rate difference between the reference and experimental device was − 1.778 ± 4.654 Beats per minute (BPM); in terms of ECG waveform, the best cases present a Pearson correlation coefficient above 0.99.

scholarly journals Effects of alcohol withdrawal on monocyte subset defects in chronic alcohol users

2016 ◽  
Vol 100 (5) ◽  
pp. 1191-1199 ◽  
Author(s):  
Hélène Donnadieu-Rigole ◽  
Thibault Mura ◽  
Pierre Portales ◽  
Isabelle Duroux-Richard ◽  
Martine Bouthier ◽  
...  
Keyword(s):  
Alcohol Withdrawal ◽  
Monocyte Subset ◽  
Chronic Alcohol ◽  
Alcohol Users

The Utility of Carbohydrate-Deficient Transferrin in Identifying Chronic Alcohol Users in the Injured Patient: Expanding the Toolkit

2021 ◽  
Vol 257 ◽  
pp. 92-100
Author(s):  
Joseph V. Sakran ◽  
Ambar Mehta ◽  
Maher M. Matar ◽  
Dulaney A. Wilson ◽  
Alistair J. Kent ◽  
...  
Keyword(s):  
Injured Patient ◽  
Chronic Alcohol ◽  
Carbohydrate Deficient Transferrin ◽  
Alcohol Users

Accelerated turnover of very low density lipoprotein triglycerides in chronic alcohol users

1984 ◽  
Vol 53 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Timo Sane ◽  
Esko A. Nikkilä ◽  
Marja-Riitta Taskinen ◽  
Matti Välimäki ◽  
Reino Ylikahri
Keyword(s):  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Very Low Density Lipoprotein ◽  
Low Density ◽  
Chronic Alcohol ◽  
Alcohol Users

Deep Learning for Detecting Sleep Apnea from ECG Signals

2020 ◽  
Vol 10 (6) ◽  
pp. 1265-1273
Author(s):  
Lili Chen ◽  
Huoyao Xu
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Ecg Signals ◽  
Novel Approach ◽  
Learning Machine ◽  
High Level ◽  
Electrocardiogram Ecg

Sleep apnea (SA) is a common sleep disorders affecting the sleep quality. Therefore the automatic SA detection has far-reaching implications for patients and physicians. In this paper, a novel approach is developed based on deep neural network (DNN) for automatic diagnosis SA. To this end, five features are extracted from electrocardiogram (ECG) signals through wavelet decomposition and sample entropy. The deep neural network is constructed by two-layer stacked sparse autoencoder (SSAE) network and one softmax layer. The softmax layer is added at the top of the SSAE network for diagnosing SA. Afterwards, the SSAE network can get more effective high-level features from raw features. The experimental results reveal that the performance of deep neural network can accomplish an accuracy of 96.66%, a sensitivity of 96.25%, and a specificity of 97%. In addition, the performance of deep neural network outperforms the comparison models including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM). Finally, the experimental results reveal that the proposed method can be valid applied to automatic SA event detection.

scholarly journals Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 63
Author(s):  
Fatima Sajid Butt ◽  
Luigi La Blunda ◽  
Matthias F. Wagner ◽  
Jörg Schäfer ◽  
Inmaculada Medina-Bulo ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Fall Detection ◽  
Daily Activities ◽  
Continuous Wavelet ◽  
Learning Approaches ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Novel Approach ◽  
The Neural Network ◽  
Electrocardiogram Ecg

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids “reinventing the wheel,” but also presents a lightweight solution to otherwise computationally heavy problems.

A Novel Approach for Detection of Myocardial Infarction From ECG Signals of Multiple Electrodes

2019 ◽  
Vol 19 (12) ◽  
pp. 4509-4517 ◽  
Author(s):  
Rajesh Kumar Tripathy ◽  
Abhijit Bhattacharyya ◽  
Ram Bilas Pachori
Keyword(s):  
Myocardial Infarction ◽  
Ecg Signals ◽  
Novel Approach ◽  
Multiple Electrodes

scholarly journals Wavelet Scattering Transform for ECG Beat Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhishuai Liu ◽  
Guihua Yao ◽  
Qing Zhang ◽  
Junpu Zhang ◽  
Xueying Zeng
Keyword(s):  
Neural Network ◽  
Time Window ◽  
Probabilistic Neural Network ◽  
Time Windows ◽  
Principal Component ◽  
Translation Invariant ◽  
Ecg Signals ◽  
Novel Approach ◽  
Scattering Transform ◽  
Reduction Methods

An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k=4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.

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