scholarly journals Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Marjan Gusev ◽  
Lidija Poposka ◽  
Gjoko Spasevski ◽  
Magdalena Kostoska ◽  
Bojana Koteska ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Glucose Level ◽  
Invasive Procedure ◽  
Development Trend ◽  
Glucose Measurement ◽  
Daily Routine ◽  
Network Methods

Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.

scholarly journals Unravelling the black box of machine learning for atrial fibrillation forecast: role of heart rate variability and of premature beats

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J.M Gregoire ◽  
C Gilon ◽  
S Carlier ◽  
H Bersini
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Deep Neural Network ◽  
Black Box ◽  
Rr Intervals ◽  
Network Layers ◽  
Premature Beats

Abstract Background The identification of patients still in sinus rhythm who will present one month later an atrial fibrillation episode is possible using machine learning (ML) techniques. However, these new ML algorithms do not provide any relevant information about the underlying pathophysiology. Purpose To compare the predictive performance for forecasting AF between a machine learning algorithm and other parameters whose pathophysiological mechanisms are known to play a role in the triggering of arrhythmias (i.e. the count of premature beats (PB) and heart rate variability (HRV) parameters) Material and methods We conducted a retrospective study from an outpatient clinic. 10484 Holter ECG recordings were screened. 250 analysable AF onsets were labelled. We developed a deep neural network model composed of convolutional neural network layers and bidirectional gated recurrent units as recurrent neural network layers that was trained for the forecast of paroxysmal AF episodes, using RR intervals variations. This model works like a black box. For comparison purposes, we used a “random forest” (RF) model of ML to obtain forecast results using HRV parameters with and without PB. This model allows the evaluation of the relevance of HRV parameters and of PB used for the forecast. We calculated the area under the curve of the receiving operating characteristic curve for the different time windows counted in RR intervals before the AF onset. Results As shown in the table, the forecasting value of the deep neural network model (ML) was not superior to the random forest algorithm. Prediction value of both decreased when analyzing the RR intervals further away from the onset of AF Conclusions These results suggest that HRV plays a predominant role in triggering AF episodes and that premature beats could add minor information. Moreover, the closer the window from AF onset, the better the accuracy, regardless of the method used. Such detection algorithms once implemented in pacemakers, might prove useful to prevent AF onset by changing pacing sequence while patients would still be in sinus rhythm, however this remains to be demonstrated Funding Acknowledgement Type of funding source: None

scholarly journals Trends in Heart-Rate Variability Signal Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Syem Ishaque ◽  
Naimul Khan ◽  
Sri Krishnan
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Well Being ◽  
Good Health ◽  
Machine Learning Techniques ◽  
Video Gaming ◽  
Clinical Events ◽  
Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

scholarly journals Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Fang ◽  
Hong-Yun Liu ◽  
Zhi-Yan Wang ◽  
Zhao Yang ◽  
Tung-Yang Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Outcome Prediction ◽  
Healthy Controls ◽  
Drug Resistant ◽  
Sleep State ◽  
Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

scholarly journals Classification among Microaneurysms, Exudates, and Lesion free Retinal Regions in the Eye Images using Transfer Learned CNNs

2019 ◽  
Vol 9 (2) ◽  
pp. 5508-5512
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Glucose Level ◽  
Vision Loss ◽  
Early Stage ◽  
Training Data ◽  
Fundus Images ◽  
Diabetic Mellitus ◽  
Start Process

When pancreas fails to secrete sufficient insulin in the human body, the glucose level in blood either becomes too high or too low. This fluctuation in glucose level affects different body organs such as kidney, brain, and eye. When the complications start appearing in the eyes due to Diabetic Mellitus (DM), it is called Diabetic Retinopathy (DR). DR can be categorized in several classes based on the severity, it can be Microaneurysms (ME), Haemorrhages (HE), Hard and Soft Exudates (EX and SE). DR is a slow start process that starts with very mild symptoms, becomes moderate with the time and results in complete vision loss, if not detected on time. Early-stage detection may greatly bolster in vision loss. However, it is impassable to detect the symptoms of DR with naked eyes. Ophthalmologist harbor to the several approaches and algorithm which makes use of different Machine Learning (ML) methods and classifiers to overcome this disease. The burgeoning insistence of Convolutional Neural Network (CNN) and their advancement in extracting features from different fundus images captivate several researchers to strive on it. Transfer Learning (TL) techniques help to use pre-trained CNN on a dataset that has finite training data, especially that in under developing countries. In this work, we propose several CNN architecture along with distinct classifiers which segregate the different lesions (ME and EX) in DR images with very eye-catching accuracies.

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