Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study

BACKGROUND: Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure. OBJECTIVE: We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black-polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD). METHODS: We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed with fluid and without fluid groups, respectively. RESULTS: Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R0; difference in extracellular-intracellular resistance, R0 - Rinfinity, and tissue heterogeneity, alpha) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R0 and R0 - Rinfinity had significantly lower values for patients with heart failure than for those without heart failure (R0: P=.006; R0 - Rinfinity: P=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation. CONCLUSIONS: This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest. Emily Ensom, Eric Ding, Anna Hayes, Jarno Riistama, Chad Darling, David McManus, Ki H. Chon. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 27.08.2020

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process

Tracking team mental workload by multimodal measurements in the operating room

Mental workload and its effects on surgical performance are underexplored topics, despite their importance for operating room (OR) efficiency and patient safety. We developed a multimodal platform that can simultaneously collect data from EEG, heart rate and breathing rate, tool handle pressure, and eye tracker from mobile subjects. We performed experiments using the Fundamentals of Laparoscopic Surgery model, with 22 subjects of varying skill levels ranging from nonsurgeon to expert. The results indicated significant modulations of the measurements depending on pupil size, heart rate variability, P300 response, tool pressure, task difficulty, time-on-task, and skill level. These provide evidence that physiology based metrics can be used in automated classification of fine gradations of skill, the assessment and certification of surgery trainees, developing real-time flags and warnings for the OR, and validating new OR technology

Automatic Classification of Full- and Reduced-Lead Electrocardiograms Using Morphological Feature Extraction

Cardiovascular diseases are the global leading cause of death. Automated electrocardiogram (ECG) analysis can support clinicians to identify abnormal excitation of the heart and prevent premature cardiovascular death. An explainable classification is particularly important for support systems. Our contribution to the PhysioNet/CinC Challenge 2021 (team name: ibmtPeakyFinders) therefore pursues an approach that is based on interpretable features to be as explainable as possible. To meet the challenge goal of developing an algorithm that works for both 12-lead and reduced lead ECGs, we processed each lead separately. We focused on signal processing techniques based on template delineation that yield the template's fiducial points to take the ECG waveform morphology into account. In addition to beat intervals and amplitudes obtained from the template, various heart rate variability and QT interval variability features were extracted and supplemented by signal quality indices. Our classification approach utilized a decision tree ensemble in a one-vs-rest approach. The model parameters were determined using an extensive grid search. Our approach achieved challenge scores of 0.47, 0.47, 0.34, 0.40, and 0.41 on hidden 12-, 6-, 4-, 3-, and 2-lead test sets, respectively, which corresponds to the ranks 12, 10, 23, 18, and 16 out of 39 teams

Quantitative Multidimensional Stress Assessment from Facial Videos

Stress has a significant impact on the physical and mental health of an individual and is a growing concern for society, especially during the COVID-19 pandemic. Facial video-based stress evaluation from non-invasive cameras has proven to be a significantly more efficient method to evaluate stress in comparison to approaches that use questionnaires or wearable sensors. Plenty of classification models have been built for stress detection. However, most do not consider individual differences. Also, the results for such models are limited by a uni-dimensional definition of stress levels lacking a comprehensive quantitative definition of stress. The dissertation focuses on building a framework that utilizes the multilevel video frame representations from deep learning and the remote photoplethysmography signals extracted from the facial videos for stress assessment. The fusion model takes the inputs of a baseline video and a target video of the subject. The physiological features such as heart rate and heart rate variability are used with the initial stress scores generated from deep learning are used to predict the stress scores in cognitive anxiety, somatic anxiety, and self-confidence. To generate stress scores with better accuracy, the signal extraction method is improved by introducing the CWT-SNR method that uses the signal-to-noise ratio to assist the adaptive bandpass filtering in the post-processing of the signals. A study on phase space reconstruction features is performed and the results show the potential for additional accuracy improvement for the heart rate variability detection. To select the best deep learning architecture, multiple deep learning architectures are tested to build the deep learning model. Support Vector Regression is used to generate the output stress score results. Testing with the data from the UBFC-Phys dataset, the fusion model shows a strong correlation between ground truth and the predicted results

Assessment of spontaneous cardiovascular oscillations in Parkinson's disease

Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population. Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains

Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76