Empirical Mode Decomposition as a Novel Approach to Study Heart Rate Variability in Congestive Heart Failure Assessment

Feasibility of a Composite Measure of Pulmonary Vascular Impedance and Application to Patients with Chronic RV Failure Post LVAD Implant

Pulmonary vascular impedance (PVZ) describes RV afterload in the frequency domain and has not been studied extensively in LVAD patients. We sought to determine (1) feasibility of calculating a composite (c)PVZ using standard of care (SoC), asynchronous, pulmonary artery pressure (PAP) and flow (PAQ) waveforms; and (2) if chronic right ventricular failure (RVF) post-LVAD implant was associated with changes in perioperative cPVZ.PAP and PAQ were obtained via SoC procedures at three landmarks: T(1), Retrospectively, pre-operative with patient conscious; and T(2) and T(3), prospectively with patient anesthetized, and either pre-sternotomy or chest open with LVAD, respectively. Additional PAP's were taken at T(4), following chest closure; and T(5), 4-24 h post chest closure. Harmonics (z) were calculated by Fast Fourier Transform (FFT) with cPVZ(z) = FFT(PAP)/FFT(PAQ). Total pulmonary resistance Z(0); characteristic impedance Zc, mean of cPVZ(2-4); and vascular stiffness PVS, sum of cPVZ(1,2), were compared at T(1,2,3) between +/-RVF groups.Out of 51 patients, nine experienced RVF. Standard hemodynamics and changes in cPVZ-derived parameters were not significant between groups at any T.In conclusion, cPVZ calculated from SoC measures is possible. Although data that could be obtained were limited it suggests no difference in RV afterload for RVF patients post-implant. If confirmed in larger studies, focus should be placed on cardiac function in these subjects.

Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets

Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.

Classification of sleep apnea using EMD-based features and PSO-trained neural networks

In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN's performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN's accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.