COMPREHENSIVE ANALYSIS OF NORMAL AND DIABETIC HEART RATE SIGNALS: A REVIEW

2012 ◽  
Vol 12 (05) ◽  
pp. 1240033 ◽  
Author(s):  
OLIVER FAUST ◽  
V. RAMANAN PRASAD ◽  
G. SWAPNA ◽  
SUBHAGATA CHATTOPADHYAY ◽  
TEIK-CHENG LIM
Keyword(s):  
Heart Rate ◽  
Organ Damage ◽  
Extraction Methods ◽  
Time Frequency ◽  
Ecg Signals ◽  
Cardiac Health ◽  
Non Linear ◽  
Non Stationary Signal ◽  
Early Diagnosis Of Diabetes ◽  
Typical Range

A large section of the world's population is affected by diabetes mellitus (DM), commonly referred to as "diabetes." Every year, the number of cases of DM is increasing. Diabetes has a strong genetic basis, hence it is very difficult to cure, but can be controlled with medications to prevent subsequent organ damage. Therefore, early diagnosis of diabetes is very important. In this paper, we examine how diabetes affects cardiac health, which is reflected through heart rate variability (HRV), as observed in electrocardiography (ECG) signals. Such signals provide clues for both the presence and severity of diabetes as well as diabetes-induced cardiac impairments. Heart rate (HR) is a non-linear and non-stationary signal. Thus, extracting useful information from HRV signals is a difficult task. We review several sophisticated signal processing and information extraction methods in order to establish measurable relationships between the presence and the extent of diabetes as well as the changes in the HRV signals. Furthermore, we discuss a typical range of values for several statistical, geometric, time domain, frequency domain, time–frequency, and non-linear features for HR signals from 15 normal and 15 diabetic subjects. We found that non-linear analysis is the most suitable approach to capture and analyze the subtle changes in HRV signals caused by diabetes.

Feature Extraction Technology for Rolling Bearings Based on Local Tangent Space Alignment

2015 ◽  
Vol 764-765 ◽  
pp. 274-279
Author(s):  
Zhi Wen ◽  
Chen Lu ◽  
Hong Mei Liu
Keyword(s):  
Tangent Space ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Linear Structure ◽  
Extraction Technology ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Non Linear ◽  
Non Stationary Signal ◽  
Local Tangent

Health assessment and fault diagnosis for rolling bearings mostly adopt traditional methods, such as time-frequency, spectral, and wavelet packet analyses, to extract the feature vector. These methods are suitable for processing data with a linear structure. However, for the non-linear and non-stationary signal, the result of these methods is not ideal. Thus, this study proposes a suitable method to extract the feature vector in nonlinear signals. Local tangent space alignment of a manifold algorithm is employed to extract the feature vector from the rolling bearings. Results verify the advantage of the manifold algorithm for non-linear and non-stationary signals.

Early prediction of serious infections in febrile infants incorporating heart rate variability in an emergency department: a pilot study

2021 ◽  
pp. emermed-2020-210675
Author(s):  
Shu-Ling Chong ◽  
Gene Yong-Kwang Ong ◽  
John Carson Allen ◽  
Jan Hau Lee ◽  
Rupini Piragasam ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vital Signs ◽  
Model Performance ◽  
Diagnostic Challenge ◽  
Poincaré Plot ◽  
Time Frequency ◽  
Young Infants ◽  
Serious Infections ◽  
Non Linear

BackgroundEarly differentiation of febrile young infants with from those without serious infections (SIs) remains a diagnostic challenge. We sought to (1) compare vital signs and heart rate variability (HRV) parameters between febrile infants with versus without SIs, (2) assess the performance of HRV and vital signs with reference to current triage tools and (3) compare HRV and vital signs to HRV, vital signs and blood biomarkers, when predicting for the presence of SIs.MethodsUsing a prospective observational design, we recruited patients <3 months old presenting to a tertiary paediatric ED in Singapore from December 2018 through November 2019. We obtained patient demographic characteristics, triage assessment (including the Severity Index Score (SIS)), HRV parameters (time, frequency and non-linear domains) and laboratory results. We performed multivariable logistic regression analyses to predict the presence of an SI, using area under the curve (AUC) with the corresponding 95% CI to assess predictive capability.ResultsAmong 203 infants with a mean age of 38.4 days (SD 27.6), 67 infants (33.0%) had an SI. There were significant differences in the time, frequency and non-linear domains of HRV parameters between infants with versus without SIs. In predicting SIs, gender, temperature and the HRV non-linear parameter Poincaré plot SD2 (AUC 0.78, 95% CI 0.71 to 0.84) performed better than SIS alone (AUC 0.61, 95% CI 0.53 to 0.68). Model performance improved with the addition of absolute neutrophil count and C reactive protein (AUC 0.82, 95% CI 0.76 to 0.89).ConclusionAn exploratory prediction model incorporating HRV and biomarkers improved prediction of SIs. Further research is needed to assess if HRV can identify which young febrile infants have an SI at ED triage.Trial registration numberNCT04103151.

Heart Rate Assessment for Posttraumatic Stress Disorder Symptoms among Military Veterans: A Narrative Literature Review

2018 ◽  
Vol 62 (1) ◽  
pp. 1727-1728
Author(s):  
Kunal Khanade ◽  
Farzan Sasangohar
Keyword(s):  
Heart Rate ◽  
Continuous Monitoring ◽  
Stress Disorder ◽  
Military Veterans ◽  
Fluctuation Analysis ◽  
Resting Heart Rate ◽  
Ptsd Symptoms ◽  
Time Frequency ◽  
Non Linear ◽  
Research Gap

Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition affecting as much as 20% of the returning veterans from the current wars in Afghanistan and Iraq (Ramchand et al., 2010). Due to its prevalence, assessment and intervention methods for PTSD symptoms among veterans are timely to ensure veterans’ faster recovery and their reintegration into society. A narrative review of literature was conducted to examine the literature on techniques and methods for detection and measurement of PTSD symptoms. Relevant reviews and seminal papers associated with psychophysiological measurements with emphasis on heart rate, the prevalence of PTSD in military veterans, and detection of PTSD were included. Psychophysiology involves the non-invasive recording of biological processes simultaneously (Pole, 2007). The measures that were found to be reliably related to PTSD are: (1) higher resting heart rate; (2) larger heart rate responses to standardized trauma cues; and (3) for idiographic cues facial muscle electromyography (EMG) and heart rate responses (Pole, 2007). Studies based on heart rate found that there were five beats per minute (bpm) increase in resting heart rates for combat veterans who suffer from PTSD compared to those who were not diagnosed (Beckham et al., 2000, Woodward et al., 2009). Buckley et al. (2004) found that for a mixed population (veterans and civilians) PTSD patients had a resting heart rate increase of 6.6 bpm compared to non-PTSD patients while adjusting for covariates. The exact mechanisms of how stress affects heart rate are not well-known; however, it is possible to observe stressful events and relate these to physiological changes in heart rate that can be measured using modern sensors (Andreoli et al., 2010). In the literature, heart rate is analyzed using statistical, geometric, frequency domain, time-frequency and non-linear feature analysis (Acharya et al., 2006). Non-linear features have been developed to quantify the dynamics of heart rate fluctuations. These include approximate entropy, Detrended Fluctuation Analysis (DFA), Lyapunov exponent, Recurrence Plots (RPs) and Correlation Dimension (CD) (Pincus, 1991; Huikuri et al., 2000; Acharya et al., 2004; Acharya et al., 2006). Our synthesis resulted in identification of three research gaps. Heart rate analysis has shown promise to link PTSD symptoms to differences in PTSD and non-PTSD subjects (Pole, 2007) but a characterization of PTSD symptoms from heart rate data seems to remain a research gap. While continuous monitoring of physiological parameters is gaining momentum, this particular method has not been studied to infer key characteristics associated with PTSD among military veterans. Continuous monitoring might be able to highlight muted response to startle or treatment. There is also a research gap in the assessment of heart rate accelerations and decelerations in response to specific PTSD symptoms (Khanade et al., 2017). The investigation into accelerations/decelerations associated with PTSD hyper-arousal triggers might have a potential to detect hyper-arousal instantaneously and would shed light on trigger-specific interventions to reduce harmful effects of PTSD triggers in a timely manner. In addition, most studies were conducted to observe differences among PTSD and non-PTSD populations. More work is warranted to focus on PTSD patients in isolation to explain variety of triggers and their specific physiological reactions.

Linear, Non-Linear and Wavelet Analysis of Cardiac Health Using Heart Rate Signals

2007 ◽  
pp. 355-375
Author(s):  
U. Rajendra Acharya ◽  
N. Kannathal ◽  
Lim Choo Min ◽  
Jasjit S. Suri
Keyword(s):  
Heart Rate ◽  
Wavelet Analysis ◽  
Cardiac Health ◽  
Non Linear

Fast Algorithm for Adaptive Gaussian Chirplet Signal Decomposition

2014 ◽  
Vol 556-562 ◽  
pp. 4755-4758
Author(s):  
Jian Feng Guo ◽  
Wei Dong Wang ◽  
Jin Zhao Liu
Keyword(s):  
Fast Algorithm ◽  
Signal Decomposition ◽  
Frequency Resolution ◽  
Decomposition Algorithms ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Non Linear ◽  
Linear Signal ◽  
Non Stationary Signal ◽  
Short Time

It is well known that adaptive Gaussian chirplet signal decomposition algorithm has the best time frequency resolution in all signal decomposition algorithms. It is widely used in non linear and non stationary signal decomposition, especially for the signal which is superposition of chirplet functions decomposition. But it has a large amount of computation. In this paper, we propose a fast algorithm based on short time Fourier transform (STFT) method and we change parameters’ domain. Using this fast algorithm to decompose a four atoms non-linear signal computes very fast and it can also avoid the cross term’s interferer of the Wigner-Ville distribution.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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 Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pablo Armañac-Julián ◽  
David Hernando ◽  
Jesús Lázaro ◽  
Candelaria de Haro ◽  
Rudys Magrans ◽  
...  
Keyword(s):  
Heart Rate ◽  
Mechanical Ventilation ◽  
Spontaneous Breathing Trial ◽  
Sinus Arrhythmia ◽  
Time Frequency ◽  
Cardiopulmonary Coupling ◽  
Cardiac Pumping ◽  
Successful Weaning ◽  
Frequency Coherence ◽  
Subspace Projections

AbstractThe ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

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