Analysis Of Heart Rate Signals Research Articles

Tsallis Entropy-Based Complexity-IPE Casualty Plane: A Novel Method for Complex Time Series Analysis.

Due to its capacity to unveil the dynamic characteristics of time series data, entropy has attracted growing interest. However, traditional entropy feature extraction methods, such as permutation entropy, fall short in concurrently considering both the absolute amplitude information of signals and the temporal correlation between sample points. Consequently, this limitation leads to inadequate differentiation among different time series and susceptibility to noise interference. In order to augment the discriminative power and noise robustness of entropy features in time series analysis, this paper introduces a novel method called Tsallis entropy-based complexity-improved permutation entropy casualty plane (TC-IPE-CP). TC-IPE-CP adopts a novel symbolization approach that preserves both absolute amplitude information and inter-point correlations within sequences, thereby enhancing feature separability and noise resilience. Additionally, by incorporating Tsallis entropy and weighting the probability distribution with parameter q, it integrates with statistical complexity to establish a feature plane of complexity and entropy, further enriching signal features. Through the integration of multiscale algorithms, a multiscale Tsallis-improved permutation entropy algorithm is also developed. The simulation results indicate that TC-IPE-CP requires a small amount of data, exhibits strong noise resistance, and possesses high separability for signals. When applied to the analysis of heart rate signals, fault diagnosis, and underwater acoustic signal recognition, experimental findings demonstrate that TC-IPE-CP can accurately differentiate between electrocardiographic signals of elderly and young subjects, achieve precise bearing fault diagnosis, and identify four types of underwater targets. Particularly in underwater acoustic signal recognition experiments, TC-IPE-CP achieves a recognition rate of 96.67%, surpassing the well-known multi-scale dispersion entropy and multi-scale permutation entropy by 7.34% and 19.17%, respectively. This suggests that TC-IPE-CP is highly suitable for the analysis of complex time series.

VARSE: Android app for real-time acquisition and analysis of heart rate signals

Background and objectivenowadays, numerous mobile applications capable of measuring the Heart Rate (HR) are continuously being launched. However, these tools do not allow to record and label the acquired HR signals while users are doing activities such as practising sports or viewing images. They do not allow to perform simultaneous HR acquisition and real-time HRV analysis, either. VARSE is an app for Android mobile devices capable of acquiring and labelling HR signals and of performing real-time HRV analysis. MethodsVARSE was developed for Android devices. It includes functionalities to acquire HR signals from any Bluetooth device that implements the Heart Rate Profile specification (such as a chest strap), while labelling segments of the HR data in different situations. Not only can these signals be stored, but also time and frequency HRV analyses can be carried out over them. The application is distributed under the MIT license [1], and it is also available to be installed via Google Play [2]. Functionality, ease-of-use and friendliness of VARSE were evaluated employing an User Experience Questionnaire (UEQ). Its reliability was proven by comparative studies against other existing acquisition (gVARVI) or HRV analysis (RHRV) software. Resultshigh values were obtained for all the dimensions evaluated in the UEQ, proving the quality of the application, as well as its ease-of-use and efficiency. Both HR signal acquisition and HRV analysis yielded results similar to the ones obtained using other applications. ConclusionsVARSE is a tool with complete functionality, that can be easily downloaded or installed on Android mobile devices. It can be used by anyone who wishes to record HR signals while performing different activities, and also by the medical scientific community to perform real-time HRV analyses easily. Future versions will improve its capabilities and allow its integration with other open source applications.

Analysis of heart rate signals during meditation using visibility graph complexity.

In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.

Fuzzy theoretic approach to signals and systems: Static systems

“Fuzzy Theoretic Approach to Signals and Systems” assumes all system variables and parameters as uncertain (i.e. being characterized by membership functions), develops a mathematical theory for analytically determining the membership functions on system variables and parameters, derives algorithms for estimating the parameters of membership functions, and establishes robustness and convergence properties of the estimation algorithms. The membership functions are analytically determined via solving a variational optimization problem that maximizes the “over-uncertainties-averaged-log-membership” of the observed data around an initial guess. This paper develops the analytical fuzzy theory for the particular case of a multi-input single-output static system affected by noises. The theory facilitates designing an adaptive filtering algorithm. The robustness of the adaptive filtering algorithm is proved theoretically via a mathematical analysis. Numerical experiments further demonstrate the robustness of the filtering algorithm. A comparison of the algorithm with the state-of-art methods is made using the practical biomedical applications related to the modeling and analysis of heart rate signals for assessing the physiological state of an individual.

Association of heart rate variability and inflammatory response in patients with cardiovascular diseases: current strengths and limitations

Many experimental and clinical studies have confirmed a continuous cross-talk between both sympathetic and parasympathetic branches of autonomic nervous system and inflammatory response, in different clinical scenarios. In cardiovascular diseases, inflammation has been proven to play a pivotal role in disease progression, pathogenesis and resolution. A few clinical studies have assessed the possible inter-relation between neuro-autonomic output, estimated with heart rate variability analysis, which is the variability of R-R in the electrocardiogram, and different inflammatory biomarkers, in patients suffering from stable or unstable coronary artery disease (CAD) and heart failure. Moreover, different indices derived from heart rate signals' processing, have been proven to correlate strongly with severity of heart disease and predict final outcome. In this review article we will summarize major findings from different investigators, evaluating neuro-immunological interactions through heart rate variability analysis, in different groups of cardiovascular patients. We suggest that markers originating from variability analysis of heart rate signals seem to be related to inflammatory biomarkers. However, a lot of open questions remain to be addressed, regarding the existence of a true association between heart rate variability and autonomic nervous system output or its adoption for risk stratification and therapeutic monitoring at the bedside. Finally, potential therapeutic implications will be discussed, leading to autonomic balance restoration in relation with inflammatory control.

Monitoring spinal cord injured patients during activity using a datalogger: Preliminary results

The Compumedics Siesta datalogger was used to set up and trial the potential and initial applications of a remote datalogger to study a range of physiological and other changes in spinal cord injured (SCI) patients undergoing rehabilitation following traumatic injury. The group of 6 patients (weeks 3 to 34 post injury) was selected due to changes expected with neurological and functional recovery. The 32 channel datalogger was used to record signals of ECG, Thoracic and Abdominal respiration, Pulse Oximetry, piezoelectric accelerometers and thermistors. Five (5) tests were completed for each subject over a period of three (3) months. This study extended on work in sleep lab testing in normals and SCI at Austin RMC. Data analysis involved pattern recognition, simple analysis of heart rate signal, and more complex focused signal analysis. Datalogger measurements have revealed some changes in physiology including autonomic failure unique to SCI patients during normal activity. Further research is required to consolidate results through a larger study group.

Hardware design for the computation of heart rate variability

The decrease in heart rate variability is an indication of abnormal heart function. Proposed here is a hardware design of a standalone system that calculates and evaluates heart rate variability, distinguishing healthy from unhealthy subjects. Previous approaches are mostly based on the fast Fourier transform and the power spectral analysis. Described is an alternative approach that follows a recently proposed idea: the analysis of heart rate signals with the use of wavelets. The proposed system follows simple gated architecture and is composed of four main units: a processing unit that prepares the input signal for analysis, a unit that manages control signals, the wavelet computation unit and the wavelet coefficient evaluation unit. The hardware design is cost-effective, simple and easy to implement. Experimental results proved that this system is efficient and produces a clean and accurate separation between the healthy and unhealthy groups of patients for the first nine scales of wavelet analysis.

Selecting measures for human factors research.

Selecting measures is a necessary component of human factors research. Proper selection must take into account the representation problem (how is the assignment of numbers to objects or phenomena justified?) and the uniqueness problem (to what degree is this assignment unique?). Other key human factors measurement issues include subject representativeness, variable representativeness, and setting representativeness. It is difficult to create a single measure that captures essential characteristics of complex systems. Several examples illustrate how theory can guide measurement selection in such diverse human factors research as vigilance, turning off warning alarms, information requirements for military command centers, subjective workload, heart-rate signal analysis, and heat stress in nuclear power plants.