A mixture of fuzzy filters applied to the analysis of heartbeat intervals

Kumar, Mohit; Weippert, Matthias; Stoll, Norbert

doi:10.1007/s10700-010-9089-7

Cited by 13 publications

(7 citation statements)

References 40 publications

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“…25 The results presented suggest that intelligent fuzzy computing-based biomedical signal analysis is a promising method. 25 The algorithms for predicting a patient's stress state from a real-time analysis of physiological data are to be optimized in terms of computational complexity, in order to facilitate the real-time applications. Intelligent fuzzy computing-based stochastic analysis of biomedical signals with potential e-health applications is the novelty of this research.…”

Section: Stress Modelingmentioning

confidence: 91%

“…The parameters of the stochastic mixture are inferred under a Bayesian framework. The probability that the given R-R interval data belong to a particular stress level-specific model can be calculated as shown by Kumar et al 25 Stress levels specific to different models are weighted by the respective probabilities in order to provide an estimate of the unknown stress level (Fig. 3b).…”

Section: Stress Modelingmentioning

confidence: 99%

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Stress and Fitness Monitoring Embedded on a Modern Telematics Platform

Takenga¹,

Berndt²,

Kuehn³

et al. 2012

Telemedicine and e-Health

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Lack of regular physical activity and high stress levels are the leading causes of several illnesses. There is thus a real need for a personal low-cost and mobile monitoring solution over extended periods to prevent health risks. Based on the above fact, this article presents a system capable of estimating and monitoring both stress and fitness levels without a physical consultation of a medical specialist. The system consists of three main subcomponents: a mobile real-time acquisition of physiological as well as subjective data, an expert model for stress and fitness estimations based on physiological signals collected from wireless vital sensors, and a secure and scalable telematics platform on which the entire system is embedded. Features and tasks performed by the telematics platform will be presented. The experimental part of the work involved a representative number of subjects. Results for 110 subjects whose fitness levels were assessed at different periods of the year and 50 individuals whose stress scores were assessed at different times of the day showed a high correlation of the estimated values with the true ones. The application of such a low-cost monitoring system will improve the quality of service in preventive medicine.

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Section: Stress Modelingmentioning

confidence: 91%

Section: Stress Modelingmentioning

confidence: 99%

Stress and Fitness Monitoring Embedded on a Modern Telematics Platform

Takenga¹,

Berndt²,

Kuehn³

et al. 2012

Telemedicine and e-Health

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results presented in [21] suggest that intelligent fuzzy computing based biomedical signal analysis is a promising method. The algorithms for predicting patient's stress state from a real-time analysis of physiological data are to be optimized in terms of computational complexity to facilitate the real-time applications.…”

Section: Stress Modeling Principlementioning

confidence: 91%

A scalable and secure Telematics Platform for the hosting of telemedical applications. Case study of a stress and fitness monitoring

Berndt¹,

Takenga²,

Kuehn³

et al. 2011

2011 IEEE 13th International Conference on E-Health Networking, Applications and Services

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Mobile telemedical applications are of crucial importance today as they do offer the potential to improve the quality of the health care related services. It has been proven that the leading cause of several illnesses and diseases are stress and the lack of fitness practices. Based on that, a system capable of estimating and monitoring both stress and fitness levels without a physical consultation of a medical specialist is presented in this work. A correlation between physiological parameters and stress or fitness is explored in the developed intelligent expert system. In order to enable the implementation of such a complex system, a flexible Telematics Platform is conceived. This Telematics Platform does guarantee a secure data communication involving encryption and a suitable visualization of the fitness and stress assessment results on both the patient's mobile phone and on a web-based portal. Moreover, the platform is scalable and can therefore be easily extended in order to support different kinds of telemedical applications. The system presented in this paper does enable the efficient and cost-effective stress and fitness monitoring of patients while being at home or practicing their daily jobs with their subsequent eventual mobility.

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“…Another technique for mitigating serial detection problems involves filtering or smoothing RR interval sequences generated by a beat detection algorithm [8,9]. However, it is not clear whether RR sequence filtering or smoothing could remedy false detections such as those shown in the bottom panel of Figure 1, because the number of detected false beats is so large.…”

Section: Introductionmentioning

confidence: 99%

Sinus rhythm heart rate estimation in high noise environments by application ofa prioriRR interval statistics

Hopenfeld

2014

Journal of Medical Engineering & Technology

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Most existing heart beat detection algorithms serially process peaks, which can be either noise or true beats. Serial processing can result in inaccurate detections in the context of high noise. The proposed method relies on the relative regularity of sinus rhythm RR interval changes to select the best sequences of peaks in a 5-10 s long segment of cardiac data. The best sequences with a current data segment are subjected to a trending analysis, to determine whether their associated RR intervals fit within a pattern of prior best segments. The RR regularity scores and the results of the trending analysis are combined into a single sequence score and the final sequence for a segment is chosen from the best sequences based on this overall score. The current heart rate estimate is updated with the final sequence's RR interval by an adaptive filter that weights the overall score. Twenty-four hour RR interval records for 54 normal individuals were parsed into 10-s segments and corrupted with spurious 'noise' peaks, which resulted in a revised RR interval series that included a number of false RR intervals. The algorithm was run on these corrupted RR interval series. The percentages of mean heart rate values within 5 beats min(-1) of the true value were 95%, 88% and 77% for 10, 20 and 30 added noise spikes, respectively. The percentages of mean heart rate values within 10 beats min(-1) of the true value were 98%, 96% and 91% for 10, 20 and 30 added noise spikes, respectively. Accuracy was higher for data segments characterized by relatively low RR interval variability. The proposed algorithm shows promise for estimating average heart rate for sinus rhythm in high noise environments.

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A mixture of fuzzy filters applied to the analysis of heartbeat intervals

Cited by 13 publications

References 40 publications

Stress and Fitness Monitoring Embedded on a Modern Telematics Platform

Stress and Fitness Monitoring Embedded on a Modern Telematics Platform

A scalable and secure Telematics Platform for the hosting of telemedical applications. Case study of a stress and fitness monitoring

Sinus rhythm heart rate estimation in high noise environments by application ofa prioriRR interval statistics

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