Robust Algorithm for Heart Rate (HR) Detection and Heart Rate Variability (HRV) Estimation

Piotrowski, Zbigniew; Różanowski, K.

doi:10.12693/aphyspola.118.131

Cited by 30 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another method proposed for event detection in LFPs (Bokil et al, 2006) was based on an algorithm with higher computational complexity O(log n!). Furthermore, algorithms used to detect PQRST complexes in ECG signals (Dota et al, 2002(Dota et al, , 2009Piotrowskia and Rozanowski, 2010) can be adapted to detect the events present in LFPs; however, these algorithms have computational complexities of O(n 3 ).…”

Section: Resultsmentioning

confidence: 99%

An automated method for detection of layer activation order in information processing pathway of rat barrel cortex under mechanical whisker stimulation

Mahmud

Pasqualotto

Bertoldo

et al. 2011

Journal of Neuroscience Methods

View full text Add to dashboard Cite

Rodents perform object localization, texture and shape discrimination very precisely through whisking. During whisking, microcircuits in corresponding barrel columns get activated to segregate and integrate tactile information through the information processing pathway. Sensory signals are projected through the brainstem and thalamus to the corresponding 'barrel columns' where different cortical layers are activated during signal projection. Therefore, having precise information about the layer activation order is desirable to better understand this signal processing pathway. This work proposes an automated, computationally efficient and easy to implement method to determine the cortical layer activation from intracortically recorded local field potentials (LFPs) and derived current source density (CSD) profiles: 1. Barrel cortex LFPs are represented by a template of four subsequent events: small positive/negative (E1) → large negative (E2) → slow positive (E3)→ slow long negative (E4). The method exploits the layer specific characteristics of LFPs to obtain latencies of the individual events (E1–E4), then taking the latency of E2 for calculating the layer activation order. 2. The corresponding CSD profile is calculated from the LFPs and the first sink’s peak is considered as a reference point to calculate latencies and evaluate the layer activation order. Other reference points require manual calculation. Similar results of layer activation sequence are found using LFPs and CSDs. Extensive tests on LFPs recorded using standard borosilicate micropipettes demonstrated the method's workability. An interpretation of layer activation order and CSD profiles on the basis of a simplified interacortical barrel column architecture is also provided.

show abstract

Section: Resultsmentioning

confidence: 99%

An automated method for detection of layer activation order in information processing pathway of rat barrel cortex under mechanical whisker stimulation

Mahmud

Pasqualotto

Bertoldo

et al. 2011

Journal of Neuroscience Methods

View full text Add to dashboard Cite

show abstract

“…These are often redundant characterized by much too entropy and with no connection with the aim of classification. In such situation what is necessary is the process of features reduction and selection and one more, the use of methods which scale multidimensionally [10,11]. Also, there are different ways of data clustering in SEI method.…”

Section: Methods Of Data Clustering In Sei Aspectmentioning

confidence: 99%

Specific emitter identification based on graphical representation of the distribution of radar signal parameters

Dudczyk¹,

Kawalec²

2015

Bulletin of the Polish Academy of Sciences Technical Sciences

View full text Add to dashboard Cite

Abstract. The article presents some possibilities of same type radar copies identification with the use of graphical representation. The procedure described by the authors is based on transformation and analysis of basic parameters distribution which are measured by the radar signal especially Pulse Repetition Interval. A radar intercept receiver passively collects incoming pulse samples from a number of unknown emitters. Information such as Pulse Repetition Interval, Angle of Arrival, Pulse Width, Radio Frequency and Doppler shifts are not usable. The most important objectives are to determine the number of emitters present and classify incoming pulses according to emitters. To classify radar emitters and precisely identification the copy of the same type of an emitter source in surrounding environment, we need to explore the detailed structure i.e. intra-pulse information, unintentional radiated electromagnetic emission and fractal features of a radar signal. An emitter has its own signal structure. This part of radar signal analysis is called Specific Emitter Identification. Utilization of some specific properties of electronic devices can cause heightening probability of a correct identification.

show abstract

“…Some algorithms used to detect QRS complexes are genetic algorithm, wavelet transform or filter banks, artificial neural networks (Köhler et al, 2002), zero crossing counts (Köhler et al, 2003), and adaptive threshold (Christov, 2004). Direct methods of detecting heart rate include spectral analyses of ECG signals (Surda et al, 2007) and the short-term autocorrelation method (Piotrowskia & Rozanowski, 2010). All of the above mentioned algorithms or methods are complicated when implemented in the detection of real-time heart rate frequency in a microprocessor unit.…”

Section: Introductionmentioning

confidence: 99%

Development of the ‘Healthcor’ System as a Cardiac Disorders Symptoms Detector using an Expert System based on Arduino Uno

Hugeng¹,

Kurniawan²

2016

IJTech

View full text Add to dashboard Cite

In the modern era, our lifestyles are very fast-moving; this makes us highly susceptible to diseases, especially those associated with heart problems. In this research, we developed a portable early detection system for cardiac disorders. This system consists of passive electrodes, named SHIELD-EKG-EMG-PA; a shield which allows Arduino-like boards to capture electrocardiography (ECG) and electromyography (EMG) signals, named SHIELD-EKG-EMG, both devices produced by Olimex; a microcontroller, based on Arduino Uno; and an expert system which is implemented by a personal computer. This system detects time intervals of various segments in ECG signals which are captured by the devices; it then analyzes the signals in order to determine whether the patient has cardiac disorders. We call our detecting system the HEALTHCOR system. A database was established, containing various possible values of parameters in ECG signals. The types of diseases that can be detected are heart rhythm disorders including sinus bradycardia, sinus tachycardia, sinus arrhythmia, and cardiac symptoms associated with intervals and the wave height, such as myocardial infarction. From our tests, the accuracy of our system is 96%. The resultant diagnoses of four patients are all appropriate, and used a commercial 12-lead electrocardiograph.

show abstract

Robust Algorithm for Heart Rate (HR) Detection and Heart Rate Variability (HRV) Estimation

Cited by 30 publications

References 6 publications

An automated method for detection of layer activation order in information processing pathway of rat barrel cortex under mechanical whisker stimulation

An automated method for detection of layer activation order in information processing pathway of rat barrel cortex under mechanical whisker stimulation

Specific emitter identification based on graphical representation of the distribution of radar signal parameters

Development of the ‘Healthcor’ System as a Cardiac Disorders Symptoms Detector using an Expert System based on Arduino Uno

Contact Info

Product

Resources

About