Prominent publications by Marimuthu Swami Palaniswami

KOL Index score: 13691

BACKGROUND: Poincaré plot is one of the important techniques used for visually representing the heart rate variability. It is valuable due to its ability to display nonlinear aspects of the data sequence. However, the problem lies in capturing temporal information of the plot quantitatively. The standard descriptors used in quantifying the Poincaré plot (SD1, SD2) measure the gross variability of the time series data. Determination of advanced methods for capturing temporal properties ...

Known for Poincaré Plot |  Complex Correlation Measure |  Sd1 Sd2 |  Congestive Heart Failure |  Groups Subjects
KOL Index score: 12292

We investigate whether pulse rate variability (PRV) extracted from finger photo-plethysmography (Pleth) waveforms can be the substitute of heart rate variability (HRV) from RR intervals of ECG signals during obstructive sleep apnea (OSA). Simultaneous measurements (ECG and Pleth) were taken from 29 healthy subjects during normal (undisturbed sleep) breathing and 22 patients with OSA during OSA events. Highly significant (p<0.01) correlations (1.0>r>0.95) were found between heart rate ...

Known for Heart Rate Variability |  Pulse Rate |  Sleep Apnea |  Hrv Prv |  Ecg Signals
KOL Index score: 12167

The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we ...

Known for Ppg Signal |  Respiratory Rate |  Blood Volume |  Existing Methods |  Proposed Algorithm
KOL Index score: 11618

Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS - ) and subjects with OSAS (OSAS +), each of approximately 8 h in duration, were analyzed. ...

Known for Support Vector Machines |  Sleep Apnea |  Ecg Recordings |  Hrv Edr |  Normal Subjects
KOL Index score: 11135

Obstructive sleep apnea or hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and hypopnea events from normal breathing events using wavelet-based features of 5-s ECG signals (sampling rate = 250 Hz) and estimate the surrogate apnea index (AI)/hypopnea index (HI) (AHI). Total 82,535 ECG epochs (each of 5-s duration) from normal breathing during sleep, 1638 ECG epochs from 689 hypopnea events, and 3151 ...

Known for Hypopnea Events |  Obstructive Sleep Apnea |  Ecg Signals |  Signal Processing |  Normal Breathing
KOL Index score: 9404

Ageing influences gait patterns causing constant threats to control of locomotor balance. Automated recognition of gait changes has many advantages including, early identification of at-risk gait and monitoring the progress of treatment outcomes. In this paper, we apply an artificial intelligence technique [support vector machines (SVM)] for the automatic recognition of young-old gait types from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly ...

Known for Support Vector Machines |  Automated Gait |  Sensitivity Specificity |  Automatic Recognition |  Early Identification
KOL Index score: 8613

Fog/Edge computing emerges as a novel computing paradigm that harnesses resources in the proximity of the Internet of Things (IoT) devices so that, alongside with the cloud servers, provide services in a timely manner. However, due to the ever-increasing growth of IoT devices with resource-hungry applications, fog/edge servers with limited resources cannot efficiently satisfy the requirements of the IoT applications. Therefore, the application placement in the fog/edge computing ...

Known for Iot Applications |  Application Placement |  Fog Computing |  Edge Servers |  Energy Consumption
KOL Index score: 8609

Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating–actuating network creates the Internet of Things (IoT), wherein sensors and actuators blend seamlessly with the environment around us, and the information is shared ...

Known for Architectural Elements |  Wsn Internet |  Things Iot |  Wireless Technologies |  Rfid Tags
KOL Index score: 8453

Heart rate complexity analysis is a powerful non-invasive means to diagnose several cardiac ailments. Non-linear tools of complexity measurement are indispensable in order to bring out the complete non-linear behavior of Physiological signals. The most popularly used non-linear tools to measure signal complexity are the entropy measures like Approximate entropy (ApEn) and Sample entropy (SampEn). But, these methods become unreliable and inaccurate at times, in particular, for short ...

Known for Distribution Entropy |  Complexity Measure |  Apen Sampen |  Data Length |  Interval Time
KOL Index score: 8386

BACKGROUND: A novel descriptor (Complex Correlation Measure (CCM)) for measuring the variability in the temporal structure of Poincaré plot has been developed to characterize or distinguish between Poincaré plots with similar shapes.

METHODS: This study was designed to assess the changes in temporal structure of the Poincaré plot using CCM during atropine infusion, 70° head-up tilt and scopolamine administration in healthy human subjects. CCM quantifies the point-to-point variation of ...

Known for Poincaré Plot |  Heart Rate |  Sd1 Sd2 |  Parasympathetic Activity |  Complex Correlation Measure
KOL Index score: 8292

In this study, we propose a non-invasive algorithm to recognize the timings of fetal cardiac events on the basis of analysis of fetal ECG (FECG) and Doppler ultrasound signals. Multiresolution wavelet analysis enabled the frequency contents of the Doppler signals to be linked to the opening (o) and closing (c) of the heart’s valves (Aortic (A) and Mitral (M)). M-mode, B-mode and pulsed Doppler ultrasound were used to verify the timings of opening and closure of these valves. In normal ...

Known for Cardiac Valves |  Closing Timings |  Fetal Heart |  Doppler Ultrasound |  Normal Fetuses
KOL Index score: 8262

In this paper, a new noninvasive method is proposed for automated estimation of fetal cardiac intervals from Doppler Ultrasound (DUS) signal. This method is based on a novel combination of empirical mode decomposition (EMD) and hybrid support vector machines-hidden Markov models (SVM/HMM). EMD was used for feature extraction by decomposing the DUS signal into different components (IMFs), one of which is linked to the cardiac valve motions, i.e. opening (o) and closing (c) of the Aortic ...

Known for Fetal Cardiac |  Doppler Ultrasound |  Timing Events |  Empirical Decomposition |  Svm Hmm
KOL Index score: 8055

We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector ...

Known for Incremental Training |  Support Vector Machines |  Automated Signal Processing |  New Algorithm |  Computer Assisted
KOL Index score: 7932

Heart rate variability (HRV) is concerned with the analysis of the intervals between heartbeats. An emerging analysis technique is the Poincaré plot, which takes a sequence of intervals and plots each interval against the following interval. The geometry of this plot has been shown to distinguish between healthy and unhealthy subjects in clinical settings. The Poincaré plot is a valuable HRV analysis technique due to its ability to display nonlinear aspects of the interval sequence. The ...

Known for Nonlinear Features |  Heart Rate Variability |  Cardiovascular Models |  Poincaré Plot |  Hrv Analysis
KOL Index score: 7489

Evidence of the short term relationship between maternal and fetal heart rates has been found in previous studies. However there is still limited knowledge about underlying mechanisms and patterns of the coupling throughout gestation. In this study, Transfer Entropy (TE) was used to quantify directed interactions between maternal and fetal heart rates at various time delays and gestational ages. Experimental results using maternal and fetal electrocardiograms showed significant coupling ...

Known for Transfer Entropy |  Fetal Heart |  Gestational Age |  Weeks Gestation |  Autonomic Nervous


Marimuthu Swami Palaniswami: Influence Statistics

Sample of concepts for which Marimuthu Swami Palaniswami is among the top experts in the world.
Concept World rank
estimation crowd density #1
hungarian cost algorithm #1
wpa fourier wavelet #1
sd1 sd1w #1
xsampen analysis #1
fhrv foetal hra #1
20 indoors #1
article techniques #1
32–41 weeks #1
mmspca ppg signal #1
poincaré descriptors hemiparesis #1
conventional mcca #1
beacon slots neighbors #1
8 subjects asymmetric #1
1svm model #1
rights boundaries #1
mother gestational age #1
drivedb #1
global decision functions #1
central tendency magnitude #1
gait mtc #1
identifying hemiparetic #1
based reprogramming #1
cardiovascular tachycardia ecg #1
normal breathing events #1
pathological hrv #1
eihave #1
multidimensional svr #1
fetal ventricular lv #1
relevance fds #1
normal single pregnancies #1
poincaré #1
wireless channel usage #1
cardiac regulations #1
disparity movement elements #1
paradigm orthodox conceptions #1
sdapen #1
nervous statistics #1
sd2w #1
theair remote installation #1
edr surrogate #1
8695 accuracy #1
coding computation #1
global convergence stability #1
hrv edr #1
balance impairments mfc #1
popular algorithm efficiency #1
paper severity #1
chou–fasman parameters #1
sreluge #1

Key People For Heart Rate

Top KOLs in the world
John Camm MD John Camm
atrial fibrillation myocardial infarction heart rate
John Thomas Bigger
myocardial infarction ventricular arrhythmias heart period variability
Alberto Malliani
heart rate spectral analysis arterial pressure
Peter John Schwartz
long qt syndrome myocardial infarction sudden death
Robert E Kleiger
heart rate variability myocardial infarction segment depression
Giuseppe Mancia
blood pressure heart rate metabolic syndrome

Marimuthu Swami Palaniswami:Expert Impact

Concepts for whichMarimuthu Swami Palaniswamihas direct influence:Heart rate,  Anomaly detection,  Poincaré plot,  Heart rate variability,  Wireless sensor networks,  Entropy profiling,  Support vector machines,  Sensor networks.

Marimuthu Swami Palaniswami:KOL impact

Concepts related to the work of other authors for whichfor which Marimuthu Swami Palaniswami has influence:Internet things,  Big data,  Smart cities,  Wireless sensor networks,  Heart rate,  Fog computing,  Machine learning.



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Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria 3010, Australia | Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia. | Department of Elec

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