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  • Marimuthu Swami Palaniswami

Marimuthu Swami Palaniswami: Influence Statistics

Marimuthu Swami Palaniswami

Marimuthu Swami Palaniswami

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

Marimuthu Swami Palaniswami: Expert Impact

Concepts for which Marimuthu Swami Palaniswami has direct influence: Heart rate , Anomaly detection , Poincaré plot , Heart rate variability , Wireless sensor networks , Entropy profiling , Support vector machines .

Marimuthu Swami Palaniswami: KOL impact

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

KOL Resume for Marimuthu Swami Palaniswami

Year
2022

Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria 3010, Australia

2021

Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, 3010, Australia

2020

Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, 3010, Australia

2019

Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Australia

2018

Department of Electrical and Electronic Engineering

Dept. of Electrical and Electronic Eng., The University of Melbourne, Australia

2017

The University of Melbourne, Melbourne, Australia

Department of Electrical and Electronic Engineering, University of Melbourne, VIC, 3052

2016

Electrical and Electronic Engineering Department, University of Melbourne, Melbourne, VIC 3010, Australia

2015

The University of Melbourne, Level 5, 161 Barry Street, 3010, Parkville, VIC, Australia

2014

Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria 3010, Australia

Dept. of Computing and Information Systems

2013

EEE, U. of Melbourne, Victoria, 3010, Australia

The University of Melbourne

Prominent publications by Marimuthu Swami Palaniswami

KOL-Index: 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 ...
Known for Poincaré Plot | Complex Correlation Measure | Sd1 Sd2 | Congestive Heart Failure
KOL-Index: 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 ...
Known for Heart Rate Variability | Pulse Rate | Sleep Apnea | Hrv Prv
KOL-Index: 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 ...
Known for Ppg Signal | Respiratory Rate | Blood Volume | Existing Methods
KOL-Index: 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 ...
Known for Support Vector Machines | Sleep Apnea | Ecg Recordings | Hrv Edr
KOL-Index: 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 ...
Known for Hypopnea Events | Obstructive Sleep Apnea | Ecg Signals | Signal Processing
KOL-Index: 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 ...
Known for Support Vector Machines | Automated Gait | Sensitivity Specificity | Automatic Recognition
KOL-Index: 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 ...
Known for Iot Applications | Application Placement | Fog Computing | Edge Servers
KOL-Index: 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 ...
Known for Architectural Elements | Wsn Internet | Things Iot | Wireless Technologies
KOL-Index: 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 ...
Known for Distribution Entropy | Complexity Measure | Apen Sampen | Data Length
KOL-Index: 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 ...
Known for Poincaré Plot | Heart Rate | Sd1 Sd2 | Parasympathetic Activity
KOL-Index: 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 ...
Known for Cardiac Valves | Closing Timings | Fetal Heart | Doppler Ultrasound
KOL-Index: 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), ...
Known for Fetal Cardiac | Doppler Ultrasound | Timing Events | Empirical Decomposition
KOL-Index: 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 ...
Known for Incremental Training | Support Vector Machines | Automated Signal Processing | New Algorithm
KOL-Index: 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 ...
Known for Nonlinear Features | Heart Rate Variability | Cardiovascular Models | Poincaré Plot
KOL-Index: 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 ...
Known for Transfer Entropy | Fetal Heart | Gestational Age | Weeks Gestation

Key People For Heart Rate

Top KOLs in the world
#1
John Camm MD John Camm
atrial fibrillation myocardial infarction heart rate
#2
John Thomas Bigger
myocardial infarction ventricular arrhythmias heart period variability
#3
Alberto Malliani
heart rate spectral analysis arterial pressure
#4
Peter John Schwartz
long qt syndrome myocardial infarction sudden death
#5
Robert E Kleiger
heart rate variability myocardial infarction segment depression
#6
Giuseppe Mancia
blood pressure heart rate metabolic syndrome

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