Analysis of myocardial infarction using wavelet transform and multiscale energy analysis

Swain, Sushree Satvatee; Patra, Dipti

doi:10.1109/tencon.2017.8228121

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In such cases where data is scarce or unavailable, the hydrologists have developed models and techniques which do not require the availability of long time series of meteorological and hydrological observations (Loukas and Vasiliades, 2014;Blöschl et al, 2013;Botero and Francés, 2010;Nag and Biswal, 2019;Post, 2004). Depending on whether flow hydrographs, statistics or quantiles are the required outputs, several methods can be used including rainfall-runoff modelling using global parameters transfer, quantile estimation from empirical methods and time series summaries from flow duration curves (FDC) regional equation (Swain and Patra, 2017;Valimba, 2016;Loukas and Vasiliades, 2014). They fall into either statistical, hydrological modelling or stochastic modelling methods (Loukas and Vasiliades, 2014) and more often employed simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Estimating Flood Magnitudes of Ungauged Urban Msimbazi River Catchment in Dar es Salaam, Tanzania

Valimba¹,

Mahé²

2020

TJET

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More often daily climate data have been used in hydrological models to estimate flood flows in small data scarce urban and rural catchments where flow peaking and recession are quick within short periods of few hours. This study assesses magnitudes of error of using daily climate data to simulate flood flow hydrographs of a small urban river catchment, Msimbazi River. Spot discharge measurements were available for Apr-May 2014 and MarMay 2015 periods.10-minutes climate records were available at one station, daily temperatures at two stations while daily rainfall records at several stations within the catchment. Visual analysis characterise rainfall events during the 2011, 2014 and 2015 floods, Thiessen-polygon was used for catchment rainfall, Hargreave-Samani model for catchment potential evapotranspiration and flow hydrographs were estimated by calibrated HBV model. Length weighted channel slope was estimated from segment slopes established from available topographical maps and used in estimating time of concentration for the catchment using Kirpich method. Results indicated that simulated flood hydrographs using 10-minutes climate inputs produced higher flood peaks for both December 2011 (peak: 471.6 m3 /s) and April 2014 (peak: 393.5 m3 /s) and expected hydrograph recession behaviour reproducing the Kirpich estimate of time of concentration of 7 hours. Simulated flood peaks using daily climate inputs were 252.3 m3 /s for December 2011 and 205 m3 /s for the April 2014 event being 53% and 52% of those simulated from 10-minutes climate inputs. Despite usefulness of 10-minute climate data, these data are required at more stations within the catchment for reliable simulation of fast receding urban floods and therefore more automatic weather stations are needed in Dar es Salaam.

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Section: Introductionmentioning

confidence: 99%

Estimating Flood Magnitudes of Ungauged Urban Msimbazi River Catchment in Dar es Salaam, Tanzania

Valimba¹,

Mahé²

2020

TJET

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“…The probability of best frequency characterisation increases upon selection of suitable wavelet filter banks. In this Letter, multiscale energy analysis of the ECG signal has been performed for detection of MI [20]. Different wavelet basis filters of different orders, i.e.…”

Section: Wavelet Transformmentioning

confidence: 99%

“…In this Letter, multiscale energy analysis of the ECG signal has been performed for detection of MI [20]. Different wavelet basis filters of different orders, i.e.…”

Section: Multiscale Energy Analysis Based Optimal Wavelet Selection Fmentioning

confidence: 99%

Multiscale energy based suitable wavelet selection for detection of myocardial infarction in ECG

Swain

Patra

2018

Healthcare Technology Letters

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Over the decades, electrocardiogram (ECG) has been proved as the chief diagnostic tool for assessment of the cardiovascular condition of human being. Myocardial Infarction (MI) is commonly known as heart attack, happens when blood supply stops to heart muscles causing occlusions in some portion or whole artery. MI is the result of three pathological changes such as elevation of ST-segment, the appearance of wide pathological Q-wave and inversion of T-wave in ECG record. Detection of MI by considering few ECG leads generally requires prior information about the pathological behaviour of the disease. The present work considers 12 leads to view the cardiac condition from various angles in ECG signal for accurate detection of MI. This Letter investigates on various wavelet basis functions, i.e. Haar, Daubechies, Symlet, Coiflet and biorthogonal basis filters of different order for selecting the most suitable one for the detection of MI. Wavelet transform of 12-lead ECG signal decomposes the signal into different subbands. A comparative study has been done based on the multiscale energy at different wavelet subbands for the selection of most suitable wavelet basis for the accurate detection of MI. The experimentation is carried out on different datasets from the PTB diagnostic ECG database.

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Predicting the Myocardial Infarction from Predictive Analytics Through Supervised Machine Learning

Raghukumar¹,

Naveen²,

Lachikarathman

2023

SN COMPUT. SCI.

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Analysis of myocardial infarction using wavelet transform and multiscale energy analysis

Cited by 5 publications

References 15 publications

Estimating Flood Magnitudes of Ungauged Urban Msimbazi River Catchment in Dar es Salaam, Tanzania

Estimating Flood Magnitudes of Ungauged Urban Msimbazi River Catchment in Dar es Salaam, Tanzania

Multiscale energy based suitable wavelet selection for detection of myocardial infarction in ECG

Predicting the Myocardial Infarction from Predictive Analytics Through Supervised Machine Learning

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