Wavelet power spectrum-based prediction of successful defibrillation from ventricular fibrillation

Author(s):  
P.S. Addison ◽  
N. Uchaipichat ◽  
J.N. Watson ◽  
G.R. Clegg ◽  
C.E. Robertson ◽  
...  
2012 ◽  
Vol 51 (21) ◽  
pp. 5216 ◽  
Author(s):  
Shuping Tao ◽  
Guang Jin ◽  
Xuyan Zhang ◽  
Hongsong Qu ◽  
Yuan An

MAUSAM ◽  
2021 ◽  
Vol 71 (1) ◽  
pp. 57-68
Author(s):  
PRAMANIK SAIKAT ◽  
SIL SOURAV ◽  
MANDAL SAMIRAN

A sixty - five year (1951-2015) long data for monthly minimum temperature (TMIN) and maximum temperature (TMAX), observed by the India Meteorological Department (IMD), is statistically analyzed at four urban stations namely Bhubaneswar, Delhi, Mumbai and Chennai of India. A bimodal nature in seasonality is noticed for TMAX and TMIN at all locations. Two peaks for TMAX and TMIN are observed in May and September. Exceptionally, Mumbai shows TMAX peaks during May and November and Delhi shows TMIN peaks during June and September. Higher standard deviations (SD) for TMAX is noted at Delhi with a maximum in March (1.78 °C), while for Chennai, the SD for TMIN is lesser compared to other cities. Two different periods 1951-1980 (P1, the first half of the study period) and 1981-2015 (P2, the second half of the study period) were identified from the time series of both TMAX and TMIN. A higher increasing trend is observed during P2 than P1 in all the cities except in TMIN at Mumbai. The highest increasing trend (0.040 °C/year) is observed for TMIN in Mumbai during P1 time, but the trend is almost constant (0.001 °C/year) during P2 time. The highest increasing trend for TMIN at Mumbai is mainly contributed by the increasing trend in post-monsoon and winter months in P1. Surprisingly, in both P1 and P2, the trends are less during monsoon months for all the cities. A consistent 5-year (3-year) band is observed throughout the wavelet power spectrum at the coastal cities Bhubaneswar, Mumbai (Chennai). However, the 5-year signal is not consistent at Delhi and it is observed only during the year 1975-1980. The global wavelet power spectrum showed that TMIN at Chennai has less power (0.6 °C2) corresponding to 3-year signal and Mumbai has highest power (12 °C2) corresponding to the 5-year signal in comparison to other cities.


2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Prabakaran Subramani ◽  
Rajendra Sahu ◽  
Shekhar Verma

2018 ◽  
Vol 7 (1) ◽  
pp. 131
Author(s):  
Lihua Ma ◽  
Zhiqiang Yin ◽  
Yanben Han

Direct observations of solar activity are available for the past four century, so some proxies reflecting solar activity such as 14C, 10Be and geomagnetic variations are used to reconstruct solar activity in the past. In this present paper, the authors use rectified wavelet power transform and time-averaged wavelet power spectrum to investigate long-term fluctuations of the reconstructed solar activity series. Results show obvious a quasi ~500-year cycle exists in the past solar activity. Three reconstructed solar activity series from 14C variations confirm the periodic signals.


2012 ◽  
Vol 30 (12) ◽  
pp. 1743-1750 ◽  
Author(s):  
Z. Zhang ◽  
J. C. Moore

Abstract. As the main result in Ge's paper, Ge announced that he proved a formula on the distribution of Morlet wavelet power spectrum of continuous-time Gaussian white noise in a rigorous statistical framework. In this paper, we will show that Ge's formula is wrong and each step of Ge's proof is wrong. Moreover, we give and prove a correct formula in a rigorous statistical framework.


2017 ◽  
Vol 29 (05) ◽  
pp. 1750039 ◽  
Author(s):  
Mohammadreza Noruzi ◽  
Malihe Sabeti ◽  
Reza Boostani

Ventricular tachycardia (VT) is a fast heart rate that arises from improper electrical activity in the ventricular of the heart. VT may eventually lead to lethal ventricular fibrillation (VF) which is characterized by fast and irregular heart rhythm. Since difference between VT and VF is diagnosed by specialist in a critical and stressful situation, the possibility of wrong decision is not low. Here, various set of ECG features belonged to different domains are implemented to investigate the predictability and discriminability of VT and VF episodes. Informative features from different domains such as correlation dimension (phase space) and power spectrum (frequency domain) were elicited from electrocardiogram (ECG) signals to describe the amount of irregularity/variation through the attack. In addition raw signal samples were used to assess the classification task based on the time domain features. Applying correlation dimension, power spectrum and the raw samples of ECGs to artificial neural network (ANN) classifier provides 91%, 92% and 71% classification accuracy between VT and VF signals, respectively. However, to enrich the time domain features, surrogate data was generated and the results of time domain is increased up to 87% which represents that ANNs are able to learn the dynamic nature of chaotic signals.


Sign in / Sign up

Export Citation Format

Share Document